Efficient digest of high-throughput sequencing data in a reproducible report.

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

Managing genomic and proteomic knowledge.

Genomic and proteomic platform data constitute a hugely important resource to current efforts in disease understanding, systems biology and drug discovery. We review prerequisites for the adequate management of 'omic' data, the means by which such data are analyzed and converted to knowledge relevant to drug discovery and issues crucial to the integration of such data, particularly with chemical, genetic and clinical data.: