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02_load_dna_rna.html
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<h1>Step 02 - Loading and Processing DNA/RNA Alignment Data</h1>
<p>Using the <code>chiptools</code> python package that I wrote, I have created a list of unique trimmed and merge reads (contigs) and the number of times they appear in each replicate library. For the 202 analysis I used <code>rna_table.py</code> in the chiptools package to create RNA/DNA measurements and remove spurrious contigs. For 203, we will use R instead, so we can be more thorough in checking. Also because while Python is the bee's knees, PANDAS is slow, lame, and missing functionality. </p>
<h2>Getting limits on RNA and DNA lengths</h2>
<p>In order to throw away spurrious reads and avoid wasting time on alignments, we want to know what the shortest and longest legitimate RNA and DNA alignments are. We can use the <code>lib_seqs</code> DataFrame that we computed in <code>01</code>. </p>
<pre><code class="r">
seq_cols <- grep(".seq", names(lib_seqs))
lib_seqs$Length <- nchar(apply(apply(lib_seqs[, seq_cols], 1, as.character),
2, paste, collapse = ""))
table(lib_seqs$Length)
</code></pre>
<pre><code>##
## 91 92 93 94
## 3562 3562 3562 3562
</code></pre>
<h3>DNA min/max lengths</h3>
<p>All of the library members are between 91 and 94 bases. For the DNA, we just check that all sequences are between 91 and 94 bases. </p>
<ul>
<li><strong>DNA min/max - 91,94</strong></li>
</ul>
<h3>RNA min/max lengths</h3>
<p>For the RNA, we want to throw away anything that might be DNA (too long) and also throw away anything that is too short. </p>
<p>If it is too short then the promoter won't be identifiable. Since we saw in <code>01</code> that the RBS length is between 18 and 21, and we only one base of the barcode, since we only have two promoters. It might be a good idea to make sure that one of the three allowed mismatches is not the last base of the barcode. Therefore the shortest RNA allowed should be 33 (CDS) + 18 (shortest RBS) + 1 (last base of barcode) = 52.</p>
<p>If it is more than 90 bases, then it could possibly be DNA, so we'll remove it. If it is less than 90, then it could be DNA with deletions, but since bowtie only matches with mismatches (not indels) we should be safe from that. </p>
<ul>
<li><strong>RNA min/max - 52,90</strong></li>
</ul>
<p>Finally, I am also filtering on there being at least 4 reads for a unique <br/>
read, meaning we need to see two reads on average in each replicate.</p>
<h2>Running Bowtie on RNA and DNA</h2>
<p>This was done on the <code>GMC</code> server, but I am putting the shell code that I ran here.</p>
<h3>Bowtie Settings</h3>
<p><code>-k 114</code> | report up to 114 alignments per contig<br/>
<code>-v 3</code> | 3 mismatches per contig (no indels)<br/>
<code>-l 10</code> | seed length of 10<br/>
<code>-p 16</code> | use all 16 processors</p>
<h3>Input</h3>
<p>My read names are 4 tab-separated fields, read number, count, bin 1, bin 2. They are in FASTA format; the perl takes them as they get fed into bowtie and filters them for size. </p>
<h3>File Locations on GMC</h3>
<pre><code class="bash">lib_prefix=/scratch/dbg/ecre/fa/203.norestrict
out_prefix=/scratch/dbg/ecre/203_hs
dna_prefix=/scratch/dbg/ecre/ct/203_hsdna/203_hsdna.counts
rna_prefix=/scratch/dbg/ecre/ct/203_hsrna/203_hsrna.counts
</code></pre>
<h3>Some read length distribution analysis</h3>
<p>Using some bash scripting I got the RNA and DNA contig length distributions before filtering:</p>
<pre><code class="console">
$ perl -pe 's/([^ATGC])\n/$1\t/' $dna_prefix.fa | perl -ne '@l = split; print length($l[4])."\n";' | sort -nr | uniq -c
$ perl -pe 's/([^ATGC])\n/$1\t/' $rna_prefix.fa | perl -ne '@l = split; print length($l[4])."\n";' | sort -nr | uniq -c
</code></pre>
<p>These of course contain the spike-ins for the 202 library, so we have to keep that in mind, but it is interesting nonetheless. I cleaned up the output and made them into tsvs, and put them in <code>data/203.dna_hist.txt</code> and <code>data/203.rna_hist.txt</code>. </p>
<pre><code class="r"># load the text files I made with bash scripts
dna_read_lengths <- read.table(file = paste(getwd(), "/data/203.dna_hist.txt",
sep = ""), sep = "\t", header = T, row.names = NULL, col.names = c("count",
"length"))
rna_read_lengths <- read.table(file = paste(getwd(), "/data/203.rna_hist.txt",
sep = ""), sep = "\t", header = T, row.names = NULL, col.names = c("count",
"length"))
# combine them into one DF
read_lengths <- melt(merge(dna_read_lengths, rna_read_lengths, by = "length",
suffixes = c(".dna", ".rna")), id.vars = "length")
# plot
ggplot(read_lengths, aes(x = length, y = value)) + geom_line(aes(colour = variable)) +
scale_y_log10(breaks = 10^(1:6), name = "contig count (log10)") + scale_x_continuous(name = "length (bp)")
</code></pre>
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" alt="plot of chunk 2.02-read_length_hist"/> </p>
<p>This looks right; there is a big hump for the RNA at about 55 bp, and most of the DNA looks like it is around 91 to 94 bp. The hump for the RNA in that same region is maybe DNA contamination. The longer things (100+ bp) are perhaps concatamers, but it is unclear. It is interesting how much junk there is in the DNA. As I mentioned, the 202 library is adding some peaks (perhaps the jagged ones between 30 and 50 bp). </p>
<h3>Run Bowtie</h3>
<blockquote>
<p>Note: the RNA/DNA min/max I arrived at above are hard-coded in the perl below. </p>
</blockquote>
<pre><code class="bash">#build the bowtie index
bowtie-build /scratch/dbg/ecre/fa/203.norestrict.fa \
/scratch/dbg/ecre/fa/203.norestrict
#perform bowtie for DNA
bowtie -v 3 -l 10 -k 114 -p 16 \
--norc --best --strata --suppress 2,6 \
--un $out_prefix.unmapped.fa -f $lib_prefix \
<(perl -pe 's/([^ATGC])\n/$1\t/' $dna_prefix.fa \
| perl -ne '@l = split; ($l[1] > 4
&& length($l[4]) <= 94 && length($l[4]) >= 91)
&& (s/\t([ATGC])/\n$1/ && print);') \
> $out_prefix.dna.bowtie
#perform bowtie for RNA
bowtie -v 3 -l 10 -k 114 -p 16 \
--norc --best --strata --suppress 2,6 \
--un $out_prefix.unmapped.fa -f $lib_prefix \
<(perl -pe 's/([^ATGC])\n/$1\t/' $rna_prefix.fa \
| perl -ne '@l = split; ($l[1] > 4
&& length($l[4]) < 90 && length($l[4]) > 52)
&& (s/\t([ATGC])/\n$1/ && print);') \
> $out_prefix.rna.bowtie
</code></pre>
<h3>Bowtie Output Summaries</h3>
<p>For DNA:</p>
<pre><code># reads processed: 1140587
# reads with at least one reported alignment: 837696 (73.44%)
# reads that failed to align: 302891 (26.56%)
Reported 842175 alignments to 1 output stream(s)
</code></pre>
<p>For RNA:</p>
<pre><code># reads processed: 1028684
# reads with at least one reported alignment: 663124 (64.46%)
# reads that failed to align: 365560 (35.54%)
Reported 689549 alignments to 1 output stream(s)
</code></pre>
<p>More contigs failed to align than I expected, but some may be due to the 202 spike-in library. I copied <code>/scratch/dbg/ecre/*.bowtie</code> to the project, though the files were pretty big. I put them in the <code>data/</code> dir and will add them to the <code>.gitignore</code> file.</p>
<h3>Output Format</h3>
<p>More info can be found in the online <a href="http://bowtie-bio.sourceforge.net/manual.shtml#default-bowtie-output">Bowtie docs</a>.</p>
<pre><code>01 Name of read that aligned
02 Total read count
03 Read count in bin 1
04 Read count in bin 2
(suppressed) Reference strand aligned to
05 Name of reference sequence where alignment occurs
06 0-based offset into the forward reference strand
07 Read sequence
(suppressed) Read qualities
08 Number of other alignments for this RNA
09 Mismatches (base:N>N, ... )
</code></pre>
<h2>Annotating the Bowtie Output</h2>
<p>Now I want to follow the path I took in <code>rna_table.py</code>. There are several steps required for both RNA and DNA:<br/>
1. Get the sequence length <br/>
2. Get the corresponding library member length<br/>
3. Get the righthand offset from the lefthand offset and member length</p>
<p>Additionally, only keep RNA that has:<br/>
1. a single best alignment<br/>
2. an offset of at least 2</p>
<p>For DNA:<br/>
1. a single best alignment<br/>
2. aligns end to end (left and right offset of 0)</p>
<pre><code class="r">
# load the raw bowtie output, add library annotation data, and get lengths
# and offset info
col.names = c("Read.num", "Count", "Count.A", "Count.B", "Name",
"Offset.L", "Read.seq", "Alts", "Mismatches")
load_raw_reads <- function(filename, is.rna, col.names) {
raw <- read.table(file = filename, sep = "\t", header = F, row.names = NULL,
stringsAsFactors = F, col.names = col.names)
raw <- merge(raw, lib_seqs, by = "Name")
raw$Read.len <- nchar(raw$Read.seq)
raw$Offset.R <- raw$Length - raw$Read.len - raw$Offset.L
raw$Mismatches[is.na(raw$Mismatches)] <- ""
raw$Mismatches.len <- unlist(lapply(raw$Mismatches, function(x) length(unlist(strsplit(x,
",", fixed = T)))))
return(raw)
}
dna.raw <- load_raw_reads(file = paste(getwd(), "/data/203.dna.bowtie",
sep = ""), is.rna = F, col.names)
rna.raw <- load_raw_reads(file = paste(getwd(), "/data/203.rna.bowtie",
sep = ""), is.rna = T, col.names)
</code></pre>
<p>Also, we should get the RNA start position relative to the Promoter/RBS junction. Take the left offset and subtract 40, the length of promoter (constant between the two).</p>
<pre><code class="r">rna.raw$Offset.RBS <- with(rna.raw, Offset.L - 40)
</code></pre>
<h3>Exploratory Plots</h3>
<p>Before we filter, let's just look at some plots. </p>
<pre><code class="r">
ggplot(rna.raw, aes(x = Offset.L)) + geom_bar(aes(fill = cut(rna.raw$Alts,
breaks = c(-Inf, 0, 1, 100, Inf), labels = c("None", "One", "> 1", "> 100")))) +
scale_x_continuous(name = "Left Offset of RNA") + scale_fill_discrete(name = "Number of Alternate Alignments")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk 2.04-plot_raw_aligns"/> </p>
<pre><code class="r">
ggplot(rna.raw, aes(x = Offset.R)) + geom_bar(aes(fill = cut(rna.raw$Alts,
breaks = c(-Inf, 0, 1, 100, Inf), labels = c("None", "One", "> 1", "> 100")))) +
scale_x_continuous(limits = c(1, 50), name = "Right Offset of RNA (hiding 0)") +
scale_fill_discrete(name = "Number of Alternate Alignments")
</code></pre>
<p><img 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" alt="plot of chunk 2.04-plot_raw_aligns"/> </p>
<pre><code class="r">
ggplot(subset(melt(cast(rna.raw, Alts ~ Promoter, fill = NA)), !is.na(value)),
aes(x = Alts, y = Promoter, fill = log10(value))) + geom_tile()
</code></pre>
<p><img 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" alt="plot of chunk 2.04-plot_raw_aligns"/> </p>
<pre><code class="r">
ggplot(subset(melt(cast(rna.raw, Alts ~ RBS, fill = NA)), !is.na(value)),
aes(x = Alts, y = RBS, fill = log10(value))) + geom_tile()
</code></pre>
<p><img 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JOAgJin0dAWAgQIECBAgEAOBATEHAyCJhAgQIAAAQIE8iQgIOZpNLSFAAECBAgQIJADAQExB4OgCQQIECBAgACBPAkIiHkaDW0hQIAAAQIECORAQEDMwSBoAgECBAgQIEAgTwICYp5GQ1sIECBAgAABAjkQEBBzMAiaQIAAAQIECBDIk4CAmKfR0BYCBAgQIECAQA4EBMQcDIImECBAgAABAgTyJCAg5mk0tIUAAQIECBAgkAMBATEHg6AJBAgQIECAAIE8CQiIeRoNbSFAgAABAgQI5EBAQMzBIGgCAQIECBAgQCBPAgJinkZDWwgQIECAAAECORAQEHMwCJpAgAABAgQIEMiTgICYp9HQFgIECBAgQIBADgQExBwMgiYQIECAAAECBPIkICDmaTS0hQABAgQIECCQAwEBMQeDoAkECBAgQIAAgTwJCIh5Gg1tIUCAAAECBAjkQEBAzMEgaAIBAgQIECBAIE8CAmKeRkNbCBAgQIAAAQI5EBAQczAImkCAAAECBAgQyJOAgJin0dAWAgQIECBAgEAOBATEHAyCJhAgQIAAAQIE8iQgIOZpNLSFAAECBAgQIJADAQExB4OgCQQIECBAgACBPAkIiHkaDW0hQIAAAQIECORAQEDMwSBoAgECBAgQIEAgTwICYp5GQ1sIECBAgAABAjkQEBBzMAiaQIAAAQIECBDIk4CAmKfR0BYCBAgQIECAQA4EBMQcDIImECBAgAABAgTyJCAg5mk0tIUAAQIECBAgkAMBATEHg6AJBAgQIECAAIE8CQiIeRoNbSFAgAABAgQI5EBAQMzBIGgCAQIECBAgQCBPAgJinkZDWwgQIECAAAECORAQEHMwCJpAgAABAgQIEMiTgICYp9HQFgIECBAgQIBADgQGrf7zkoN2tKwJc+fOTcuWLWv48Y+5Za2yOl+efXXZ+vKFL5Wtr1z6etn6C3de1rF+702XdjyOB1MP+qey9dsv+WbZejUrI0eOTEuXLs2Kbn/gCR273HHpOR2PW/Fg/PjxadiwYemVV15pxeF7PGZnsx4L9vOTEyZMSEOGDEmvvvpq2nHGaWVHv/UHXytb7++VvJpNnDgxDRo0KM2fP79fSXb+9Fllx7t51oll67HC7C0kvW7Iq9mkSZNS/IpdsGBBr33o7wLVmu118kVlTfvVvx9Stt6IlaFDh6YNNtigEVWpo4ECZhAbiKkqAgQIECBAgEARBATEIoyiPhAgQIAAAQIEGiggIDYQU1UECBAgQIAAgSIICIhFGEV9IECAAAECBAg0UEBAbCCmqggQIECAAAECRRAQEIswivpAgAABAgQIEGiggIDYQExVESBAgAABAgSKICAgFmEU9YEAAQIECBAg0EABAbGBmKoiQIAAAQIECBRBQEAswijqAwECBAgQIECggQICYgMxVUWAAAECBAgQKIKAgFiEUdQHAgQIECBAgEADBQTEBmKqigABAgQIECBQBAEBsQijqA8ECBAgQIAAgQYKCIgNxFQVAQIECBAgQKAIAgJiEUZRHwgQIECAAAECDRQQEBuIqSoCBAgQIECAQBEEBMQijKI+ECBAgAABAgQaKCAgNhBTVQQIECBAgACBIggIiEUYRX0gQIAAAQIECDRQQEBsIKaqCBAgQIAAAQJFEBAQizCK+kCAAAECBAgQaKCAgNhATFURIECAAAECBIogICAWYRT1gQABAgQIECDQQAEBsYGYqiJAgAABAgQIFEFAQCzCKOoDAQIECBAgQKCBAgJiAzFVRYAAAQIECBAogoCAWIRR1AcCBAgQIECAQAMFBMQGYqqKAAECBAgQIFAEAQGxCKOoDwQIECBAgACBBgoIiA3EVBUBAgQIECBAoAgCAmIRRlEfCBAgQIAAAQINFBAQG4ipKgIECBAgQIBAEQQExCKMoj4QIECAAAECBBooICA2EFNVBAgQIECAAIEiCAiIRRhFfSBAgAABAgQINFBAQGwgpqoIECBAgAABAkUQEBCLMIr6QIAAAQIECBBooICA2EBMVREgQIAAAQIEiiAgIBZhFPWBAAECBAgQINBAAQGxgZiqIkCAAAECBAgUQUBALMIo6gMBAgQIECBAoIECAmIDMVVFgAABAgQIECiCgIBYhFHUBwIECBAgQIBAAwUExAZiqooAAQIECBAgUAQBAbEIo6gPBAgQIECAAIEGCgiIDcRUFQECBAgQIECgCAICYhFGUR8IECBAgAABAg0UEBAbiKkqAgQIECBAgEARBATEIoyiPhAgQIAAAQIEGijQ1IB47bXXpn/7t39LZ555ZvrGN76Rbrjhhqzpy5cvT6eeemq2/d///d/Td7/73fTyyy9nz7355pvppz/9aTr33HPTL3/5y46u3nTTTenb3/52uvDCC9PSpUuz7bNnz872Pf/889NLL73UUTYePPLII+knP/lJ2TYrBAgQIECAQHEErrvuurTxxhun1atX19Wpxx57LNv/5ptvzvZ/4403snyy/fbbp1mzZnXUGcf56Ec/2rFezYN627brrrumO++8