CaPSID: A bioinformatics platform for computational pathogen sequence identification in human genomes and transcriptomes
1 Ontario Institute for Cancer Research, MaRS Centre,South Tower, 101 College Street, Suite 800, Toronto, Ontario M5G 0A3, Canada
2 Ontario Cancer Institute and the Campbell Family Cancer Research Institute, Toronto Medical Discovery Tower, University of Toronto, 101 College Street, Rm 8-703, Toronto, Ontario M5G 1L7, Canada
3 Department of Biochemistry, McGill University, McIntyre Medical Building, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada
4 Department of Oncology, McGill University, McIntyre Medical Building, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada
5 The Goodman Cancer Research Centre, McGill University, McIntyre Medical Building, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada
BMC Bioinformatics 2012, 13:206 doi:10.1186/1471-2105-13-206Published: 17 August 2012
It is now well established that nearly 20% of human cancers are caused by infectious agents, and the list of human oncogenic pathogens will grow in the future for a variety of cancer types. Whole tumor transcriptome and genome sequencing by next-generation sequencing technologies presents an unparalleled opportunity for pathogen detection and discovery in human tissues but requires development of new genome-wide bioinformatics tools.
Here we present CaPSID (Computational Pathogen Sequence IDentification), a comprehensive bioinformatics platform for identifying, querying and visualizing both exogenous and endogenous pathogen nucleotide sequences in tumor genomes and transcriptomes. CaPSID includes a scalable, high performance database for data storage and a web application that integrates the genome browser JBrowse. CaPSID also provides useful metrics for sequence analysis of pre-aligned BAM files, such as gene and genome coverage, and is optimized to run efficiently on multiprocessor computers with low memory usage.
To demonstrate the usefulness and efficiency of CaPSID, we carried out a comprehensive analysis of both a simulated dataset and transcriptome samples from ovarian cancer. CaPSID correctly identified all of the human and pathogen sequences in the simulated dataset, while in the ovarian dataset CaPSID’s predictions were successfully validated in vitro.