KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping

Abstract

Motivation The growing number of microbial reference genomes enables the improvement of metagenomic profiling accuracy but also imposes greater requirements on the indexing efficiency, database size, and runtime of taxonomic profilers. Additionally, most profilers focus mainly on bacterial, archaeal, and fungal populations, while less attention is paid to viral communities. Results We present KMCP, a novel k-mer-based metagenomic profiling tool that utilizes genome coverage information by splitting the reference genomes into chunks and stores k-mers in a modified and optimized COBS index for fast alignment-free sequence searching. KMCP combines k-mer similarity and genome coverage information to reduce the false positive rate of k-mer-based taxonomic classification and profiling methods. Benchmarking results based on simulated and real data demonstrate that KMCP, despite a longer running time than all other methods, not only allows the accurate taxonomic profiling of prokaryotic and viral populations but also provides more confident pathogen detection in clinical samples of low depth. Availability The software is open-source under the MIT license and available at https://github.com/shenwei356/kmcp. Supplementary information Supplementary data are available at Bioinformatics online.

Metagenome profiling and containment estimation through abundance-corrected k-mer sketching with sylph

“…Specifically, these synthetic communities had 50 present species at 95-97.5% ANI and 150 genomes between 85-90% ANI to the nearest ANI genome in the database ( Methods ). We compared sylph’s species-leveling profiling against three other methods, Bracken [10] (with Kraken2 [8]), KMCP [30], and ganon [9]. We chose Bracken due to its wide use, KMCP because it achieved the best CAMI2 marine benchmarks in its publication, and ganon because it is relatively time and memory-efficient.…”

nf-core/taxprofiler: highly parallelised and flexible pipeline for metagenomic taxonomic classification and profiling

“…The following classifiers and profilers are supported in version 1.1.0 of nf-core/taxprofiler: Kraken2 (Wood, Lu, and Langmead 2019), Bracken (Lu et al 2017), KrakenUniq (F. P. Breitwieser, Baker, and Salzberg 2018), Centrifuge (Kim et al 2016), MALT (Vågene et al 2018), DIAMOND (Buchfink, Reuter, and Drost 2021), Kaiju (Menzel, Ng, and Krogh 2016), MetaPhlAn (Blanco-Míguez et al 2023), mOTUs Ruscheweyh et al 2022), ganon (Piro et al 2020), KMCP (Shen et al 2023).…”

“…Additionally on top of this, also simultaneously for each of the classifiers, an arbitrary number of databases as supplied by the user. As of version 1.1.0, the following classifiers and profilers are available: Kraken2 (Wood, Lu, and Langmead 2019), Bracken (Lu et al 2017), KrakenUniq (F. P. Breitwieser, Baker, and Salzberg 2018), Centrifuge (Kim et al 2016), MALT (Vågene et al 2018), DIAMOND (Buchfink, Reuter, and Drost 2021), Kaiju (Menzel, Ng, and Krogh 2016), MetaPhlAn (Blanco-Míguez et al 2023), mOTUs (Ruscheweyh et al 2022), ganon (Piro et al 2020), and KMCP (Shen et al 2023). Databases are also supplied via a input TSV file, which also allows per-database custom classification parameters - meaning a given database can be supplied multiple times each with different parameters or multiple different databases per profiler.…”

ganon2: up-to-date and scalable metagenomics analysis

“…We compare ganon2 (v2.0.0) with the state-of-the-art methods: kmcp (v0.9.2) [18], kraken2 (v2.1.2) [19], bracken (v2.8) [20], and metacache (v2.3.1) [21]. Methods were selected based on the following criteria: open-source code, actively maintained and/or highly used, scalable to handle very large data in terms of database construction and sequence classification, execution in doable time (hours/few days for building, minutes/hours per sample), possibility to create custom databases with nucleotide sequences, ability to perform taxonomic binning and/or profiling.…”