s5pDNLVMUwPi4sWL0/Tp09NJJ52Ujj/++HTfffel5557LhvIQYMGZdtPPvnkNHXq1HTFFVdkHb3xxhvTOuusk4477rj04osvpocffjg9+eST6YknnkjHHntsNpCBHnXfeuut6eijj0577LFHuvzyyzug4rmf//zn6fXXX+/Y5gEBAgQIECBQLIH4ff/UU0+VdSomobouq1atSjEB1Xn5zW9+k+WH2H/ZsmXZUzE59eMf/zideOKJ6bOf/WwqBccvf/nL6Ygjjui8e8XHixYt6theqW0dT3Z50Llthx9+eDrllFO6lOj/1aH9dcgVK1akIUOGpFGjRpUdMgYtBnP8+PHZ9jlz5qTAibJTpkxJjz76aBo9enTacssts21bbbVVihnDfffdN51wwglp5cqVWYCcOHFiR72XXnpp2n333dMf/vCHjm3x4MEHH0y//e1vy7Z9/OMfTxMmTCjblreVtdZaq8cm9fZ8pZ0HDx6cwr7rUk9dXevoy/rQoUNTtK3V7ajUh+7MKpXtz21hFn9wVTKrtK0/25ZXs2HDhmUMpa/9adL5WJXGh1lnoeoe591s+PDh1XWkH0vVa1bpNdvXZlf6XVRPna+++mo68MADs8mjbbfdNgtZe+65Z/rFL36R5YX58+dnuSJ+XsZEU2SFM844Ix122GEdh4tAuOOOO6b9998/bbrppinOXr7jHe/IJrhiBvFf//Vfs7OTDz30UHbmc6ONNkpXXnlllm0iQMbE1tprr53iDGrnJSa9It/EmdEf/vCH6Z/+6Z+y/aNtMYkWE1oxCRbBNHJJTH49++yzacMNN+xcTb8+bnpAvPrqq1PMCkaqXn/99dO4ceOyGcQYqDj1HKl54cKFaebMmVnHFyxYkAXCWIlgGPtFgFxvvfWy5yNgdk7oMYt42223ZYExCsS0bBynVD7b6S//W3fdddMHPvCBzpuy43eur+zJPq2M6dPenXfurX29Pd+5rtLj+MUYob3rUk9dXevoy3qMb/xx0Op2VOpDd2aVyvbntvg+iR/2lcwqbevPtuXZLH5JtNqn0vHzajZmzP/9TKvU5v58TVU6Vp7N4tRnzCTlbanXrBnjH9+LY8eO7TPRP//zP2eXm8VZxwsuuCCbAYyzj6eddlqWEeLStr/9279Nb3/727NjRZklS5aUHTcuV4sJqVhi4ikuf7v//vuzTBG/n4466qj09a9/Pd1+++3prrvuSmuuuWaaNm1adgndf/7nf6btttsuve9978sCaATL0hKTWaVZwgjEpd+/0eYIjzHptdlmm6VPf/rTWfsmTZqUHnjggWIHxH322Sdts802mdE111yTpfGdd945RecjNccS5/zPPvvsLO3HAMRUb7x4IxjGiyZ++ZWmfwM1QmZp2WWXXdIOO+yQvQAi7UcgjcGKgXvllVeyaxE333zzrPgaa6yR4q+KzsvcuXOb9Esi3wFx5MiRHddydvZoxjd/5/p7exzhMMa+1e2o1M7uzCqV7c9tMYPYXahutWNezeI1lteAyKz27568msXMYQTEVn8fVhKt16wZfYmfYY1Y7r333uyytph1i9m8008/Pd1zzz0p7lf4yle+kp0tjAwQM3ndLXFGsdTHmNWLkPj4449nl77FPlH3Xnvtlc0CRs6Iya1SRonjRV8iDEau6bqUrpMs3UcRM4Rxv8R//dd/Zdc7xsxjnPn8yEc+kh3v6aef7lpFv6439RrErj2JH8qloNf5uRJopOoNNtig43qC+CsgZgO7bovZwXnz5qUL/3zDSmmJF3vUH1PA8ZdurMcvzTxO7Zfa7CsBAgQIECDQGIH3v//92UzeCy+8kC666KIUp38/9KEPpfe+973ZeoTF66+/vseDRR0RNGPy6JlnnsnOOr7zne9McXaztBxzzDHZaeaYWfzUpz6VbY77KWJyKk5Rl/4wKJWPr3EZXdQZITH2iyWyTcxAxj0acaY1Zifj0rpYImB2noHMNvbz/xoT23todOkUc/y1HsFtxowZWenSKebYHml77733TiNGjMiAL7vssuy0cQS+mIEM0LhZJe52fu2111IMTmBH0o+7jGKmMW5UiVNtcY1iLDFNHKk/7m6yECBAgAABAsUWOOuss7Lr92JiKYLheeedl50piMvZIjdE8HvXu97VI0JcA/j5z38+u/4w7of42Mc+lmWJmM2Lmb+YfIq8EjOLcQazdDnbkUcemc1YRuaJ0Bc3vkQwLS1xXeF3vvOd9La3vS076xnbI//ENZARECNgxk29cZNuzFw+//zz2anq0v6t+NrUgHjAAQek+FdpiTuFKi1x6jmmbCP0lWb/AvGQQw4p2xb7Rt1RrnS6qHN9cSFtvCAsBAgQIECAQDEF9ttvv2wSKXoXp2jjRtS45jMmjErL73//+yy8HXTQQenggw/uuAYxno/L2kqnfmM9Zu2uuuqq7GxnTFrFssUWW2STT1H3brvtlp1SjtnFzsuXvvSlLFhGgIxTzqWlVPfWW2+dXfYW1yFGmdISeeeTn/xkFj5L1/nGWwLGaeZWzyD+tRel1ubkaykcdm5Od9siQFoIECBAgAABAp3DYWhMnjw5ex/luA8hgt0Xv/jFXpFK4bBU8Ktf/WrZ+yKWtnf+WrphsPO2zo/jcrrO4bD0XFwOVwqHse173/texz0apTKt+NrUGcRWdMgxCRAgQIAAAQIlgTgDGf/6ssTb5cTsYX8s8bY5jbpxpy/tFRD7omdfAgQIECBAoGUCpVO4fW1ANWci+yu09ddxejMTEHsT8jwBAgQIECCQS4E1Ntm6z+169Ym7+lxHESvI7TWIRcTWJwIECBAgQIBAOwgIiO0wStpIgAABAgQIEOhHAQGxH7EdigABAgQIECDQDgICYjuMkjYSIECAAAECBPpRQEDsR2yHIkCAAAECBPIr8MQTT2SfxrLzzjunyy+/PL8N7YeWuYu5H5AdggABAgQIEMi/wLHHHpt9/F18ikl89N2uu+6afaxe/lve+BaaQWy8qRoJECBAgACBNhSYO3dumjJlSvaZyR/+8IfTnXfe2Ya9aEyTBcTGOKqFAAECBAgQaGOB+fPnl30U3qRJk9K8efPauEd9a7qA2Dc/exMgQIAAAQIFEBg3blxavHhxR08WLVqU1lprrY71gfZAQBxoI66/BAgQIECAwFsE4iPuRo0alV555ZUUH+E3e/bs9O53v/st5QbKBjepDJSR1k8CBAgQIECgR4Gzzz47HXHEEWnp0qVp7733Tuuvv36P5Yv8pIBY5NHVNwIECBAgQKBqgZ122inFvwiII0eOrHq/IhZ0irmIo6pPBAgQIECAQN0CAz0cBpyAWPfLx44ECBAgQIAAgWIKCIjFHFe9IkCAAAECBAjULeAaxLrp7EiAAAECBAi0UuA73/x6GjLYXFczxkBAbIaqOgkQIECAAIGmC5xw+gV9PsaB++3Z5zqKWIHYXcRR1ScCBAgQIECAQB8EBMQ+4NmVAAECBAgQIFBEAQGxiKOqTwQIECBAgACBPggIiH3AsysBAgQIECBAoIgCAmIRR1WfCBAgQIAAgboEli9fnvbYY480f/78uvYvyk4CYlFGUj8IECBAgACBPgk89thjafr06emuu+5Kq1at6lNd7b6zgNjuI6j9BAgQIECAQEMEXnjhhXT++een97znPQ2pr50r8T6I7Tx62k6AAAECBAg0TGCHHXZoWF3tXpEZxHYfQe0nQIAAAQIECDRYQEBsMKjqCBAgQIAAAQLtLiAgtvsIaj8BAgQIECBAoMECrkFsMKjqCBAgQIAAgfYW+O1vf9veHWhA680gNgBRFQQIECBAgACBIgkIiEUaTX0hQIAAAQIECDRAQEBsAKIqCBAgQIAAAQJFEnANYpFGU18IECBAgMAAEthn2tQ0aNCgAdTj/utqzQFx9erVaeXKlWno0KFpxYoVKS7knDBhQvrgBz/Yf612JAIECBAgQGDAC9xw//MD3qBZADWdYr7kkkvSeuutl+69997sMwo/+tGPphkzZqQDDjggfeYzn2lWG9VLgAABAgQIECDQjwJVzyC++OKLaebMmWnWrFnpQx/6ULr88svT/fffnx5++OFsNnHy5MnZh1tvvfXW/dh8hyJAgAABAgQIEGi0QNUziLNnz04f+MAH0oEHHpgGDx6cfvWrX6WDDjoojRs3Lo0aNSpNmzYt3X333Y1un/oIECBAgAABAgT6WaDqgLhkyZI0duzYjubdcMMNaY899uhYX7BgQRo/fnzHugcECBAgQIAAAQLtKVB1QIzTyrfffnv64x//mC677LIUgTBmDWN5+umn0/XXX5+mTJnSngpaTYAAAQIECBAg0CFQ9TWIG220UTr11FPTdtttl+184YUXpuHDh6ef/exn6e///u/T5z73ufSe97yno2IPCBAgQIAAAQLtJBCXyn3rW99KixYtSvvtt1/61Kc+1U7Nb2hbqw6IcdTjjz8+HXPMMdk1iBEOY3nve9+bbrnllvS+970vW3lhO/YAACAASURBVPc/AgQIECBAgEA7CpxwwgnpJz/5SfaOLdOnT0+77bZbesc73tGOXelzm6s+xVw60siRI7OZw1iP90N89dVX0+LFi1O8P6KFAAECBAgQINCuAhEON9xwwzRkyJDsDbhfeOGFdu1Kn9tdU0D89a9/nbbccsu07bbbZtci7r777unggw/OEvYnPvGJPjdGBQQIECBAgACBVglEOIzlG9/4Rpo0aVLaZpttWtWUlh+36lPMcRfzoYcems4555wMLW5Qifc8jHQds4hx/WG8J+IWW2zR8k5pAAECBAgQIECgHoGvf/3r6cEHH8xONdezf1H2qXoG8ZFHHsmmXT/5yU+m/fffPwuHe+65ZzYF+7a3vS1L2XPmzCmKi34QIECAAAECA0wgwuFLL72U3YBbutdigBF0dLfqGcT58+dnM4elPePzl+NfaRk2bFh2LWJpfaB/XbX09TKClYtfK1tf/MJjZesrly0qWx+99t90rG9/4Akdj+PBHZeeU7bedWW7A44r2/T7K/5f2XrXld7q61x+6kFf7Lyabr/kG2XrVmoX2PHIr5ftdOuP/qVsvZaVW3/wtVqKK9vPAjfPOrGfj+hwBPomMGzMpL5V0EZ7P//88+n000/PzojGW/vFcvbZZ6e4WWUgLlUHxBLOm2++mT2Mm1LiJpXO66UyvhIgQIAAAQIE2klgvfXWSytWrGinJje1rTUFxNtuuy3FTGFp+elPf5rilHNpOfzww0sPfSVAgAABAgQIEGhTgaoD4tSpU7NPTOmpn29/+9t7etpzBAgQIECAAAECbSBQdUAcMWJEj28WGXcyxx3Nm2yySRt0WxMJECBAgACBdhcYPXL4nz+8Y1C7dyOX7a86IEbr77vvvvQf//Ef2ZtiH3fccWn77bfPOnXRRRelf/iHf0jnnXeegJjLYdYoAgQIECBQQIEJG6ZVBexWHrpU9dvcLFu2LO21115p6NChafPNN08HHXRQev3119PMmTPTpz/96XTiiSdmb3+Th05pAwECBAgQIECAQP0CVc8gPvnkk1k4/MEPfpAd7Zlnnkk777xzio/eu//++80c1j8G9iRAgAABAgQI5Eqg6hnEuL5wypQpHY3fdNNN/3zef3C68cYbhcMOFQ8IECBAgAABAu0vUHVAjPc9HDTorxeCxgdZ77bbbiluXrEQIECAAAECBAgUR6DqU8zR5eXLl6d58+ZlvV+0aFGK6xJL67Fx/PjxaaB/NE2G438ECBAgQIAAgTYWqCkg/vrXv05rrrlmWXfPOeevH/t28cUXZzevlBWwQoAAAQIECBBoA4HZs2ens846Ky1cuDDFu7Xss88+bdDq5jSx6oC44447ZmA9NWP06NE9Pe05AgQIECBAgEBuBb70pS+luBl3jTXWSJMnT87evSUuqRuIS9UBMYDiFLKFAAECBAgQIFBEgRtuuCHr1m9/+9s0duzYNFDDYSBUfZNKEV8I+kSAAAECBAgQ6Cxw3XXXpS984Qtp2rRpaeXKlZ2fGlCPBcQBNdw6S4AAAQIECPQkMH369HTnnXemp556Kt1yyy09FS30cwJioYdX5wgQIECAAIFqBFatWpV23333tGTJkqx4zB52vTG3mnqKUqbqaxCL0mH9IECAAAECBAh0FYgP/5gxY0Y68MADs/d4jptU3ve+93UtNmDWBcQBM9Q6SoAAAQIECPQkcMQRR6TDDjssrVixYsB/EIhTzD29UjxHgAABAgQIDCiBmEn0KXHuYh5QL3qdJUCAAAECBAhUI2AGsRolZQgQIECAAAECA0jANYgDaLB1lQABAgQIFEng0n89LA0ZPKhIXcpNXwTE3AyFhhAgQIAAAQK1CMw4/55ailcs+/szdq+4faBvdIp5oL8C9J8AAQIECBAg0EVAQOwCYpUAAQIECBAgMNAFBMSB/grQfwIECBAgQIBAFwEBsQuIVQIECBAgQIDAQBcQEAf6K0D/CRAgQIAAgTKBM844I82aNats20BbERAH2ojrLwECBAgQINCtwB133JHOO++8tHDhwm7LDIQnBMSBMMr6SIAAAQIECPQq8MYbb6TTTjstffGLX+y1bNELCIhFH2H9I0CAAAECBKoS+MIXvpBOPfXUNGbMmKrKF7mQgFjk0dU3AgQIECBAoCqBG2+8Md1zzz3p7rvvTjfddFP63e9+lx5++OGq9i1iIZ+kUsRR1ScCBAgQIECgJoGNNtooxQxiLKNHj06DBw9Ow4cPr6mOIhUWEIs0mvpCgAABAgQI1CWwySabpPgXy6JFi9Lrr7/esV5XhW2+k4DY5gOo+QQIECBAgEBjBY4++ujGVtiGtbkGsQ0HTZMJECBAgAABAs0UEBCbqatuAgQIECBAgEAbCgiIbThomkyAAAECBAgQaKaAaxCbqatuAgQIECBAoGkCn9p1kzToz/9ZGi8gIDbeVI0ECBAgQIBAPwhc/MioPh/l6N36XEUhK3CKuZDDqlMECBAgQIAAgfoFBMT67exJgAABAgQIECikgIBYyGHVKQIECBAgQIBA/QICYv129iRAgAABAgQIFFJAQCzksOoUAQIECBAgQKB+AQGxfjt7EiBAgAABAgUSuPbaa9P222/f8e/5558vUO9q64q3uanNS2kCBAgQIECgoAK33HJLOumkk9Kee+6Z9XDEiBEF7Wnv3TKD2LuREgQIECBAgMAAELj33nvT/Pnz03nnnZcWLFgwAHrcfRcFxO5tPEOAAAECBAgMIIEhQ4ak1atXp7XXXjt9+MMfTm+88cYA6n15V51iLvewRoAAAQIECAxQgauvvrqj5/fcc0/6xS9+kQ477LCObQPpQVMDYlzseffdd6dI5EOHDk1TpkxJ06ZNS8uXL09nnHFGGjlyZJbU11hjjfSxj30srbnmmunNN99Ml1xySXrxxRfTZpttlvbee+9sPG666ab0wAMPpPHjx6dDDz0023f27NlZ/ZH2Y/+11lorvf766+maa67JpobXW2+9bP84toUAAQIECBAg0J3AqlWr0j777JMuvfTSNHr06PTII4+ko446qrvihd/e1FPMixcvTtOnT88u+Dz++OPTfffdl5577rksFA4aNCjbfvLJJ6epU6emK664IsO+8cYb0zrrrJOOO+64LCQ+/PDD6cknn0xPPPFEOvbYY9PGG2+crrvuuhR133rrrenoo49Oe+yxR7r88ss79n/Xu96VPvvZz2brcT2BhQABAgQIECDQk8DgwYOzCaiPf/zjaZdddknrr79+mjx5ck+7FPq5fptaW7FiRTaTOGpU+QdrR2KPGcWYGYxlzpw56fDDD8/Kxozjo48+miX5LbfcMtu21VZbpfPPPz/tu+++6YQTTkgrV67MAuTEiROz/Xfeeec0duzY7PGwYcPKLjK944470s9//vPsudL/IqDGzGPjl3mNr/IvNW644YY11V2pfAT0mHnt61Kp7r7UGe2KJf56y9vSKLPe+lWraclszJgxvVXd78/3l1mtHSuZlX5W1Lp/M8szq10372bjxo2rvVNN3qNes1p/PlXTjfg9npfl7/7u79InPvGJtGTJkpTHn6n96dT0gBjn82NWcNGiRVkaj2+UCCZxl9CZZ56ZnVJeuHBhmjlzZtbvuGuoFA7ia+wXATJOF8cSATO2lZaYRbztttuywBjbJkyYkD312GOPZTOW//iP/1gqmrbeeuu06aabdqzHg5iJjNPSjV+GNb7Kv9Q4d+7cmuquVD5u3V+2bFlN9VQqXKnuSuWq3Ra/sCPYx+sjb0ujzHrrV62m8T0Vl3Hk8Y67/jLrzbTr8/EHafyCjJ89eVuY1T4ieTWL30fx++61116rvVNN3qNes1p/PlXTjfj5te6661ZTtF/KxEziQA+HAd30gBjn87fZZptsUOPawLiWMGb5Jk2alJ1ijifiLqGzzz47nXLKKVkAjOASISGCYQSGGKxSmImZyM5/jcU08A477JBOO+20bCp4+PDh6U9/+lOKYPq5z30uqy87+J//F9cidp0tjBd7XPfY+KV5AbHW9lYqHxaVttfq0Ig6Oh8zZpTjX6Pr7XyMeh83yqy349fa9/CKsFPrfr21oxHP95dZrW1lVqtYyr4vvc5qc4vXWQTEIn1v5rEvtY2K0tUKNPUaxK6NiNBXCnqdn4tfIjHFHN9MG2ywQXrqqaeyp+Paw7gGoOu2mE2cN29euvDCCzuqiRte4q+QuGYxrlH8/Oc/n0qnnTsKeUCAAAECBAgQINCrQNNnEEunmOMvz5iynTFjRtao0inm2B7hMO5WjinvuMv5sssuy04bR6CMGcj4CyyC33e/+91sqv6YY47JrlmM6ftZs2ZlM41xo0oExLj7KP7C+fa3v50d54Mf/GDafffde4VQgAABAgQIEGgvgeuPXb+9GtxGrW1qQDzggANS/Ku0nHvuuZU2Z6ee43rEOL0cp4tjiRB5yCGHlG2L7VF3lIsgGWVi+epXv5p99T8CBAgQIECg2AKl3/3F7mVretfUgNiXLpXCYec6qt3WeR+PCRAgQIAAAQIEahPo12sQa2ua0gQIECBAgAABAq0QEBBboe6YBAgQIECAAIEcCwiIOR4cTSNAgAABAgQItEJAQGyFumMSIECAAAECBHIsICDmeHA0jQABAgQIECDQCgEBsRXqjkmAAAECBAgQyLGAgJjjwdE0AgQIECBAgEArBATEVqg7JgECBAgQIEAgxwICYo4HR9MIECBAgAABAq0QEBBboe6YBAgQIECAAIEcCwiIOR4cTSNAgAABAgQItEJAQGyFumMSIECAAAECBHIsICDmeHA0jQABAgQIECDQCgEBsRXqjkmAAAECBAgQyLGAgJjjwdE0AgQIECBAgEArBATEVqg7JgECBAgQIEAgxwICYo4HR9MIECBAgAABAq0QEBBboe6YBAgQIECAAIEcCwiIOR4cTSNAgAABAgQItEJAQGyFumMSIECAAAECBHIsICDmeHA0jQABAgQIECDQCgEBsRXqjkmAAAECBAgQyLGAgJjjwdE0AgQIECBAgEArBATEVqg7JgECBAgQIEAgxwICYo4HR9MIECBAgAABAq0QEBBboe6YBAgQIECAAIEcCwiIOR4cTSNAgAABAgQItEJAQGyFumMSIECAAAECBHIsICDmeHA0jQABAgQIECDQCgEBsRXqjkmAAAECBAgQyLGAgJjjwdE0AgQIECBAgEArBATEVqg7JgECBAgQIEAgxwICYo4HR9MIECBAgAABAq0QEBBboe6YBAgQIECAAIEcCwiIOR4cTSNAgAABAgQItEJAQGyFumMSIECAAAECBHIsICDmeHA0jQABAgQIECDQCgEBsRXqjkmAAAECBAgQyLGAgJjjwdE0AgQIECBAgEArBATEVqg7JgECBAgQIEAgxwICYo4HR9MIECBAgAABAq0QEBBboe6YBAgQIECAAIEcCwiIOR4cTSNAgAABAgQItEJAQGyFumMSIECAAAECBHIsICDmeHA0jQABAgQIECDQCgEBsRXqjkmAAAECBAgQyLGAgJjjwdE0AgQIECBAgEArBATEVqg7JgECBAgQIEAgxwJDc9y2tm7a6tXlzR88YkzZhsFDR5Stp0FdsnqnClYNGV5etsvadvt/vmzL0DETy9ZrWdnlM98qK37TBV8oW7/9km+UrVvpu8CtP/qXvleiBgIECDRB4Kqv7t6EWlXZDgJdUkk7NFkbCRAgQIAAAQIEmikgIDZTV90ECBAgQIAAgTYUEBDbcNA0mQABAgQIECDQTAEBsZm66iZAgAABAgQItKGAgNiGg6bJBAgQIECAAIFmCgiIzdRVNwECBAgQIECgDQUExDYcNE0mQIAAAQIECDRTQEBspq66CRAgQIAAAQJtKCAgtuGgaTIBAgQIECBAoJkCAmIzddVNgAABAgQIEGhDAQGxDQdNkwkQIECAAAECzRQQEJupq24CBAgQIECAQBsKCIhtOGiaTIAAAQIECBBopoCA2ExddRMgQIAAAQIE2lBAQGzDQdNkAgQIECBAgEAzBQTEZuqqmwABAgQIECDQhgICYhsOmiYTIECAAAECBJopICA2U1fdBAgQIECAAIE2FBAQ23DQNJkAAQIECBAg0EwBAbGZuuomQIAAAQIECLShgIDYhoOmyQQIECBAgACBZgoIiM3UVTcBAgQIECBAoA0FBMQ2HDRNJkCAAAECBAg0U0BAbKauugkQIECAAAECbSggILbhoGkyAQIECBAgQKCZAgJiM3XVTYAAAQIECBBoQwEBsQ0HTZMJECBAgAABAs0UEBCbqatuAgQIECBAgEAbCgiIbThomkyAAAECBAgQaKaAgNhMXXUTIECAAAECBNpQQEBsw0HTZAIECBAgQIBAMwUExGbqqpsAAQIECBAg0IYCAmIbDpomEyBAgAABAgSaKSAgNlNX3QQIECBAgACBNhQQENtw0DSZAAECBAgQINBMAQGxmbrqJkCAAAECBAi0oYCA2IaDpskECBAgQIAAgWYKCIjN1FU3AQIECBAgQKANBQTENhw0TSZAgAABAgQINFNAQGymrroJECBAgAABAm0oICC24aBpMgECBAgQIECgmQICYjN11U2AAAECBAgQaEMBAbENB02TCRAgQIAAAQLNFBAQm6mrbgIECBAgQIBAGwoIiG04aJpMgAABAgQIEGimgIDYTF11EyBAgAABAgTaUEBAbMNB02QCBAgQIECAQDMFBMRm6qqbAAECBAgQINCGAgJiGw6aJhMgQIAAAQIEmikwtJmVN7LuWbNmpf322y+9/e1vTwsXLkzf+ta30syZM9OGG26YlixZkr7//e+nyZMnpzvuuCOtWrUqvfnmm2n48OFp1KhR6fjjj29kU9RFgAABAgQIECi0QNsExLXWWivNmTMnC4h/+tOf0tprr53++Mc/ZgExtsfzO+20U/bvkUceyYLiUUcdVejB0zkCBAgQIECAQDME2iYgbrHFFunuu+9O2267bXrooYfSRz/60XTppZemvfbaKz3++OMpnu9tiVnF+fPnv6XY0KFtw5C1vdb2Vio/ZMiQVGl7V5xqynTdpy/rgwcPTvGvv49bTZurNaumrkaWYVa7ZpgNGjTI66wGOmY1YP2laJitXr3a66wXuvjZasmfQNsko4022ihdeeWV2enjCHkbbLBB9o23ePHi9OSTT2ZBsTfeO++8M1111VVlxU4++eQ0duzYsm2NWZnXmGoq1LLuuutW2Nr9pkrl45dj/ODqbam0b2/79OX5aFcsI0eO7Es1Tdm3WrOmHLyHSktmcTlF3pa8m40ePTpvZFlwreZ7s78bXnqdMatevmQ2ZsyY6nfqp5J5+t5cuXJlP/XaYWoRaJuAOGzYsBTfZHF6+Z3vfGfWx8033zw9+uij2V9nI0aM6LXfU6dOTfGv8zJ37tyKs4qdy9T3uHm/rJ999tmamlSpfASwpUuX9lpPpX173akPBcaPH59irF955ZU+1NKcXas1a87Ru691woQJKf4Cf/XVV7sv1KJn8mo2ceLELIhVOqPQIqqOwzLroKj6QV7NJk2alP0hvmDBgqr70l8F82QWZ4xi0seSL4G2uot5s802S9dff31697vfnSnG15tvvjnFdgsBAgQIECBAgEBjBNoqIMaM4dNPP53e9a53Zb2PmcTnn3++IzA2hkQtBAgQIECAAIGBLdA2p5hjmOItbc4555yOEYsLgM8+++yO9dKDCJLxz0KAAAECBAgQIFC7QFvNINbePXsQIECAAAECBAjUKiAg1iqmPAECBAgQIECg4AICYsEHWPcIECBAgAABArUKCIi1iilPgAABAgQIECi4gIBY8AHWPQIECBAgQIBArQICYq1iyhMgQIAAAQIECi4gIBZ8gHWPAAECBAgQIFCrgIBYq5jyBAgQIECAAIGCCwiIBR9g3SNAgAABAgQI1CogINYqpjwBAgQIECBAoOACAmLBB1j3CBAgQIAAAQK1CgiItYopT4AAAQIECBAouICAWPAB1j0CBAgQIECAQK0CAmKtYsoTIECAAAECBAouICAWfIB1jwABAgQIECBQq4CAWKuY8gQIECBAgACBggsIiAUfYN0jQIAAAQIECNQqICDWKqY8AQIECBAgQKDgAgJiwQdY9wgQIECAAAECtQoIiLWKKU+AAAECBAgQKLiAgFjwAdY9AgQIECBAgECtAgJirWLKEyBAgAABAgQKLiAgFnyAdY8AAQIECBAgUKuAgFirmPIECBAgQIAAgYILCIgFH2DdI0CAAAECBAjUKiAg1iqmPAECBAgQIECg4AICYsEHWPcIECBAgAABArUKCIi1iilPgAABAgQIECi4gIBY8AHWPQIECBAgQIBArQICYq1iyhMgQIAAAQIECi4gIBZ8gHWPAAECBAgQIFCrgIBYq5jyBAgQIECAAIGCCwiIBR9g3SNAgAABAgQI1CogINYqpjwBAgQIECBAoOACAmLBB1j3CBAgQIAAAQK1CgiItYopT4AAAQIECBAouICAWPAB1j0CBAgQIECAQK0CAmKtYsoTIECAAAECBAouICAWfIB1jwABAgQIECBQq4CAWKuY8gQIECBAgACBggsIiAUfYN0jQIAAAQIECNQqICDWKqY8AQIECBAgQKDgAgJiwQdY9wgQIECAAAECtQoIiLWKKU+AAAECBAgQKLiAgFjwAdY9AgQIECBAgECtAgJirWLKEyBAgAABAgQKLiAgFnyAdY8AAQIECBAgUKuAgFirmPIECBAgQIAAgYILCIgFH2DdI0CAAAECBAjUKjC01h2Ur07ge3suSePGjUsjRoxI8+bNS2nP7bvs2HW9y9M1rP7+yv+qoXTPRW+64As9F/AsAQIECBAgUHgBM4iFH2IdJECAAAECBAjUJiAg1ualNAECBAgQIECg8AICYuGHWAcJECBAgAABArUJCIi1eSlNgAABAgQIECi8gIBY+CHWQQIECBAgQIBAbQICYm1eShMgQIAAAQIECi8gIBZ+iHWQAAECBAgQIFCbgIBYm5fSBAgQIECAAIHCCwiIhR9iHSRAgAABAgQI1Cbgk1Rq86qp9Pz589Py5cvT6NGja9pvIBd+9dVX04oVK9KoUaMGMkNNfX/llVeY1SSW0ssvv5xWrlyZRo4cWeOeA7f4iy++mAYNGpSGDx8+cBFq7PkLL7zArEYzxfMjMGj1n5f8NKdYLbn55pvTs88+m4488sjcdSyGPX7Y52254YYbUvwiOvzww/PWtJRXs2uvvTYtXLgwHXzwwcyqFPjlL3+ZlixZkj7+8Y9XuUf/Fcvr6+yqq67Kvgf222+//sOo8kh5Nbv88suzj1v9yEc+UmVP+q9YXs36T8CRehNwirk3oT4+H9+EeVzyGA7z6NS5Tcw6a1T3mFl1Tp1LMeusUd1jZtU5dS7FrLOGx5UEBMRKKrYRIECAAAECBAawgIA4gAdf1wkQIECAAAEClQTcpFJJpUHb1l577bRq1aoG1TYwqgmzoUO9LGsZ7XXWWSeNGTOmll0GfNkwW7Zs2YB3qAVg3XXXza5BrGWfgV52vfXWS8OGDRvoDPrfpgJuUmnTgdNsAgQIECBAgECzBJxibpasegkQIECAAAECbSogILbpwGk2AQIECBAgQKBZAi72apLsQw89lOJ9EN9888100EEHpbjmyfJWgWeeeSbdeOON2fVg73//+9P222+fFbrpppvSAw88kMaPH58OPfRQb2j8VroU7384bty4NHXqVGYVfDpvev3119Ntt92W4vX2nve8J+24447MOgNVeBxv8hyvsXiz/2222SZtueWWzCo4lTbF+0RusskmafLkydmmSr8D5s2bl6688sq0aNGitNNOO6UPfOADpd19JZA7ATOITRiSpUuXpl/84hdpxowZ6YADDkg/+9nPmnCUYlR52WWXpXjj3aOPPjrdddddKT5J5cknn0xPPPFEOvbYY9PGG2+crrvuumJ0toG9CKMIPPFai4VZz7gXXXRR2mKLLdJnPvOZ7LX1xhtvMOuZLF1zzTVp1113TZ/85CfTr371q+zTerzO3ooWNzv98Ic/TH/4wx+yCYEo0d3vgIsvvjjtscceaebMmVn4Xrx48VsrtIVATgQExCYMRHwSyIYbbph9xN4GG2yQfWJDzCRa3ipw1FFHpUmTJqXBgwdnn+zy2muvpTlz5mSzFUOGDElbbbVVevTRR9+64wDeEr+Q4hf2tGnTOhSYdVC85UG8k0B8T8bdpPfcc0/2KT1jx471OnuLVPmG+LjLCIRz587NPpYwvke9zsqNYi1mA+Pn1HbbbdfxZHe/A+LnW/xuiHcdiNnGp59+umMfDwjkTUBAbMKILFiwoOzzl+MHbfwQsbxVIMJhLPERe+G00UYbpc5+7N5qFh/ftffee5d9Ji6ztzqVtsTp5dIp5pg5/OY3v5kFHmYlocpf45RyXOoRZ0DitHx88gazt1qtscYamU/nZzo7xfb4ORanlzu/hdfo0aP9XuiM5nHuBFyD2IQhGTlyZNl7rK1YscL71PXgHJ+LG9c7xWxiLPHDtPQedWEX19lZ/k/gkUceyT7fe/31189OlcbMdPzyZtb9K2T48OHZ+/cdcsghKWalX3rppRSOzLo3W7lyZbrkkkvSV77ylWzm9fvf/356/PHHmXVPVvZMpd8B8cdwXM9ZWuJxzGRbCORVwAxiE0Ym3lD2f//3f7NfSkuWLMm+dv7LsQmHbNsqIxzGrE6Ew5JRnJZ/6qmnsj7FKa54s1nL/wm87W1vy64Li9mHCD5x2jTcmHX/CokgGDc7la7XXLhwYXbTE7PuzeKZ+MMsTivHEq+z+Mcs4+j1f5V+B4wYMSL7no2zSatXr85umHLzYq+UCrRQwBtlNwk/biD44x//mJ3aipswNt988yYdqX2rjV/UX/va17I7vEsfHL///vunzTbbLJu9iNM0cc3OMccck/2CMxrBlQAAA8BJREFUb9+eNqflv/vd77KZ1riRIK6zixkfZpWtY/brjjvuyP4YmThxYnZnfPySZlbZK7befffd6X/+53+yU8sRFuPdGLzOuveKGxPj+sLSncmVfgc89thj6Te/+U12M0vM/E+fPr37Cj1DoMUCAmITByBO/8Vf4KW/wpt4qEJWHadgYpbMUr0As56tKvlU2tZzLQPn2QjR8XMsZg87L8w6a3T/uLvfAXHpTFfT7mvxDIHWCAiIrXF3VAIECBAgQIBAbgVcg5jbodEwAgQIECBAgEBrBATE1rg7KgECNQgcf/zx2VtHzZ8/v2yvuIEp7kiO5fTTTy+7S7SsoBUCBAgQqElAQKyJS2ECBPpbIK7jio8ni49c/O///u+Kh4+3ZTnllFOymygqFrCRAAECBGoSEBBr4lKYAIH+FohPjZkyZUr20Yvf+973Kh7+sMMOy7bHHaTxhsQ//vGP0zve8Y4Ub2J88MEHZ3d3V9zRRgIECBCoKCAgVmSxkQCBvAjE59xGyPvQhz6UffJEvP1K1+U73/lOtik+kSc+xuxzn/tcik+cibe3ibdTKj3fdT/rBAgQIFBZwCepVHaxlQCBHAjE+zped911KT7JI5YIijGLGJ9923mJ9zaMJd5IPN5TM045/+EPf0ibbrppdno63qTYQoAAAQLVC5hBrN5KSQIE+lng4osvzt4M/MMf/nCKzwaOaxB/+tOf9vgZthEGf/SjH6Vzzz03rb322tm1i88880w/t9zhCBAg0N4CAmJ7j5/WEyi0QJxejptPLrjgguzfRRddlOJjzOITUHpadt999xSfWhGfZhFvSvzZz362p+KeI0CAAIEuAgJiFxCrBAjkQ2DOnDlp9uzZ2c0p2267bSr9O/LII7PTzJ1bOWTIkI7PuX3hhRfSxhtvnN2sEvvE3c+lz2HuvI/HBAgQINC9gGsQu7fxDAECLRSI08QxEzhhwoSyVhxxxBHZrOJDDz1Utn2XXXbJgmFce/jlL385TZ06NcVnCMd7J3b39jhlFVghQIAAgQ4BH7XXQeEBAQLtLrBo0aLsLuZSP15++eW05pprllZ9JUCAAIEqBQTEKqEUI0CAAAECBAgMFAHXIA6UkdZPAgQIECBAgECVAgJilVCKESBAgAABAgQGioCAOFBGWj8JECBAgAABAlUKCIhVQilGgAABAgQIEBgoAgLiQBlp/SRAgAABAgQIVCkgIFYJpRgBAgQIECBAYKAICIgDZaT1kwABAgQIECBQpYCAWCWUYgQIECBAgACBgSLw/wEraJL+BNt+zQAAAABJRU5ErkJggg==" alt="plot of chunk 2.04-plot_raw_aligns"/> </p>
<pre><code class="r">
ggplot(subset(melt(cast(rna.raw, Alts ~ Offset.L, fill = NA)), !is.na(value)),
aes(x = Alts, y = Offset.L)) + geom_point()
</code></pre>
<p><img 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" alt="plot of chunk 2.04-plot_raw_aligns"/> </p>
<pre><code class="r">
ggplot(subset(melt(cast(rna.raw, Alts ~ Offset.R, value = "Name",
fill = NA)), !is.na(value)), aes(x = Alts, y = Offset.R)) + geom_point()
</code></pre>
<p><img 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" alt="plot of chunk 2.04-plot_raw_aligns"/> </p>
<pre><code class="r">
ggplot(subset(melt(cast(rna.raw, Alts ~ Mismatches.len, value = "Name",
fill = NA)), !is.na(value)), aes(x = Alts, y = Mismatches.len, fill = log10(value))) +
geom_tile()
</code></pre>
<p><img 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" alt="plot of chunk 2.04-plot_raw_aligns"/> </p>
<pre><code class="r">
ggplot(subset(melt(cast(rna.raw, Offset.L ~ Offset.R, value = "Name",
fill = NA)), !is.na(value)), aes(x = Offset.L, y = Offset.R, fill = log10(value))) +
geom_tile()
</code></pre>
<p><img 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" alt="plot of chunk 2.04-plot_raw_aligns"/> </p>
<p>So, a lot is going on here. </p>
<ul>
<li>Most of the reads with multiple alignments have a left offset of 40 or greater, corresponding to a late RNA start perhaps. </li>
<li>The <code>BBaJ23108</code> promoter has a lot more multiple alignments than <code>BBaJ23100</code>.</li>
<li>There are also differences in multiple alignments per RBS, with <code>BB0030</code> having the most, and the WT having none of the super-high multiple aligners (aligning to 120 seqs).</li>
</ul>
<h2>Filtering contigs</h2>
<p>We could make these plots all night, but let's just subset based on our aforementioned criteria and see where we are. </p>
<pre><code class="r">
rna.criteria <- with(rna.raw, Alts == 0 & Offset.L > 2 & Offset.R ==
0)
dna.criteria <- with(dna.raw, Alts == 0 & Offset.L == 0 & Offset.R ==
0)
rna.subset <- subset(rna.raw, rna.criteria)
dna.subset <- subset(dna.raw, dna.criteria)
rna.discard <- subset(rna.raw, !rna.criteria)
dna.discard <- subset(dna.raw, !dna.criteria)
# Before and after Subsetting:
print(xtable(rbind(dna = cbind(read = "dna", `before subset` = dim(dna.raw)[1],
`after subset` = dim(dna.subset)[1]), rna = cbind(read = "rna", `before subset` = dim(rna.raw)[1],
`after subset` = dim(rna.subset)[1]))), "html")
</code></pre>
<pre><code>## <!-- html table generated in R 2.14.1 by xtable 1.7-0 package -->
## <!-- Fri Oct 12 09:41:09 2012 -->
## <TABLE border=1>
## <TR> <TH> </TH> <TH> read </TH> <TH> before subset </TH> <TH> after subset </TH> </TR>
## <TR> <TD align="right"> 1 </TD> <TD> dna </TD> <TD> 842175 </TD> <TD> 800414 </TD> </TR>
## <TR> <TD align="right"> 2 </TD> <TD> rna </TD> <TD> 689549 </TD> <TD> 567351 </TD> </TR>
## </TABLE>
</code></pre>
<pre><code class="r">
# Missing from DNA
dna.missing <- lib_seqs$Name[which(!(lib_seqs$Name %in% dna.subset$Name))]
dna.missing
</code></pre>
<pre><code>## [1] BBaJ23100-bamA-4 BBaJ23108-bamA-4 BBaJ23100-bamA-1
