Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank

Steinegger, Martin; Salzberg, Steven L.

doi:10.1186/s13059-020-02023-1

Cited by 183 publications

(212 citation statements)

References 38 publications

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“…Although the inference of GCCs is using very sensitive methods to compare profile HMMs, low sequence diversity in GCs can limit its effectiveness. Our approach is affected by the presence and propagation of contamination in reference databases, a significant problem in 'omics 66,67 . In our case, we only use Pfam as a source for annotation owing to its highquality and manual curation process.…”

Section: Discussionmentioning

confidence: 99%

Unifying the known and unknown microbial coding sequence space

Vanni

Schechter

Acinas

et al. 2020

Preprint

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AbstractBridging the gap between the known and the unknown coding sequence space is one of the biggest challenges in molecular biology today. This challenge is especially extreme in microbiome analyses where between 40% to 60% of the coding sequences detected are of unknown function, and ignoring this fraction limits our understanding of microbial systems. Discarding the uncharacterized fraction is not an option anymore. Here, we present an in-depth exploration of the microbial unknown fraction through the lenses of a conceptual framework and a computational workflow we developed to unify the microbial known and unknown coding sequence space. Our approach partitions the coding sequence space in gene clusters and contextualizes them with genomic and environmental information. We analyzed 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes putting into perspective the extent of the unknown fraction, its diversity, and its relevance in a genomic and environmental context. With the identification of a target gene of unknown function for antibiotic resistance, we demonstrate how a contextualized unknown coding sequence space provides a robust framework for the generation of hypotheses that can be used to augment experimental data.

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Section: Discussionmentioning

confidence: 99%

Unifying the known and unknown microbial coding sequence space

Vanni

Schechter

Acinas

et al. 2020

Preprint

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“…Taxonomy was assigned to each of the tags by comparing tags to the GenBank database (28) (Fig 2). A large proportion of the reads did not have taxonomy assigned (ApeKI: 61.92%, PstI: 70.73%) which is partially due to the absence of genome assemblies in the GenBank database, particularly for uncultured microbes [15], but may also be due to the contamination of sequences in the GenBank database [36], whereby a sequence is assigned to the incorrect organism (e.g. microbial genome incorrectly inserted into a "host" genome assembly)-in these cases the sequence may match to reads in two different kingdoms (one correct, and one incorrect) and therefore be unassigned.…”

Section: Comparison Of Tags Against the Genbank Databasementioning

confidence: 99%

A restriction enzyme reduced representation sequencing approach for low-cost, high-throughput metagenome profiling

et al. 2020

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Microbial community profiles have been associated with a variety of traits, including methane emissions in livestock. These profiles can be difficult and expensive to obtain for thousands of samples (e.g. for accurate association of microbial profiles with traits), therefore the objective of this work was to develop a low-cost, high-throughput approach to capture the diversity of the rumen microbiome. Restriction enzyme reduced representation sequencing (RE-RRS) using ApeKI or PstI, and two bioinformatic pipelines (referencebased and reference-free) were compared to bacterial 16S rRNA gene sequencing using repeated samples collected two weeks apart from 118 sheep that were phenotypically extreme (60 high and 58 low) for methane emitted per kg dry matter intake (n = 236). DNA was extracted from freeze-dried rumen samples using a phenol chloroform and bead-beating protocol prior to RE-RRS. The resulting sequences were used to investigate the repeatability of the rumen microbial community profiles, the effect of laboratory and analytical method, and the relationship with methane production. The results suggested that the best method was PstI RE-RRS analyzed with the reference-free approach, which accounted for 53.3±5.9% of reads, and had repeatabilities of 0.49±0.07 and 0.50±0.07 for the first two principal components (PC1 and PC2), phenotypic correlations with methane yield of 0.43±0.06 and 0.46±0.06 for PC1 and PC2, and explained 41±8% of the variation in methane yield. These results were significantly better than for bacterial 16S rRNA gene sequencing of the same samples (p<0.05) except for the correlation between PC2 and methane yield. A Sensitivity study suggested approximately 2000 samples could be sequenced in a single lane on an Illumina HiSeq 2500, meaning the current work using 118 samples/lane and future proposed 384 samples/lane are well within that threshold. With minor adaptations, our approach could be used to obtain microbial profiles from other metagenomic samples.

