Strain level microbial detection and quantification with applications to single cell metagenomics.

ABSTRACT: Computational identification and quantification of distinct microbes from high throughput sequencing data is crucial for our understanding of human health. Existing methods either use accurate but computationally expensive alignment-based approaches or less accurate but computationally fast alignment-free approaches, which often fail to correctly assign reads to genomes. Here we introduce CAMMiQ, a combinatorial optimization framework to identify and quantify distinct genomes (specified by a database) in a metagenomic dataset. As a key methodological innovation, CAMMiQ uses substrings of variable length and those that appear in two genomes in the database, as opposed to the commonly used fixed-length, unique substrings. These substrings allow to accurately decouple mixtures of highly simil

SUBMITTER: Zhu K 

PROVIDER: S-EPMC9616933 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature