ganon: precise metagenomics classification against large and up-to-date sets of reference sequences

Abstract

11The exponential growth of assembled genome sequences greatly benets metagenomics 12 studies, providing a broader catalog of reference organisms on a variety of environments. 13 However, currently available methods struggle to manage the increasing amount of sequences 14 and their frequent updates. Indexing the current RefSeq is no longer possible on standard 15 infrastructures and it can take days and hundreds of GB of memory on large servers. Few 16 methods address these issues thus far, and even though many can theoretically handle large 17 amounts of references, time/memory requirements are prohibitive in practice. As a result, 18 many studies that require sequence classication use the available tools in conjunction with 19 often outdated and almost never truly up-to-date indices. This also means that the taxonomic 20 composition of the reference database is not being adjusted based on the study performed. 21 These factors can lead to unnecessary performance problems in the sequence classication. 22 Motivated by those limitations we created ganon, a k-mer based read classication tool that 23 uses Interleaved Bloom Filters in conjunction with a taxonomic clustering and a k-mer count-24 ing/ltering scheme. Ganon provides an ecient method for indexing references, keeping them 25 updated. It requires less than 55 minutes to index the complete RefSeq of bacteria, archaea, 26 fungi and viruses. The tool can further keep these indices up-to-date in a fraction of the time 27 necessary to create them, allowing researchers to always work with the most recent references. 28 Ganon makes it possible to query against very large reference sets and therefore it classies 29 signicantly more reads and identies more species than similar methods. When classifying a 30 high-complexity real dataset from the CAMI challenge against complete genomes from RefSeq, 31 ganon shows strongly increased precision while exhibiting equal or better sensitivity compared 32 with state-of-the-art tools. When classifying the same dataset against the complete RefSeq, 33 ganon improved the F1-Score by 65% at the genus level. Ganon supports taxonomy-and 34 assembly-level classication as well as multiple indices and hierarchical classication. The 35 software is open-source and available at: https://gitlab.com/rki_bioinformatics/ganon 36 42 prede(ned nd stti set of referene sequenesF wny of those pprohes re txonomyEsed 43 U nd use this lssi(tion to etter understnd the omposition of smplesF 44 * PiroV@rki.de † RenardB@rki.de I he mount of omplete or drft genomi sequenes in puli repositories is rpidly growing 45 @pigure IA due to dvnes in genome sequeningD improvements in red qulityD length nd ovE 46 erge nd lso etter lgorithms for genome ssemlyF sn dditionD mny prtil nd omplete 47

Centrifuger: lossless compression of microbial genomes for efficient and accurate metagenomic sequence classification

“…Kraken2 used its own built-in masking module. Though we used Ganon v2.0.0 in evaluations, we failed to create the index with the hierarchical interleaved bloom filter data structure implemented in this version [50]. Therefore, we used the index based on the interleaved bloom filter (--filter-type ibf ) for Ganon, and we referred to this method as "Ganon" rather than Ganon2.…”

TAXPASTA: TAXonomic Profile Aggregation and STAndardisation

“…TAXPASTA supports reading a wide range of formats of primarily shotgun-metagenomic profiling tools and formats, and it is designed to be used as a building block in metagenomic analysis workflows. At the time of writing, it is able to read profiles from nine different profilers, namely Bracken (Lu et al, 2017), Centrifuge (Kim et al, 2016), DIAMOND (Buchfink et al, 2021), ganon (Piro et al, 2020), Kaiju (Menzel et al, 2016), Kraken2 (Wood et al, 2019), KrakenUniq (Breitwieser et al, 2018), MALT/MEGAN6 (Huson et al, 2016;Vågene et al, 2018), MetaPhlAn (Blanco-Míguez et al, 2023,and mOTUs (Ruscheweyh et al, 2022). Supporting more profilers is already planned, and detailed documentation for community contributions is provided 5 .…”

Raptor: A fast and space-efficient pre-filter for querying very large collections of nucleotide sequences

“…Using the counts for k-mers given by the BD, we can infer the composition of a metagenomic sample. Indeed, (Piro et al, 2020) already applied this idea as described in (Dadi et al, 2018) for this task. This resolved the problem of uneven bin sizes by applying a preprocessing step to distribute the k-mer content of bins more evenly.…”

“…While not shown in this paper, the update operation on an IBF was already used in DREAM-Yara (Dadi et al, 2018) and ganon (Piro et al, 2020). Adding data is trivial since we just need to set the corresponding bits in the x-PIBF.…”