Find specific gene or transcript kmers. And more.
Project description
Kmerator2
DEPRECATED
kmerator2 is now deprecated, and replaced by kmerator (without number)
Take a look at github: https://github.com/Transipedia/kmerator
Prototype for decomposition of transcript or gene sequences and extraction of their specific k-mers
Kmerator is a prototype tool designed for the prediction of specific k-mers (also called tags) from input sequences, considering a reference genome and an ENSEMBL-like transcriptome. From these specific k-mers, it also outputs their corresponding specific contigs which are sequences of consecutive k-mers (overlapping length between k-mers must be k-1, otherwise, it's a new contig). Kmerator first uses Jellyfish [1] to create 2 requestable indexes from the reference genome and transcriptome, and second, decomposes your input transcript or gene sequences to count the occurences of each k-mer in the genome and transcriptome. Number of occurrences are then interpreted, in different manners, to select specific k-mer from your input.
Kmerator strictly depends on a reference genome (fasta or jellyfish index format) and on an Ensembl fasta format transcriptome, you can find it there: https://www.ensembl.org/info/data/ftp/index.html. For a more complete k-mer filtering, we advice to merge the coding (cDNA) and non-coding (ncRNA) as one unique reference transcript.
Kmerator2 is new version of kmerator, written in python3 (julia with first version), several options have changed. It is compatible with last versions of Ensembl transcriptome (version 103 max for kmerator). The output is improved and a file report is produced.
Specific kmers
Specific config
Dependencies
- Python >= v3.6
- Jellyfish >= 2.0
Installation
Solution 1 (preferred)
Install with pip
pip3 install kmerator2
If installed as user, ensure the directory $HOME/.local/bin
is in your $PATH.
Solution 2
Installation from github
git clone https://github.com/Transipedia/kmerator2.git
cp kmerator2/kmerator/kmerator.py /usr/local/bin/kmerator2 # or somewhere in your $PATH
cp kmerator2/kmerator/ktools.py /usr/local/bin/ktools # or somewhere in your $PATH
Usage
kmerator2 [-h] (-s SELECTION [SELECTION ...] | -f FASTA_FILE) -g GENOME -t TRANSCRIPTOME
-l {gene,transcript,chimera} [-a APPRIS] [-u] [-k KMER_LENGTH] [--stringent]
[--threshold THRESHOLD] [-o OUTPUT] [-c CORES] [--verbose] [-v]
How use kmerator2
There are two main cases:
- you find for specific k-mers for annotated genes or transcripts : use the
--selection
option, followed by:- the list of gene and/or transcripts
- or a file with the list of genes/transcripts
- you find for specific k-mers of unannotated sequences : use the
--fasta-file
option, followed by a fasta file containing yours requests. In case of you focuses on chimeras, add the--chimera
option
Differences between genes and transcripts
- When you find for a gene (symbol or Ensembl name), kmerator fetch sequence of its canonical transcript, extracts kmers and keep those that found only in the gene.
- When you find for a transcript, kmerator only keeps the kmer found in the transcript, and only in that transcript. If isoforms completely cover the transcript, no kmer will be kept.
arguments
Choose one of the two options:
-s SELECTION [SELECTION ...], --selection SELECTION [SELECTION ...]
list of gene IDs or transcript IDs (ENST, ENS or gene Symbol) to
select inside your fasta transcriptome file and that you want to
extract specific kmers from. For genes, kmerator search specific kmers
along the gene. For transcripts, it search specific kmers to the
transcript. You can also give a file with yours genes/transcripts
separated by space, tab or newline. If you want to use your own
unannotated sequences, you must give your fasta file with --fasta_file
option.
-f FASTA_FILE, --fasta-file FASTA_FILE
Use this option when yours sequences are unannonated or provided by a
annotation file external from Ensembl. Otherwise, use --selection
option.
Mandatory:
-g GENOME, --genome GENOME
genome fasta file or jellyfish index (.jf) to use for k-mers requests.
-t TRANSCRIPTOME, --transcriptome TRANSCRIPTOME
transcriptome fasta file (ENSEMBL fasta format ONLY) to use for k-mers
request and transcriptional variants informations.
optional arguments:
-j JELLYFISH_TRANSCRIPTOME, --jellyfish-transcriptome JELLYFISH_TRANSCRIPTOME
if your transcriptome (-t option) has already been converted by
jellyfish as a jf file, this avoids redoing the operation (be
careful,it must be the same transcriptome!).
-S SPECIE, --specie SPECIE
indicate a specie referenced in Ensembl, to help, follow the link
https://rest.ensembl.org/documentation/info/species. You can use the
'name', the 'display_name' or any 'aliases'. For example human,
homo_sapiens or homsap are valid.
-k KMER_LENGTH, --kmer-length KMER_LENGTH
k-mer length that you want to use (default 31).
--chimera Only if with --fasta-file option.
--stringent FOR ANNOTATED GENE ONLY: use this option if you want to select gene-
specific k-mers present in ALL known transcripts for your gene. If
false, a k-mer is considered as gene-specific if present in at least
one isoform of your gene of interest.
-o OUTPUT, --output OUTPUT
output directory (default: 'output')
-p PROCS, --procs PROCS
run n process simultaneously (default: 1)
-d, --debug if you want some details while Kmerator is running.
--keep keep intermediate files (sequences, indexes, separate tags and contigs
files).
-e, --edit-config Edit config file
-h, --help show this help message and exit
-v, --version show program's version number and exit
Nota: kmerator2
has lost the --level
option specifying the level (gene, transcript or chimera). It's now semi-automatic: if you give a gene symbol
or a ENSGxxx
the level is gene, if you give a ENSTxxx
the level is transcript. You can mix gene symbols, ENSGxxxx and ENSTxxx. You need to use the --fasta-file
in association with --chimera
to operate at chimera
level.
Examples
Get specific kmers from a set of known gene and/or transcripts:
kmerator2 --selection BRCA2 npm1 ENSG00000159216 ENST00000614774 \
--transcriptome /indexes/Homo_sapiens.GRCh38.cdna+ncrna-altchr.fa \
-g /indexes/GRCh38_with_MT.jf \
-o my-output \
-p 8
Notes
- Genes and transcripts are mixed, but keep in mind that the behaviour is different between the two: in the case of genes, kmerator looks for gene-specific kmers. For transcripts, only kmers specific to the transcript are retained, excluding kmers found in isoforms of the transcript.
- The list of genes/transcript could be in file, separated by spaces, tab or newlines, like
--selection my-gene-file
. - kmerator will search the file
/indexes/Homo_sapiens.GRCh38.cdna+ncrna-altchr.jf
, If not exists, it creates it in the output.
Get specific kmers of unannotated sequences/transcripts:
kmerator2 --fasta-file my-unannotated-seqs.fa \
--transcriptome /indexes/Homo_sapiens.GRCh38.cdna+ncrna-altchr.fa \
-g /indexes/GRCh38_with_MT.jf \
-o my-output \
-p 8
To many arguments?
kmerator2
needs many required arguments. Fortunately, you can define some of them as default in a configuration file. You can overwrite arguments by the command-line.
the configuration file is given with examples as comment, don't hesitate to use it:
kmerator2 --edit-config
ktools, a companion tool for kmerator
ktools can help you with some kmerator related tasks. For example, build the transcriptome could be tricky and repetitive (updated quaterly).
usage: ktools [-h] [-v] {mk-transcripts} ...
positional arguments:
{mk-transcripts}
mk-transcripts make transcriptome
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
build transcriptome
What does ktools do?
- gets cDNA and ncRNA Ensembl transcriptome fasta files (last release by default),
- concatenates this files
- remove alternative chromosomes.
- build jellyfish index
Example:
# help of subcommand mk-transcripts
ktools mk-transcripts --help
# build transcriptome (fasta and jellyfish index)
ktools mk-transcripts -o /path/to/transcriptomes -t 10
References
[1] Guillaume Marçais, Carl Kingsford, A fast, lock-free approach for efficient parallel counting of occurrences of k-mers, Bioinformatics, Volume 27, Issue 6, 15 March 2011, Pages 764–770, https://doi.org/10.1093/bioinformatics/btr011 [2] Rodriguez JM, et al. Nucleic Acids Res. Database issue; 2017 Oct 23
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