diff --git a/_images/10981181533700c500305d703e650337ee63863e408661d79dbe69b2be8cdca0.png b/_images/10981181533700c500305d703e650337ee63863e408661d79dbe69b2be8cdca0.png deleted file mode 100644 index c9bde59..0000000 Binary files a/_images/10981181533700c500305d703e650337ee63863e408661d79dbe69b2be8cdca0.png and /dev/null differ diff --git a/_sources/workshop-6/module-4-primers.ipynb b/_sources/workshop-6/module-4-primers.ipynb index df054c3..55a68f8 100644 --- a/_sources/workshop-6/module-4-primers.ipynb +++ b/_sources/workshop-6/module-4-primers.ipynb @@ -351,115 +351,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "528ae85d", "metadata": { "id": "528ae85d", "tags": [] }, - "outputs": [], - "source": [ - "# Import libraries \n", - "import malariagen_data\n", - "import pandas as pd\n", - "import primer3\n", - "import AnoPrimer\n", - "import gget\n", - "\n", - "#configure plotting with matplotlib\n", - "%matplotlib inline\n", - "%config InlineBackend.figure_format = \"retina\"" - ] - }, - { - "cell_type": "markdown", - "id": "bf14ab7c", - "metadata": { - "id": "746LdtO9W8lE" - }, - "source": [ - "\n", - "## Worked example: Designing primers for the *ace1*-280S mutation\n", - "\n", - "In the rest of this module, we will design primers and probes for the *ace1*-280S mutation (previously *ace1*-119S). This mutation is known to be important in resistance to organophosphates and carbamates, insecticides which are widely used in indoor residual spraying (IRS). The resistance mutation has spread throughout much of west and central Africa, including introgressing from *An. gambiae* to *An. coluzzii*, and is often found on the background of large duplications which often pair wild-type and mutant alleles [11]. We see very large signals of selection at this locus in many contemporary populations of *An. gambiae*. " - ] - }, - { - "cell_type": "markdown", - "id": "5d32c5b4", - "metadata": {}, - "source": [ - "### Selecting primer parameters\n", - "\n", - "In the below cells, replace the values with those desired for your primers." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "SV03ewwc9de8", - "metadata": { - "cellView": "form", - "id": "SV03ewwc9de8", - "tags": [] - }, - "outputs": [], - "source": [ - "#@title **Primer parameters** { run: \"auto\" }\n", - "\n", - "# N.B., this cell will be rendered as a form when running on colab\n", - "\n", - "assay_type = \"gDNA primers + probe\" #@param [\"gDNA primers\", \"gDNA primers + probe\", \"probe\", \"qPCR primers\"]\n", - "assay_name = 'ace1-280s' #@param {type:\"string\"}\n", - "min_amplicon_size = 60 #@param {type:\"integer\"}\n", - "max_amplicon_size = 120 #@param {type:\"integer\"}\n", - "amplicon_size_range = [[min_amplicon_size, max_amplicon_size]]\n", - "n_primer_pairs = 6 #@param {type:\"slider\", min:1, max:20, step:1}\n", - "\n", - "#@markdown \n", - "#@markdown target_loc is required for gDNA primers and probes, and transcript required for qPCR primers.\n", - "\n", - "contig = \"2R\" #@param ['2L', '2R', '3L', '3R', 'X']\n", - "target_loc = '3492074' #@param {type:\"string\"}\n", - "target_loc = int(target_loc)\n", - "transcript = '' #@param {type:\"string\"} \n", - "\n", - "if any(item in assay_type for item in ['gDNA', 'probe']):\n", - " assert target_loc > 0, \"Target location must be above 0 and less than the contig length\"\n", - "elif assay_type == 'qPCR primers':\n", - " assert len(transcript) > 2, \"Transcript ID is not valid, should be vectorbase ID such as 'AGAP004707-RD'\"" - ] - }, - { - "cell_type": "markdown", - "id": "c2073e67", - "metadata": { - "id": "c2073e67" - }, - "source": [ - "Configure access to the MalariaGEN Ag3 data resource.\n", - "Note that authentication is required to access data through the package, please follow the instructions [here](https://malariagen.github.io/vector-data/vobs/vobs-data-access.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "338d8c1a", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 233 - }, - "id": "338d8c1a", - "outputId": "c39c3494-8072-4421-c3c0-44b3f34ee7e6", - "tags": [] - }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Tue May 14 04:48:39 2024 WARNING No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n" + "Wed May 22 06:52:06 2024 WARNING No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n" ] }, { @@ -782,8777 +685,1459 @@ "output_type": "display_data" }, { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
MalariaGEN Ag3 API client
\n", - " Please note that data are subject to terms of use,\n", - " for more information see \n", - " the MalariaGEN website or contact support@malariagen.net.\n", - " See also the Ag3 API docs.\n", - "
\n", - " Storage URL\n", - " gs://vo_agam_release/
\n", - " Data releases available\n", - " 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9
\n", - " Results cache\n", - " None
\n", - " Cohorts analysis\n", - " 20240418
\n", - " AIM analysis\n", - " 20220528
\n", - " Site filters analysis\n", - " dt_20200416
\n", - " Software version\n", - " malariagen_data 9.0.0
\n", - " Client location\n", - " unknown
\n", - " " - ], - "text/plain": [ - "\n", - "Storage URL : gs://vo_agam_release/\n", - "Data releases available : 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9\n", - "Results cache : None\n", - "Cohorts analysis : 20240418\n", - "AIM analysis : 20220528\n", - "Site filters analysis : dt_20200416\n", - "Software version : malariagen_data 9.0.0\n", - "Client location : unknown\n", - "---\n", - "Please note that data are subject to terms of use,\n", - "for more information see https://www.malariagen.net/data\n", - "or contact support@malariagen.net. For API documentation see \n", - "https://malariagen.github.io/malariagen-data-python/v9.0.0/Ag3.html" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ag3 = malariagen_data.Ag3()\n", - "ag3" - ] - }, - { - "cell_type": "markdown", - "id": "aa733775", - "metadata": { - "id": "aa733775" - }, - "source": [ - "Load sequence data for the chromosomal arm of choice, using the [malariagen_data API](https://malariagen.github.io/vector-data/ag3/api.html):" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5fa0be8d", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5fa0be8d", - "outputId": "eb3fbe20-0f9d-4909-d1ac-04e7ba9f04c3", - "tags": [] - }, - "outputs": [ - { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Our genome sequence for 2R is 61545105 bp long\n" + "Wed May 22 06:52:07 2024 WARNING No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n" ] - } - ], - "source": [ - "genome_seq = ag3.genome_sequence(region=contig)\n", - "print(f\"Our genome sequence for {contig} is {genome_seq.shape[0]} bp long\")" - ] - }, - { - "cell_type": "markdown", - "id": "144a37ce", - "metadata": { - "id": "144a37ce" - }, - "source": [ - "Now we need to extract the bit of genome sequence we need. We will use functions in the [AnoPrimer](https://pypi.org/project/AnoPrimer/) package. For genomic DNA primers, this is quite simple and we can make direct use of the `ag3.genome_sequence()` function, but for cDNA qPCR primers, we must only include coding sequence, and so we must concatenate exons together.\n", - "\n", - "With qPCR primers for cDNA, we also must ensure that one primer spans an exon-exon junction, to ensure that any residual genomic DNA in our samples does not get amplified. We must therefore make a note of where the exon junctions are, and we store that as a list in the `exon_junctions` variable." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "R0CkEd38VXGY", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "R0CkEd38VXGY", - "outputId": "98dbbc29-e598-44da-ba33-75ba0f853171", - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The target sequence is 239 bases long\n", - "the target snp is 119 bp into our target sequence\n" - ] - } - ], - "source": [ - "if any(item in assay_type for item in ['gDNA', 'probe']):\n", - " # genomic DNA\n", - " target_sequence, gdna_pos, seq_parameters = AnoPrimer.prepare_gDNA_sequence(\n", - " target_loc=target_loc, \n", - " amplicon_size_range=amplicon_size_range, \n", - " genome_seq=genome_seq, \n", - " assay_name=assay_name, \n", - " assay_type=assay_type\n", - " )\n", - "\n", - "elif assay_type == 'qPCR primers':\n", - " # RT-quantitative PCR, cDNA\n", - " target_sequence, exon_junctions, gdna_pos, seq_parameters = AnoPrimer.prepare_cDNA_sequence(\n", - " transcript=transcript, \n", - " gff=ag3.geneset(), \n", - " genome_seq=genome_seq, \n", - " assay_name=assay_name\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "_aaCJGAXUCSx", - "metadata": { - "id": "_aaCJGAXUCSx" - }, - "source": [ - "Now we have our target sequence. Lets take a look..." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "nDaWXq7f9hyA", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - }, - "id": "nDaWXq7f9hyA", - "outputId": "635d7c7a-6014-4107-a671-0e2503cdb9c4", - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'CGGGCGCGACCATGTGGAACCCGAACACGCCCCTGTCCGAGGACTGTCTGTACATTAACGTGGTGGCACCGCGACCCCGGCCCAAGAATGCGGCCGTCATGCTGTGGATCTTCGGCGGCGGCTTCTACTCCGGCACCGCCACCCTGGACGTGTACGACCACCGGGCGCTTGCGTCGGAGGAGAACGTGATCGTGGTGTCGCTGCAGTACCGCGTGGCCAGTCTGGGCTTCCTGTTTCTC'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_parameters['SEQUENCE_TEMPLATE']" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d7JYKNXW1zla", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "d7JYKNXW1zla", - "outputId": "1f8a39e9-e3f9-4204-f4be-08da01d88d4c", - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'SEQUENCE_ID': 'ace1-280s',\n", - " 'SEQUENCE_TEMPLATE': 'CGGGCGCGACCATGTGGAACCCGAACACGCCCCTGTCCGAGGACTGTCTGTACATTAACGTGGTGGCACCGCGACCCCGGCCCAAGAATGCGGCCGTCATGCTGTGGATCTTCGGCGGCGGCTTCTACTCCGGCACCGCCACCCTGGACGTGTACGACCACCGGGCGCTTGCGTCGGAGGAGAACGTGATCGTGGTGTCGCTGCAGTACCGCGTGGCCAGTCTGGGCTTCCTGTTTCTC',\n", - " 'SEQUENCE_TARGET': [119, 10],\n", - " 'GENOMIC_TARGET': 3492074,\n", - " 'SEQUENCE_INTERNAL_EXCLUDED_REGION': [[1, 99], [139, 100]]}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_parameters" - ] - }, - { - "cell_type": "markdown", - "id": "38fe2530", - "metadata": { - "id": "38fe2530" - }, - "source": [ - "We need to set up a second python dictionary, which will be our input to primer3. This contains our preferred primer parameters. In the below cell, you can modify or add primer3 parameters, such as optimal primer size, TM, GC content etc etc. A full list of possible parameters and their functions can be found in the [primer3 2.6.1 manual](https://htmlpreview.github.io/?https://github.com/primer3-org/primer3/blob/v2.6.1/src/primer3_manual.htm)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "622ac6d9", - "metadata": { - "id": "622ac6d9", - "tags": [] - }, - "outputs": [], - "source": [ - "primer_parameters = {\n", - " 'PRIMER_OPT_SIZE': 20,\n", - " 'PRIMER_TASK':'generic',\n", - " 'PRIMER_MIN_SIZE': 17,\n", - " 'PRIMER_MAX_SIZE': 24,\n", - " 'PRIMER_OPT_TM': 60.0,\n", - " 'PRIMER_MIN_TM': 55.0,\n", - " 'PRIMER_MAX_TM': 64.0,\n", - " 'PRIMER_MIN_GC': 30.0,\n", - " 'PRIMER_MAX_GC': 75.0,\n", - " 'PRIMER_MIN_THREE_PRIME_DISTANCE': 3, # this parameter is the minimum distance between successive pairs. If 1, it means successive primer pairs could be identical bar one base shift\n", - " 'PRIMER_INTERNAL_OPT_SIZE': 16, # Probe size preferences if selected, otherwise ignored\n", - " 'PRIMER_INTERNAL_MIN_SIZE': 10,\n", - " 'PRIMER_INTERNAL_MAX_SIZE': 22,\n", - " 'PRIMER_INTERNAL_MIN_TM': 45,\n", - " 'PRIMER_INTERNAL_MAX_TM': 65, # Probe considerations are quite relaxed, assumed that LNAs / Taqman will be used later to affect TM\n", - " # Extra primer3 parameters can go here\n", - " # In the same format as above \n", - "}\n", - "\n", - "# adds some necessary parameters depending on assay type\n", - "primer_parameters = AnoPrimer.primer_params(\n", - " assay_type=assay_type, \n", - " primer_parameters=primer_parameters, \n", - " n_primer_pairs=n_primer_pairs, \n", - " amplicon_size_range=amplicon_size_range,\n", - ") " - ] - }, - { - "cell_type": "markdown", - "id": "0819ba76", - "metadata": { - "id": "0819ba76" - }, - "source": [ - "### Run the primer3 algorithm!" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ae6e7525", - "metadata": { - "id": "ae6e7525", - "tags": [] - }, - "outputs": [], - "source": [ - "primer_dict = primer3.designPrimers(\n", - " seq_args=seq_parameters, \n", - " global_args=primer_parameters\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "56945ca6", - "metadata": { - "id": "56945ca6" - }, - "source": [ - "It should be *fast*. The output, which we store as 'primer_dict', is a python dictionary containing the full results from primer3. We will turn this into a pandas dataframe containing just the necessary bits of information. First, we'll print some information from the primer3 run." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1bb40846", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "1bb40846", - "outputId": "052f3437-9a32-47d5-d15f-604aa756e8ee", - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "primer_forward_explain : considered 796, GC content failed 84, low tm 72, high tm 347, ok 293 \n", - "\n", - "primer_reverse_explain : considered 724, GC content failed 68, low tm 17, high tm 395, high hairpin stability 17, ok 227 \n", - "\n", - "primer_probe_explain : considered 524, overlap excluded region 212, GC content failed 20, low tm 100, high tm 2, high hairpin stability 1, ok 189 \n", - "\n", - "primer_pair_explain : considered 3107, unacceptable product size 3099, primer in pair overlaps a primer in a better pair 1279, ok 8 \n", - "\n", - "primer_forward_num_returned : 6 \n", - "\n", - "primer_reverse_num_returned : 6 \n", - "\n", - "primer_probe_num_returned : 6 \n", - "\n", - "primer_pair_num_returned : 6 \n", - "\n", - "primer_pair : [{'PENALTY': 0.7107151008710275, 'COMPL_ANY_TH': 0.0, 'COMPL_END_TH': 0.0, 'PRODUCT_SIZE': 95, 'PRODUCT_TM': 88.90156407263927}, {'PENALTY': 1.8268188261876048, 'COMPL_ANY_TH': 0.0, 'COMPL_END_TH': 0.0, 'PRODUCT_SIZE': 66, 'PRODUCT_TM': 86.70188305190561}, {'PENALTY': 3.1582579651147853, 'COMPL_ANY_TH': 0.0, 'COMPL_END_TH': 1.562469314775342, 'PRODUCT_SIZE': 103, 'PRODUCT_TM': 89.70217725608332}, {'PENALTY': 3.3586929277500417, 'COMPL_ANY_TH': 12.973825966488278, 'COMPL_END_TH': 17.231667178159967, 'PRODUCT_SIZE': 112, 'PRODUCT_TM': 90.19160166662422}, {'PENALTY': 3.997840573385986, 'COMPL_ANY_TH': 12.56535604041153, 'COMPL_END_TH': 5.976322718447193, 'PRODUCT_SIZE': 117, 'PRODUCT_TM': 90.31415966418224}, {'PENALTY': 4.920050397896603, 'COMPL_ANY_TH': 0.0, 'COMPL_END_TH': 6.284961991996681, 'PRODUCT_SIZE': 109, 'PRODUCT_TM': 90.03744046085752}] \n", - "\n", - "primer_forward : [{'PENALTY': 0.46258069149581615, 'SEQUENCE': 'TCATGCTGTGGATCTTCGGC', 'COORDS': [96, 20], 'TM': 60.462580691495816, 'GC_PERCENT': 55.0, 'SELF_ANY_TH': 12.259940761643293, 'SELF_END_TH': 12.259940761643293, 'HAIRPIN_TH': 36.59415215911514, 'END_STABILITY': 5.54}, {'PENALTY': 0.7458448421797357, 'SEQUENCE': 'GCCGTCATGCTGTGGATCTT', 'COORDS': [92, 20], 'TM': 60.745844842179736, 'GC_PERCENT': 55.0, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 2.4}, {'PENALTY': 1.0630056029440311, 'SEQUENCE': 'CAAGAATGCGGCCGTCATG', 'COORDS': [82, 19], 'TM': 59.93699439705597, 'GC_PERCENT': 57.89473684210526, 'SELF_ANY_TH': 27.740677587638743, 'SELF_END_TH': 17.565181116897747, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 3.07}, {'PENALTY': 1.2319584737745117, 'SEQUENCE': 'ACATTAACGTGGTGGCACCG', 'COORDS': [51, 20], 'TM': 61.23195847377451, 'GC_PERCENT': 55.0, 'SELF_ANY_TH': 30.138122022371874, 'SELF_END_TH': 21.521828989913672, 'HAIRPIN_TH': 42.57679566489054, 'END_STABILITY': 4.94}, {'PENALTY': 2.1245702891837936, 'SEQUENCE': 'GGCCGTCATGCTGTGGAT', 'COORDS': [91, 18], 'TM': 60.124570289183794, 'GC_PERCENT': 61.111111111111114, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 3.41}, {'PENALTY': 2.8924249804836677, 'SEQUENCE': 'CTGTGGATCTTCGGCGGC', 'COORDS': [101, 18], 'TM': 60.89242498048367, 'GC_PERCENT': 66.66666666666667, 'SELF_ANY_TH': 7.2145116769710285, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 6.53}] \n", - "\n", - "primer_reverse : [{'PENALTY': 0.24813440937521136, 'SEQUENCE': 'GATCACGTTCTCCTCCGACG', 'COORDS': [190, 20], 'TM': 60.24813440937521, 'GC_PERCENT': 60.0, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 42.12619709637721, 'END_STABILITY': 5.12}, {'PENALTY': 1.0809739840078691, 'SEQUENCE': 'GTCGTACACGTCCAGGGTG', 'COORDS': [157, 19], 'TM': 60.08097398400787, 'GC_PERCENT': 63.1578947368421, 'SELF_ANY_TH': 1.1902412874118795, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 36.90501393969038, 'END_STABILITY': 4.61}, {'PENALTY': 2.095252362170754, 'SEQUENCE': 'GTTCTCCTCCGACGCAAGC', 'COORDS': [184, 19], 'TM': 61.095252362170754, 'GC_PERCENT': 63.1578947368421, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 45.62213695404927, 'END_STABILITY': 4.01}, {'PENALTY': 2.12673445397553, 'SEQUENCE': 'CGGTGGTCGTACACGTCC', 'COORDS': [162, 18], 'TM': 60.12673445397553, 'GC_PERCENT': 66.66666666666667, 'SELF_ANY_TH': 10.508612029603285, 'SELF_END_TH': 10.508612029603285, 'HAIRPIN_TH': 45.42514212470502, 'END_STABILITY': 4.79}, {'PENALTY': 1.8732702842021922, 'SEQUENCE': 'TACTGCAGCGACACCACGAT', 'COORDS': [207, 20], 'TM': 61.87327028420219, 'GC_PERCENT': 55.0, 'SELF_ANY_TH': 15.265667197231778, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 35.08910707969903, 'END_STABILITY': 3.73}, {'PENALTY': 2.027625417412935, 'SEQUENCE': 'GGTACTGCAGCGACACCAC', 'COORDS': [209, 19], 'TM': 61.027625417412935, 'GC_PERCENT': 63.1578947368421, 'SELF_ANY_TH': 15.265667197231778, 'SELF_END_TH': 0.405162456403275, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 4.16}] \n", - "\n", - "primer_probe : [{'PENALTY': 4.161169114371262, 'SEQUENCE': 'GGCTTCTACTCCGGCACCGC', 'COORDS': [119, 20], 'TM': 59.83883088562874, 'GC_PERCENT': 70.0, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 36.28074618918981}, {'PENALTY': 3.1023652706057874, 'SEQUENCE': 'GCGGCGGCTTCTACTCCGG', 'COORDS': [114, 19], 'TM': 59.89763472939421, 'GC_PERCENT': 73.6842105263158, 'SELF_ANY_TH': 7.734280912202962, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 42.29746586682808}, {'PENALTY': 2.194535966840135, 'SEQUENCE': 'TGGATCTTCGGCGGCGGC', 'COORDS': [104, 18], 'TM': 59.805464033159865, 'GC_PERCENT': 72.22222222222223, 'SELF_ANY_TH': 28.91330394666676, 'SELF_END_TH': 21.80273259353129, 'HAIRPIN_TH': 38.904208273950076}, {'PENALTY': 2.194535966840135, 'SEQUENCE': 'TGGATCTTCGGCGGCGGC', 'COORDS': [104, 18], 'TM': 59.805464033159865, 'GC_PERCENT': 72.22222222222223, 'SELF_ANY_TH': 28.91330394666676, 'SELF_END_TH': 21.80273259353129, 'HAIRPIN_TH': 38.904208273950076}, {'PENALTY': 3.1023652706057874, 'SEQUENCE': 'GCGGCGGCTTCTACTCCGG', 'COORDS': [114, 19], 'TM': 59.89763472939421, 'GC_PERCENT': 73.6842105263158, 'SELF_ANY_TH': 7.734280912202962, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 42.29746586682808}, {'PENALTY': 4.161169114371262, 'SEQUENCE': 'GGCTTCTACTCCGGCACCGC', 'COORDS': [119, 20], 'TM': 59.83883088562874, 'GC_PERCENT': 70.0, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 36.28074618918981}] \n", - "\n" - ] - } - ], - "source": [ - "AnoPrimer.primer3_run_statistics(primer_dict, assay_type)" - ] - }, - { - "cell_type": "markdown", - "id": "CbVMwOdCrNMf", - "metadata": { - "id": "CbVMwOdCrNMf" - }, - "source": [ - "Now lets convert this into an easy to read pandas dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "bf8c5970", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 488 - }, - "id": "bf8c5970", - "outputId": "6d83511f-35a3-409a-ead3-cfa867029fc0", - "tags": [] - }, - "outputs": [ { "data": { - "text/html": [ - "
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primer_pair012345
parameter
primer_forward_sequenceTCATGCTGTGGATCTTCGGCGCCGTCATGCTGTGGATCTTCAAGAATGCGGCCGTCATGACATTAACGTGGTGGCACCGGGCCGTCATGCTGTGGATCTGTGGATCTTCGGCGGC
primer_reverse_sequenceGATCACGTTCTCCTCCGACGGTCGTACACGTCCAGGGTGGTTCTCCTCCGACGCAAGCCGGTGGTCGTACACGTCCTACTGCAGCGACACCACGATGGTACTGCAGCGACACCAC
primer_probe_sequenceGGCTTCTACTCCGGCACCGCGCGGCGGCTTCTACTCCGGTGGATCTTCGGCGGCGGCTGGATCTTCGGCGGCGGCGCGGCGGCTTCTACTCCGGGGCTTCTACTCCGGCACCGC
primer_forward_tm60.46258160.74584559.93699461.23195860.1245760.892425
primer_reverse_tm60.24813460.08097461.09525260.12673461.8732761.027625
primer_probe_tm59.83883159.89763559.80546459.80546459.89763559.838831
primer_forward_gc_percent55.055.057.89473755.061.11111166.666667
primer_reverse_gc_percent60.063.15789563.15789566.66666755.063.157895
primer_probe_gc_percent70.073.68421172.22222272.22222273.68421170.0
primer_forward[96, 20][92, 20][82, 19][51, 20][91, 18][101, 18]
primer_reverse[190, 20][157, 19][184, 19][162, 18][207, 20][209, 19]
primer_probe[119, 20][114, 19][104, 18][104, 18][114, 19][119, 20]
primer_pair_product_size9566103112117109
\n", - "
" - ], - "text/plain": [ - "primer_pair 0 1 \\\n", - "parameter \n", - "primer_forward_sequence TCATGCTGTGGATCTTCGGC GCCGTCATGCTGTGGATCTT \n", - "primer_reverse_sequence GATCACGTTCTCCTCCGACG GTCGTACACGTCCAGGGTG \n", - "primer_probe_sequence GGCTTCTACTCCGGCACCGC GCGGCGGCTTCTACTCCGG \n", - "primer_forward_tm 60.462581 60.745845 \n", - "primer_reverse_tm 60.248134 60.080974 \n", - "primer_probe_tm 59.838831 59.897635 \n", - "primer_forward_gc_percent 55.0 55.0 \n", - "primer_reverse_gc_percent 60.0 63.157895 \n", - "primer_probe_gc_percent 70.0 73.684211 \n", - "primer_forward [96, 20] [92, 20] \n", - "primer_reverse [190, 20] [157, 19] \n", - "primer_probe [119, 20] [114, 19] \n", - "primer_pair_product_size 95 66 \n", "\n", - "primer_pair 2 3 \\\n", - "parameter \n", - "primer_forward_sequence CAAGAATGCGGCCGTCATG ACATTAACGTGGTGGCACCG \n", - "primer_reverse_sequence GTTCTCCTCCGACGCAAGC CGGTGGTCGTACACGTCC \n", - "primer_probe_sequence TGGATCTTCGGCGGCGGC TGGATCTTCGGCGGCGGC \n", - "primer_forward_tm 59.936994 61.231958 \n", - "primer_reverse_tm 61.095252 60.126734 \n", - "primer_probe_tm 59.805464 59.805464 \n", - "primer_forward_gc_percent 57.894737 55.0 \n", - "primer_reverse_gc_percent 63.157895 66.666667 \n", - "primer_probe_gc_percent 72.222222 72.222222 \n", - "primer_forward [82, 19] [51, 20] \n", - "primer_reverse [184, 19] [162, 18] \n", - "primer_probe [104, 18] [104, 18] \n", - "primer_pair_product_size 103 112 \n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim()\n", + " drop(id)\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", "\n", - "primer_pair 4 5 \n", - "parameter \n", - "primer_forward_sequence GGCCGTCATGCTGTGGAT CTGTGGATCTTCGGCGGC \n", - "primer_reverse_sequence TACTGCAGCGACACCACGAT GGTACTGCAGCGACACCAC \n", - "primer_probe_sequence GCGGCGGCTTCTACTCCGG GGCTTCTACTCCGGCACCGC \n", - "primer_forward_tm 60.12457 60.892425 \n", - "primer_reverse_tm 61.87327 61.027625 \n", - "primer_probe_tm 59.897635 59.838831 \n", - "primer_forward_gc_percent 61.111111 66.666667 \n", - "primer_reverse_gc_percent 55.0 63.157895 \n", - "primer_probe_gc_percent 73.684211 70.0 \n", - "primer_forward [91, 18] [101, 18] \n", - "primer_reverse [207, 20] [209, 19] \n", - "primer_probe [114, 19] [119, 20] \n", - "primer_pair_product_size 117 109 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "primer_df = AnoPrimer.primer3_to_pandas(primer_dict, assay_type)\n", - "primer_df" - ] - }, - { - "cell_type": "markdown", - "id": "Lf0CeEVrtaxP", - "metadata": { - "id": "Lf0CeEVrtaxP" - }, - "source": [ - "\n", - "We can write this to .tsv and excel files, which can be explored in other editors." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "09eQYSeT7lHG", - "metadata": { - "id": "09eQYSeT7lHG", - "tags": [] - }, - "outputs": [], - "source": [ - "primer_df.to_csv(f\"{assay_name}.{assay_type}.primers.tsv\", sep=\"\\t\")\n", - "primer_df.to_excel(f\"{assay_name}.{assay_type}.primers.xlsx\")" - ] - }, - { - "cell_type": "markdown", - "id": "b4a9776c", - "metadata": { - "id": "b4a9776c" - }, - "source": [ - "### Looking for variation using the Ag1000G data resource\n", - "\n", - "As we've seen in earlier workshops, Ag1000G samples are organised into sample sets. Lets look at what each sample set contains, breaking it down by species, year and country. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "61286685", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "bbf351cef55f45a9af635ae3fdef4545", - "00fd40decfb447c98307cf817055cc83", - "3df8d1664f99432e90a3521ffaa54fe3", - "e73718d89be642e7adbb8abab441cd71", - "bf628ae9b7df45c59dcd381d179746dd", - "13e0faac5a534973a737f2c11158110c", - "edf49ec3949047ab902b8ab2d6981bdf", - "5b2e179c55794344bc6231fc3537d264", - "e3f78396230249a5b4e08a8fe93c9bb4", - "25344ace43974c59be0eee35f4915b3a", - "fa94ca372db84ebb898eede7aa8fd855" - ] - }, - "id": "61286685", - "outputId": "fa90b592-e22e-43af-82f3-c7b83a34ffd9", - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "text/html": [ - "
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taxonarabiensiscoluzziigambiaegcx1gcx3unassigned
sample_setcountryyear
AG1000G-AOAngola20090810000
AG1000G-BF-ABurkina Faso201208299000
AG1000G-BF-BBurkina Faso201435346000
AG1000G-BF-CBurkina Faso20040013000
AG1000G-CDDemocratic Republic of the Congo20150076000
AG1000G-CFCentral African Republic1993052000
199401353000
AG1000G-CICote d'Ivoire20120800000
AG1000G-CM-ACameroon200900303000
AG1000G-CM-BCameroon20050790000
AG1000G-CM-CCameroon201321923000
AG1000G-FRMayotte20110023000
AG1000G-GA-AGabon20000069000
AG1000G-GHGhana201206436000
AG1000G-GM-AGambia, The20110606800
AG1000G-GM-BGambia, The20060220900
AG1000G-GM-CGambia, The201201722000
AG1000G-GN-AGuinea20120441000
AG1000G-GN-BGuinea20120783001
Mali201202765002
AG1000G-GQEquatorial Guinea20020010000
AG1000G-GWGuinea-Bissau20100079301
AG1000G-KEKenya20000019000
2007300000
201210000540
AG1000G-ML-AMali201402733000
AG1000G-ML-BMali200423633000
AG1000G-MWMalawi20154100000
AG1000G-MZMozambique2003003000
20040071000
AG1000G-TZTanzania20128700000
201310320100
2015137032010
AG1000G-UGUganda2012820207001
AG1000G-XLab Cross-100000297
\n", - "
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For the purposes of this notebook, let's use the Ghana sample set. If we wanted to use all sample sets, we could supply '3.0' instead of a sample set, which will load all samples from the Ag3.0 release." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "I6MEculr4OFi", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "I6MEculr4OFi", - "outputId": "9874c6e7-869e-4b5d-8b8e-60f36397fc77", - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['sample_id', 'partner_sample_id', 'contributor', 'country', 'location',\n", - " 'year', 'month', 'latitude', 'longitude', 'sex_call', 'sample_set',\n", - " 'release', 'quarter', 'study_id', 'study_url',\n", - " 'aim_species_fraction_arab', 'aim_species_fraction_colu',\n", - " 'aim_species_fraction_colu_no2l', 'aim_species_gambcolu_arabiensis',\n", - " 'aim_species_gambiae_coluzzii', 'aim_species', 'country_iso',\n", - " 'admin1_name', 'admin1_iso', 'admin2_name', 'taxon',\n", - " 'cohort_admin1_year', 'cohort_admin1_month', 'cohort_admin1_quarter',\n", - " 'cohort_admin2_year', 'cohort_admin2_month', 'cohort_admin2_quarter'],\n", - " dtype='object')" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metadata.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "49ca7f2b", - "metadata": { - "id": "49ca7f2b", - "tags": [] - }, - "outputs": [], - "source": [ - "sample_set = ['AG1000G-BF-A', 'AG1000G-GH', 'AG1000G-GN-A']\n", - "# here we could subset to specific values in the metadata e.g.: \"taxon == 'gambiae'\" , or \"taxon == 'arabiensis'\"\n", - "sample_query = None " - ] - }, - { - "cell_type": "markdown", - "id": "67coxQKoo82x", - "metadata": { - "id": "67coxQKoo82x" - }, - "source": [ - "### Plot allele frequencies in primers locations\n", - "\n", - "Now we can plot the primers pairs, and the frequency of any alternate alleles in the Ag1000G sample set of choice. When calculating allele frequencies, we will take the sum of all alternate alleles, as we are interested here in any mutations which are different from the reference genome. We can see the frequencies of specific alleles by hovering over the points of the plot - in some cases it may be preferable to design degenerate primers rather than avoid a primer pair completely.\n", - "\n", - "We will also plot the primer Tm, GC and genomic spans of each primer binding site. We can use this plot to identify primers pairs and probes which may be suitable, particularly trying to avoid SNPs in the 3' end. 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i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n\n } catch (error) {throw error;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Import libraries \n", + "import malariagen_data\n", + "import pandas as pd\n", + "import primer3\n", + "import AnoPrimer\n", + "import gget\n", + "\n", + "#configure plotting with matplotlib\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = \"retina\"" + ] + }, + { + "cell_type": "markdown", + "id": "bf14ab7c", + "metadata": { + "id": "746LdtO9W8lE" + }, + "source": [ + "\n", + "## Worked example: Designing primers for the *ace1*-280S mutation\n", + "\n", + "In the rest of this module, we will design primers and probes for the *ace1*-280S mutation (previously *ace1*-119S). This mutation is known to be important in resistance to organophosphates and carbamates, insecticides which are widely used in indoor residual spraying (IRS). The resistance mutation has spread throughout much of west and central Africa, including introgressing from *An. gambiae* to *An. coluzzii*, and is often found on the background of large duplications which often pair wild-type and mutant alleles [11]. We see very large signals of selection at this locus in many contemporary populations of *An. gambiae*. " + ] + }, + { + "cell_type": "markdown", + "id": "5d32c5b4", + "metadata": {}, + "source": [ + "### Selecting primer parameters\n", + "\n", + "In the below cells, replace the values with those desired for your primers." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "SV03ewwc9de8", + "metadata": { + "cellView": "form", + "id": "SV03ewwc9de8", + "tags": [] + }, + "outputs": [], + "source": [ + "#@title **Primer parameters** { run: \"auto\" }\n", + "\n", + "# N.B., this cell will be rendered as a form when running on colab\n", + "\n", + "assay_type = \"gDNA primers + probe\" #@param [\"gDNA primers\", \"gDNA primers + probe\", \"probe\", \"cDNA primers\"]\n", + "assay_name = 'ace1-280s' #@param {type:\"string\"}\n", + "min_amplicon_size = 60 #@param {type:\"integer\"}\n", + "max_amplicon_size = 120 #@param {type:\"integer\"}\n", + "amplicon_size_range = [[min_amplicon_size, max_amplicon_size]]\n", + "n_primer_pairs = 6 #@param {type:\"slider\", min:1, max:20, step:1}\n", + "\n", + "#@markdown \n", + "#@markdown target_loc is required for gDNA primers and probes, and transcript required for qPCR primers.\n", + "\n", + "contig = \"2R\" #@param ['2L', '2R', '3L', '3R', 'X']\n", + "target_loc = '3492074' #@param {type:\"string\"}\n", + "target_loc = int(target_loc)\n", + "transcript = '' #@param {type:\"string\"} \n", + "\n", + "if any(item in assay_type for item in ['gDNA', 'probe']):\n", + " assert target_loc > 0, \"Target location must be above 0 and less than the contig length\"\n", + "elif assay_type == 'cDNA primers':\n", + " assert len(transcript) > 2, \"Transcript ID is not valid, should be vectorbase ID such as 'AGAP004707-RD'\"" + ] + }, + { + "cell_type": "markdown", + "id": "c2073e67", + "metadata": { + "id": "c2073e67" + }, + "source": [ + "Configure access to the MalariaGEN Ag3 data resource.\n", + "Note that authentication is required to access data through the package, please follow the instructions [here](https://malariagen.github.io/vector-data/vobs/vobs-data-access.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "338d8c1a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 233 + }, + "id": "338d8c1a", + "outputId": "c39c3494-8072-4421-c3c0-44b3f34ee7e6", + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Wed May 22 06:52:41 2024 WARNING No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n" + ] + }, + { + "data": { + "application/javascript": [ + "'use strict';\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " function drop(id) {\n", + " const view = Bokeh.index.get_by_id(id)\n", + " if (view != null) {\n", + " view.model.document.clear()\n", + " Bokeh.index.delete(view)\n", + " }\n", + " }\n", + "\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + "\n", + " // Clean up Bokeh references\n", + " if (id != null) {\n", + " drop(id)\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim()\n", + " drop(id)\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? 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MalariaGEN Ag3 API client
\n", + " Please note that data are subject to terms of use,\n", + " for more information see \n", + " the MalariaGEN website or contact support@malariagen.net.\n", + " See also the Ag3 API docs.\n", + "
\n", + " Storage URL\n", + " gs://vo_agam_release/
\n", + " Data releases available\n", + " 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9
\n", + " Results cache\n", + " None
\n", + " Cohorts analysis\n", + " 20240418
\n", + " AIM analysis\n", + " 20220528
\n", + " Site filters analysis\n", + " dt_20200416
\n", + " Software version\n", + " malariagen_data 9.0.0
\n", + " Client location\n", + " unknown
\n", + " " + ], + "text/plain": [ + "\n", + "Storage URL : gs://vo_agam_release/\n", + "Data releases available : 3.0, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9\n", + "Results cache : None\n", + "Cohorts analysis : 20240418\n", + "AIM analysis : 20220528\n", + "Site filters analysis : dt_20200416\n", + "Software version : malariagen_data 9.0.0\n", + "Client location : unknown\n", + "---\n", + "Please note that data are subject to terms of use,\n", + "for more information see https://www.malariagen.net/data\n", + "or contact support@malariagen.net. For API documentation see \n", + "https://malariagen.github.io/malariagen-data-python/v9.0.0/Ag3.html" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ag3 = malariagen_data.Ag3()\n", + "ag3" + ] + }, + { + "cell_type": "markdown", + "id": "aa733775", + "metadata": { + "id": "aa733775" + }, + "source": [ + "Load sequence data for the chromosomal arm of choice, using the [malariagen_data API](https://malariagen.github.io/vector-data/ag3/api.html):" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5fa0be8d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5fa0be8d", + "outputId": "eb3fbe20-0f9d-4909-d1ac-04e7ba9f04c3", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Our genome sequence for 2R is 61545105 bp long\n" + ] + } + ], + "source": [ + "genome_seq = ag3.genome_sequence(region=contig)\n", + "print(f\"Our genome sequence for {contig} is {genome_seq.shape[0]} bp long\")" + ] + }, + { + "cell_type": "markdown", + "id": "144a37ce", + "metadata": { + "id": "144a37ce" + }, + "source": [ + "Now we need to extract the bit of genome sequence we need. We will use functions in the [AnoPrimer](https://pypi.org/project/AnoPrimer/) package. For genomic DNA primers, this is quite simple and we can make direct use of the `ag3.genome_sequence()` function, but for cDNA qPCR primers, we must only include coding sequence, and so we must concatenate exons together.\n", + "\n", + "With qPCR primers for cDNA, we also must ensure that one primer spans an exon-exon junction, to ensure that any residual genomic DNA in our samples does not get amplified. We must therefore make a note of where the exon junctions are, and we store that as a list in the `exon_junctions` variable." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "R0CkEd38VXGY", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R0CkEd38VXGY", + "outputId": "98dbbc29-e598-44da-ba33-75ba0f853171", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The target sequence is 239 bases long\n", + "the target snp is 119 bp into our target sequence\n" + ] + } + ], + "source": [ + "if any(item in assay_type for item in ['gDNA', 'probe']):\n", + " # genomic DNA\n", + " target_sequence, gdna_pos, seq_parameters = AnoPrimer.prepare_gDNA_sequence(\n", + " target_loc=target_loc, \n", + " amplicon_size_range=amplicon_size_range, \n", + " genome_seq=genome_seq, \n", + " assay_name=assay_name, \n", + " assay_type=assay_type\n", + " )\n", + "\n", + "elif assay_type == 'qPCR primers':\n", + " # RT-quantitative PCR, cDNA\n", + " target_sequence, exon_junctions, gdna_pos, seq_parameters = AnoPrimer.prepare_cDNA_sequence(\n", + " transcript=transcript, \n", + " gff=ag3.geneset(), \n", + " genome_seq=genome_seq, \n", + " assay_name=assay_name\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "_aaCJGAXUCSx", + "metadata": { + "id": "_aaCJGAXUCSx" + }, + "source": [ + "Now we have our target sequence. Lets take a look..." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "nDaWXq7f9hyA", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "nDaWXq7f9hyA", + "outputId": "635d7c7a-6014-4107-a671-0e2503cdb9c4", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'CGGGCGCGACCATGTGGAACCCGAACACGCCCCTGTCCGAGGACTGTCTGTACATTAACGTGGTGGCACCGCGACCCCGGCCCAAGAATGCGGCCGTCATGCTGTGGATCTTCGGCGGCGGCTTCTACTCCGGCACCGCCACCCTGGACGTGTACGACCACCGGGCGCTTGCGTCGGAGGAGAACGTGATCGTGGTGTCGCTGCAGTACCGCGTGGCCAGTCTGGGCTTCCTGTTTCTC'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seq_parameters['SEQUENCE_TEMPLATE']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d7JYKNXW1zla", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d7JYKNXW1zla", + "outputId": "1f8a39e9-e3f9-4204-f4be-08da01d88d4c", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'SEQUENCE_ID': 'ace1-280s',\n", + " 'SEQUENCE_TEMPLATE': 'CGGGCGCGACCATGTGGAACCCGAACACGCCCCTGTCCGAGGACTGTCTGTACATTAACGTGGTGGCACCGCGACCCCGGCCCAAGAATGCGGCCGTCATGCTGTGGATCTTCGGCGGCGGCTTCTACTCCGGCACCGCCACCCTGGACGTGTACGACCACCGGGCGCTTGCGTCGGAGGAGAACGTGATCGTGGTGTCGCTGCAGTACCGCGTGGCCAGTCTGGGCTTCCTGTTTCTC',\n", + " 'SEQUENCE_TARGET': [119, 10],\n", + " 'GENOMIC_TARGET': 3492074,\n", + " 'SEQUENCE_INTERNAL_EXCLUDED_REGION': [[1, 99], [139, 100]]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seq_parameters" + ] + }, + { + "cell_type": "markdown", + "id": "38fe2530", + "metadata": { + "id": "38fe2530" + }, + "source": [ + "We need to set up a second python dictionary, which will be our input to primer3. This contains our preferred primer parameters. In the below cell, you can modify or add primer3 parameters, such as optimal primer size, TM, GC content etc etc. A full list of possible parameters and their functions can be found in the [primer3 2.6.1 manual](https://htmlpreview.github.io/?https://github.com/primer3-org/primer3/blob/v2.6.1/src/primer3_manual.htm)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "622ac6d9", + "metadata": { + "id": "622ac6d9", + "tags": [] + }, + "outputs": [], + "source": [ + "primer_parameters = {\n", + " 'PRIMER_OPT_SIZE': 20,\n", + " 'PRIMER_TASK':'generic',\n", + " 'PRIMER_MIN_SIZE': 17,\n", + " 'PRIMER_MAX_SIZE': 24,\n", + " 'PRIMER_OPT_TM': 60.0,\n", + " 'PRIMER_MIN_TM': 55.0,\n", + " 'PRIMER_MAX_TM': 64.0,\n", + " 'PRIMER_MIN_GC': 30.0,\n", + " 'PRIMER_MAX_GC': 75.0,\n", + " 'PRIMER_MIN_THREE_PRIME_DISTANCE': 3, # this parameter is the minimum distance between successive pairs. If 1, it means successive primer pairs could be identical bar one base shift\n", + " 'PRIMER_INTERNAL_OPT_SIZE': 16, # Probe size preferences if selected, otherwise ignored\n", + " 'PRIMER_INTERNAL_MIN_SIZE': 10,\n", + " 'PRIMER_INTERNAL_MAX_SIZE': 22,\n", + " 'PRIMER_INTERNAL_MIN_TM': 45,\n", + " 'PRIMER_INTERNAL_MAX_TM': 65, # Probe considerations are quite relaxed, assumed that LNAs / Taqman will be used later to affect TM\n", + " # Extra primer3 parameters can go here\n", + " # In the same format as above \n", + "}\n", + "\n", + "# adds some necessary parameters depending on assay type\n", + "primer_parameters = AnoPrimer.primer_params(\n", + " assay_type=assay_type, \n", + " primer_parameters=primer_parameters, \n", + " n_primer_pairs=n_primer_pairs, \n", + " amplicon_size_range=amplicon_size_range,\n", + ") " + ] + }, + { + "cell_type": "markdown", + "id": "0819ba76", + "metadata": { + "id": "0819ba76" + }, + "source": [ + "### Run the primer3 algorithm!" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ae6e7525", + "metadata": { + "id": "ae6e7525", + "tags": [] + }, + "outputs": [], + "source": [ + "primer_dict = primer3.designPrimers(\n", + " seq_args=seq_parameters, \n", + " global_args=primer_parameters\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "56945ca6", + "metadata": { + "id": "56945ca6" + }, + "source": [ + "It should be *fast*. The output, which we store as 'primer_dict', is a python dictionary containing the full results from primer3. We will turn this into a pandas dataframe containing just the necessary bits of information. First, we'll print some information from the primer3 run." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1bb40846", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1bb40846", + "outputId": "052f3437-9a32-47d5-d15f-604aa756e8ee", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "primer_forward_explain : considered 796, GC content failed 84, low tm 72, high tm 347, ok 293 \n", + "\n", + "primer_reverse_explain : considered 724, GC content failed 68, low tm 17, high tm 395, high hairpin stability 17, ok 227 \n", + "\n", + "primer_probe_explain : considered 524, overlap excluded region 212, GC content failed 20, low tm 100, high tm 2, high hairpin stability 1, ok 189 \n", + "\n", + "primer_pair_explain : considered 3107, unacceptable product size 3099, primer in pair overlaps a primer in a better pair 1279, ok 8 \n", + "\n", + "primer_forward_num_returned : 6 \n", + "\n", + "primer_reverse_num_returned : 6 \n", + "\n", + "primer_probe_num_returned : 6 \n", + "\n", + "primer_pair_num_returned : 6 \n", + "\n", + "primer_pair : [{'PENALTY': 0.7107151008710275, 'COMPL_ANY_TH': 0.0, 'COMPL_END_TH': 0.0, 'PRODUCT_SIZE': 95, 'PRODUCT_TM': 88.90156407263927}, {'PENALTY': 1.8268188261876048, 'COMPL_ANY_TH': 0.0, 'COMPL_END_TH': 0.0, 'PRODUCT_SIZE': 66, 'PRODUCT_TM': 86.70188305190561}, {'PENALTY': 3.1582579651147853, 'COMPL_ANY_TH': 0.0, 'COMPL_END_TH': 1.562469314775342, 'PRODUCT_SIZE': 103, 'PRODUCT_TM': 89.70217725608332}, {'PENALTY': 3.3586929277500417, 'COMPL_ANY_TH': 12.973825966488278, 'COMPL_END_TH': 17.231667178159967, 'PRODUCT_SIZE': 112, 'PRODUCT_TM': 90.19160166662422}, {'PENALTY': 3.997840573385986, 'COMPL_ANY_TH': 12.56535604041153, 'COMPL_END_TH': 5.976322718447193, 'PRODUCT_SIZE': 117, 'PRODUCT_TM': 90.31415966418224}, {'PENALTY': 4.920050397896603, 'COMPL_ANY_TH': 0.0, 'COMPL_END_TH': 6.284961991996681, 'PRODUCT_SIZE': 109, 'PRODUCT_TM': 90.03744046085752}] \n", + "\n", + "primer_forward : [{'PENALTY': 0.46258069149581615, 'SEQUENCE': 'TCATGCTGTGGATCTTCGGC', 'COORDS': [96, 20], 'TM': 60.462580691495816, 'GC_PERCENT': 55.0, 'SELF_ANY_TH': 12.259940761643293, 'SELF_END_TH': 12.259940761643293, 'HAIRPIN_TH': 36.59415215911514, 'END_STABILITY': 5.54}, {'PENALTY': 0.7458448421797357, 'SEQUENCE': 'GCCGTCATGCTGTGGATCTT', 'COORDS': [92, 20], 'TM': 60.745844842179736, 'GC_PERCENT': 55.0, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 2.4}, {'PENALTY': 1.0630056029440311, 'SEQUENCE': 'CAAGAATGCGGCCGTCATG', 'COORDS': [82, 19], 'TM': 59.93699439705597, 'GC_PERCENT': 57.89473684210526, 'SELF_ANY_TH': 27.740677587638743, 'SELF_END_TH': 17.565181116897747, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 3.07}, {'PENALTY': 1.2319584737745117, 'SEQUENCE': 'ACATTAACGTGGTGGCACCG', 'COORDS': [51, 20], 'TM': 61.23195847377451, 'GC_PERCENT': 55.0, 'SELF_ANY_TH': 30.138122022371874, 'SELF_END_TH': 21.521828989913672, 'HAIRPIN_TH': 42.57679566489054, 'END_STABILITY': 4.94}, {'PENALTY': 2.1245702891837936, 'SEQUENCE': 'GGCCGTCATGCTGTGGAT', 'COORDS': [91, 18], 'TM': 60.124570289183794, 'GC_PERCENT': 61.111111111111114, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 3.41}, {'PENALTY': 2.8924249804836677, 'SEQUENCE': 'CTGTGGATCTTCGGCGGC', 'COORDS': [101, 18], 'TM': 60.89242498048367, 'GC_PERCENT': 66.66666666666667, 'SELF_ANY_TH': 7.2145116769710285, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 6.53}] \n", + "\n", + "primer_reverse : [{'PENALTY': 0.24813440937521136, 'SEQUENCE': 'GATCACGTTCTCCTCCGACG', 'COORDS': [190, 20], 'TM': 60.24813440937521, 'GC_PERCENT': 60.0, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 42.12619709637721, 'END_STABILITY': 5.12}, {'PENALTY': 1.0809739840078691, 'SEQUENCE': 'GTCGTACACGTCCAGGGTG', 'COORDS': [157, 19], 'TM': 60.08097398400787, 'GC_PERCENT': 63.1578947368421, 'SELF_ANY_TH': 1.1902412874118795, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 36.90501393969038, 'END_STABILITY': 4.61}, {'PENALTY': 2.095252362170754, 'SEQUENCE': 'GTTCTCCTCCGACGCAAGC', 'COORDS': [184, 19], 'TM': 61.095252362170754, 'GC_PERCENT': 63.1578947368421, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 45.62213695404927, 'END_STABILITY': 4.01}, {'PENALTY': 2.12673445397553, 'SEQUENCE': 'CGGTGGTCGTACACGTCC', 'COORDS': [162, 18], 'TM': 60.12673445397553, 'GC_PERCENT': 66.66666666666667, 'SELF_ANY_TH': 10.508612029603285, 'SELF_END_TH': 10.508612029603285, 'HAIRPIN_TH': 45.42514212470502, 'END_STABILITY': 4.79}, {'PENALTY': 1.8732702842021922, 'SEQUENCE': 'TACTGCAGCGACACCACGAT', 'COORDS': [207, 20], 'TM': 61.87327028420219, 'GC_PERCENT': 55.0, 'SELF_ANY_TH': 15.265667197231778, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 35.08910707969903, 'END_STABILITY': 3.73}, {'PENALTY': 2.027625417412935, 'SEQUENCE': 'GGTACTGCAGCGACACCAC', 'COORDS': [209, 19], 'TM': 61.027625417412935, 'GC_PERCENT': 63.1578947368421, 'SELF_ANY_TH': 15.265667197231778, 'SELF_END_TH': 0.405162456403275, 'HAIRPIN_TH': 0.0, 'END_STABILITY': 4.16}] \n", + "\n", + "primer_probe : [{'PENALTY': 4.161169114371262, 'SEQUENCE': 'GGCTTCTACTCCGGCACCGC', 'COORDS': [119, 20], 'TM': 59.83883088562874, 'GC_PERCENT': 70.0, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 36.28074618918981}, {'PENALTY': 3.1023652706057874, 'SEQUENCE': 'GCGGCGGCTTCTACTCCGG', 'COORDS': [114, 19], 'TM': 59.89763472939421, 'GC_PERCENT': 73.6842105263158, 'SELF_ANY_TH': 7.734280912202962, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 42.29746586682808}, {'PENALTY': 2.194535966840135, 'SEQUENCE': 'TGGATCTTCGGCGGCGGC', 'COORDS': [104, 18], 'TM': 59.805464033159865, 'GC_PERCENT': 72.22222222222223, 'SELF_ANY_TH': 28.91330394666676, 'SELF_END_TH': 21.80273259353129, 'HAIRPIN_TH': 38.904208273950076}, {'PENALTY': 2.194535966840135, 'SEQUENCE': 'TGGATCTTCGGCGGCGGC', 'COORDS': [104, 18], 'TM': 59.805464033159865, 'GC_PERCENT': 72.22222222222223, 'SELF_ANY_TH': 28.91330394666676, 'SELF_END_TH': 21.80273259353129, 'HAIRPIN_TH': 38.904208273950076}, {'PENALTY': 3.1023652706057874, 'SEQUENCE': 'GCGGCGGCTTCTACTCCGG', 'COORDS': [114, 19], 'TM': 59.89763472939421, 'GC_PERCENT': 73.6842105263158, 'SELF_ANY_TH': 7.734280912202962, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 42.29746586682808}, {'PENALTY': 4.161169114371262, 'SEQUENCE': 'GGCTTCTACTCCGGCACCGC', 'COORDS': [119, 20], 'TM': 59.83883088562874, 'GC_PERCENT': 70.0, 'SELF_ANY_TH': 0.0, 'SELF_END_TH': 0.0, 'HAIRPIN_TH': 36.28074618918981}] \n", + "\n" + ] + } + ], + "source": [ + "AnoPrimer.primer3_run_statistics(primer_dict, assay_type)" + ] + }, + { + "cell_type": "markdown", + "id": "CbVMwOdCrNMf", + "metadata": { + "id": "CbVMwOdCrNMf" + }, + "source": [ + "Now lets convert this into an easy to read pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bf8c5970", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488 + }, + "id": "bf8c5970", + "outputId": "6d83511f-35a3-409a-ead3-cfa867029fc0", + "tags": [] + }, + "outputs": [ + { + "data": { "text/html": [ - "
" + "primer_pair 4 5 \n", + "parameter \n", + "primer_forward_sequence GGCCGTCATGCTGTGGAT CTGTGGATCTTCGGCGGC \n", + "primer_reverse_sequence TACTGCAGCGACACCACGAT GGTACTGCAGCGACACCAC \n", + "primer_probe_sequence GCGGCGGCTTCTACTCCGG GGCTTCTACTCCGGCACCGC \n", + "primer_forward_tm 60.12457 60.892425 \n", + "primer_reverse_tm 61.87327 61.027625 \n", + "primer_probe_tm 59.897635 59.838831 \n", + "primer_forward_gc_percent 61.111111 66.666667 \n", + "primer_reverse_gc_percent 55.0 63.157895 \n", + "primer_probe_gc_percent 73.684211 70.0 \n", + "primer_forward [91, 18] [101, 18] \n", + "primer_reverse [207, 20] [209, 19] \n", + "primer_probe [114, 19] [119, 20] \n", + "primer_pair_product_size 117 109 " ] }, + "execution_count": 14, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "results_dict = AnoPrimer.plot_primer_snp_frequencies(\n", - " species='gambiae_sl',\n", - " primer_df=primer_df,\n", - " gdna_pos=gdna_pos,\n", - " contig=contig,\n", - " sample_sets=sample_set, \n", - " sample_query=sample_query,\n", - " assay_type=assay_type,\n", - " seq_parameters=seq_parameters,\n", - " out_dir=\".\"\n", - ")" + "primer_df = AnoPrimer.primer3_to_pandas(primer_dict, assay_type)\n", + "primer_df" ] }, { "cell_type": "markdown", - "id": "aCVuE1CiAR4h", + "id": "Lf0CeEVrtaxP", "metadata": { - "id": "aCVuE1CiAR4h" + "id": "Lf0CeEVrtaxP" }, "source": [ - "Now lets plot these primer pairs across the genome, highlighting where they are in relation to any nearby exons..." + "\n", + "We can write this to .tsv and excel files, which can be explored in other editors." ] }, { "cell_type": "code", - "execution_count": 21, - "id": "cAacbw-q8Sgm", + "execution_count": 15, + "id": "09eQYSeT7lHG", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 286 - }, - "id": "cAacbw-q8Sgm", - "outputId": "aeb67689-9587-4bab-f8c0-97f66aea0699" + "id": "09eQYSeT7lHG", + "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "image/png": { - "height": 379, - "width": 1288 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "AnoPrimer.plot_primer_locs(\n", - " species='gambiae_sl',\n", - " primer_res_dict=results_dict, \n", - " primer_df=primer_df, \n", - " assay_type=assay_type, \n", - " contig=contig, \n", - " seq_parameters=seq_parameters, \n", - " legend_loc='lower left'\n", - ")" + "primer_df.to_csv(f\"{assay_name}.{assay_type}.primers.tsv\", sep=\"\\t\")\n", + "primer_df.to_excel(f\"{assay_name}.{assay_type}.primers.xlsx\")" ] }, { "cell_type": "markdown", - "id": "-aIy4IyoBc7-", + "id": "b4a9776c", "metadata": { - "id": "-aIy4IyoBc7-" + "id": "b4a9776c" }, "source": [ - "### Checking our primers for specificity to the *Anopheles gambiae* genome\n", + "### Looking for variation using the Ag1000G data resource\n", "\n", - "We can use a cool new python package, [gget](https://github.com/pachterlab/gget), to rapidly search our primers against the AgamP3 genome, to ensure they will not amplify any other regions of the genome. gget rapidly queries large databases - in this case we can use the program BLAT to align primer sequences. Unfortunately as the lengths of primer sequences are so short, it is at the limit of BLATs sensitivity, and in some cases, matches are not returned. gget can also currently only query the older AgamP3 assembly. Therefore, it is also recommended run a more exhaustive search in [Primer-BLAST](https://www.ncbi.nlm.nih.gov/tools/primer-blast/)." + "As we've seen in earlier workshops, Ag1000G samples are organised into sample sets. Lets look at what each sample set contains, breaking it down by species, year and country. " ] }, { "cell_type": "code", - "execution_count": 22, - "id": "f9fedfe4", + "execution_count": 16, + "id": "61286685", "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 631 + "height": 1000, + "referenced_widgets": [ + "bbf351cef55f45a9af635ae3fdef4545", + "00fd40decfb447c98307cf817055cc83", + "3df8d1664f99432e90a3521ffaa54fe3", + "e73718d89be642e7adbb8abab441cd71", + "bf628ae9b7df45c59dcd381d179746dd", + "13e0faac5a534973a737f2c11158110c", + "edf49ec3949047ab902b8ab2d6981bdf", + "5b2e179c55794344bc6231fc3537d264", + "e3f78396230249a5b4e08a8fe93c9bb4", + "25344ace43974c59be0eee35f4915b3a", + "fa94ca372db84ebb898eede7aa8fd855" + ] }, - "id": "f9fedfe4", - "outputId": "b8baa2e0-6636-4022-b940-b033e7fdb4ce" + "id": "61286685", + "outputId": "fa90b592-e22e-43af-82f3-c7b83a34ffd9", + "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Tue May 14 04:49:16 2024 INFO No DNA BLAT matches were found for this sequence in genome anoGam3.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "No hit for forward - pair 2\n" + " \r" ] }, { @@ -9577,430 +2162,667 @@ " \n", " \n", " \n", - " genome\n", - " query_size\n", - " aligned_start\n", - " aligned_end\n", - " matches\n", - " mismatches\n", - " %_aligned\n", - " %_matched\n", - " chromosome\n", - " strand\n", - " start\n", - " end\n", + " taxon\n", + " arabiensis\n", + " coluzzii\n", + " gambiae\n", + " gcx1\n", + " gcx3\n", + " unassigned\n", + " \n", + " \n", + " sample_set\n", + " country\n", + " year\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " AG1000G-AO\n", + " Angola\n", + " 2009\n", + " 0\n", + " 81\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-BF-A\n", + " Burkina Faso\n", + " 2012\n", + " 0\n", + " 82\n", + " 99\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-BF-B\n", + " Burkina Faso\n", + " 2014\n", + " 3\n", + " 53\n", + " 46\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-BF-C\n", + " Burkina Faso\n", + " 2004\n", + " 0\n", + " 0\n", + " 13\n", + " 0\n", + " 0\n", + " 0\n", " \n", " \n", - " \n", - " primer\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " AG1000G-CD\n", + " Democratic Republic of the Congo\n", + " 2015\n", + " 0\n", + " 0\n", + " 76\n", + " 0\n", + " 0\n", + " 0\n", " \n", - " \n", - " \n", " \n", - " 0\n", - " forward_0\n", - " anoGam3\n", - " 20\n", - " 1\n", - " 20\n", - " 20\n", + " AG1000G-CF\n", + " Central African Republic\n", + " 1993\n", + " 0\n", + " 5\n", + " 2\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492051\n", - " 3492070\n", " \n", " \n", - " reverse_0\n", - " anoGam3\n", - " 20\n", - " 1\n", - " 20\n", - " 20\n", + " 1994\n", + " 0\n", + " 13\n", + " 53\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " -\n", - " 3492126\n", - " 3492145\n", " \n", " \n", - " probe_0\n", - " anoGam3\n", - " 20\n", - " 1\n", - " 20\n", - " 20\n", + " AG1000G-CI\n", + " Cote d'Ivoire\n", + " 2012\n", + " 0\n", + " 80\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492074\n", - " 3492093\n", " \n", " \n", - " 1\n", - " forward_1\n", - " anoGam3\n", - " 20\n", - " 1\n", - " 20\n", - " 20\n", + " AG1000G-CM-A\n", + " Cameroon\n", + " 2009\n", + " 0\n", + " 0\n", + " 303\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492047\n", - " 3492066\n", " \n", " \n", - " reverse_1\n", - " anoGam3\n", + " AG1000G-CM-B\n", + " Cameroon\n", + " 2005\n", + " 0\n", + " 7\n", + " 90\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-CM-C\n", + " Cameroon\n", + " 2013\n", + " 2\n", " 19\n", + " 23\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-FR\n", + " Mayotte\n", + " 2011\n", + " 0\n", + " 0\n", + " 23\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-GA-A\n", + " Gabon\n", + " 2000\n", + " 0\n", + " 0\n", + " 69\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-GH\n", + " Ghana\n", + " 2012\n", + " 0\n", + " 64\n", + " 36\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-GM-A\n", + " Gambia, The\n", + " 2011\n", + " 0\n", + " 6\n", + " 0\n", + " 68\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-GM-B\n", + " Gambia, The\n", + " 2006\n", + " 0\n", + " 22\n", + " 0\n", + " 9\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-GM-C\n", + " Gambia, The\n", + " 2012\n", + " 0\n", + " 172\n", + " 2\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-GN-A\n", + " Guinea\n", + " 2012\n", + " 0\n", + " 4\n", + " 41\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-GN-B\n", + " Guinea\n", + " 2012\n", + " 0\n", + " 7\n", + " 83\n", + " 0\n", + " 0\n", " 1\n", - " 19\n", - " 19\n", + " \n", + " \n", + " Mali\n", + " 2012\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " -\n", - " 3492094\n", - " 3492112\n", + " 27\n", + " 65\n", + " 0\n", + " 0\n", + " 2\n", " \n", " \n", - " probe_1\n", - " anoGam3\n", - " 19\n", + " AG1000G-GQ\n", + " Equatorial Guinea\n", + " 2002\n", + " 0\n", + " 0\n", + " 10\n", + " 0\n", + " 0\n", + " 0\n", + " \n", + " \n", + " AG1000G-GW\n", + " Guinea-Bissau\n", + " 2010\n", + " 0\n", + " 0\n", + " 7\n", + " 93\n", + " 0\n", " 1\n", - " 19\n", + " \n", + " \n", + " AG1000G-KE\n", + " Kenya\n", + " 2000\n", + " 0\n", + " 0\n", " 19\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492069\n", - " 3492087\n", + " 0\n", + " 0\n", " \n", " \n", - " 2\n", - " reverse_2\n", - " anoGam3\n", - " 19\n", - " 1\n", - " 19\n", - " 19\n", + " 2007\n", + " 3\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " -\n", - " 3492121\n", - " 3492139\n", " \n", " \n", - " probe_2\n", - " anoGam3\n", - " 18\n", - " 1\n", - " 18\n", - " 18\n", + " 2012\n", + " 10\n", + " 0\n", + " 0\n", + " 0\n", + " 54\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492059\n", - " 