https://github.com/bcgsc/chopstitch
Finding putative exons and constructing splicegraphs using Trans-ABySS contigs
https://github.com/bcgsc/chopstitch
bloom-filter exon putative-exons splice splice-graphs transcriptome-assembly
Last synced: 15 days ago
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Finding putative exons and constructing splicegraphs using Trans-ABySS contigs
- Host: GitHub
- URL: https://github.com/bcgsc/chopstitch
- Owner: bcgsc
- License: gpl-3.0
- Created: 2016-08-23T19:05:30.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-11-08T22:10:20.000Z (over 6 years ago)
- Last Synced: 2025-04-19T19:34:42.060Z (about 1 month ago)
- Topics: bloom-filter, exon, putative-exons, splice, splice-graphs, transcriptome-assembly
- Language: C++
- Homepage: http://www.bcgsc.ca/platform/bioinfo/software/chopstitch
- Size: 349 KB
- Stars: 11
- Watchers: 14
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog
- License: LICENSE
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README
# ChopStitch 1.0.0.
### Exon annotation and splice graph construction using transcriptome assembly and whole genome sequencing data
ChopStitch is a new method for finding putative exons and constructing splice graphs using an assembled transcriptome and whole genome shotgun sequencing (WGSS) data. ChopStitch identifies exon-exon boundaries in *de novo* assembled RNA-seq data with the help of a Bloom filter that represents the *k*-mer spectrum of WGSS reads. The algorithm also detects base substitutions in transcript sequences corresponding to sequencing or assembly errors, haplotype variations, or putative RNA editing events. The primary output of our tool is a FASTA file containing putative exons. Further, exon edges are interrogated for alternative exon-exon boundaries to detect transcript isoforms, which are reported as splice graphs in dot output format.
### Requirements:
Install [pip](https://pip.pypa.io/en/latest/installing/)
Install requirements by running:
```
pip install -r requirements.txt
```
Install [Graphviz version 2.4.0](http://www.graphviz.org/download/) command line tools
### Install ChopStitch:
When installing ChopStitch from GitHub source the following tools are required:
* [Autoconf](http://www.gnu.org/software/autoconf)
* [Automake](http://www.gnu.org/software/automake)
To generate the configure script and make files:
```
./autogen.sh
```
To compile and install ChopStitch in /usr/local:
```
$ ./configure
$ make
$ sudo make install
```
To install ChopStitch in a specified directory:
```
$ ./configure --prefix=/opt/ChopStitch
$ make
$ make install
```
ChopStitch uses OpenMP for parallelization, which requires a modern compiler such as GCC 4.2 or greater. If you have an older compiler, it is best to upgrade your compiler if possible. If you have multiple versions of GCC installed, you can specify a different compiler:
```
$ ./configure CC=gcc-xx CXX=g++-xx
```
For the best performance of ChopStitch, pass `-O3` flag:
```
$ ./configure CFLAGS='-g -O3' CXXFLAGS='-g -O3'
```
To run ChopStitch, its executables, `CreateBloom` and `FindExons`, should be found in your PATH. If you installed ChopStitch in /opt/ChopStitch, add /opt/ChopStitch/bin to your PATH:
```
$ PATH=/opt/ChopStitch/bin:$PATH
```
### Run CreateBloom
```
Usage: CreateBloom [OPTION]... FILES...
Creates a Bloom filter (BF) to be used for FindExons.
Acceptable file formats: fastq, fasta, sam, bam, gz, bz, zip.
Options:
-t, --threads=N use N parallel threads [1]
-k, --kmer=N the length of kmer [50]
-d, --fpr1=N primary BF fpr [0.01]
-s, --fpr2=N secondary BF fpr [0.01]
-r, --ref using FASTA reference as input instead of FASTQ reads. Don't use fpr2 in this case
--help display help and exit
--version output version information and exit
`FILES`: input file or set of files seperated by space, in fasta, fastq, sam, and bam formats. The files can also be in compressed (`.gz`, `.bz2`, `.xz`) formats . A list of files containing file names in each row can be passed with `@` prefix.
```
Example:
```
./CreateBloom -t 32 -k 50 --fpr1 0.01 --fpr2 0.01
```
To pass a list of files, `list.in`, as input:
```
./CreateBloom -t 32 -k 50 --fpr1 0.01 --fpr2 0.01 @list.in
```
To pass a reference fasta file as input:
```
./CreateBloom --ref -t 32 -k 50 --fpr1 0.01
```
Output:
Bfilter.bf : Bloom filter file
Bfilter.inf : Info file required for FindExons
### Run FindExons
Find putative exons in TransAbySS Transcriptome assembly file
Acceptable file formats: FASTA
```
Options:
-i, --input-bloom=FILE load bloom filter from FILE
-l, --leniency=N leniency for exon-exon juction detection [10]
-f, --lfactor=N leniency calculated as ceil(FPR*lfactor*k)
-s, --lsplicesignals=csv Comma separated 3' splicesignals \n"
-r, --rsplicesignals=csv Comma separated 5' splicesignals \n"
--allexons Also output exons on either ends of contigs\n"
--help display this help and exit
--version output version information and exit
````
Example:
```
./FindExons -i Bfilter.bf
./FindExons -i Bfilter.bf -s AG,TG,AC,GC,GG -r GT,TT,AT
./FindExons -i Bfilter.bf --allexons
```
Output:
A FASTA file of exons with headers in this format -
```
>TranscriptName_startCoordinate_Endcoordinate
```
### Run MakeSplicegraph.py with the putative exons FASTA file outputted by FindExons(confident-exons.fa)
Example:
```
python MakeSplicegraph.py -i -o
```
### Run Graphviz ccomps to obtain Splice sub-graphs
Example:
```
ccomps -o
```
### OR Run FindSubcomponents.py -w to generate a DOT file with Splicegraph sub-graphs
The script can also generate a file with mappings of transcripts to genes (denoted by random numbers) with the -m option
By default, it generates a file with mappings of putative exons to genes (also denoted by random numbers)
```
Usage: FindSubcomponents.py [-h] -g DOTFILE [-m] [-w]
Find graph subcomponents and write output
optional arguments:
-h, --help show this help message and exit
-g DOTFILE, --dotfile DOTFILE
Graph DOT file from MakeSplicegraph.py
-m, --geneMap Write a file with mappings of transcripts to genes
-w, --writesplicesubgraphs
Write splice subgraphs to DOT file
```