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Short Read Micro re-Aligner


Welcome to the SRMA home page. SRMA is a short read micro re-aligner for next-generation high throughput sequencing data.

Sequence alignment algorithms examine each read independently. When indels occur towards the ends of reads, the alignment can lead to false SNPs as well as improperly placed indels. This tool aims to perform a re-alignment of each read to a graphical representation of all alignments within a local region to provide a better overall base-resolution consensus.

Currently this tool works well with and has been tested on 30x diploid coverage genome sequencing data from Illumina and ABI SOLiD technology. This tool may not work well with 454 data, as indels are a significant error mode for 454 data.

A Quick Example

Here is an example of SRMA applied to the alignments of a human cancer cell line: U87MG. The image is generated from IGV, and shows a before and after picture of alignments over a 15bp deletion with a SNP eight bases upstream (see the alignments). Notice how after applying SRMA the reads now agree on the deletion and SNP, and other spurious variants observed only once are removed. Sanger sequencing was used to validate the deletion and SNP (see the traces).

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Current Release

The project is in beta stage development. The current release can be obtained by clicking this link.

Get the Code

The code is available via GIT:

git clone git://srma.git.sourceforge.net/gitroot/srma/srma srma


Please see the Installation page.

Getting Help

See our mailing lists. Alternatively, see our User Guide.

How does it work ?

See: http://dx.doi.org/10.1186/gb-2010-11-10-r99 

Project Page

Project Page

Call for Developers

If you would like to become a developer of SRMA, please do not hesitate to ask. There is a substantial opportunity for both Java and C developers. The former could work on reducing the memory usage, while the latter could work on improving run time performance and consistency with the Java version.


Please cite:

Homer N, Nelson SF.
Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA.
Genome Biol. 2010 Oct 8;11(10):R99
PMID: 20932289

as well as this website:

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