Fast, lightweight, and accurate metagenomic functional profiling using FracMinHash sketches

Abstract

MotivationFunctional profiling of metagenomic samples is essential to decipher the functional capabilities of these microbial communities. Traditional and more widely used functional profilers in the context of metagenomics rely on aligning reads against a known reference database. However, aligning sequencing reads against a large and fast-growing database is computationally expensive. In general,k-mer-based sketching techniques have been successfully used in metagenomics to address this bottleneck, notably in taxonomic profiling. In this work, we describe leveraging FracMinHash (implemented insourmash, a publicly available software), ak-mer-sketching algorithm, to obtain functional profiles of metagenome samples. We show how pieces of thesourmashsoftware (and the resulting FracMinHash sketches) can be put together in a pipeline to functionally profile a metagenomic sample.ResultsWe report that the functional profiles obtained using this pipeline demonstrate superior completeness and purity compared to the profiles obtained using other alignment-based methods when applied to simulated metagenomic data. At the same time, we also report that our functional profiling pipeline is 42-51x faster in CPU time, 10-15.8x faster in running time, and consumes up to 20% less memory. Coupled with the KEGG database, this method not only replicates fundamental biological insights but also highlights novel signals from the Human Microbiome Project datasets.ReproducibilityThis fast and lightweight metagenomic functional profiler is freely available and can be accessed here:https://github.com/KoslickiLab/funprofiler. All scripts of the analyses we present in this manuscript can be found onGitHub.

A survey of k-mer methods and applications in bioinformatics

“… [249] BBMap [250] , Bowtie2 [251] , [252] , BWA [253] , [254] , [255] , iMOKA [256] , MiniMap2 [71] Taxonomic Classification In sequence composition-based methods, the frequency and distribution of k-mers in metagenomic data are analyzed to assess genome similarity across various taxonomic ranks. [257] , [258] ARK [259] , BinDash [122] , Bracken [260] , CDKAM [261] , CLARK [262] , Dashing [124] , fmh-funprofiler [128] , Genometa [263] , Kaiju [138] , KMCP [264] , KmerFinder [265] , Kraken2 [136] , KrakenUniq [19] , LMAT [266] , Mash [72] , Mash Screen [34] , Matchtigs [267] , MetaCache [268] , MetaPalette [269] , MetaProFi [50] , NIQKI [126] , SEK [270] , StrainSeeker [271] , SuperSampler [127] , TACOA [272] , Taxonomer [273] , TETRA [274] , VirFinder [18] , WGSQuikr [275] Phylogeny Reconstruction Pairwise evolutionary distances between protein or nucleic acid sequences and phylogenetic distances can be estimated from the number of k-mer matches between two sequences. Alignment-free sequence comparison quantifies distance using the decay of the number of k-mer matches between two sequences and compares the results to known phylogenetic trees.…”

“…However, Kmer-db is roughly 26 times faster than Mash and is subsequently better equipped to process larger datasets [121] . Additional sketch-based methods that utilize k-mers in comparative genomics include Bindash 1.0 [122] and 2.0 [123] , Dashing 1.0 [124] and 2.0 [125] , NIQKI [126] , SuperSampler [127] , and fmh-funprofiler [128] ( Table 2 ).…”

On branch lengths assignment methods for trees with fixed topology and related biological applications

“…Each of these samples were converted into a functional profile in the form of a probability vector indexed by KOs, representing the abundances of the KOs in the sample. This is done using FracMinHash, [9] a sketch-based pipeline that uses sourmash methods to estimate the abundance of each KO present in each sample. The details for this process can be found in Supplement Section 3.2.…”

“…This procedure can be adapted with the help of branch lengths assignment to answer a different yet equally meaningful question in metagenomic studies: the difference in functions that microbial communities are capable of performing in two given environments regardless of the actual organisms that carry out those functions. To do so, instead of clustering DNA into OTUs, we clustered them into functional units of orthologous genes through a process called functional profiling [9]. Next, instead of a phylogenetic tree, we obtained the KEGG Orthology (KO) hierarchy from the KEGG database [13,11,12].…”