===================================================================
km : a software for RNA-seq investigation using k-mer decomposition
===================================================================
+-------------+-----------+
| |pyversion| | |codecov| |
+-------------+-----------+
.. |pyversion| image:: https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue.svg
:target: https://pypi.org/project/km-walk/
.. |codecov| image:: https://codecov.io/gh/iric-soft/km/branch/master/graph/badge.svg
:target: https://codecov.io/gh/iric-soft/km/
.. |travis| image:: https://travis-ci.org/iric-soft/km.svg?branch=master
:target: https://travis-ci.org/iric-soft/km
-------------
Introduction:
-------------
This tool was developed to identify and quantify the occurence of single
nucleotide variants, insertions, deletions and duplications in RNA-seq data. Contrary to most tools that try to report all variants in a complete genome, here we instead propose to focus the analysis on small regions of interest.
Given a reference sequence (typically a few hundred base pairs) around a
known or suspected mutation in a gene of interest, all possible sequences
that can be be created between the two end k-mers according to the
sequenced reads will be reported. A ratio of variant allele vs WT will be
computed for each possible sequence constructed.
-------
Citing:
-------
* Targeted variant detection using unaligned RNA-Seq reads. Life science Alliance 2019 Aug 19;2(4); doi: https://doi.org/10.26508/lsa.201900336
* Target variant detection in leukemia using unaligned RNA-Seq reads. bioRxiv 295808; doi: https://doi.org/10.1101/295808
--------
Install:
--------
Recommended method - using pip:
-------------------------------
.. code:: shell
python3 -m venv $HOME/.virtualenvs/km
source $HOME/.virtualenvs/km/bin/activate
pip install --upgrade pip setuptools wheel
pip install km-walk
Alternative method - easy install script:
-----------------------------------------
`easy_install.sh`_ will install jellyfish with python binding, km in a virtual
environement, and test it. Without modification, all the code source will be
downloaded in your $HOME/software directory and all executable will be available
in the virtual environement directory: $HOME/.virtualenvs/km.
Requirements:
*************
* Python 3.6.0 or later with `pip`_ installed.
.. _pip: https://pip.pypa.io/en/stable/installing/
Usage:
******
* Copy/paste each line in a terminal.
* The virtual environment needs to be loaded each time you open a new terminal, with this command:
.. code:: shell
$ source $HOME/.virtualenvs/km/bin/activate
Test:
*****
* 4bp insertion in NPM1
.. code:: shell
$ cd [your_km_folder]
$ km find_mutation ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa ./data/jf/02H025_NPM1.jf | km find_report -t ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa
Sample Region Location Type Removed Added Abnormal Normal Ratio Min_coverage Exclu_min_cov Variant Target InfoVariant_sequence Reference_sequence
./data/jf/02H025_NPM1.jf chr5:171410540-171410543 chr5:171410544 ITD 0 4 | 4 2870.6 3055.2 0.484 2428 /TCTG NPM1_4ins_exons_10-11utr vs_ref AATTGCTTCCGGATGACTGACCAAGAGGCTATTCAAGATCTCTGTCTGGCAGTGGAGGAAGTCTCTTTAAGAAAATAGTTTAAA AATTGCTTCCGGATGACTGACCAAGAGGCTATTCAAGATCTCTGGCAGTGGAGGAAGTCTCTTTAAGAAAATAGTTTAAA
./data/jf/02H025_NPM1.jf - Reference 0 0 0.0 2379.0 1.000 2379 - NPM1_4ins_exons_10-11utr vs_ref
# To display kmer coverage
$ km find_mutation ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa ./data/jf/02H025_NPM1.jf -g
* ITD of 75 bp
.. code:: shell
$ cd [your_km_folder]
$ km find_mutation ./data/catalog/GRCh38/FLT3-ITD_exons_13-15.fa ./data/jf/03H116_ITD.jf | km find_report -t ./data/catalog/GRCh38/FLT3-ITD_exons_13-15.fa
Sample Region Location Type Removed Added Abnormal Normal Ratio Min_coverage Exclu_min_cov Variant Target Info Variant_sequence Reference_sequence
./data/jf/03H116_ITD.jf - Reference 0 0 0.0 443.0 1.000 912 - FLT3-ITD_exons_13-15 vs_ref
./data/jf/03H116_ITD.jf chr13:28034105-28034179 chr13:28034180 ITD 0 75 | 75 417.6 1096.7 0.276 443 /AACTCCCATTTGAGATCATATTCATATTCTCTGAAATCAACGTAGAAGTACTCATTATCTGAGGAGCCGGTCACC FLT3-ITD_exons_13-15 vs_ref CTTTCAGCATTTTGACGGCAACCTGGATTGAGACTCCTGTTTTGCTAATTCCATAAGCTGTTGCGTTCATCACTTTTCCAAAAGCACCTGATCCTAGTACCTTCCCAAACTCTAAATTTTCTCTTGGAAACTCCCATTTGAGATCATATTCATATTCTCTGAAATCAACGTAGAAGTACTCATTATCTGAGGAGCCGGTCACCAACTCCCATTTGAGATCATATTCATATTCTCTGAAATCAACGTAGAAGTACTCATTATCTGAGGAGCCGGTCACCTGTACCATCTGTAGCTGGCTTTCATACCTAAATTGCTTTTTGTACTTGTGACAAATTAGCAGGGTTAAAACGACAATGAAGAGGAGACAAACACCAATTGTTGCATAGAATGAGATGTTGTCTTGGATGAAAGGGAAGGGGC CTTTCAGCATTTTGACGGCAACCTGGATTGAGACTCCTGTTTTGCTAATTCCATAAGCTGTTGCGTTCATCACTTTTCCAAAAGCACCTGATCCTAGTACCTTCCCAAACTCTAAATTTTCTCTTGGAAACTCCCATTTGAGATCATATTCATATTCTCTGAAATCAACGTAGAAGTACTCATTATCTGAGGAGCCGGTCACCTGTACCATCTGTAGCTGGCTTTCATACCTAAATTGCTTTTTGTACTTGTGACAAATTAGCAGGGTTAAAACGACAATGAAGAGGAGACAAACACCAATTGTTGCATAGAATGAGATGTTGTCTTGGATGAAAGGGAAGGGGC
.. _easy_install.sh: https://github.com/iric-soft/km/blob/master/easy_install.sh
Without install:
----------------
km can be executed directly from source code.
Requirements:
*************
* Python 3.6.0 or later
* pyJellyfish python module or Jellyfish 2.2 or later **with** Python `bindings`_.
Usage:
******
.. code:: shell
$ cd [your_km_folder]
$ python -m km find_mutation ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa ./data/jf/02H025_NPM1.jf | km find_report -t ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa
.. _bindings: https://github.com/gmarcais/Jellyfish#binding-to-script-languages
----------------------------
Design your target sequence:
----------------------------
* km is designed to make targeted analysis based on **target sequences**. These target sequences **need to be designed** and given to km as input.
* A target sequence is a nucleotide sequence saved in a fasta file. Some target sequences are provided in `catalog <https://github.com/iric-soft/km/tree/master/km/data/catalog>`_.
* To fit your specific needs, you will have to create your own target sequences.
* On generic cases, you can follow some good practices described below:
.. image:: https://github.com/iric-soft/km/blob/master/data/figure/doc_target_sequence.png
* A web portal is available to assist you in the creation of your target sequences (for cases 1 and 2).
- km-target: https://bioinfo.iric.ca/km-target/
* You could also extract nucleotide sequences from genome using severals methods, two of them are discribe below:
- Using `samtools <http://www.htslib.org/doc/samtools.html>`_: samtools faidx chr2:25234341-25234405 GRCh38/genome.fa
- Using get DNA from `ucsc <https://genome.ucsc.edu/cgi-bin/hgc?hgsid=730614743_K2u5W9UIMXrPzrUlC5KaXmWjzf4R&o=25234340&g=getDna&i=mixed&c=chr2&l=25234340&r=25234405&db=hg38&hgsid=730614743_K2u5W9UIMXrPzrUlC5KaXmWjzf4R>`_.
-------------
Display help:
-------------
.. code:: shell
$ km -h
usage: PROG [-h] {find_mutation,find_report,linear_kmin,min_cov} ...
positional arguments:
{find_mutation,find_report,linear_kmin,min_cov}
sub-command help
find_mutation Identify and quantify mutations from a target sequence
and a k-mer database.
find_report Parse find_mutation output to reformat it in tabulated
file more user friendly.
linear_kmin Find min k length to decompose a target sequence in a
linear graph.
min_cov Compute coverage of target sequences.
optional arguments:
-h, --help show this help message and exit
--------------------
km's tools overview:
--------------------
For more detailed documentation click `here <https://github.com/iric-soft/km/tree/master/km/tools>`_.
find_mutation:
--------------
This is the main tool of km, to identify and quantify mutations from
a target sequence and a k-mer jellyfish database.
