Mohamad Koohi-Moghadam1,2,3, Mitesh J Borad4, Nhan L Tran5, Kristin R Swanson6, Lisa A Boardman7, Hongzhe Sun2, Junwen Wang1,8. 1. Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA. 2. Department of Chemistry, The University of Hong Kong, Hong Kong SAR, China. 3. Center for Genomic Sciences, The University of Hong Kong, Hong Kong SAR, China. 4. Department of Hematology, Mayo Clinic, Scottsdale, AZ, USA. 5. Department of Cancer Biology, Mayo Clinic, Scottsdale, AZ, USA. 6. Department of Neurologic Surgery, Mayo Clinic, Scottsdale, AZ, USA. 7. Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA. 8. College of Health Solutions, Arizona State University, Scottsdale, AZ, USA.
Abstract
SUMMARY: We present MetaMarker, a pipeline for discovering metagenomic biomarkers from whole-metagenome sequencing samples. Different from existing methods, MetaMarker is based on a de novo approach that does not require mapping raw reads to a reference database. We applied MetaMarker on whole-metagenome sequencing of colorectal cancer (CRC) stool samples from France to discover CRC specific metagenomic biomarkers. We showed robustness of the discovered biomarkers by validating in independent samples from Hong Kong, Austria, Germany and Denmark. We further demonstrated these biomarkers could be used to build a machine learning classifier for CRC prediction. AVAILABILITY AND IMPLEMENTATION: MetaMarker is freely available at https://bitbucket.org/mkoohim/metamarker under GPLv3 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: We present MetaMarker, a pipeline for discovering metagenomic biomarkers from whole-metagenome sequencing samples. Different from existing methods, MetaMarker is based on a de novo approach that does not require mapping raw reads to a reference database. We applied MetaMarker on whole-metagenome sequencing of colorectal cancer (CRC) stool samples from France to discover CRC specific metagenomic biomarkers. We showed robustness of the discovered biomarkers by validating in independent samples from Hong Kong, Austria, Germany and Denmark. We further demonstrated these biomarkers could be used to build a machine learning classifier for CRC prediction. AVAILABILITY AND IMPLEMENTATION: MetaMarker is freely available at https://bitbucket.org/mkoohim/metamarker under GPLv3 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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