## [4] BBaJ23108-bamA-1 BBaJ23100-bamA-13 BBaJ23108-bamA-13
## [7] BBaJ23100-bamA-15 BBaJ23108-bamA-15 BBaJ23100-bamA-22
## [10] BBaJ23108-bamA-22 BBaJ23100-yejM-5 BBaJ23108-yejM-5
## [13] BBaJ23100-yejM-15 BBaJ23108-yejM-15 BBaJ23100-cysS-5
## [16] BBaJ23108-cysS-5 BBaJ23100-cysS-18 BBaJ23108-cysS-18
## [19] BBaJ23100-bamA-2 BBaJ23108-bamA-2 BBaJ23108-bamA-23
## [22] BBaJ23100-serS-6 BBaJ23108-serS-6 BBaJ23100-serS-27
## [25] BBaJ23108-serS-27 BBaJ23100-aspS-5 BBaJ23108-aspS-5
## [28] BBaJ23100-aspS-16 BBaJ23108-aspS-16 BBaJ23100-rpsB-7
## [31] BBaJ23108-rpsB-7 BBaJ23100-rpsB-40 BBaJ23108-rpsB-40
## [34] BBaJ23100-bamA-3 BBaJ23108-bamA-3 BBaJ23100-mrdB-7
## [37] BBaJ23108-mrdB-7 BBaJ23100-mrdB-39 BBaJ23108-mrdB-39
## [40] BBaJ23100-lolA-7 BBaJ23108-lolA-7 BBaJ23100-lolA-41
## [43] BBaJ23108-lolA-41
## 14248 Levels: BBaJ23100-accA-1 BBaJ23100-accA-10 ... BBaJ23108-zipA-9
</code></pre>
<pre><code class="r">
# Missing from RNA
rna.missing <- lib_seqs$Name[which(!(lib_seqs$Name %in% rna.subset$Name))]
rna.missing
</code></pre>
<pre><code>## [1] BBaJ23100-yejM-5 BBaJ23108-yejM-5 BBaJ23100-yejM-15
## [4] BBaJ23108-yejM-15 BBaJ23100-cysS-5 BBaJ23108-cysS-5
## [7] BBaJ23100-cysS-18 BBaJ23108-cysS-18 BBaJ23100-serS-6
## [10] BBaJ23108-serS-6 BBaJ23100-serS-27 BBaJ23108-serS-27
## [13] BBaJ23108-dnaX-9 BBaJ23108-metK-18 BBaJ23108-lnt-16
## [16] BBaJ23108-nadK-16 BBaJ23108-murG-5 BBaJ23100-aspS-5
## [19] BBaJ23108-aspS-5 BBaJ23100-aspS-16 BBaJ23108-aspS-16
## [22] BBaJ23108-murJ-15 BBaJ23108-ileS-42 BBaJ23108-lptD-3
## [25] BBaJ23108-murC-42 BBaJ23100-rpsB-7 BBaJ23108-rpsB-7
## [28] BBaJ23100-rpsB-40 BBaJ23108-rpsB-40 BBaJ23108-pyrH-33
## [31] BBaJ23108-ispA-34 BBaJ23108-hemH-40 BBaJ23100-mrdB-7
## [34] BBaJ23108-mrdB-7 BBaJ23100-mrdB-39 BBaJ23108-mrdB-39
## [37] BBaJ23100-lolA-7 BBaJ23108-lolA-7 BBaJ23100-lolA-41
## [40] BBaJ23108-lolA-41 BBaJ23108-aspS-37 BBaJ23108-rplS-33
## [43] BBaJ23108-holA-19 BBaJ23108-ispH-5 BBaJ23108-serS-20
## 14248 Levels: BBaJ23100-accA-1 BBaJ23100-accA-10 ... BBaJ23108-zipA-9
</code></pre>
<pre><code class="r">
# Missing from both
both.missing <- lib_seqs$Name[which(!(lib_seqs$Name %in% dna.subset$Name |
lib_seqs$Name %in% rna.subset$Name))]
both.missing
</code></pre>
<pre><code>## [1] BBaJ23100-yejM-5 BBaJ23108-yejM-5 BBaJ23100-yejM-15
## [4] BBaJ23108-yejM-15 BBaJ23100-cysS-5 BBaJ23108-cysS-5
## [7] BBaJ23100-cysS-18 BBaJ23108-cysS-18 BBaJ23100-serS-6
## [10] BBaJ23108-serS-6 BBaJ23100-serS-27 BBaJ23108-serS-27
## [13] BBaJ23100-aspS-5 BBaJ23108-aspS-5 BBaJ23100-aspS-16
## [16] BBaJ23108-aspS-16 BBaJ23100-rpsB-7 BBaJ23108-rpsB-7
## [19] BBaJ23100-rpsB-40 BBaJ23108-rpsB-40 BBaJ23100-mrdB-7
## [22] BBaJ23108-mrdB-7 BBaJ23100-mrdB-39 BBaJ23108-mrdB-39
## [25] BBaJ23100-lolA-7 BBaJ23108-lolA-7 BBaJ23100-lolA-41
## [28] BBaJ23108-lolA-41
## 14248 Levels: BBaJ23100-accA-1 BBaJ23100-accA-10 ... BBaJ23108-zipA-9
</code></pre>
<p>28 are missing from both, 43 are missing from DNA, and 45 are missing from RNA. Let's see if they appear in the unfiltered reads.</p>
<h2>Recovering Lost Contigs</h2>
<pre><code class="r">rna.reads_for_missing <- subset(rna.raw, Read.num %in% subset(rna.raw,
Name %in% rna.missing & Count > 200)$Read.num)[, !grepl("seq", names(rna.raw))]
rna.reads_for_missing[order(rna.reads_for_missing$Read.num), ]
</code></pre>
<pre><code>## Name Read.num Count Count.A Count.B Offset.L Alts
## 557099 BBaJ23100-yejM-15 104353 777 441 336 37 1
## 559798 BBaJ23100-yejM-5 104353 777 441 336 37 1
## 58022 BBaJ23100-cysS-18 108136 958 556 402 37 1
## 61889 BBaJ23100-cysS-5 108136 958 556 402 37 1
## 513203 BBaJ23100-serS-27 125037 927 520 407 37 1
## 515988 BBaJ23100-serS-6 125037 927 520 407 37 1
## 28575 BBaJ23100-aspS-16 153619 509 308 201 37 1
## 31395 BBaJ23100-aspS-5 153619 509 308 201 37 1
## 302265 BBaJ23100-lolA-41 168777 1705 1011 694 37 1
## 304797 BBaJ23100-lolA-7 168777 1705 1011 694 37 1
## 365419 BBaJ23100-mrdB-39 172333 1124 638 486 37 1
## 366936 BBaJ23100-mrdB-7 172333 1124 638 486 37 1
## 502802 BBaJ23100-rpsB-40 181908 896 492 404 37 1
## 504244 BBaJ23100-rpsB-7 181908 896 492 404 37 1
## 677855 BBaJ23108-serS-27 250029 307 187 120 36 1
## 678482 BBaJ23108-serS-6 250029 307 187 120 36 1
## 557098 BBaJ23100-yejM-15 1012393 288 130 158 36 1
## 559754 BBaJ23100-yejM-5 1012393 288 130 158 36 1
## 57973 BBaJ23100-cysS-18 1014195 1046 633 413 36 1
## 61915 BBaJ23100-cysS-5 1014195 1046 633 413 36 1
## 513222 BBaJ23100-serS-27 1025817 662 406 256 36 1
## 516021 BBaJ23100-serS-6 1025817 662 406 256 36 1
## 28566 BBaJ23100-aspS-16 1047189 884 545 339 36 1
## 31404 BBaJ23100-aspS-5 1047189 884 545 339 36 1
## 302286 BBaJ23100-lolA-41 1055530 1589 1062 527 36 1
## 304778 BBaJ23100-lolA-7 1055530 1589 1062 527 36 1
## 365425 BBaJ23100-mrdB-39 1058615 669 447 222 36 1
## 366875 BBaJ23100-mrdB-7 1058615 669 447 222 36 1
## 502768 BBaJ23100-rpsB-40 1066710 564 333 231 36 1
## 504254 BBaJ23100-rpsB-7 1066710 564 333 231 36 1
## 557129 BBaJ23100-yejM-15 1276876 759 441 318 35 1
## 559780 BBaJ23100-yejM-5 1276876 759 441 318 35 1
## 58045 BBaJ23100-cysS-18 1282988 4636 2751 1885 35 1
## 61949 BBaJ23100-cysS-5 1282988 4636 2751 1885 35 1
## 513144 BBaJ23100-serS-27 1330825 3596 2108 1488 35 1
## 515921 BBaJ23100-serS-6 1330825 3596 2108 1488 35 1
## 28550 BBaJ23100-aspS-16 1417790 4341 2339 2002 35 1
## 31476 BBaJ23100-aspS-5 1417790 4341 2339 2002 35 1
## 302248 BBaJ23100-lolA-41 1443162 5090 3002 2088 35 1
## 304741 BBaJ23100-lolA-7 1443162 5090 3002 2088 35 1
## 365454 BBaJ23100-mrdB-39 1452520 2646 1597 1049 35 1
## 366902 BBaJ23100-mrdB-7 1452520 2646 1597 1049 35 1
## 502755 BBaJ23100-rpsB-40 1477479 2236 1305 931 35 1
## 504242 BBaJ23100-rpsB-7 1477479 2236 1305 931 35 1
## Mismatches Promoter Gene CDS.num RBS CDS.type RBS.len Length
## 557099 BBaJ23100 yejM 15 WT ∆G 3 20 93
## 559798 BBaJ23100 yejM 5 WT Min Rare 20 93
## 58022 BBaJ23100 cysS 18 WT ∆G 6 20 93
## 61889 BBaJ23100 cysS 5 WT Min Rare 20 93
## 513203 BBaJ23100 serS 27 BB0030 ∆G 5 21 94
## 515988 BBaJ23100 serS 6 BB0030 Min Rare 21 94
## 28575 BBaJ23100 aspS 16 WT ∆G 4 20 93
## 31395 BBaJ23100 aspS 5 WT Min Rare 20 93
## 302265 BBaJ23100 lolA 41 BB0032 ∆G 9 19 92
## 304797 BBaJ23100 lolA 7 BB0032 Min Rare 19 92
## 365419 BBaJ23100 mrdB 39 BB0032 ∆G 7 19 92
## 366936 BBaJ23100 mrdB 7 BB0032 Min Rare 19 92
## 502802 BBaJ23100 rpsB 40 BB0032 ∆G 8 19 92
## 504244 BBaJ23100 rpsB 7 BB0032 Min Rare 19 92
## 677855 BBaJ23108 serS 27 BB0030 ∆G 5 21 94
## 678482 BBaJ23108 serS 6 BB0030 Min Rare 21 94
## 557098 BBaJ23100 yejM 15 WT ∆G 3 20 93
## 559754 BBaJ23100 yejM 5 WT Min Rare 20 93
## 57973 BBaJ23100 cysS 18 WT ∆G 6 20 93
## 61915 BBaJ23100 cysS 5 WT Min Rare 20 93
## 513222 BBaJ23100 serS 27 BB0030 ∆G 5 21 94
## 516021 BBaJ23100 serS 6 BB0030 Min Rare 21 94
## 28566 BBaJ23100 aspS 16 WT ∆G 4 20 93
## 31404 BBaJ23100 aspS 5 WT Min Rare 20 93
## 302286 