.. code:: shell
$ km find_mutation -h
$ km find_mutation [your_fasta_targetSeq] [your_jellyfish_count_table]
$ km find_mutation [your_catalog_directory] [your_jellyfish_count_table]
find_report:
------------
This tool parse find_mutation output to reformat it in more user friendly
tabulated file.
.. code:: shell
$ km find_report -h
$ km find_report -t [your_fasta_targetSeq] [find_mutation_output]
$ km find_mutation [your_fasta_targetSeq] [your_jellyfish_count_table] | km find_report -t [your_fasta_targetSeq]
min_cov:
--------
This tools display some k-mer's coverage stats of a target sequence and a list of jellyfish database.
.. code:: shell
$ km min_cov -h
$ km min_cov [your_fasta_targetSeq] [[your_jellyfish_count_table]...]
linear_kmin:
------------
Length of k-mers is a central parameter:
* To produce a linear directed graph from the target sequence.
* To avoid false-positive. find_mutation shouldn't be use on jellyfish count table build with k<21 bp (we recommand k=31 bp, by default)
linear_kmin tool is design to give you the minimun k length to allow a
decomposition of a target sequence in a linear graph.
.. code:: shell
$ km linear_kmin -h
$ km linear_kmin [your_catalog_directory]
-------------------------------------------------
Runing km on a real sample from downloaded fastq:
-------------------------------------------------
In the `example`_ folder you can find a script to help you to
run a km analysis on one Leucegene sample.
.. _example: https://github.com/iric-soft/km/tree/master/example
Raw data
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"description": "\n===================================================================\nkm : a software for RNA-seq investigation using k-mer decomposition\n===================================================================\n\n+-------------+-----------+\n| |pyversion| | |codecov| |\n+-------------+-----------+\n\n.. |pyversion| image:: https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue.svg\n :target: https://pypi.org/project/km-walk/\n\n.. |codecov| image:: https://codecov.io/gh/iric-soft/km/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/iric-soft/km/\n\n.. |travis| image:: https://travis-ci.org/iric-soft/km.svg?branch=master\n :target: https://travis-ci.org/iric-soft/km\n\n-------------\nIntroduction:\n-------------\n\nThis tool was developed to identify and quantify the occurence of single\nnucleotide variants, insertions, deletions and duplications in RNA-seq data. Contrary to most tools that try to report all variants in a complete genome, here we instead propose to focus the analysis on small regions of interest.\n\nGiven a reference sequence (typically a few hundred base pairs) around a\nknown or suspected mutation in a gene of interest, all possible sequences\nthat can be be created between the two end k-mers according to the\nsequenced reads will be reported. A ratio of variant allele vs WT will be\ncomputed for each possible sequence constructed.\n\n-------\nCiting:\n-------\n* Targeted variant detection using unaligned RNA-Seq reads. Life science Alliance 2019 Aug 19;2(4); doi: https://doi.org/10.26508/lsa.201900336\n* Target variant detection in leukemia using unaligned RNA-Seq reads. bioRxiv 295808; doi: https://doi.org/10.1101/295808\n\n--------\nInstall:\n--------\n\nRecommended method - using pip:\n-------------------------------\n\n.. code:: shell\n\n python3 -m venv $HOME/.virtualenvs/km\n source $HOME/.virtualenvs/km/bin/activate\n pip install --upgrade pip setuptools wheel\n pip install km-walk\n\nAlternative method - easy install script:\n-----------------------------------------\n\n`easy_install.sh`_ will install jellyfish with python binding, km in a virtual\nenvironement, and test it. Without modification, all the code source will be\ndownloaded in your $HOME/software directory and all executable will be available\nin the virtual environement directory: $HOME/.virtualenvs/km.\n\nRequirements:\n*************\n* Python 3.6.0 or later with `pip`_ installed.\n\n.. _pip: https://pip.pypa.io/en/stable/installing/\n\nUsage:\n******\n\n* Copy/paste each line in a terminal.\n* The virtual environment needs to be loaded each time you open a new terminal, with this command:\n\n.. code:: shell\n\n $ source $HOME/.virtualenvs/km/bin/activate\n\nTest:\n*****\n\n* 4bp insertion in NPM1\n\n.. code:: shell\n\n $ cd [your_km_folder]\n $ km find_mutation ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa ./data/jf/02H025_NPM1.jf | km find_report -t ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa\n Sample\tRegion\tLocation\tType\tRemoved\tAdded\tAbnormal\tNormal\tRatio\tMin_coverage\tExclu_min_cov\tVariant\tTarget\tInfoVariant_sequence\tReference_sequence\n ./data/jf/02H025_NPM1.jf\tchr5:171410540-171410543\tchr5:171410544\tITD\t0\t4 | 4\t2870.6\t3055.2\t0.484\t2428\t\t/TCTG\tNPM1_4ins_exons_10-11utr\tvs_ref\tAATTGCTTCCGGATGACTGACCAAGAGGCTATTCAAGATCTCTGTCTGGCAGTGGAGGAAGTCTCTTTAAGAAAATAGTTTAAA\tAATTGCTTCCGGATGACTGACCAAGAGGCTATTCAAGATCTCTGGCAGTGGAGGAAGTCTCTTTAAGAAAATAGTTTAAA\n ./data/jf/02H025_NPM1.jf\t\t-\tReference\t0\t0\t0.0\t2379.0\t1.000\t2379\t\t-\tNPM1_4ins_exons_10-11utr\tvs_ref\n # To display kmer coverage\n $ km find_mutation ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa ./data/jf/02H025_NPM1.jf -g\n\n* ITD of 75 bp\n\n.. code:: shell\n\n $ cd [your_km_folder]\n $ km find_mutation ./data/catalog/GRCh38/FLT3-ITD_exons_13-15.fa ./data/jf/03H116_ITD.jf | km find_report -t ./data/catalog/GRCh38/FLT3-ITD_exons_13-15.fa\n Sample\tRegion\tLocation\tType\tRemoved\tAdded\tAbnormal\tNormal\tRatio\tMin_coverage\tExclu_min_cov\tVariant\tTarget\tInfo\tVariant_sequence\tReference_sequence\n ./data/jf/03H116_ITD.jf\t\t-\tReference\t0\t0\t0.0\t443.0\t1.000\t912\t\t-\tFLT3-ITD_exons_13-15\tvs_ref\n ./data/jf/03H116_ITD.jf\tchr13:28034105-28034179\tchr13:28034180\tITD\t0\t75 | 75\t417.6\t1096.7\t0.276\t443\t\t/AACTCCCATTTGAGATCATATTCATATTCTCTGAAATCAACGTAGAAGTACTCATTATCTGAGGAGCCGGTCACC\tFLT3-ITD_exons_13-15\tvs_ref\tCTTTCAGCATTTTGACGGCAACCTGGATTGAGACTCCTGTTTTGCTAATTCCATAAGCTGTTGCGTTCATCACTTTTCCAAAAGCACCTGATCCTAGTACCTTCCCAAACTCTAAATTTTCTCTTGGAAACTCCCATTTGAGATCATATTCATATTCTCTGAAATCAACGTAGAAGTACTCATTATCTGAGGAGCCGGTCACCAACTCCCATTTGAGATCATATTCATATTCTCTGAAATCAACGTAGAAGTACTCATTATCTGAGGAGCCGGTCACCTGTACCATCTGTAGCTGGCTTTCATACCTAAATTGCTTTTTGTACTTGTGACAAATTAGCAGGGTTAAAACGACAATGAAGAGGAGACAAACACCAATTGTTGCATAGAATGAGATGTTGTCTTGGATGAAAGGGAAGGGGC\tCTTTCAGCATTTTGACGGCAACCTGGATTGAGACTCCTGTTTTGCTAATTCCATAAGCTGTTGCGTTCATCACTTTTCCAAAAGCACCTGATCCTAGTACCTTCCCAAACTCTAAATTTTCTCTTGGAAACTCCCATTTGAGATCATATTCATATTCTCTGAAATCAACGTAGAAGTACTCATTATCTGAGGAGCCGGTCACCTGTACCATCTGTAGCTGGCTTTCATACCTAAATTGCTTTTTGTACTTGTGACAAATTAGCAGGGTTAAAACGACAATGAAGAGGAGACAAACACCAATTGTTGCATAGAATGAGATGTTGTCTTGGATGAAAGGGAAGGGGC\n\n.. _easy_install.sh: https://github.com/iric-soft/km/blob/master/easy_install.sh\n\nWithout install:\n----------------\nkm can be executed directly from source code.\n\nRequirements:\n*************\n* Python 3.6.0 or later\n* pyJellyfish python module or Jellyfish 2.2 or later **with** Python `bindings`_.\n\nUsage:\n******\n\n.. code:: shell\n\n $ cd [your_km_folder]\n $ python -m km find_mutation ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa ./data/jf/02H025_NPM1.jf | km find_report -t ./data/catalog/GRCh38/NPM1_4ins_exons_10-11utr.fa\n\n.. _bindings: https://github.com/gmarcais/Jellyfish#binding-to-script-languages\n\n----------------------------\nDesign your target sequence:\n----------------------------\n\n* km is designed to make targeted analysis based on **target sequences**. These target sequences **need to be designed** and given to km as input.