A few years back, Veli Mkinen’s group proposed using min-flow for RNAseq instead of expectation-maximization (EM). I suspect this new new arxiv paper is related algorithmically, but developed for a different context (metagenomics).
High-throughput sequencing (HTS) of metagenomes is proving essential in understanding the environment and diseases. State-of-the-art methods for discovering the species and their abundances in an HTS metagenomic sample are based on genome-specific markers, which can lead to skewed results, especially at species level. We present MetaFlow, the first method based on coverage analysis across entire genomes that also scales to HTS samples. We formulated this problem as an NP-hard matching problem in a bipartite graph, which we solved in practice by min-cost flows. On synthetic data sets of varying complexity and similarity, MetaFlow is more precise and sensitive than popular tools such as MetaPhlAn, mOTU, GSMer and BLAST, and its abundance estimations at species level are two to four times better in terms of L1-norm. On a real human stool data set, MetaFlow identifies B.uniformis as most predominant, in line with previous human gut studies, whereas marker-based methods report it as rare. MetaFlow is freely available at http://cs.helsinki.fi/gsa/metaflow