3492076\n", " \n", " \n", - " 3\n", - " forward_3\n", - " anoGam3\n", - " 20\n", - " 1\n", - " 20\n", - " 20\n", + " AG1000G-ML-A\n", + " Mali\n", + " 2014\n", + " 0\n", + " 27\n", + " 33\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492006\n", - " 3492025\n", " \n", " \n", - " reverse_3\n", - " anoGam3\n", - " 18\n", - " 1\n", - " 18\n", - " 18\n", + " AG1000G-ML-B\n", + " Mali\n", + " 2004\n", + " 2\n", + " 36\n", + " 33\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " -\n", - " 3492100\n", - " 3492117\n", " \n", " \n", - " probe_3\n", - " anoGam3\n", - " 18\n", - " 1\n", - " 18\n", - " 18\n", + " AG1000G-MW\n", + " Malawi\n", + " 2015\n", + " 41\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492059\n", - " 3492076\n", " \n", " \n", - " 4\n", - " forward_4\n", - " anoGam3\n", - " 18\n", - " 1\n", - " 18\n", - " 18\n", + " AG1000G-MZ\n", + " Mozambique\n", + " 2003\n", + " 0\n", + " 0\n", + " 3\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492046\n", - " 3492063\n", " \n", " \n", - " reverse_4\n", - " anoGam3\n", - " 20\n", - " 1\n", - " 20\n", - " 20\n", + " 2004\n", + " 0\n", + " 0\n", + " 71\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " -\n", - " 3492143\n", - " 3492162\n", " \n", " \n", - " probe_4\n", - " anoGam3\n", - " 19\n", - " 1\n", - " 19\n", - " 19\n", + " AG1000G-TZ\n", + " Tanzania\n", + " 2012\n", + " 87\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492069\n", - " 3492087\n", " \n", " \n", - " 5\n", - " forward_5\n", - " anoGam3\n", - " 18\n", + " 2013\n", " 1\n", - " 18\n", - " 18\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492056\n", - " 3492073\n", + " 32\n", + " 0\n", + " 10\n", + " 0\n", " \n", " \n", - " reverse_5\n", - " anoGam3\n", - " 19\n", + " 2015\n", + " 137\n", + " 0\n", + " 32\n", + " 0\n", " 1\n", - " 19\n", - " 19\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " -\n", - " 3492146\n", - " 3492164\n", " \n", " \n", - " probe_5\n", - " anoGam3\n", - " 20\n", - " 1\n", - " 20\n", - " 20\n", + " AG1000G-UG\n", + " Uganda\n", + " 2012\n", + " 82\n", " 0\n", - " 100.0\n", - " 100.0\n", - " chr2R\n", - " +\n", - " 3492074\n", - " 3492093\n", + " 207\n", + " 0\n", + " 0\n", + " 1\n", " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " genome query_size aligned_start aligned_end matches \\\n", - " primer \n", - "0 forward_0 anoGam3 20 1 20 20 \n", - " reverse_0 anoGam3 20 1 20 20 \n", - " probe_0 anoGam3 20 1 20 20 \n", - "1 forward_1 anoGam3 20 1 20 20 \n", - " reverse_1 anoGam3 19 1 19 19 \n", - " probe_1 anoGam3 19 1 19 19 \n", - "2 reverse_2 anoGam3 19 1 19 19 \n", - " probe_2 anoGam3 18 1 18 18 \n", - "3 forward_3 anoGam3 20 1 20 20 \n", - " reverse_3 anoGam3 18 1 18 18 \n", - " probe_3 anoGam3 18 1 18 18 \n", - "4 forward_4 anoGam3 18 1 18 18 \n", - " reverse_4 anoGam3 20 1 20 20 \n", - " probe_4 anoGam3 19 1 19 19 \n", - "5 forward_5 anoGam3 18 1 18 18 \n", - " reverse_5 anoGam3 19 1 19 19 \n", - " probe_5 anoGam3 20 1 20 20 \n", + " \n", + " AG1000G-X\n", + " Lab Cross\n", + " -1\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 297\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + "taxon arabiensis coluzzii \\\n", + "sample_set country year \n", + "AG1000G-AO Angola 2009 0 81 \n", + "AG1000G-BF-A Burkina Faso 2012 0 82 \n", + "AG1000G-BF-B Burkina Faso 2014 3 53 \n", + "AG1000G-BF-C Burkina Faso 2004 0 0 \n", + "AG1000G-CD Democratic Republic of the Congo 2015 0 0 \n", + "AG1000G-CF Central African Republic 1993 0 5 \n", + " 1994 0 13 \n", + "AG1000G-CI Cote d'Ivoire 2012 0 80 \n", + "AG1000G-CM-A Cameroon 2009 0 0 \n", + "AG1000G-CM-B Cameroon 2005 0 7 \n", + "AG1000G-CM-C Cameroon 2013 2 19 \n", + "AG1000G-FR Mayotte 2011 0 0 \n", + "AG1000G-GA-A Gabon 2000 0 0 \n", + "AG1000G-GH Ghana 2012 0 64 \n", + "AG1000G-GM-A Gambia, The 2011 0 6 \n", + "AG1000G-GM-B Gambia, The 2006 0 22 \n", + "AG1000G-GM-C Gambia, The 2012 0 172 \n", + "AG1000G-GN-A Guinea 2012 0 4 \n", + "AG1000G-GN-B Guinea 2012 0 7 \n", + " Mali 2012 0 27 \n", + "AG1000G-GQ Equatorial Guinea 2002 0 0 \n", + "AG1000G-GW Guinea-Bissau 2010 0 0 \n", + "AG1000G-KE Kenya 2000 0 0 \n", + " 2007 3 0 \n", + " 2012 10 0 \n", + "AG1000G-ML-A Mali 2014 0 27 \n", + "AG1000G-ML-B Mali 2004 2 36 \n", + "AG1000G-MW Malawi 2015 41 0 \n", + "AG1000G-MZ Mozambique 2003 0 0 \n", + " 2004 0 0 \n", + "AG1000G-TZ Tanzania 2012 87 0 \n", + " 2013 1 0 \n", + " 2015 137 0 \n", + "AG1000G-UG Uganda 2012 82 0 \n", + "AG1000G-X Lab Cross -1 0 0 \n", "\n", - " mismatches %_aligned %_matched chromosome strand start \\\n", - " primer \n", - "0 forward_0 0 100.0 100.0 chr2R + 3492051 \n", - " reverse_0 0 100.0 100.0 chr2R - 3492126 \n", - " probe_0 0 100.0 100.0 chr2R + 3492074 \n", - "1 forward_1 0 100.0 100.0 chr2R + 3492047 \n", - " reverse_1 0 100.0 100.0 chr2R - 3492094 \n", - " probe_1 0 100.0 100.0 chr2R + 3492069 \n", - "2 reverse_2 0 100.0 100.0 chr2R - 3492121 \n", - " probe_2 0 100.0 100.0 chr2R + 3492059 \n", - "3 forward_3 0 100.0 100.0 chr2R + 3492006 \n", - " reverse_3 0 100.0 100.0 chr2R - 3492100 \n", - " probe_3 0 100.0 100.0 chr2R + 3492059 \n", - "4 forward_4 0 100.0 100.0 chr2R + 3492046 \n", - " reverse_4 0 100.0 100.0 chr2R - 3492143 \n", - " probe_4 0 100.0 100.0 chr2R + 3492069 \n", - "5 forward_5 0 100.0 100.0 chr2R + 3492056 \n", - " reverse_5 0 100.0 100.0 chr2R - 3492146 \n", - " probe_5 0 100.0 100.0 chr2R + 3492074 \n", + "taxon gambiae gcx1 gcx3 \\\n", + "sample_set country year \n", + "AG1000G-AO Angola 2009 0 0 0 \n", + "AG1000G-BF-A Burkina Faso 2012 99 0 0 \n", + "AG1000G-BF-B Burkina Faso 2014 46 0 0 \n", + "AG1000G-BF-C Burkina Faso 2004 13 0 0 \n", + "AG1000G-CD Democratic Republic of the Congo 2015 76 0 0 \n", + "AG1000G-CF Central African Republic 1993 2 0 0 \n", + " 1994 53 0 0 \n", + "AG1000G-CI Cote d'Ivoire 2012 0 0 0 \n", + "AG1000G-CM-A Cameroon 2009 303 0 0 \n", + "AG1000G-CM-B Cameroon 2005 90 0 0 \n", + "AG1000G-CM-C Cameroon 2013 23 0 0 \n", + "AG1000G-FR Mayotte 2011 23 0 0 \n", + "AG1000G-GA-A Gabon 2000 69 0 0 \n", + "AG1000G-GH Ghana 2012 36 0 0 \n", + "AG1000G-GM-A Gambia, The 2011 0 68 0 \n", + "AG1000G-GM-B Gambia, The 2006 0 9 0 \n", + "AG1000G-GM-C Gambia, The 2012 2 0 0 \n", + "AG1000G-GN-A Guinea 2012 41 0 0 \n", + "AG1000G-GN-B Guinea 2012 83 0 0 \n", + " Mali 2012 65 0 0 \n", + "AG1000G-GQ Equatorial Guinea 2002 10 0 0 \n", + "AG1000G-GW Guinea-Bissau 2010 7 93 0 \n", + "AG1000G-KE Kenya 2000 19 0 0 \n", + " 2007 0 0 0 \n", + " 2012 0 0 54 \n", + "AG1000G-ML-A Mali 2014 33 0 0 \n", + "AG1000G-ML-B Mali 2004 33 0 0 \n", + "AG1000G-MW Malawi 2015 0 0 0 \n", + "AG1000G-MZ Mozambique 2003 3 0 0 \n", + " 2004 71 0 0 \n", + "AG1000G-TZ Tanzania 2012 0 0 0 \n", + " 2013 32 0 10 \n", + " 2015 32 0 1 \n", + "AG1000G-UG Uganda 2012 207 0 0 \n", + "AG1000G-X Lab Cross -1 0 0 0 \n", "\n", - 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"execution_count": 22, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "blat_result_df = AnoPrimer.gget_blat_genome(primer_df, assay_type, assembly='anoGam3')\n", - "blat_result_df" + "metadata = ag3.sample_metadata(sample_sets='3.0')\n", + "\n", + "pivot_country_year_taxon = (\n", + " metadata\n", + " .pivot_table(\n", + " index=[\"sample_set\", \"country\", \"year\"], \n", + " columns=[\"taxon\"], \n", + " values=\"sample_id\",\n", + " aggfunc=\"count\",\n", + " fill_value=0\n", + " )\n", + ")\n", + "\n", + "pivot_country_year_taxon" ] }, { "cell_type": "markdown", - "id": "1f7ca79e", + "id": "BLwvXBT28NYE", "metadata": { - "id": "1f7ca79e" + "id": "BLwvXBT28NYE" }, "source": [ - "### Further considerations\n", - "\n", - "We may now have designed suitable primers. However, there are some further considerations...\n", - "\n", - "- Primers should be run in [**Primer-BLAST**](https://www.ncbi.nlm.nih.gov/tools/primer-blast/), to ensure specificity against the host organism, and specificity for the genomic location of interest.\n", - "\n", - "- If in multiplexed use with other primers or probes, primers must not interact with each other. This can be investigated on a one by one basis using the IDT tool [oligoanalyzer](https://eu.idtdna.com/calc/analyzer), though higher throughput algorithms may be required.\n", - "\n", - "- If designing TaqMan or Locked Nucleic Acid (LNA) probes for SNP detection, further modification will be required, such as adding fluorophores and ensuring the two probes (one for wild-type, one for mutant) can discriminate between SNPs. for LNA probes, you will want to play around with the placement of LNAs in the oligo sequence, which can allow short probes (~10-14 bases) to bind with high affinity and discriminate between SNPs. IDT have a tool for this which allow you to check the binding affinity between mismatches, though it requires a log in https://eu.idtdna.com/calc/analyzer/lna. \n", - "\n", - "- Many more considerations.... [IDT - How to design primers and probes for PCR and qPCR](https://eu.idtdna.com/pages/education/decoded/article/designing-pcr-primers-and-probes) \n" + "Here, we can see the breakdown by sample set for country, species and year. For the purposes of this notebook, let's use the Ghana sample set. If we wanted to use all sample sets, we could supply '3.0' instead of a sample set, which will load all samples from the Ag3.0 release." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "I6MEculr4OFi", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I6MEculr4OFi", + "outputId": "9874c6e7-869e-4b5d-8b8e-60f36397fc77", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['sample_id', 'partner_sample_id', 'contributor', 'country', 'location',\n", + " 'year', 'month', 'latitude', 'longitude', 'sex_call', 'sample_set',\n", + " 'release', 'quarter', 'study_id', 'study_url',\n", + " 'aim_species_fraction_arab', 'aim_species_fraction_colu',\n", + " 'aim_species_fraction_colu_no2l', 'aim_species_gambcolu_arabiensis',\n", + " 'aim_species_gambiae_coluzzii', 'aim_species', 'country_iso',\n", + " 'admin1_name', 'admin1_iso', 'admin2_name', 'taxon',\n", + " 'cohort_admin1_year', 'cohort_admin1_month', 'cohort_admin1_quarter',\n", + " 'cohort_admin2_year', 'cohort_admin2_month', 'cohort_admin2_quarter'],\n", + " dtype='object')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metadata.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "49ca7f2b", + "metadata": { + "id": "49ca7f2b", + "tags": [] + }, + "outputs": [], + "source": [ + "sample_set = ['AG1000G-BF-A', 'AG1000G-GH', 'AG1000G-GN-A']\n", + "# here we could subset to specific values in the metadata e.g.: \"taxon == 'gambiae'\" , or \"taxon == 'arabiensis'\"\n", + "sample_query = None " ] }, { "cell_type": "markdown", - "id": "7FUld8V3GQRk", + "id": "67coxQKoo82x", "metadata": { - "id": "7FUld8V3GQRk" + "id": "67coxQKoo82x" }, "source": [ - "## Worked example: Primers to avoid\n", + "### Plot allele frequencies in primers locations\n", "\n", - "In the above *ace1* example, all of the primer pairs look reasonable. Lets have a look at an example where some of the primers do not look so good. A UGT detoxification gene, AGAP006222, is overexpressed very highly in populations of *An. coluzzii* - lets try and design some qPCR primers to measure its expression. The below `AnoPrimer.designPrimers()` function integrates the entire notebook into one function, for convenience." + "Now we can plot the primers pairs, and the frequency of any alternate alleles in the Ag1000G sample set of choice. When calculating allele frequencies, we will take the sum of all alternate alleles, as we are interested here in any mutations which are different from the reference genome. We can see the frequencies of specific alleles by hovering over the points of the plot - in some cases it may be preferable to design degenerate primers rather than avoid a primer pair completely.\n", + "\n", + "We will also plot the primer Tm, GC and genomic spans of each primer binding site. We can use this plot to identify primers pairs and probes which may be suitable, particularly trying to avoid SNPs in the 3' end. " ] }, { "cell_type": "code", - "execution_count": 23, - "id": "hKugtREjGU8-", + "execution_count": 19, + "id": "shXKBpG49LSU", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, - "id": "hKugtREjGU8-", - "outputId": "f6cbfa18-b3e7-41d0-f01b-2347dd317015" + "id": "shXKBpG49LSU", + "outputId": "b9b2466f-67d0-40e4-c9da-e50ee12299d6", + "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Our genome sequence for 2L is 49364325 bp long\n", - "Exon junctions for AGAP006222-RA: [ 238 325 911 1353] [28524463, 28524621, 28525282, 28525790] \n", - "\n", - "Subsetting allele frequencies to taxon == 'coluzzii'\n", - " \r" + " \r" ] }, + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "application/vnd.plotly.v1+json": { @@ -10008,6 +2830,1842 @@ "plotlyServerURL": "https://plot.ly" }, "data": [ + { + "customdata": [ + [ + 0, + 0, + 0, + 1, + "T_3492051" + ], + [ + 0, + 1, + 0, + 0, + "C_3492052" + ], + [ + 1, + 0, + 0, + 0, + "A_3492053" + ], + [ + 0, + 0, + 0, + 1, + "T_3492054" + ], + [ + 0, + 0, + 1, + 0, + "G_3492055" + ], + [ + 0, + 1, + 0, + 0, + "C_3492056" + ], + [ + 0, + 0, + 0, + 1, + "T_3492057" + ], + [ + 0, + 0, + 1, + 0, + "G_3492058" + ], + [ + 0, + 0, + 0, + 1, + "T_3492059" + ], + [ + 0, + 0, + 1, + 0, + "G_3492060" + ], + [ + 0, + 0, + 1, + 0, + "G_3492061" + ], + [ + 1, + 0, + 0, + 0, + "A_3492062" + ], + [ + 0, + 0, + 0, + 1, + "T_3492063" + ], + [ + 0, + 0.99, + 0, + 0.01, + "C_3492064" + ], + [ + 0, + 0, + 0, + 1, + "T_3492065" + ], + [ + 0, + 0, + 0, + 1, + "T_3492066" + ], + [ + 0, + 0.99, + 0, + 0.01, + "C_3492067" + ], + [ + 0, + 0, + 1, + 0, + "G_3492068" + ], + [ + 0, + 0, + 1, + 0, + "G_3492069" + ], + [ + 0, + 1, + 0, + 0, + "C_3492070" + ] + ], + "hovertemplate": "Base / Position: %{customdata[4]}
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0, 1 ], - "showticklabels": false, "tickvals": [ 0, 0.2, 0.4, 0.6000000000000001, 0.8 - ] + ], + "title": { + "text": "Alternate allele frequency" + } }, "yaxis5": { "anchor": "x5", "domain": [ - 0.43199999999999994, - 0.568 + 0.72, + 0.82 ], "mirror": true, "range": [ 0, 1 ], + "showticklabels": false, "tickvals": [ 0, 0.2, @@ -14501,8 +9987,8 @@ "yaxis6": { "anchor": "x6", "domain": [ - 0.43199999999999994, - 0.568 + 0.72, + 0.82 ], "mirror": true, "range": [ @@ -14521,8 +10007,8 @@ "yaxis7": { "anchor": "x7", "domain": [ - 0.21599999999999997, - 0.352 + 0.54, + 0.64 ], "mirror": true, "range": [ @@ -14535,16 +10021,13 @@ 0.4, 0.6000000000000001, 0.8 - ], - "title": { - "text": "Alternate allele frequency" - } + ] }, "yaxis8": { "anchor": "x8", "domain": [ - 0.21599999999999997, - 0.352 + 0.54, + 0.64 ], "mirror": true, "range": [ @@ -14563,14 +10046,15 @@ "yaxis9": { "anchor": "x9", "domain": [ - 0, - 0.13599999999999998 + 0.54, + 0.64 ], "mirror": true, "range": [ 0, 1 ], + "showticklabels": false, "tickvals": [ 0, 0.2, @@ -14582,101 +10066,587 @@ } }, "text/html": [ - "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results_dict = AnoPrimer.plot_primer_snp_frequencies(\n", + " species='gambiae_sl',\n", + " primer_df=primer_df,\n", + " gdna_pos=gdna_pos,\n", + " contig=contig,\n", + " sample_sets=sample_set, \n", + " sample_query=sample_query,\n", + " assay_type=assay_type,\n", + " seq_parameters=seq_parameters,\n", + " out_dir=\".\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "aCVuE1CiAR4h", + "metadata": { + "id": "aCVuE1CiAR4h" + }, + "source": [ + "Now lets plot these primer pairs across the genome, highlighting where they are in relation to any nearby exons..." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cAacbw-q8Sgm", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "id": "cAacbw-q8Sgm", + "outputId": "aeb67689-9587-4bab-f8c0-97f66aea0699" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "image/png": { + "height": 379, + "width": 1288 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "AnoPrimer.plot_primer_locs(\n", + " species='gambiae_sl',\n", + " primer_res_dict=results_dict, \n", + " primer_df=primer_df, \n", + " assay_type=assay_type, \n", + " contig=contig, \n", + " seq_parameters=seq_parameters, \n", + " legend_loc='lower left'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "-aIy4IyoBc7-", + "metadata": { + "id": "-aIy4IyoBc7-" + }, + "source": [ + "### Checking our primers for specificity to the *Anopheles gambiae* genome\n", + "\n", + "We can use a cool new python package, [gget](https://github.com/pachterlab/gget), to rapidly search our primers against the AgamP3 genome, to ensure they will not amplify any other regions of the genome. gget rapidly queries large databases - in this case we can use the program BLAT to align primer sequences. Unfortunately as the lengths of primer sequences are so short, it is at the limit of BLATs sensitivity, and in some cases, matches are not returned. gget can also currently only query the older AgamP3 assembly. Therefore, it is also recommended run a more exhaustive search in [Primer-BLAST](https://www.ncbi.nlm.nih.gov/tools/primer-blast/)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f9fedfe4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 631 + }, + "id": "f9fedfe4", + "outputId": "b8baa2e0-6636-4022-b940-b033e7fdb4ce" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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genomequery_sizealigned_startaligned_endmatchesmismatches%_aligned%_matchedchromosomestrandstartend
primer
0forward_0anoGam320120200100.0100.00chr2R+34920513492070
reverse_0anoGam320120200100.0100.00chr2R-34921263492145
probe_0anoGam320120200100.0100.00chr2R+34920743492093
1forward_1anoGam320120200100.0100.00chr2R+34920473492066
reverse_1anoGam319119190100.0100.00chr2R-34920943492112
probe_1anoGam319119190100.0100.00chr2R+34920693492087
2forward_2anoGam319119181100.094.74chrX-1750419017504208
reverse_2anoGam319119190100.0100.00chr2R-34921213492139
probe_2anoGam318118180100.0100.00chr2R+34920593492076
3forward_3anoGam320120200100.0100.00chr2R+34920063492025
reverse_3anoGam318118180100.0100.00chr2R-34921003492117
probe_3anoGam318118180100.0100.00chr2R+34920593492076
4forward_4anoGam318118180100.0100.00chr2R+34920463492063
reverse_4anoGam320120200100.0100.00chr2R-34921433492162
probe_4anoGam319119190100.0100.00chr2R+34920693492087
5forward_5anoGam318118180100.0100.00chr2R+34920563492073
reverse_5anoGam319119190100.0100.00chr2R-34921463492164
probe_5anoGam320120200100.0100.00chr2R+34920743492093
\n", + "
" + ], + "text/plain": [ + " genome query_size aligned_start aligned_end matches \\\n", + " primer \n", + "0 forward_0 anoGam3 20 1 20 20 \n", + " reverse_0 anoGam3 20 1 20 20 \n", + " probe_0 anoGam3 20 1 20 20 \n", + "1 forward_1 anoGam3 20 1 20 20 \n", + " reverse_1 anoGam3 19 1 19 19 \n", + " probe_1 anoGam3 19 1 19 19 \n", + "2 forward_2 anoGam3 19 1 19 18 \n", + " reverse_2 anoGam3 19 1 19 19 \n", + " probe_2 anoGam3 18 1 18 18 \n", + "3 forward_3 anoGam3 20 1 20 20 \n", + " reverse_3 anoGam3 18 1 18 18 \n", + " probe_3 anoGam3 18 1 18 18 \n", + "4 forward_4 anoGam3 18 1 18 18 \n", + " reverse_4 anoGam3 20 1 20 20 \n", + " probe_4 anoGam3 19 1 19 19 \n", + "5 forward_5 anoGam3 18 1 18 18 \n", + " reverse_5 anoGam3 19 1 19 19 \n", + " probe_5 anoGam3 20 1 20 20 \n", "\n", - "// Listen for the clearing of the current output cell\n", - "var outputEl = gd.closest('.output');\n", - "if (outputEl) {{\n", - " x.observe(outputEl, {childList: true});\n", - "}}\n", + " mismatches %_aligned %_matched chromosome strand start \\\n", + " primer \n", + "0 forward_0 0 100.0 100.00 chr2R + 3492051 \n", + " reverse_0 0 100.0 100.00 chr2R - 3492126 \n", + " probe_0 0 100.0 100.00 chr2R + 3492074 \n", + "1 forward_1 0 100.0 100.00 chr2R + 3492047 \n", + " reverse_1 0 100.0 100.00 chr2R - 3492094 \n", + " probe_1 0 100.0 100.00 chr2R + 3492069 \n", + "2 forward_2 1 100.0 94.74 chrX - 17504190 \n", + " reverse_2 0 100.0 100.00 chr2R - 3492121 \n", + " probe_2 0 100.0 100.00 chr2R + 3492059 \n", + "3 forward_3 0 100.0 100.00 chr2R + 3492006 \n", + " reverse_3 0 100.0 100.00 chr2R - 3492100 \n", + " probe_3 0 100.0 100.00 chr2R + 3492059 \n", + "4 forward_4 0 100.0 100.00 chr2R + 3492046 \n", + " reverse_4 0 100.0 100.00 chr2R - 3492143 \n", + " probe_4 0 100.0 100.00 chr2R + 3492069 \n", + "5 forward_5 0 100.0 100.00 chr2R + 3492056 \n", + " reverse_5 0 100.0 100.00 chr2R - 3492146 \n", + " probe_5 0 100.0 100.00 chr2R + 3492074 \n", "\n", - " }) }; }); " + " end \n", + " primer \n", + "0 forward_0 3492070 \n", + " reverse_0 3492145 \n", + " probe_0 3492093 \n", + "1 forward_1 3492066 \n", + " reverse_1 3492112 \n", + " probe_1 3492087 \n", + "2 forward_2 17504208 \n", + " reverse_2 3492139 \n", + " probe_2 3492076 \n", + "3 forward_3 3492025 \n", + " reverse_3 3492117 \n", + " probe_3 3492076 \n", + "4 forward_4 3492063 \n", + " reverse_4 3492162 \n", + " probe_4 3492087 \n", + "5 forward_5 3492073 \n", + " reverse_5 3492164 \n", + " probe_5 3492093 " ] }, + "execution_count": 21, "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Tue May 14 04:50:39 2024 INFO No DNA BLAT matches were found for this sequence in genome anoGam3.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No hit for reverse - pair 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Tue May 14 04:50:40 2024 INFO No DNA BLAT matches were found for this sequence in genome anoGam3.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No hit for forward - pair 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Tue May 14 04:50:40 2024 INFO No DNA BLAT matches were found for this sequence in genome anoGam3.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No hit for forward - pair 3\n" - ] - }, - { - "data": { - "image/png": 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dN3nnnXeKzDVz5sxkwIABSXZ2drHj+vbtW+7/y4KCguT+++9Pdt111xL3uUGDBsnpp5+eTJ8+vczzrl69OrnpppuKPf6bPnbcccfk3HPPTb788staibek1+g///nP5LDDDit2G/Xq1UuuvPLKZM2aNUXm+8tf/pK0bdu22HHNmzdPHnrooTIfxw3Wr1+fDB06NNl7771L3O/s7OykT58+yXvvvVfu+atTo0aNUjFut912pfadMWNG2j5t+HuuaSW9JkaPHp1079692OOfk5OTnHjiicnMmTPLvb0JEyYkxx57bJKTk1Pi/+8BBxyQ/P3vfy/XvKNGjUp69eqVZGVllfo3mJubm+Tn5yfDhg0rca5zzjkn1b99+/ZF1t94442b/Vvf9FHc/29x7+ebU5PvC1WZuwAAAGBLtvH3OBu+9yzNiSeeWO7vDiIiGT9+fJni+dGPfpQa89lnn1Vy79hAmSgAAABQ48aPHx+rVq1KtXv27FmmX8BWpaeffjq6du0ar776ahQWFhbb54MPPoijjz46nnrqqXLPv3Tp0sjPz4/f/OY38fXXX5dpzH333RddunSJYcOGxerVq0vsN23atLjgggvi8MMPj4ULF5Y7tjlz5kS3bt3i9ttvjyVLlhTbZ9KkSdG7d+947bXXUsvefPPN2H///eP//u//Yv369cWOe/HFF6NHjx4xc+bMMsUyb9686N69ewwcODCmT59eYr9Vq1bF008/HZ07d067tfnm5r322mvLdPwXLFgQQ4cOjQkTJtRKvMW57bbbok+fPvHOO+8Uu37NmjXxxz/+Mfr27Rvr1q2LiIh169bFaaedFhdccEHMnj272HGLFi2KCy+8MG644YYyx/LNN99E165d49xzz41PP/20xH7r16+PkSNHxiGHHBJ/+MMfyjx/dZo+fXqsWLEi1a7MVQhr2+233x5HH310vPfee8WuLygoiOeeey66dOkSo0aNKvO8v/vd76Jbt27x6quvRkFBQYn9Pvzwwzj55JPjhBNOSHsPL8lvf/vbOProo2Ps2LFpV+wtztq1a2PMmDHx8MMPlznuTFGT7wvVnbsAAABgazZ//vwKjVu5cuVm+3z77bep72O6du0anTt3rtC2KMotqgEAAIAa9+abb6a1u3XrVqPb/9e//hUDBw6MgoKCqF+/fvTu3Ts6d+4cjRo1ilmzZsXLL7+cKhwsKCiICy64IA4++ODYfffdyzR/kiRx5plnxltvvRURETvvvHMcddRR0aZNm1i1alVMmTIl6tatmzbmjjvuiP/6r/9KW7bDDjvEscceG+3bt4/ly5fH+++/H++8806qUOjdd9+Nww8/PMaNGxdNmzYtU2xr1qyJn/zkJzFlypSIiDjwwAOjR48e0bRp05g7d268/PLLMWfOnFTfM844I6ZOnRrz58+Pn/zkJ/H9999H3bp144gjjoj99tsvGjZsGF9++WW8+OKLsXz58oiImD17dpx33nkxevToUmNZtGhRHHbYYfHVV1+llmVlZcVhhx0WBx98cDRu3Di+/vrreOWVV1L/H2vXro3zzjsvVq1aFZdcckmJc5922mnx8ccfp9r16tWLww8/PPbaa69o1qxZrF+/Pr777rv44osv4oMPPohFixZt9thVZ7ybeuaZZ+Kaa66JiB9usdynT5/YbbfdYv369fHRRx/FqFGjUsVN//znP+N//ud/YvDgwXHJJZfEs88+GxER7dq1i6OPPjpat24dK1asiNGjR8dHH32U2sbvf//7OPLIIyM/P7/UWKZOnRq9e/eOf//736lljRo1Sh3PvLy8+O6772LChAnx7rvvRpIkkSRJ/O53v4u1a9fG4MGDy7zf1eGJJ55Iax955JG1FEnlvPTSS3HVVVdFkiRRt27dOOqoo2LvvfeO7OzsmDp1arz66quposMVK1bE8ccfH6+//noccsghpc77i1/8Iu666660ZW3atIkf/ehH0apVq1i6dGm89dZbaa+dESNGRJ8+feL1118v8l62waOPPhpDhgxJW9a5c+fo3r17tG7dOnJzc2PZsmXxzTffxKRJk1LvSZVRp06dyM7OjohIK8LOysoq8ZZQG/pXVE2+L1R37gIAAICt3YbvCzp06BAjRowo87gOHTpsts+TTz6Zmv/ss8+uWIAUrzYvHwkAAABsm37605+m3eLjmWeeqdbtbXqbz9zc3CQiktNOOy2ZO3dukf7ff/99cuqpp6aNOeuss0rdxsa3NN1w++Z69eol9913X7G3kt74tsITJkxI6tatmxqflZWVXH/99cXeenjChAnJ7rvvnhbbqaeeWmpsG9+iesN22rRpU+wtTFetWpWceeaZafP/9re/TQ444IAkIpIjjjii2Ns4z5kzJ9l3333Txv3zn/8sNa6TTjoprf9ee+2VTJw4sUi/1atXJ7/97W+L3J75o48+KnbesWPHpvU97rjjknnz5pUYx/r165N33303ufDCC5Pnn3++xuNNkqKv0Q3/T5deemmybNmyIv3feuutpEmTJqn+jRo1Sh555JHU67uk193dd9+dtp0ePXqUGFOS/PB62Pj/tW7dusn111+fLF26tNj+H3/8cbLffvul+tepUycZPXp0qduoTosWLUqaN2+ets9vvPFGqWMy9RbV9erVSyIiOfzww1O3Kt7Y/PnzkxNOOCFtzO67756sWrWqxG0899xzRd4b77nnnqSwsLBI39deey3Zaaed0vr/5je/KXHujh07pvrtuOOOyeuvv17q/n799dfJ7bffnlx66aUl9tncLao3tvH7XlluUbWx8tyiuibfF6ojdwEAAMCWrLy3qD7uuOOSiEgaNmyYrFu3rkpj2fAdWt26dZOFCxdW6dzbOgWOAAAAQI078sgjy1VwVFmbFolERHL++eeXOmbNmjXJnnvumerfoEGDZOXKlSX237ggZsNjxIgRZYrviCOOSBt36623ltp/zpw5SZs2bcp8DDcu9ImIpGnTpslXX31VYv/Vq1cnu+yyS1qRWkQk3bt3T1avXl3iuM8++yzVd3NfKr7++utpMXXo0KHUIsQkSZLBgwenjTnyyCOL7Xf99den+jRp0iT5/vvvS523LKoz3iQp/jX6i1/8otT5H3jggbT+G479P/7xj1LHnX766WnjiiuW2+DGG29Mm/+5554rde4kSZKlS5em/e307Nlzs2Oqy6bFuqX9H2yQqQWOEZEcdNBByfLly0scs27duuTYY49NG/PHP/6x2L7r169POnTokNZ32LBhpcb0ySefJHl5ean+2dnZxb5+pk+fnjbv448/Xq59L0mmFTjWxvtCVecuAAAA2JKVt8DxuuuuS/Uv7sffFfXhhx+m5j3xxBOrbF5+UPx9OQAAAACq0eLFi9PaTZo0qdHt77TTTnHPPfeU2ic3Nzcuv/zyVHvVqlVpt2jdnNNPPz369eu32X4ff/xxvPHGG6n2IYccEldeeWWpY1q3bl3klrJ/+tOfyhzbH/7wh1Jvq1KvXr0499xzU+3CwsLIysqKhx9+OOrVq1fiuM6dO0fPnj1T7XfffbfEvpvGf++990aLFi1Kjfu6666LAw44INV+/fXX45NPPinSb968eanne+65ZzRu3LjUecuiOuMtTps2beKWW24ptc+ZZ54ZDRs2TLULCwvjpz/9aZx44omljrvooovS2iX9P61cuTLt7+Tiiy+OE044YTOR//D3fMcdd6Tab775ZkyePHmz46ra0KFD025PXb9+/fjzn/9c43FUlaysrHjooYeiUaNGJfbJycmJBx98MBo0aJBadv/996dua7+xF154IWbMmJFqn3rqqTFgwIBSY9h7773Tbjm+fv36Yt9LN/4bjIg4+OCDS513S1XT7ws1kbsAAABga7bxd1ub++6tPP7617+mnp9zzjlVNi8/UOAIAAAA1Ljvv/8+rV1awU51OP/889MKgErSq1evtPbnn39e5m1ccsklZer3/PPPp7V/9atfRVZW1mbHnXTSSbHbbrul2i+//HKsW7dus+Pq16+fVrxYku7du6e1e/bsGV26dNnsuEMOOST1fNq0acXGtHbt2nj11VdT7c6dO8exxx672bmzs7Pjv/7rv9KWjRgxoki/jQsav/jii1i5cuVm5y5NdcdbnPPOOy9yc3NL7dOwYcPYZ5990pYNHDhws3N369Yt7TX22WefFdvvxRdfjEWLFqXaV1xxxWbn3qBPnz7RrFmzVHv06NFlHlsV3nvvvSJ/g7fddlvsueeeNRpHVerZs2daYVxJ2rZtGyeffHKqPW3atPj000+L9Nv0vWfT12pJLr744sjLyytxnogoUlT84YcflmnuLUltvC/URO4CAACArVnXrl2jT58+EfHD96k33nhjqf1nzpwZTz/9dKl91q9fn+rTvHnz+MlPflI1wZKiwBEAAACocRsXx0RErFixoka3v2nxR0l22WWXtPaSJUvKNK5evXpphX6l2fjqednZ2dG3b98yjYv4ochxg7JepevAAw9Mu+pfSdq0aZPWPvzww8sU08bjkiSJZcuWFenz4YcfxurVq1PtslwVcIMTTzwx6tT5z1da77zzTpE+3bp1Sz1funRpDBgwIObMmVPmbWyquuMtzsZXwizNxsc7Ozs7Dj300M2OadiwYWy//fap9tKlS4vtN3bs2NTzHXbYoVzFgXXq1ImOHTum2jV5BbmpU6dG37590/7PzjvvvLjssstqLIbqUJYrwm5w/PHHp7Xfe++9In02fu/Zaaed0v5uStOwYcO0Qr4vv/wyFixYkNZnzz33THufv+KKK+Kf//xnmebfUtTG+0J15y4AAADIdG+99VYMHTo09Rg+fHhq3Zdffpm2bujQocXO8eijj0arVq0iIuK///u/45BDDokHH3ww3n333fjwww9j1KhRcfvtt0efPn1it912i7/97W+lxvTaa6/F3LlzI+KHu/rUrVu3anaWlJzaDgAAAADY9mxcXBUR8d1339Xo9nfeeecy9dv0ypLLly8v07hdd921zF9kffHFF6nnu+++e5mKDzc48MAD09pTpkyJrl27ljqmovtemWPWvHnztGUb73NElOmqdBs0btw4OnXqlJpjypQpRfqceOKJscsuu8TMmTMj4odfY7dv3z569+4dP/rRj+Lwww+PAw88cLNXSKypeItTkeO9/fbbR/369cs8bsPVGUt6XW981b2FCxdGTk75vkpcv3596vnGV4KsTt9880306dMnFi5cmFrWr1+/ePDBB2tk+9Vpv/32K3Pf/fffP6296VU6kySJadOmpdrleU1H/PDe83//93+p9pQpU2LHHXdMtevWrRuXX3553HzzzRHxw+unT58+0alTp+jXr1/06tUrDj300LQxW5ot5X0houy5CwAAADLdX/7yl3jssceKXff222/H22+/nbasuDvZtG7dOt59990YMGBATJgwId57771ifxy6wXbbbVdqTBvfnvrss88utS8V4wqOAAAAQI3bYYcd0trffvttjW6/rLfE3vRW0UmSlGlc06ZNyxzLxlfW2mmnnco8rrj+ZblKV1kLKDfd94qOK+6YbRpnZfa7uH2uV69ePPfcc9G6devUsvXr18eoUaPi17/+dRx66KHRpEmT6N27d9x0003x1Vdflbq96o63OBU53uUpjt14XEmv642LBCN+OIbleWysJoqYNxTRzZo1K7XsiCOOiGHDhpW7ODMTtWzZssJ9N33dLVu2LO3/qDreewYPHpx2ldmIH26Xffvtt8cJJ5wQLVq0iM6dO8ell14a//rXv6KwsLBcMdS22nhfqO7cBQAAANuK9u3bx3vvvRf/+Mc/4rTTTosOHTpEw4YNo27durHjjjvGYYcdFldeeWWMHTs2Hn744RLnWbZsWYwYMSIifrijxeZ+fE7FKHAEAAAAatymVyKbOHFiLUVSPcpzG5Lvv/8+9bysxSsbNG7cuMS5MtmmcVZmv0va5/322y8++eSTuPrqq4sU1EZErF69OsaMGRPXXXdd7LbbbtG/f/+0wriajjcTlXTr6oqo7uK1ZcuWxbHHHhuff/55atmBBx4YL7zwQpmvapnpyvO629wV/KryNV3cfBE/vA/+7W9/i6effrrI1WY3mDJlStx3331x9NFHx5577hn/+Mc/yhVHbdpW3xcAAACgNg0dOjSSJCnzozRZWVlx4oknxtNPPx1fffVVrFixItauXRvz58+Pt99+O2677bY44ogjivyQcGPbbbddrFy5MpIkSfteiqqlwBEAAACocT179kxrl3YLkK1dXl5e6vmKFSvKNXbToqWN58pkm8ZZmf0ubZ+33377GDJkSPz73/+O0aNHx6BBg+JHP/pRkdvKJEkSf//732P//fePjz76qNbizTQbXxGyR48e5fryeNPHmDFjqi3OVatWRb9+/dIKpffcc8949dVXN3sLoS1JeV53m/bdtCCxKl/Txc23QVZWVpx22mkxceLEmDp1atx///1x5plnRseOHYv0nTZtWpx88skxaNCgcsVSW7bV9wUAAACAmqbAEQAAAKhx3bp1iwYNGqTab775ZsyePbsWI6o9zZo1Sz2fO3duucZu2n/juTLZpnFWZr/Lss9169aN/Pz8uPHGG+PVV1+NxYsXx7hx44pc3XHp0qVx2mmnFbm9ck3Hmyk2PjbTp0+vxUhKtm7dujjllFPijTfeSC3bZZddYtSoUbHjjjvWYmRVb/78+WXuO2/evLT2pq+77bbbLrKzs1Ptmnjv6dSpU1x88cXx+OOPx/Tp02P27Nlx3333Fbl10+DBg+Ott94qVzy1YVt9XwAAAACoaQocAQAAgBpXr169OPPMM1PtwsLC+Mtf/lKLEdWePfbYI/V86tSpsXLlyjKP/eCDD9Lae+65Z5XFVZ023ueIiA8//LDMY5cvXx7Tpk1LtSuyz9nZ2dG9e/cYMmRITJs2LfbZZ5/Uui+++CKtWC4T4q0te+21V+r53LlzY+rUqbUYTVGFhYVx1llnxcsvv5xa1qpVqxg1alTsvPPOtRhZ9Sju6qIl+fjjj9PaG/9fRvxwZcVOnTql2uV5TUdUzXtPmzZtYuDAgTF+/Pi4/vrr09Y99NBD5Z6vpm2r7wsAAAAANU2BIwAAAFArrrzyysjKykq1b7vttkpfJW7NmjWVDavGHXbYYann69evjxdffLHMY5977rnU8wYNGsT+++9fhZFVnwMPPDDq16+faj///PNlHvv8889HYWFhqr3x8auIpk2bxk033ZS2bNPisEyKtyYdddRRae2nnnqqliIp3sCBA+PZZ59NtZs3bx7//Oc/Y9ddd63FqKpPed4bRowYkdbu3r17kT4bvxbnzp0b48ePL9Pcq1atitdeey3V3m233Sp9tczBgwenzbHp32BF1K1bN/V806uyVoVt9X0BAAAAoKYpcAQAAABqxR577BFnn312qr1y5co466yzYvny5RWa79tvv438/Pwqiq7mnHDCCWntO+64I5Ik2ey4ESNGpF0BrG/fvmkFPZmsbt268eMf/zjV/vzzz+PVV1/d7LjCwsK444470pZtevwqYtOCuLVr16a1My3emtKvX7/Iy8tLtf/0pz+V+za81eXqq69Ou8pfkyZN4rXXXosuXbrUYlTV64033ihT4d8333wTf//731PtTp06xd57712k36avxdtvv71McTz00EOxbNmyVPukk04q07jSZGVlRYcOHVLtTf8GK2Lj1+6SJUsqPd+mttX3BQAAAICapsARAAAAqDX33HNP7L777qn2u+++G8ccc0zMnz+/XPOMGDEiunbtGuPGjavqEKvdvvvuG7169Uq1x40bF3/84x9LHfPtt9/G5ZdfnrbsF7/4RbXEV12uuOKKtPall14aCxYsKHXMzTffHBMnTky1jz766GILtyZOnFiuK7ZtekvqXXbZpUbjzVTbb7992uts6dKlcfLJJ8d3331Xrnn+9a9/VWlcN998c9x6662pdsOGDeOll16Kgw46qEq3k2mSJIkLL7wwVq1aVWKf9evXx8CBA9NudT9w4MC0q+Vu0Ldv37SiwmeffTaGDx9eagyff/552u2ks7Oz47LLLivSb8aMGZv9+9jY4sWLY/Lkyal2cX+D5bXxHB988EHaFROryrb4vgAAAABQ0xQ4AgAAALWmcePG8X//93/RvHnz1LJx48bF7rvvHkOGDCm1UGT16tXx4osvRq9eveKEE06IOXPm1ETI1eKPf/xj2tUXr7766hg0aFCsW7euSN8PPvggjjzyyJg9e3Zq2amnnho9evSokVirSn5+ftqV32bMmBG9e/eOjz76qEjftWvXxu9+97v43e9+l1pWr169uO2224qd+8orr4xOnTrFTTfdFFOnTi01jn/84x/x61//OtVu0KBBHHvssTUabya77rrrYt99902133333ejatWu8+OKLpV5p9Ntvv42777479ttvv+jfv3+VxfPggw/Gtddem2rXq1cvnn/++S3u9V8R9erViwkTJsSPf/zjmDVrVpH1CxcujAEDBsRLL72UWrb77rvHpZdeWux8derUKXIlwf/3//5f3HfffcX+344aNSqOOuqotKs3XnXVVdG+ffsifceOHRvt2rWL888/P0aOHBkFBQUl7tfMmTPj+OOPjxUrVqSWnXzyySX2L6tDDz009fzbb7+Nyy67LGbMmFHpeTe2rb4vAAAAANSknNoOAAAAANi27bvvvvHuu+/GcccdF19++WVERHz33Xfx29/+Nq699to4+OCDY6+99oodd9wxcnNzY968eTF79ux48803065SFhHFXqVsS3DQQQfFkCFD4r/+678i4ocrtQ0ePDjuvffe+PGPfxzt2rWLFStWxIQJE+Ltt99OKz7aY4894v7776+t0CvloYceio8//ji++uqriIiYPHlyHHjggXH44YfHQQcdFI0bN47Zs2fHyy+/XKTY9Y477oj99tuvxLlnzJgR1113XVx33XXRtm3bOPDAA2OXXXaJpk2bRkFBQcyZMyfeeuut1Gtug5tuuim22267Go83UzVq1Cief/75OOKII1JFtdOmTYt+/frFzjvvHEcccUS0adMmGjZsGN999118++238eGHH8a0adNSr9MmTZpUWTw333xzWnvNmjXFFqRuzvTp04stzCvO2LFjIyen/F+j3nDDDXHDDTeUe1xJbrvttrjiiiti7Nixsfvuu8fRRx8dXbp0iezs7Jg6dWq8+uqrae+JDRo0iMceeyzq169f4pwnnHBCXHHFFXHXXXdFxA9FeJdeemncfPPN8aMf/ShatWoVS5cujbfffjs++OCDtLGHH354/P73vy9x7tWrV8ejjz4ajz76aOTl5cX+++8fnTt3jubNm0dubm4sWbIkPv7443j77bfTCiAPOuigOPfccyt4lP7jzDPPjBtvvDF1xcv7778/7r///sjJyYl69eql+vXs2TNeeeWVCm9nW3xfAAAAAKhJChwBAACAWtepU6cYN25cXH/99fGXv/wldeXCwsLCGD9+fIwfP36zcxxzzDFxyy23VHeo1eZXv/pV1K9fP6644opUsc+CBQvir3/9a4ljDjnkkHjhhReiadOmNRRl1WrevHm8/fbb8ZOf/CRVPJUkSbz55pvx5ptvFjumbt26cf/998f5559f5u3Mnj077YqXxalTp07ccMMN8ctf/rLW4800u+yyS0ycODHOOOOMGDVqVGr5nDlz4umnn97s+I2LySqruCsLlud25KXNU5qKbKOqb4nct2/fWLNmTVx99dWxdu3aePnll+Pll18utm+jRo3iH//4RxxyyCGbnfdPf/pTNG7cOG6++ebUcZk9e3b85S9/KXFMv3794plnnkm78mxpvv/++1L/Tjbo3r17vPDCCxUqKN1Uq1at4tFHH41zzz03Vq9enVpeUFCQVlBZ2i2/y2JbfV8AAAAAqCluUQ0AAABkhObNm8e9994bn3/+eVxxxRXRqVOnzY5p1apVXH755fH+++/HyJEjY//996/+QKv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- "text/plain": [ - "
" - ] - }, - "metadata": { - "image/png": { - "height": 379, - "width": 1308 - } - }, - "output_type": "display_data" + "output_type": "execute_result" } ], + "source": [ + "blat_result_df = AnoPrimer.gget_blat_genome(primer_df, assay_type, assembly='anoGam3')\n", + "blat_result_df" + ] + }, + { + "cell_type": "markdown", + "id": "1f7ca79e", + "metadata": { + "id": "1f7ca79e" + }, + "source": [ + "### Further considerations\n", + "\n", + "We may now have designed suitable primers. However, there are some further considerations...\n", + "\n", + "- Primers should be run in [**Primer-BLAST**](https://www.ncbi.nlm.nih.gov/tools/primer-blast/), to ensure specificity against the host organism, and specificity for the genomic location of interest.\n", + "\n", + "- If in multiplexed use with other primers or probes, primers must not interact with each other. This can be investigated on a one by one basis using the IDT tool [oligoanalyzer](https://eu.idtdna.com/calc/analyzer), though higher throughput algorithms may be required.\n", + "\n", + "- If designing TaqMan or Locked Nucleic Acid (LNA) probes for SNP detection, further modification will be required, such as adding fluorophores and ensuring the two probes (one for wild-type, one for mutant) can discriminate between SNPs. for LNA probes, you will want to play around with the placement of LNAs in the oligo sequence, which can allow short probes (~10-14 bases) to bind with high affinity and discriminate between SNPs. IDT have a tool for this which allow you to check the binding affinity between mismatches, though it requires a log in https://eu.idtdna.com/calc/analyzer/lna. \n", + "\n", + "- Many more considerations.... [IDT - How to design primers and probes for PCR and qPCR](https://eu.idtdna.com/pages/education/decoded/article/designing-pcr-primers-and-probes) \n" + ] + }, + { + "cell_type": "markdown", + "id": "7FUld8V3GQRk", + "metadata": { + "id": "7FUld8V3GQRk" + }, + "source": [ + "## Worked example: Primers to avoid\n", + "\n", + "In the above *ace1* example, all of the primer pairs look reasonable. Lets have a look at an example where some of the primers do not look so good. A UGT detoxification gene, AGAP006222, is overexpressed very highly in populations of *An. coluzzii* - lets try and design some qPCR primers to measure its expression. The below `AnoPrimer.designPrimers()` function integrates the entire notebook into one function, for convenience." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "hKugtREjGU8-", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "hKugtREjGU8-", + "outputId": "f6cbfa18-b3e7-41d0-f01b-2347dd317015" + }, + "outputs": [], "source": [ "primer_parameters = {\n", " 'PRIMER_OPT_SIZE': 20,\n", @@ -14748,7 +10718,7 @@ "\n", "For the exercises, you can either re-run this notebook or use the function in the cell above, which integrates the above notebook into one single function, for convenience.\n", "\n", - "1. Design a set of qPCR primers to measure gene expression of the carboxylesterase gene, COEAE2F (contig='2L', transcript='AGAP006228-RA'), restricting the samples to just *Anopheles gambiae*.\n", + "1. Design a set of qPCR primers to measure gene expression of the carboxylesterase gene, COEAE2F (contig='2L', transcript='AGAP006228-RA'), restricting the samples to just *Anopheles gambiae*. HINT: use 'cDNA primers'.\n", "\n", "2. Design a set of primers and a probe for the *vgsc*-995F *kdr* mutation (contig = 2L, genome bp = 2422652)\n", "\n", @@ -14758,7 +10728,7 @@ "\n", "En tant qu'exercices, vous pouvez soit exécuter à nouveau ce notebook ou utiliser la fonction de la cellule ci-dessus, qui intègre le contenu du reste du notebook dans une seule cellule, pour une plus grande aisance.\n", "\n", - "1. Créer un ensemble d'amorces pour qPCR afin de mesurer l'expression génique du gène carboxylesterase COEAE2F (contig='2L', transcript='AGAP006228-RA'), en limitant les échantillons aux Anopheles gambiae.\n", + "1. Créer un ensemble d'amorces pour qPCR afin de mesurer l'expression génique du gène carboxylesterase COEAE2F (contig='2L', transcript='AGAP006228-RA'), en limitant les échantillons aux Anopheles gambiae. HINT: use 'cDNA primers'.\n", "\n", "2. Créer un ensemble d'amorces et une sonde pour la mutation vgsc-995F kdr (contig = 2L, genome bp = 2422652)\n", "\n", @@ -14767,7 +10737,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "C0wyXmfVQmVv", "metadata": { "id": "C0wyXmfVQmVv" @@ -14797,9 +10767,9 @@ "\n", "# primer_df, blat_df = AnoPrimer.designPrimers(\n", "# species='gambiae_sl',\n", - "# assay_type='gDNA primers + probe', # assay_type options are: 'qPCR primers', 'gDNA primers', 'gDNA primers + probe', 'probe'\n", - "# target='X:9_500_000' ,#'AGAP000818-RA', # target should be an AGAP transcript identifier in for qPCR, otherwise should be an integer in genome\n", - "# assay_name='x_locus',\n", + "# assay_type='cDNA primers', # assay_type options are: 'cDNA primers', 'gDNA primers', 'gDNA primers + probe', 'probe'\n", + "# target='AGAP000818-RA', # target should be an AGAP transcript identifier in for cDNA primers, otherwise should be an integer in genome\n", + "# assay_name='COEAE2F',\n", "# n_primer_pairs=8,\n", "# min_amplicon_size=60,\n", "# max_amplicon_size=120,\n", diff --git a/searchindex.js b/searchindex.js index 977288b..4d57ee8 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["home", "workshop-1/about", "workshop-1/journal-club", "workshop-1/module-1-colab", "workshop-1/module-2-sample-metadata", "workshop-1/module-3-genome", "workshop-1/module-4-vgsc-snps", "workshop-2/about", "workshop-2/journal-club", "workshop-2/module-1-pandas", "workshop-2/module-2-metabolic-ir", "workshop-2/module-3-cnv-calling", "workshop-2/module-4-cnv-frequencies", "workshop-3/about", "workshop-3/journal-club", "workshop-3/module-1-plotly", "workshop-3/module-2-population-structure", "workshop-3/module-3-snps", "workshop-3/module-4-pca", "workshop-4/about", "workshop-4/journal-club", "workshop-4/module-1-numpy", "workshop-4/module-2-cryptic-species", "workshop-4/module-3-aims", "workshop-4/module-4-taxon-analysis", "workshop-5/about", "workshop-5/module-1-xarray", "workshop-5/module-2-diversity", "workshop-5/module-3-stats", "workshop-5/module-4-roh", "workshop-6/about", "workshop-6/module-1-haplotypes", "workshop-6/module-2-selection", "workshop-6/module-3-gwss", "workshop-6/module-4-primers", "workshop-7/about", "workshop-7/module-1-op-ir", "workshop-7/module-2-gene-flow", "workshop-7/module-3-haplotype-clustering", "workshop-7/module-4-haplotype-networks", "workshop-8/about", "workshop-8/module-1-planning-analysis", "workshop-8/module-2-presenting-results", "workshop-8/module-3-genomic-surveillance-tanzania", "workshop-8/module-4-genomic-surveillance-ghana"], "filenames": ["home.md", "workshop-1/about.ipynb", "workshop-1/journal-club.ipynb", "workshop-1/module-1-colab.ipynb", "workshop-1/module-2-sample-metadata.ipynb", "workshop-1/module-3-genome.ipynb", "workshop-1/module-4-vgsc-snps.ipynb", "workshop-2/about.ipynb", "workshop-2/journal-club.ipynb", "workshop-2/module-1-pandas.ipynb", "workshop-2/module-2-metabolic-ir.ipynb", "workshop-2/module-3-cnv-calling.ipynb", "workshop-2/module-4-cnv-frequencies.ipynb", "workshop-3/about.ipynb", "workshop-3/journal-club.ipynb", "workshop-3/module-1-plotly.ipynb", "workshop-3/module-2-population-structure.ipynb", "workshop-3/module-3-snps.ipynb", "workshop-3/module-4-pca.ipynb", "workshop-4/about.ipynb", "workshop-4/journal-club.ipynb", "workshop-4/module-1-numpy.ipynb", "workshop-4/module-2-cryptic-species.ipynb", "workshop-4/module-3-aims.ipynb", "workshop-4/module-4-taxon-analysis.ipynb", "workshop-5/about.ipynb", "workshop-5/module-1-xarray.ipynb", "workshop-5/module-2-diversity.ipynb", "workshop-5/module-3-stats.ipynb", "workshop-5/module-4-roh.ipynb", "workshop-6/about.ipynb", "workshop-6/module-1-haplotypes.ipynb", "workshop-6/module-2-selection.ipynb", "workshop-6/module-3-gwss.ipynb", "workshop-6/module-4-primers.ipynb", "workshop-7/about.ipynb", "workshop-7/module-1-op-ir.ipynb", "workshop-7/module-2-gene-flow.ipynb", "workshop-7/module-3-haplotype-clustering.ipynb", "workshop-7/module-4-haplotype-networks.ipynb", "workshop-8/about.ipynb", "workshop-8/module-1-planning-analysis.ipynb", "workshop-8/module-2-presenting-results.ipynb", "workshop-8/module-3-genomic-surveillance-tanzania.ipynb", "workshop-8/module-4-genomic-surveillance-ghana.ipynb"], "titles": ["Home", "Workshop 1 - Pyrethroid target-site resistance", "Journal club - Clarkson et al. 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"102": [4, 6, 11, 12, 26, 29], "inspect": [4, 6, 9, 11, 26, 31, 41], "condit": [4, 9, 34], "262": [4, 9], "ab0326": 4, "bf18": 4, "09_colu_2014": [4, 6, 9, 12, 26, 36], "09_colu_2014_07": 4, "09_colu_2014_q3": 4, "09_houet_colu_2014": [4, 33, 37], "09_houet_colu_2014_07": 4, "09_houet_colu_2014_q3": 4, "263": [4, 29], "ab0327": 4, "264": [4, 29], "ab0328": 4, "265": 4, "ab0329": 4, "266": 4, "ab0330": 4, "359": 4, "ab0533": 4, "bf13": 4, "souroukoudinga": 4, "238": [4, 6, 12, 26, 34], "09_gamb_2014": [4, 6, 9, 12, 26], "09_gamb_2014_07": 4, "09_gamb_2014_q3": 4, "09_houet_gamb_2014": [4, 33, 37], "09_houet_gamb_2014_07": 4, "09_houet_gamb_2014_q3": 4, "360": 4, "ab0536": 4, "31": [4, 6, 9, 10, 11, 12, 15, 17, 18, 26, 28, 31, 38], "361": 4, "ab0537": 4, "362": [4, 6, 17], "ab0538": 4, "33": [4, 6, 9, 12, 15, 26, 34, 37], "363": [4, 23], "ab0408": 4, "bf14": 4, "break": [4, 7, 10, 26, 34, 36], "arabiensi": [4, 6, 10, 12, 17, 23, 24, 26, 28, 34, 37, 41], "46": [4, 6, 12, 15, 17, 26, 28, 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24, 26, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39, 41, 42], "restart": [4, 18, 26, 28, 29, 31, 33, 34, 36, 37, 39], "kernel": [4, 18, 26, 28, 29, 31, 33, 34, 36, 37], "straightforward": [4, 13, 34], "center": [4, 5, 11], "pan": [4, 15], "zoom": [4, 5, 6, 12, 15, 17, 24, 33, 38, 39], "basemap": [4, 28], "openstreetmap": 4, "mapnik": 4, "marker": [4, 10, 11, 12, 19, 29, 34, 35], "pivot_location_taxon": 4, "89": [4, 12, 26, 29, 34], "38": [4, 6, 12, 26, 28, 34], "35": [4, 6, 9, 12, 15, 18, 26, 34, 36], "muheza": [4, 24, 26, 28], "940": [4, 28], "948": [4, 6, 12, 26, 28], "muleba": [4, 24, 28], "962": [4, 6, 12, 26, 28], "621": [4, 6, 12, 26, 28], "tarim": [4, 28], "431": [4, 6, 9, 12, 26, 28], "34": [4, 6, 9, 11, 12, 15, 26, 28], "199": [4, 6, 12, 26, 28], "kihihi": 4, "751": [4, 6, 12, 26, 28], "701": [4, 6, 12, 17, 26, 28], "95": [4, 6, 12, 15, 26, 28, 29, 33, 34], "nagongera": 4, "770": [4, 28], "026": [4, 6, 12, 26, 28], "112": [4, 6, 12, 26, 28, 34], "127": [4, 29], "reset_index": [4, 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Setup# +
+
Wed May 22 06:52:06 2024 WARNING No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable
+
+
+
Wed May 22 06:52:07 2024 WARNING No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable
+
+
+
@@ -707,7 +1333,7 @@

