PathSeq: A comprehensive computational tool for the identification or discovery of microorganisms by deep sequencing of human tissue

By A. Kostic, A. Ojesina, C. Pedamallu, Joonil Jung, Gad Getz, Matthew Meyerson. Nature Biotechnology, 2011

314 citations recorded by the source corpus.

Many human diseases are believed to be caused by undiscovered pathogens1–3. The advent of next-generation sequencing technology presents an unprecedented opportunity to identify pathogens in hitherto idiopathic diseases. Here we present PathSeq, a highly scalable software tool that performs computational subtraction on high-throughput sequencing data to identify non-human nucleic acids that may indicate candidate microbes. PathSeq exhibits high sensitivity and specificity in its ability to discriminate human from non-human sequences using both simulated and experimental transcriptome and whole-genome sequencing data. PathSeq is implemented in a cloud computing environment making it readily accessible by the scientific community.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/878092ff00d71a70adc15fbf7ca65d1de34488b5

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Citations in the corpus

NCBI Viral Genomes Resource (2014, background): “Whereas primary databases are archival repositories of sequence data, reference databases provide curated datasets that enable a number of activities, among them are transfer annotation to related genomes (11) (12) (13) , sequence assembly and virus discovery (14) (15) (16) (17) , viral dynamics and”
Integrated Genomic Characterization of Papillary Thyroid Carcinoma (2014, methodology): “We also looked for the presence of viral pathogens in PTC using two independent methods to assess the RNA-seq data: PathSeq (Kostic et al., 2011) and BioBloom Tools (Chu et al.”
Personalized Oncology Through Integrative High-Throughput Sequencing: A Pilot Study (2011, background): “org/software/pathseq/) to our sequencing pipelines, and expect that this increase confidence in the results(34).”
Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. (2012, methodology): “To determine the microbial composition of human colorectal cancer, we analyzed whole genome sequences of nine colorectal cancers and matched normal colons (Bass et al. 2011) using PathSeq, a computational subtraction pipeline that culls out can- didate microbial sequences (Kostic et al. 2011).”
Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor immune microenvironment (2013, methodology): “All non-human sequencing reads were applied to PathSeq, a computational tool that identifies and quantifies the abundance of all bacteria present in each tumor http://www.broadinstitute.org/software/pathseq/ (Kostic et al., 2011).”
Lessons from the cancer genome. (2013, unknown): “of foreign DNA (such as from viruses) (Beroukhim et al., 2007; Chen et al., 2009; Cibulskis et al., 2013; Dees et al., 2012; Kim and Salzberg, 2011; Kostic et al., 2011; Trapnell et al., 2009).”
A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples (2014, methodology): “…the most widely used approach is computational subtraction of reads corresponding to the host (e.g., human), followed by alignment to reference databases that contain sequences from candidate pathogens (MacConaill and Meyerson 2008; Greninger et al. 2010; Kostic et al. 2011; Zhao et al. 2013).”
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma (2017, unknown): “…106 We applied a threshold of 2 RPM for identifying samples that were positive for hepatitis B. PathSeq - Broad Institute: The PathSeq algorithm (Kostic et al., 2011) was used to perform computational subtraction of human reads, followed by alignment of residual reads to a combined database of…”
Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. (2017, unknown): “PathSeq Microbial Detection: The PathSeq algorithm (Kostic et al., 2011) (https:// github.”