a statistical framework for accurate taxonomic assignment of metagenomic sequencing reads准确的分类任务的统计框架宏基因组测序读.pdfVIP

A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads 1 . 2,3. 4,5 6 1 Hongmei Jiang * , Lingling An , Simon M. Lin , Gang Feng , Yuqing Qiu 1 Department of Statistics, Northwestern University, Evanston, Illinois, United States of America, 2 Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, Arizona, United States of America, 3 Interdisciplinary Program in Statistics, University of Arizona, Tucson, Arizona, United States of America, 4 Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America, 5 Institute for Clinical and Translational Research, University of Wisconsin at Madison, Madison, Wisconsin, United States of America, 6 Biomedical Informatics Center, Northwestern University, Chicago, Illinois, United States of America Abstract The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we pro