Selecting primer parameters# N.B., this cell will be rendered as a form when running on colab -assay_type = "gDNA primers + probe" #@param ["gDNA primers", "gDNA primers + probe", "probe", "qPCR primers"] +assay_type = "gDNA primers + probe" #@param ["gDNA primers", "gDNA primers + probe", "probe", "cDNA primers"] assay_name = 'ace1-280s' #@param {type:"string"} min_amplicon_size = 60 #@param {type:"integer"} max_amplicon_size = 120 #@param {type:"integer"} @@ -724,7 +1350,7 @@

Selecting primer parametersif any(item in assay_type for item in ['gDNA', 'probe']): assert target_loc > 0, "Target location must be above 0 and less than the contig length" -elif assay_type == 'qPCR primers': +elif assay_type == 'cDNA primers': assert len(transcript) > 2, "Transcript ID is not valid, should be vectorbase ID such as 'AGAP004707-RD'" @@ -740,7 +1366,7 @@

Selecting primer parameters -
Tue May 14 04:48:39 2024 WARNING No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable
+
Wed May 22 06:52:41 2024 WARNING No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable
 
-
                                     
-
-
-
Tue May 14 04:50:39 2024 INFO No DNA BLAT matches were found for this sequence in genome anoGam3.
-
-
-
No hit for reverse - pair 1
-
-
-
Tue May 14 04:50:40 2024 INFO No DNA BLAT matches were found for this sequence in genome anoGam3.
-
-
-
No hit for forward - pair 2
-
-
-
Tue May 14 04:50:40 2024 INFO No DNA BLAT matches were found for this sequence in genome anoGam3.
-
-
-
No hit for forward - pair 3
-
-
-../_images/10981181533700c500305d703e650337ee63863e408661d79dbe69b2be8cdca0.png -

We can see that only primer pair 4 really looks suitable, as all the other primer pairs contain SNPs at their 3’ end in at least one of the forward or reverse primers.

@@ -2471,7 +3051,7 @@

Exercises#

For the exercises, you can either re-run this notebook or use the function in the cell above, which integrates the above notebook into one single function, for convenience.

    -
  1. Design a set of qPCR primers to measure gene expression of the carboxylesterase gene, COEAE2F (contig=’2L’, transcript=’AGAP006228-RA’), restricting the samples to just Anopheles gambiae.

  2. +
  3. Design a set of qPCR primers to measure gene expression of the carboxylesterase gene, COEAE2F (contig=’2L’, transcript=’AGAP006228-RA’), restricting the samples to just Anopheles gambiae. HINT: use ‘cDNA primers’.

  4. Design a set of primers and a probe for the vgsc-995F kdr mutation (contig = 2L, genome bp = 2422652)

  5. Design a set of qPCR primers to target the gene from module 3’s practical exercise.

@@ -2480,7 +3060,7 @@

English#

Français#

En tant qu’exercices, vous pouvez soit exécuter à nouveau ce notebook ou utiliser la fonction de la cellule ci-dessus, qui intègre le contenu du reste du notebook dans une seule cellule, pour une plus grande aisance.

    -
  1. Créer un ensemble d’amorces pour qPCR afin de mesurer l’expression génique du gène carboxylesterase COEAE2F (contig=’2L’, transcript=’AGAP006228-RA’), en limitant les échantillons aux Anopheles gambiae.

  2. +
  3. Créer un ensemble d’amorces pour qPCR afin de mesurer l’expression génique du gène carboxylesterase COEAE2F (contig=’2L’, transcript=’AGAP006228-RA’), en limitant les échantillons aux Anopheles gambiae. HINT: use ‘cDNA primers’.

  4. Créer un ensemble d’amorces et une sonde pour la mutation vgsc-995F kdr (contig = 2L, genome bp = 2422652)

  5. Créer un ensemble d’amorces pour qPCR ciblant le gène de l’exercice pratique du module 3

@@ -2509,9 +3089,9 @@

Français# primer_df, blat_df = AnoPrimer.designPrimers( # species='gambiae_sl', -# assay_type='gDNA primers + probe', # assay_type options are: 'qPCR primers', 'gDNA primers', 'gDNA primers + probe', 'probe' -# target='X:9_500_000' ,#'AGAP000818-RA', # target should be an AGAP transcript identifier in for qPCR, otherwise should be an integer in genome -# assay_name='x_locus', +# assay_type='cDNA primers', # assay_type options are: 'cDNA primers', 'gDNA primers', 'gDNA primers + probe', 'probe' +# target='AGAP000818-RA', # target should be an AGAP transcript identifier in for cDNA primers, otherwise should be an integer in genome +# assay_name='COEAE2F', # n_primer_pairs=8, # min_amplicon_size=60, # max_amplicon_size=120,