Biology subjects

Seiler, E.

Publications and source records attributed to Seiler, E..

3 recordsLinked to original sources

bioRxiv2018

ganon: continuously up-to-date with database growth for precise short read classification in metagenomics

MotivationThe exponential growth of assembled genome sequences greatly benefits metagenomics studies. However, currently available methods struggle to manage the increasing amount of sequences and their frequent updates. Indexing the current RefSeq can take days and hundreds of GB of memory on large servers. Few methods address these issues thus far, and even though many can theoretically handle large amounts of references, time/memory requirements are prohibitive in practice. As a result, many studies that require sequence classification use often outdated and almost never truly up-to-date indices. ResultsMotivated by those limitations we created ganon, a k-mer based read classification tool that uses Interleaved Bloom Filters in conjunction with a taxonomic clustering and a k-mer counting/filtering scheme. Ganon provides an efficient method for indexing references, keeping them updated. It requires less than 55 minutes to index the complete RefSeq of bacteria, archaea, fungi and viruses. The tool can further keep these indices up-to-date in a fraction of the time necessary to create them. Ganon makes it possible to query against very large reference sets and therefore it classifies significantly more reads and identifies more species than similar methods. When classifying a high-complexity CAMI challenge dataset against complete genomes from RefSeq, ganon shows strongly increased precision with equal or better sensitivity compared with state-of-the-art tools. With the same dataset against the complete RefSeq, ganon improved the F1-Score by 65% at the genus level. It supports taxonomy- and assembly-level classification, multiple indices and hierarchical classification. AvailabilityThe software is open-source and available at: https://gitlab.com/rki_bioinformatics/ganon Contactbernhard.renard@hpi.de

bioinformatics↗

bioRxiv2018

Where did you come from, where did you go: Refining Metagenomic Analysis Tools for HGT characterisation

Horizontal gene transfer (HGT) has changed the way we regard evolution. Instead of waiting for the next generation to establish new traits, especially bacteria are able to take a shortcut via HGT that enables them to pass on genes from one individual to another, even across species boundaries. Existing HGT detection approaches usually first identify genes of foreign nature, e.g., using composition-based methods, and then exploit phylogenetic discrepancies of the corresponding gene tree compared to a species tree. These approaches depend on fully sequenced HGT organisms and computable phylogenetic species trees. The tool Daisy offers a different approach based on read mapping that provides complementary evidence compared to existing methods at the cost of relying on the acceptor and donor references of the HGT organism being known. Acceptor and donor identification is akin to species identification in metagenomic samples based on sequencing reads, a problem addressed by metagenomic profiling tools. However, acceptor and donor references have certain properties such that these methods can not be directly applied. We propose DaisyGPS, a mapping-based pipeline that is able to identify acceptor and donor candidates of an HGT organism based on sequencing reads. To do that, DaisyGPS leverages metagenomic profiling strategies and refines them for HGT candidate identification. These candidates can then be further evaluated by tools like Daisy to establish HGT regions. We successfully validated our approach on both simulated and real data, and show its benefits in an investigation of MRSA outbreak data. DaisyGPS is freely available from https://gitlab.com/rki_bioinformatics/.

bioinformatics↗

bioRxiv2018

DREAM-Yara: An implementation of an exact read mapper for very large databases

MotivationMapping-based approaches have become limited in their application to very large sets of references since computing an FM-index for very large databases (e.g. > 10 GB) has become a bottleneck. This affects many analyses that need such index as an essential step for approximate matching of the NGS reads to reference databases. For instance, in typical metagenomics analysis, the size of the reference sequences has become prohibitive to compute a single full-text index on standard machines. Even on large memory machines, computing such index takes about one day of computing time. As a result, updates of indices are rarely performed. Hence, it is desirable to create an alternative way of indexing while preserving fast search times.\n\nResultsTo solve the index construction and update problem we propose the DREAM (Dynamic seaRchablE pArallel coMpressed index) framework and provide an implementation. The main contributions are the introduction of an approximate search distributor directories via a novel use of Bloom filters. We combine several Bloom filters to form an interleaved Bloom filter and use this new data structure to quickly exclude reads for parts of the databases where they cannot match. This allows us to keep the databases in several indices which can be easily rebuilt if parts are updated while maintaining a fast search time. The second main contribution is an implementation of DREAM-Yara a distributed version of a fully sensitive read mapper under the DREAM framework.\n\nContacttemesgen.dadi@fu-berlin.de\n\nAvailabilityhttps://gitlab.com/pirovc/dream_yara/

bioinformatics↗