Tool:Bamchop: Efficient Digest Of High-Throughput Sequencing Data In A Reproducible Report

Question

[Jeremy Leipzig]

http://www.biomedcentral.com/qc/1471-2105/14/S11/S3

Background

High-throughput sequencing (HTS) technologies are spearheading the accelerated development of biomedical research. Processing and summarizing the large amount of data generated by HTS presents a non-trivial challenge to bioinformatics. A commonly adopted standard is to store sequencing reads aligned to a reference genome in SAM (Sequence Alignment/Map) or BAM (Binary Alignment/Map) files. Quality control of SAM/BAM files is a critical checkpoint before downstream analysis. The goal of the current project is to facilitate and standardize this process.

Results

We developed bamchop, a robust program to efficiently summarize key statistical metrics of HTS data stored in BAM files, and to visually present the results in a formatted report. The report documents information about various aspects of HTS data, such as sequencing quality, mapping to a reference genome, sequencing coverage, and base frequency. Bamchop uses the R language and Bioconductor packages to calculate statistical matrices and the Sweave utility and associated LaTeX markup for documentation. Bamchop's efficiency and robustness were tested on BAM files generated by local sequencing facilities and the 1000 Genomes Project. Source code, instruction and example reports of bamchop are freely available from https://github.com/CBMi-BiG/bamchop website.

Conclusions

Bamchop enables biomedical researchers to quickly and rigorously evaluate HTS data by providing a convenient synopsis and user-friendly reports.

[Malachi Griffith]

For those interested and too lazy to find it, an example report can be found here: http://www.biomedcentral.com/content/supplementary/1471-2105-14-s11-s3-s1.pdf

[Istvan Albert]

I am going to look at this more closely, having to summarize alignment files is a task that we face all the time,

[Aaronquinlan]

Do you have a brief example of how to run it on a single BAM file? Also not entirely clear how to install it if not at BiC.

[Ian]

Looks interesting, but the documentation is non-existent (please correct me if I am wrong). For example, how does one create mm10.rdata?

[Jeremy Leipzig]

repository updated

Answer 1

[Jeremy Leipzig]

to create mm10:

source('source/CreateGenome.R')
biocLite("BSgenome.Mmusculus.UCSC.mm10")
library(BSgenome.Mmusculus.UCSC.mm10)
genome<-CreateGenome(BSgenome.Mmusculus.UCSC.mm10)
save(genome,file="database/mm10.rdata")