\n* A target sequence is a nucleotide sequence saved in a fasta file. Some target sequences are provided in `catalog <https://github.com/iric-soft/km/tree/master/km/data/catalog>`_.\n* To fit your specific needs, you will have to create your own target sequences.\n* On generic cases, you can follow some good practices described below:\n\n.. image:: https://github.com/iric-soft/km/blob/master/data/figure/doc_target_sequence.png\n\n* A web portal is available to assist you in the creation of your target sequences (for cases 1 and 2).\n\n - km-target: https://bioinfo.iric.ca/km-target/\n\n* You could also extract nucleotide sequences from genome using severals methods, two of them are discribe below:\n\n - Using `samtools <http://www.htslib.org/doc/samtools.html>`_: samtools faidx chr2:25234341-25234405 GRCh38/genome.fa\n - Using get DNA from `ucsc <https://genome.ucsc.edu/cgi-bin/hgc?hgsid=730614743_K2u5W9UIMXrPzrUlC5KaXmWjzf4R&o=25234340&g=getDna&i=mixed&c=chr2&l=25234340&r=25234405&db=hg38&hgsid=730614743_K2u5W9UIMXrPzrUlC5KaXmWjzf4R>`_.\n\n\n-------------\nDisplay help:\n-------------\n\n.. code:: shell\n\n $ km -h\n usage: PROG [-h] {find_mutation,find_report,linear_kmin,min_cov} ...\n\n positional arguments:\n {find_mutation,find_report,linear_kmin,min_cov}\n sub-command help\n find_mutation Identify and quantify mutations from a target sequence\n and a k-mer database.\n find_report Parse find_mutation output to reformat it in tabulated\n file more user friendly.\n linear_kmin Find min k length to decompose a target sequence in a\n linear graph.\n min_cov Compute coverage of target sequences.\n\n optional arguments:\n -h, --help show this help message and exit\n\n\n--------------------\nkm's tools overview:\n--------------------\n\nFor more detailed documentation click `here <https://github.com/iric-soft/km/tree/master/km/tools>`_.\n\nfind_mutation:\n--------------\n\nThis is the main tool of km, to identify and quantify mutations from\na target sequence and a k-mer jellyfish database.\n\n.. code:: shell\n\n $ km find_mutation -h\n $ km find_mutation [your_fasta_targetSeq] [your_jellyfish_count_table]\n $ km find_mutation [your_catalog_directory] [your_jellyfish_count_table]\n\nfind_report:\n------------\n\nThis tool parse find_mutation output to reformat it in more user friendly\ntabulated file.\n\n.. code:: shell\n\n $ km find_report -h\n $ km find_report -t [your_fasta_targetSeq] [find_mutation_output]\n $ km find_mutation [your_fasta_targetSeq] [your_jellyfish_count_table] | km find_report -t [your_fasta_targetSeq]\n\nmin_cov:\n--------\n\nThis tools display some k-mer's coverage stats of a target sequence and a list of jellyfish database.\n\n.. code:: shell\n\n $ km min_cov -h\n $ km min_cov [your_fasta_targetSeq] [[your_jellyfish_count_table]...]\n\nlinear_kmin:\n------------\n\nLength of k-mers is a central parameter:\n\n* To produce a linear directed graph from the target sequence.\n* To avoid false-positive. find_mutation shouldn't be use on jellyfish count table build with k<21 bp (we recommand k=31 bp, by default)\n\nlinear_kmin tool is design to give you the minimun k length to allow a\ndecomposition of a target sequence in a linear graph.\n\n.. code:: shell\n\n $ km linear_kmin -h\n $ km linear_kmin [your_catalog_directory]\n\n-------------------------------------------------\nRuning km on a real sample from downloaded fastq:\n-------------------------------------------------\nIn the `example`_ folder you can find a script to help you to\nrun a km analysis on one Leucegene sample.\n\n .. _example: https://github.com/iric-soft/km/tree/master/example\n",
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