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“…Eukaryotic reference genomes and proteomes have many sequences that are derived from bacteria, which have entered these genomes either spuriously through contamination during sequencing and assembly, or represent true biology of horizontal transfer from bacteria to eukaryotes (Lu and Salzberg 2018;Steinegger and Salzberg 2020). In either case, these bacterial-derived sequences overwhelm the ability of either k-mer matching or read mapping-based approaches to detect eukaryotes from microbiome sequencing, as bacteria represent the majority of the sequencing library from most microbiomes.…”

Section: Bacterial Sequences Are Ubiquitous In Eukaryotic Genomesmentioning

confidence: 99%

“…Research questions about eukaryotes in microbiomes have primarily been addressed by using eukaryotic reference genomes, genes, or proteins for either k-mer matching or read mapping approaches (Nash et al 2017;Chehoud et al 2015). However, databases of eukaryotic genomes and proteins have widespread contamination from bacterial sequences, and these methods therefore frequently misattribute bacterial reads to eukaryotic species (Lu and Salzberg 2018;Steinegger and Salzberg 2020). Some gene-based taxonomic profilers, such as Metaphlan2, can detect eukaryotic species, but these target a small subset of microbial eukaryotes (122 eukaryotes detectable at the species level as of the mpa_v30 release) (Truong et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…These taxa and others would not be identifiable by aligning reads to existing gene or protein databases. These results demonstrate that EukDetect can be used to detect microbial eukaryotes in non-human associated microbiomes and reveal more information than aligning reads to gene or protein databases.DiscussionHere we present EukDetect, an analysis pipeline that leverages a database of conserved eukaryotic genes to identify eukaryotes present in metagenomic sequencing.By using conserved eukaryotic genes, this pipeline avoids inaccurately classifying sequences based on bacterial contamination of eukaryotic genomes, which is widespread(Figure 1)(Lu and Salzberg 2018;Steinegger and Salzberg 2020). The EukDetect pipeline is sensitive and can detect fungi, protists, and worms present at low sequencing coverage in metagenomic sequencing data.…”

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Accurate and sensitive detection of microbial eukaryotes from whole metagenome shotgun sequencing

Lind

Pollard

2020

Preprint

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Microbial eukaryotes are found alongside bacteria and archaea in natural microbial systems, including host-associated microbiomes. While microbial eukaryotes are critical to these communities, they are often not included in metagenomic analyses. Here we present EukDetect, a bioinformatics approach based on universal eukaryotic marker genes, to identify eukaryotes in shotgun metagenomic sequencing data. EukDetect is accurate, sensitive, has a broad taxonomic coverage of microbial eukaryotes, and is resilient against bacterial contamination in eukaryotic genomes. This enables us to identify and make observations about eukaryotes in public human and plant microbiome datasets. Using EukDetect, we describe the spatial distribution of eukaryotes in the human gut, finding that fungi and protists are present in the lumen and mucosa throughout the large intestine. We find that there is a succession of eukaryotes that colonize the human gut during the first years of life, similar to patterns of succession observed in bacteria in the developing gut. By comparing DNA and RNA sequencing of paired samples from the human gut, we find that many eukaryotes continue active transcription after passage through the gut, while others do not, suggesting they are dormant or nonviable. Finally, we identify eukaryotes in Arabidopsis leaf samples, many of which are not identifiable from public protein databases. EukDetect is an accurate and sensitive pipeline to routinely characterize eukaryotes in shotgun sequencing datasets from diverse microbiomes.

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Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank

Cited by 183 publications

References 38 publications

Unifying the known and unknown microbial coding sequence space

Unifying the known and unknown microbial coding sequence space

A restriction enzyme reduced representation sequencing approach for low-cost, high-throughput metagenome profiling

Accurate and sensitive detection of microbial eukaryotes from whole metagenome shotgun sequencing

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