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PathSeq: software to identify or discover microbes by deep sequencing of human tissue

Nature Biotechnology volume 29, pages 393–396 (2011)Cite this article

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To the Editor:

Many human diseases are believed to be caused by undiscovered pathogens1,2. 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 nonhuman nucleic acids that may indicate candidate microbes. PathSeq exhibits high sensitivity and specificity in its ability to discriminate human from nonhuman 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.

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Figure 1: The PathSeq workflow.
Figure 2: PathSeq performance on simulated and experimental sequence data.

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Acknowledgements

This work was supported by the National Cancer Institute (RC2CA148317), the Starr Cancer Consortium and the Claudia Adams Barr Program in Innovative Cancer Research. A.D.K. was supported by a Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship. A.I.O. was supported by the Rebecca Ridley Kry Fellowship of the Damon Runyon Cancer Research Foundation. We thank M. Berger, C. Mermel, J. Levin, V. Fusaro and J. Shendure for helpful discussions and S. Hart for illustrations.

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Authors and Affiliations

  1. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA

    Aleksandar D Kostic, Akinyemi I Ojesina, Chandra Sekhar Pedamallu, Joonil Jung, Roel G W Verhaak, Gad Getz & Matthew Meyerson

  2. Department of Pathology, Harvard Medical School, Boston, Massachusetts, USA

    Aleksandar D Kostic & Matthew Meyerson

  3. Department of Medical Oncology and Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Akinyemi I Ojesina, Chandra Sekhar Pedamallu, Joonil Jung, Roel G W Verhaak & Matthew Meyerson

Authors
  1. Aleksandar D Kostic
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  2. Akinyemi I Ojesina
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  3. Chandra Sekhar Pedamallu
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  7. Matthew Meyerson
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Correspondence to Matthew Meyerson.

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Supplementary Figures 1–4, Supplementary Tables 1–4, Supplementary Methods, and Supplementary Data Sets 1–3 (PDF 456 kb)

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Kostic, A., Ojesina, A., Pedamallu, C. et al. PathSeq: software to identify or discover microbes by deep sequencing of human tissue. Nat Biotechnol 29, 393–396 (2011). https://doi.org/10.1038/nbt.1868

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