BBaJ23100 lolA 41 BB0032 ∆G 9 19 92
## 304778 BBaJ23100 lolA 7 BB0032 Min Rare 19 92
## 365425 BBaJ23100 mrdB 39 BB0032 ∆G 7 19 92
## 366875 BBaJ23100 mrdB 7 BB0032 Min Rare 19 92
## 502768 BBaJ23100 rpsB 40 BB0032 ∆G 8 19 92
## 504254 BBaJ23100 rpsB 7 BB0032 Min Rare 19 92
## 557129 BBaJ23100 yejM 15 WT ∆G 3 20 93
## 559780 BBaJ23100 yejM 5 WT Min Rare 20 93
## 58045 BBaJ23100 cysS 18 WT ∆G 6 20 93
## 61949 BBaJ23100 cysS 5 WT Min Rare 20 93
## 513144 BBaJ23100 serS 27 BB0030 ∆G 5 21 94
## 515921 BBaJ23100 serS 6 BB0030 Min Rare 21 94
## 28550 BBaJ23100 aspS 16 WT ∆G 4 20 93
## 31476 BBaJ23100 aspS 5 WT Min Rare 20 93
## 302248 BBaJ23100 lolA 41 BB0032 ∆G 9 19 92
## 304741 BBaJ23100 lolA 7 BB0032 Min Rare 19 92
## 365454 BBaJ23100 mrdB 39 BB0032 ∆G 7 19 92
## 366902 BBaJ23100 mrdB 7 BB0032 Min Rare 19 92
## 502755 BBaJ23100 rpsB 40 BB0032 ∆G 8 19 92
## 504242 BBaJ23100 rpsB 7 BB0032 Min Rare 19 92
## Read.len Offset.R Mismatches.len Offset.RBS
## 557099 56 0 0 -3
## 559798 56 0 0 -3
## 58022 56 0 0 -3
## 61889 56 0 0 -3
## 513203 57 0 0 -3
## 515988 57 0 0 -3
## 28575 56 0 0 -3
## 31395 56 0 0 -3
## 302265 55 0 0 -3
## 304797 55 0 0 -3
## 365419 55 0 0 -3
## 366936 55 0 0 -3
## 502802 55 0 0 -3
## 504244 55 0 0 -3
## 677855 58 0 0 -4
## 678482 58 0 0 -4
## 557098 57 0 0 -4
## 559754 57 0 0 -4
## 57973 57 0 0 -4
## 61915 57 0 0 -4
## 513222 58 0 0 -4
## 516021 58 0 0 -4
## 28566 57 0 0 -4
## 31404 57 0 0 -4
## 302286 56 0 0 -4
## 304778 56 0 0 -4
## 365425 56 0 0 -4
## 366875 56 0 0 -4
## 502768 56 0 0 -4
## 504254 56 0 0 -4
## 557129 58 0 0 -5
## 559780 58 0 0 -5
## 58045 58 0 0 -5
## 61949 58 0 0 -5
## 513144 59 0 0 -5
## 515921 59 0 0 -5
## 28550 58 0 0 -5
## 31476 58 0 0 -5
## 302248 57 0 0 -5
## 304741 57 0 0 -5
## 365454 57 0 0 -5
## 366902 57 0 0 -5
## 502755 57 0 0 -5
## 504242 57 0 0 -5
</code></pre>
<h3>Duplicate Library Members</h3>
<p>It looks like there are some high-count reads that map doubly. It appears that some of the Min Rare CDSes have the exact same sequences as the ∆G RBSes. To confirm, I will check the original library FASTA file. </p>
<pre><code class="console">$ grep -Pi '^[ATGC]+' $lib_prefix.fa | sort | uniq -c | grep -Pv '^\s+1' 2 ctgacagctagctcagtcctaggtataatgctagcCACCGAGGGAAACAGATAACAGGTTATGGTGACCCATCGCCAGCGCTATCGCGAAAAA
2 ctgacagctagctcagtcctaggtataatgctagcCACCGATGTCTAAACGGAATCTTCGATGCTGAAAATTTTTAACACCCTGACCCGCCAG
2 ctgacagctagctcagtcctaggtataatgctagcCACCGATTAAAGAGGAGAAAtactagATGCTGGATCCGAACCTGCTGCGCAACGAACCG
2 ctgacagctagctcagtcctaggtataatgctagcCACCGGCTAAGTTAAGGGATATCTCATGCGCACCGAATATTGCGGCCAGCTGCGCCTG
2 ctgacagctagctcagtcctaggtataatgctagcCACCGTCACACAGGAAAGtactagATGAAAAAAATTGCGATTACCTGCGCGCTGCTG
2 ctgacagctagctcagtcctaggtataatgctagcCACCGTCACACAGGAAAGtactagATGACCGATAACCCGAACAAAAAAACCTTTTGG
2 ctgacagctagctcagtcctaggtataatgctagcCACCGTCACACAGGAAAGtactagATGGCGACCGTGAGCATGCGCGATATGCTGAAA
2 ttgacggctagctcagtcctaggtacagtgctagcTTAATAGGGAAACAGATAACAGGTTATGGTGACCCATCGCCAGCGCTATCGCGAAAAA
2 ttgacggctagctcagtcctaggtacagtgctagcTTAATATGTCTAAACGGAATCTTCGATGCTGAAAATTTTTAACACCCTGACCCGCCAG
2 ttgacggctagctcagtcctaggtacagtgctagcTTAATATTAAAGAGGAGAAAtactagATGCTGGATCCGAACCTGCTGCGCAACGAACCG
2 ttgacggctagctcagtcctaggtacagtgctagcTTAATGCTAAGTTAAGGGATATCTCATGCGCACCGAATATTGCGGCCAGCTGCGCCTG
2 ttgacggctagctcagtcctaggtacagtgctagcTTAATTCACACAGGAAAGtactagATGAAAAAAATTGCGATTACCTGCGCGCTGCTG
2 ttgacggctagctcagtcctaggtacagtgctagcTTAATTCACACAGGAAAGtactagATGACCGATAACCCGAACAAAAAAACCTTTTGG
2 ttgacggctagctcagtcctaggtacagtgctagcTTAATTCACACAGGAAAGtactagATGGCGACCGTGAGCATGCGCGATATGCTGAAA
$ grep -Pi '^[ATGC]+' $lib_prefix.fa | sort | uniq -c | grep -Pv '^\s+1' | wc -l
</code></pre>
<p>There are indeed 14 pairs of sequences that are not unique. Looking at the dataframe printout above, there are 28 genes where the Min Rare CDS sequence is the same as one of the ∆G types. Here are their names:</p>
<pre><code class="console">$ grep -Pi '^[ATGC]+' $lib_prefix.fa | sort | uniq -c | grep -P '^\s+2' | \
| perl -ne '@l = split; print $l[1]."\n"' | grep -B1 -f - $lib_prefix.fa \
| grep -P '^>' | sort
>BBaJ23100-aspS-16
>BBaJ23100-aspS-5
>BBaJ23100-cysS-18
>BBaJ23100-cysS-5
>BBaJ23100-lolA-41
>BBaJ23100-lolA-7
>BBaJ23100-mrdB-39
>BBaJ23100-mrdB-7
>BBaJ23100-rpsB-40
>BBaJ23100-rpsB-7
>BBaJ23100-serS-27
>BBaJ23100-serS-6
>BBaJ23100-yejM-15
>BBaJ23100-yejM-5
>BBaJ23108-aspS-16
>BBaJ23108-aspS-5
>BBaJ23108-cysS-18
>BBaJ23108-cysS-5
>BBaJ23108-lolA-41
>BBaJ23108-lolA-7
>BBaJ23108-mrdB-39
>BBaJ23108-mrdB-7
>BBaJ23108-rpsB-40
>BBaJ23108-rpsB-7
>BBaJ23108-serS-27
>BBaJ23108-serS-6
>BBaJ23108-yejM-15
>BBaJ23108-yejM-5
</code></pre>
<p>Filtering on exact matches (no right offset, no mismatches) with Alts > 0, here they are in R:</p>
<pre><code class="r">
# get reads that align perfectly to constructs missing from the subset
rna.reads_for_missing <- subset(rna.raw, Read.num %in% subset(rna.raw,
Name %in% rna.missing & Alts > 0 & Offset.R == 0 & Mismatches.len == 0 &
Offset.RBS < 0)$Read.num)[, !grepl("seq", names(rna.raw))]
# print df ordered by read number
cols_to_show <- names(rna.reads_for_missing)[!(names(rna.reads_for_missing) %in%
c("Length", "RBS.len", "Offset.R", "Mismatches.len", "Mismatches"))]
rna.reads_for_missing[order(rna.reads_for_missing$Read.num), cols_to_show]
</code></pre>
<pre><code>## Name Read.num Count Count.A Count.B Offset.L Alts
## 557099 BBaJ23100-yejM-15 104353 777 441 336 37 1
## 559798 BBaJ23100-yejM-5 104353 777 441 336 37 1
## 58022 BBaJ23100-cysS-18 108136 958 556 402 37 1
## 61889 BBaJ23100-cysS-5 108136 958 556 402 37 1
## 513203 BBaJ23100-serS-27 125037 927 520 407 37 1
## 515988 BBaJ23100-serS-6 125037 927 520 407 37 1
## 28575 BBaJ23100-aspS-16 153619 509 308 201 37 1
## 31395 BBaJ23100-aspS-5 153619 509 308 201 37 1
## 687091 BBaJ23108-yejM-15 154558 9 0 9 26 1
## 687662 BBaJ23108-yejM-5 154558 9 0 9 26 1
## 677853 BBaJ23108-serS-27 155028 6 2 4 26 1
## 678494 BBaJ23108-serS-6 155028 6 2 4 26 1
## 302265 BBaJ23100-lolA-41 168777 1705 1011 694 37 1
## 304797 BBaJ23100-lolA-7 168777 1705 1011 694 37 1
## 365419 BBaJ23100-mrdB-39 172333 1124 638 486 37 1
## 366936 BBaJ23100-mrdB-7 172333 1124 638 486 37 1
## 502802 BBaJ23100-rpsB-40 181908 896 492 404 37 1
## 504244 BBaJ23100-rpsB-7 181908 896 492 404 37 1
## 675280 BBaJ23108-rpsB-40 208668 28 25 3 3 1
## 675625 BBaJ23108-rpsB-7 208668 28 25 3 3 1
## 513157 BBaJ23100-serS-27 214555 29 26 3 24 1
## 515991 BBaJ23100-serS-6 214555 29 26 3 24 1
## 28576 BBaJ23100-aspS-16 215428 7 7 0 24 1
## 31422 BBaJ23100-aspS-5 215428 7 7 0 24 1
## 302337 BBaJ23100-lolA-41 215611 16 11 5 24 1
## 304842 BBaJ23100-lolA-7 215611 16 11 5 24 1
## 502757 BBaJ23100-rpsB-40 216263 36 17 19 24 1
## 504239 BBaJ23100-rpsB-7 216263 36 17 19 24 1
## 687094 BBaJ23108-yejM-15 244030 116 67 49 36 1
## 687659 BBaJ23108-yejM-5 244030 116 67 49 36 1
## 580838 BBaJ23108-cysS-18 244661 181 115 66 36 1
## 581585 BBaJ23108-cysS-5 244661 181 115 66 36 1
## 677855 BBaJ23108-serS-27 250029 307 187 120 36 1
## 678482 BBaJ23108-serS-6 250029 307 187 120 36 1
## 575164 BBaJ23108-aspS-16 257066 143 79 64 36 1
## 575766 BBaJ23108-aspS-5 257066 143 79 64 36 1
## 632781 BBaJ23108-lolA-41 258156 65 46 19 36 1
## 633211 BBaJ23108-lolA-7 258156 65 46 19 36 1
## 645915 BBaJ23108-mrdB-39 258651 75 40 35 36 1
## 646182 BBaJ23108-mrdB-7 258651 75 40 35 36 1
## 675271 BBaJ23108-rpsB-40 259871 46 37 9 36 1
## 675626 BBaJ23108-rpsB-7 259871 46 37 9 36 1
## 302257 BBaJ23100-lolA-41 265062 22 20 2 3 1
## 304734 BBaJ23100-lolA-7 265062 22 20 2 3 1
## 687097 BBaJ23108-yejM-15 277480 5 5 0 32 1
## 687668 BBaJ23108-yejM-5 277480 5 5 0 32 1
## 575174 BBaJ23108-aspS-16 278251 8 8 0 9 1
## 575777 BBaJ23108-aspS-5 278251 8 8 0 9 1
## 365390 BBaJ23100-mrdB-39 278508 8 8 0 32 1
## 366870 BBaJ23100-mrdB-7 278508 8 8 0 32 1
## 58020 BBaJ23100-cysS-18 286284 7 7 0 26 1
## 61923 BBaJ23100-cysS-5 286284 7 7 0 26 1
## 513141 BBaJ23100-serS-27 286672 19 6 13 26 1
## 515963 BBaJ23100-serS-6 286672 19 6 13 26 1
## 28641 BBaJ23100-aspS-16 287257 11 0 11 26 1
## 31446 BBaJ23100-aspS-5 287257 11 0 11 26 1
## 302218 BBaJ23100-lolA-41 287419 20 6 14 26 1
## 304716 BBaJ23100-lolA-7 287419 20 6 14 26 1
## 365394 BBaJ23100-mrdB-39 287531 13 13 0 26 1
## 366840 BBaJ23100-mrdB-7 287531 13 13 0 26 1
## 502795 BBaJ23100-rpsB-40 287853 7 2 5 26 1
## 504274 BBaJ23100-rpsB-7 287853 7 2 5 26 1
## 580833 BBaJ23108-cysS-18 298345 6 5 1 24 1
## 581579 BBaJ23108-cysS-5 298345 6 5 1 24 1
## 557113 BBaJ23100-yejM-15 307043 19 10 9 38 1
## 559773 BBaJ23100-yejM-5 307043 19 10 9 38 1
## 58071 BBaJ23100-cysS-18 310764 18 11 7 38 1
## 61926 BBaJ23100-cysS-5 310764 18 11 7 38 1
## 513175 BBaJ23100-serS-27 313053 34 23 11 38 1
## 516009 BBaJ23100-serS-6 313053 34 23 11 38 1
## 28623 BBaJ23100-aspS-16 322170 5 4 1 38 1
## 31481 BBaJ23100-aspS-5 322170 5 4 1 38 1
## 302305 BBaJ23100-lolA-41 338572 163 96 67 38 1
## 304794 BBaJ23100-lolA-7 338572 163 96 67 38 1
## 365450 BBaJ23100-mrdB-39 339062 28 22 6 38 1
## 366863 BBaJ23100-mrdB-7 339062 28 22 6 38 1
## 502782 BBaJ23100-rpsB-40 340361 21 8 13 38 1
## 504281 BBaJ23100-rpsB-7 340361 21 8 13 38 1
## 687088 BBaJ23108-yejM-15 350953 19 9 10 35 1
## 687663 BBaJ23108-yejM-5 350953 19 9 10 35 1
## 580834 BBaJ23108-cysS-18 351087 24 10 14 35 1
## 581576 BBaJ23108-cysS-5 351087 24 10 14 35 1
## 677841 BBaJ23108-serS-27 352670 45 20 25 35 1
## 678477 BBaJ23108-serS-6 352670 45 20 25 35 1
## 575173 BBaJ23108-aspS-16 354512 46 26 20 35 1
## 575776 BBaJ23108-aspS-5 354512 46 26 20 35 1
## 632791 BBaJ23108-lolA-41 354894 18 7 11 35 1
## 633199 BBaJ23108-lolA-7 354894 18 7 11 35 1
## 645916 BBaJ23108-mrdB-39 355086 6 0 6 35 1
## 646190 BBaJ23108-mrdB-7 355086 6 0 6 35 1
## 675283 BBaJ23108-rpsB-40 357579 5 5 0 4 1
## 675614 BBaJ23108-rpsB-7 357579 5 5 0 4 1
## 28568 BBaJ23100-aspS-16 358579 5 5 0 25 1
## 31417 BBaJ23100-aspS-5 358579 5 5 0 25 1
## 677856 BBaJ23108-serS-27 445294 5 5 0 37 1
## 678492 BBaJ23108-serS-6 445294 5 5 0 37 1
## 675276 BBaJ23108-rpsB-40 448104 12 7 5 17 1
## 675613 BBaJ23108-rpsB-7 448104 12 7 5 17 1
## 687098 BBaJ23108-yejM-15 540694 7 4 3 30 1
## 687658 BBaJ23108-yejM-5 540694 7 4 3 30 1
## 677852 BBaJ23108-serS-27 541294 5 2 3 30 1
## 678489 BBaJ23108-serS-6 541294 5 2 3 30 1
## 575168 BBaJ23108-aspS-16 542023 40 27 13 30 1
## 575771 BBaJ23108-aspS-5 542023 40 27 13 30 1
## 632786 BBaJ23108-lolA-41 542239 17 12 5 30 1
## 633203 BBaJ23108-lolA-7 542239 17 12 5 30 1
## 645917 BBaJ23108-mrdB-39 542404 11 2 9 30 1
## 646189 BBaJ23108-mrdB-7 542404 11 2 9 30 1
## 302288 BBaJ23100-lolA-41 543857 8 0 8 30 1
## 304718 BBaJ23100-lolA-7 543857 8 0 8 30 1
## 58087 BBaJ23100-cysS-18 548114 70 49 21 34 1
## 61957 BBaJ23100-cysS-5 548114 70 49 21 34 1
## 28593 BBaJ23100-aspS-16 549809 6 6 0 34 1
## 31406 BBaJ23100-aspS-5 549809 6 6 0 34 1
## 502736 BBaJ23100-rpsB-40 550608 15 8 7 34 1
## 504213 BBaJ23100-rpsB-7 550608 15 8 7 34 1
## 677847 BBaJ23108-serS-27 928160 21 13 8 10 1
## 678495 BBaJ23108-serS-6 928160 21 13 8 10 1
## 557098 BBaJ23100-yejM-15 1012393 288 130 158 36 1
## 559754 BBaJ23100-yejM-5 1012393 288 130 158 36 1
## 57973 BBaJ23100-cysS-18 1014195 1046 633 413 36 1
## 61915 BBaJ23100-cysS-5 1014195 1046 633 413 36 1
## 513222 BBaJ23100-serS-27 1025817 662 406 256 36 1
## 516021 BBaJ23100-serS-6 1025817 662 406 256 36 1
## 28566 BBaJ23100-aspS-16 1047189 884 545 339 36 1
## 31404 BBaJ23100-aspS-5 1047189 884 545 339 36 1
## 302286 BBaJ23100-lolA-41 1055530 1589 1062 527 36 1
## 304778 BBaJ23100-lolA-7 1055530 1589 1062 527 36 1
## 365425 BBaJ23100-mrdB-39 1058615 669 447 222 36 1
## 366875 BBaJ23100-mrdB-7 1058615 669 447 222 36 1
## 502768 BBaJ23100-rpsB-40 1066710 564 333 231 36 1
## 504254 BBaJ23100-rpsB-7 1066710 564 333 231 36 1
## 687090 BBaJ23108-yejM-15 1089174 8 3 5 31 1
## 687657 BBaJ23108-yejM-5 1089174 8 3 5 31 1
## 580845 BBaJ23108-cysS-18 1089278 12 10 2 31 1
## 581578 BBaJ23108-cysS-5 1089278 12 10 2 31 1
## 677838 BBaJ23108-serS-27 1090290 30 13 17 31 1
## 678476 BBaJ23108-serS-6 1090290 30 13 17 31 1
## 575170 BBaJ23108-aspS-16 1091578 8 4 4 31 1
## 575763 BBaJ23108-aspS-5 1091578 8 4 4 31 1
## 632788 BBaJ23108-lolA-41 1091910 13 0 13 31 1
## 633204 BBaJ23108-lolA-7 1091910 13 0 13 31 1
## 645914 BBaJ23108-mrdB-39 1092126 7 5 2 31 1
## 646183 BBaJ23108-mrdB-7 1092126 7 5 2 31 1
## 675275 BBaJ23108-rpsB-40 1092630 9 8 1 31 1
## 675620 BBaJ23108-rpsB-7 1092630 9 8 1 31 1
## 502759 BBaJ23100-rpsB-40 1093806 8 4 4 8 1
## 504276 BBaJ23100-rpsB-7 1093806 8 4 4 8 1
## 557090 BBaJ23100-yejM-15 1096785 7 7 0 31 1
## 559778 BBaJ23100-yejM-5 1096785 7 7 0 31 1
## 57986 BBaJ23100-cysS-18 1096893 58 35 23 31 1
## 61855 BBaJ23100-cysS-5 1096893 58 35 23 31 1
## 513193 BBaJ23100-serS-27 1098032 32 25 7 31 1
## 515904 BBaJ23100-serS-6 1098032 32 25 7 31 1
## 302255 BBaJ23100-lolA-41 1099791 114 45 69 31 1
## 304768 BBaJ23100-lolA-7 1099791 114 45 69 31 1
## 502727 BBaJ23100-rpsB-40 1101020 28 21 7 31 1
## 504198 BBaJ23100-rpsB-7 1101020 28 21 7 31 1
## 557085 BBaJ23100-yejM-15 1102553 9 0 9 39 1
## 559784 BBaJ23100-yejM-5 1102553 9 0 9 39 1
## 557130 BBaJ23100-yejM-15 1108185 14 5 9 19 1
## 559776 BBaJ23100-yejM-5 1108185 14 5 9 19 1
## 58007 BBaJ23100-cysS-18 1108341 21 14 7 19 1
## 61909 BBaJ23100-cysS-5 1108341 21 14 7 19 1
## 513212 BBaJ23100-serS-27 1109798 31 21 10 19 1
## 515966 BBaJ23100-serS-6 1109798 31 21 10 19 1
## 28642 BBaJ23100-aspS-16 1111907 31 10 21 19 1
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