EM based transcript abundance from nanopore reads mapped to a transcriptome with minimap2 Python package adapted from https://github.com/jts/nanopore-rna-analysis by Jared Simpson
NanoCount estimates transcript abundance from ONT direct-RNA Sequencing reads mapped to a transcriptome. It uses an expectation-maximization approach like RSEM, Kallisto, salmon, etc to handle multi-mapping reads. The reads must be mapped to the transcript set using minimap2.
- Author: Adrien Leger - aleg {at} ebi.ac.uk (based on a prototype written by Jared Simpson)
- Licence: MIT
- Python version: 3.5 +
Ideally, before installation, create a clean python3 virtual environment to deploy the package, using virtualenvwrapper for example (see http://www.simononsoftware.com/virtualenv-tutorial-part-2/).
pip3 install git+https://github.com/a-slide/NanoCount.gitpip3 install git+https://github.com/a-slide/NanoCountbash.git --upgradeusage: NanoCount [-h][--version] -i ALIGNMENT_FILE -o COUNT_FILE
[--min_read_length MIN_READ_LENGTH]
[--min_query_fraction_aligned MIN_QUERY_FRACTION_ALIGNED]
[--equivalent_threshold EQUIVALENT_THRESHOLD]
[--scoring_value SCORING_VALUE]
[--convergence_target CONVERGENCE_TARGET] [--verbose]
Calculate transcript abundance for a dRNA-Seq dataset from a BAM/SAM alignment file generated by minimap2
optional arguments:
-h, --help show this help message and exit
--version, -v show program's version number and exit
-i ALIGNMENT_FILE, --alignment_file ALIGNMENT_FILE
BAM or SAM file containing aligned ONT dRNA-Seq reads including secondary and supplementary alignment
-o COUNT_FILE, --count_file COUNT_FILE
Output count file
--min_read_length MIN_READ_LENGTH
Minimal length of the read to be considered valid
--min_query_fraction_aligned MIN_QUERY_FRACTION_ALIGNED
Minimal fraction of the primary hit query aligned to consider the read valid
--equivalent_threshold EQUIVALENT_THRESHOLD
Fraction of the alignment score or the alignment
length of secondary hits compared to the primary hit to be considered valid hits
--scoring_value SCORING_VALUE
Value to use for score thresholding of secondary hits. Either alignment_score or alignment_length
--convergence_target CONVERGENCE_TARGET
Convergence target value of the cummulative difference between abundance values of successive EM round to trigger the end of the EM loop
--verbose If True will be chatty
Output file exemple
transcript_name raw est_count tpm
ENSDART00000023156|ENSDARG00000020850 0.009360321697698902 277.0000000000036 277000000.0000036
ENSDART00000093609|ENSDARG00000063908 0.0075017740681919 222.0000000000029 222000000.0000029
ENSDART00000052511|ENSDARG00000036161 0.0063190619403238075 187.00000000000244 187000000.00000244
ENSDART00000093606|ENSDARG00000063905 0.00615010306491408 182.00000000000236 182000000.00000235
ENSDART00000146211|ENSDARG00000020504 0.006076357615676965 179.81765092072843 179817650.92072842
ENSDART00000028885|ENSDARG00000028335 0.005410518932099315 160.11348675761502 160113486.75761503
ENSDART00000058715|ENSDARG00000040141 0.005237725137701552 155.00000000000202 155000000.00000203
ENSDART00000074787|ENSDARG00000052856 0.004385241613334473 129.77245506340708 129772455.06340708
ENSDART00000093613|ENSDARG00000063912 0.0036495117088501134 108.0000000000014 108000000.0000014
The same options as for the command line are available, but it allows to use the package as an API and access more internal information
from NanoCount.NanoCount import NanoCount_main
n = NanoCount_main (alignment_file="transcriptome_aligned_reads.bam", verbose=True)Parse Bam file and filter low quality hits
Mapped hits:118237
Wrong strand hits:1139
Unmapped hits:38325
Valid best hit:72427
Valid secondary hit:31298
Invalid secondary hit:12695
Best hit with low query fraction aligned:1066
Generate initial read/transcript compatibility index
Start EM abundance estimate
........
Convergence target reached after 8 rounds
Convergence value = 0.004801809595549253
The count results are stored in a Pandas Dataframe that can be conveniently rendered in Jupyter
display(n.count_df)| transcript_name | raw | est_count | tpm |
|---|---|---|---|
| ENSDART00000010144|ENSDARG00000002768 | 0.0319356 | 2313 | 2313000000 |
| ENSDART00000055062|ENSDARG00000037789 | 0.02688224 | 1947 | 1947000000 |
| ENSDART00000055136|ENSDARG00000037840 | 0.01905367 | 1380 | 1380000000 |
| ENSDART00000093609|ENSDARG00000063908 | 0.01818383 | 1317 | 1317000000 |
| ENSDART00000023156|ENSDARG00000020850 | 0.01186022 | 859 | 859000000 |
| ENSDART00000093612|ENSDARG00000063911 | 0.008146133 | 590 | 590000000 |
| ENSDART00000123003|ENSDARG00000067990 | 0.007874959 | 570.3596 | 570359600 |
| ENSDART00000012644|ENSDARG00000017624 | 0.007097575 | 514.0561 | 514056100 |
| ENSDART00000093613|ENSDARG00000063912 | 0.006862082 | 497 | 497000000 |
Please be aware this package is experimental . It was tested under Linux Ubuntu 16.04 and in an HPC environment running under Red Hat Enterprise 7.1.
You are welcome to contribute by requesting additional functionalities, reporting bugs or by forking and submitting patches or updates pull requests
Thank you