Note
MethylCoder has been superseded by bwa-meth: https://github.com/brentp/bwa-meth/ That repo also contains an improved runner script for gsnap in compare/src/gsnap-meth.py
MethylCoder is a single program that takes of bisulfite-treated reads and outputs per-base methylation data. It also includes scripts for analysis and visualization. In addition to a binary output and a SAM alignment file, the direct output of methylcoder is a text file that looks like
#seqid mt bp c t 1 3 1354 0 1 1 3 1358 0 1 1 3 1393 0 1 1 3 1394 0 1 1 3 1402 0 1 1 3 1409 0 1
where columns are reference seqid methylation context (type) basepair location(bp) number of reads where a (c)ytosine was unconverted, number of reads where where a cytosine was converted to (t)hymine. Making methylation at every methylable basepair easily calculated as c / (c + t).
Contents
This software is developed in the Fischer Lab . At UC Berkeley. Please report any requests, bugs, patches, problems, docs to [email protected]
It is distributed under the New BSD License
MethylCoder: Software Pipeline for Bisulfite-Treated Sequences Brent Pedersen; Tzung-Fu Hsieh; Christian Ibarra; Robert L. Fischer Bioinformatics 2011; doi: 10.1093/bioinformatics/btr394
Python 2.6 must be installed along with the following modules. all of these are available from pypi and as such are installable via
$ easy_install [module]
- numpy to handle arrays and binary data in python
- pyfasta to access/index fasta files
- (required if bsddb is unavailable) py-tcdb replace bsddb
matplotlib is required to plot the per-chromosome methylation levels.
- bowtie to align the reads to the genome.
- (required if bsddb is unavailable) tokyocabinet modern implementation of DBM database.
- (optional) gsnap (>= 2011-11-17) preferred aligner. (part of gmap).
- (optional) sam-tools to view the alignments and processing the reads
once the above python and c libraries are installed, download methylcoder from:
http://github.com/brentp/methylcode/tarball/master (tar ball)
and untar; or clone the repository via git:
git clone git://github.com/brentp/methylcode.git
Then, from the methylcode directory, it is still necessary to run
$ sudo python setup.py install
to install the package into your path. After that, the executable 'methylcoder' will be available on your path. Running with no arguments will print help.
The input to the pipeline is:
- a reference fasta file with one entry per chromosome in the genome to which the reads are to be mapped.
- a fastq or fasta reads file. If reads are not from the from Eckers/Zilberman bisulfite process (with only 2 possibilities) use "--mode=cmet-stranded" in the --extra-args to GSNAP
- If 2 read files are specified, they are assumed to be pair ends and the aligner is called appropriately.
- a textfile containing columns:
- seqid (chromosome)
- methylation type (1 to 6 see below)
- basepair position (0-indexed)
- reads with C at this position
- reads with T at this position
methylation can be calculated as column 4 / (column 5 + column 4) 4 and 5 are corrected for strand (G, A respectively for - strand).
- a set of binary files for each chromosome in the fasta file. each file
contains a value for each basepair in the chromosome--many of which will be 0 if the position is not a C or G. these files contain no headers and can be read in any language by specifying the file-type (listed in [square brackets] below. these include:
- methyltype.bin with values between 1 and 6 as described below (value of 0 means no methylation is possible at this basepair). [encoded as uint8]
- cs.bin containing the number of reads with C's at each position (same as column 4 above). [encoded as uint32]
- ts.bin containing the number of reads with T's at each position (same as column 5 above). [encoded as uint32]
- Methylation type is a value between 1 and 6:
- CG on + strand
- CHG on + strand
- CHH on + strand
- CG on - strand
- CHG on - strand
- CHH on - strand
You must have:
- input reference fasta file to which to align the reads. here: thaliana_v9.fasta
- a reads file in fastq or fasta format. here: reads.fastq. if you have paired end reads, they must be specified in order 1, 2.
- a directory containing the bowtie and bowtie-build executables. (or the path to the gmap/gsnap install directory the gsnap utilities
An example command to run the pipeline is:
$ methylcoder --bowtie /usr/local/src/bowtie/ \
--extra-args "-m 1"
--reference /path/to/thaliana_v9.fasta \
/path/to/reads.fastq
or using the gsnap aligner on paired-end reads.:
$ methylcoder --gsnap /usr/local/bin/ \
--reference /path/to/thaliana_v9.fasta \
/path/to/reads_1.fastq /path/to/reads_2.fastq
Where you must adjust /path/to/reads.fastq to point to your BS-treated reads. This will create the files specified in Output above, sending the text to path/to/reads_methylcoder/methy-data-DATE.txt where DATE is the current date. The binary files will be sent to, that same directory as: thaliana_v9.fasta.[CHR].methyl.bin where [CHR] is substituted by each chromosome in the fasta file. Once bowtie is run once, its output is not deleted, and methylcoder.py will only re-run bowtie if its input has been modified since it was run last. NOTE if the methylcoder executable is called without any options, it will print help and available command-line arguments.
Additional args can be sent directly to the aligner as a string to methylcoder.py's --extra-args parameter. This would look like.
--extra-args "--solexa-quals -k 1 -m 1 --strata"
and that string will be passed directly to the bowtie invocation when it is called from methylcoder. Whenever 2 fastq files are sent, they are assumed to be paired-end reads.
For stranded reads, send "--mode=cmet-stranded" to gsnap via --extra-args.
- when using bowtie, the reference size must be less than about 2 Gigabases. This limitation can be circumvented by splitting the reference into 2 smaller reference sequences. For example with human, splitting into 2 fasta files, one with chromosomes 1-9 and the other with chromosomes 10+ works well. This limitation does not exist when GSNAP is used as the aligner.
Don't do BS-Seq in colorspace.
See: http://github.com/brentp/methylcode/wikis/using-samtools-to-view-alignments
- Eckers paper. http://www.nature.com/nature/journal/v462/n7271/extref/nature08514-s1.pdf
- Bowtie Paper: Langmead B, Trapnell C, Pop M, Salzberg SL. 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25.
- GSNAP paper: Wu TD, Nacu S. 2010 Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 26(7):873-81.
warning Methylcoder assumes that the Bisulfite converted reads are created using the Zilberman/Ecker method in which BS conversion occurs after conversion to solexa library--giving only 2 possibibilities. This is in contrast to the Jacobsen method which gives 4 possiblities. If you have a library generated using the Jacobsen method, you can use scripts/tagged_reads_prep.py to convert the reads to a format that MethylCoder can map.