FDA-ARGOS is a database with public quality-controlled reference genomes for diagnostic use and regulatory science

Sichtig, Heike; Minogue, Timothy D.; Yan, Yi; Stefan, Christopher P.; Hall, Adrienne T.; Tallon, Luke J.; Sadzewicz, Lisa; Nadendla, Suvarna; Klimke, William; Hatcher, Eneida L.; Shumway, Martin; Aldea, Dayanara Lebron; Allen, Jonathan; Koehler, Jeffrey W.; Slezak, Tom; Lovell, Stephen; Schoepp, Randal J.; Scherf, Uwe

doi:10.1038/s41467-019-11306-6

Cited by 100 publications

(83 citation statements)

References 43 publications

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“…As example of contaminant contig, PDBU01060288.1 (25,547 bp) matches the genome of Achromobacter xylosoxidans strain FDAARGOS_147 in positions 3,319,249-3,344,796. This assembly is included in the Database for Reference Grade Microbial Sequences (FDA-ARGOS) 21 which collects high quality bacterial genomes. While this assembly was deposited in NCBI in 2019, PDBU01060288.1 also gives hits to several Achromobacter and other bacterial genomes that were deposited in public databases since 2013.…”

Section: Figmentioning

confidence: 99%

Microbial contaminants cataloged as novel human sequences in recent human pan-genomes

Manni

Zdobnov

2020

Preprint

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Human pan-genome studies offer the opportunity to identify human non-reference sequences (NRSs) which are, by definition, not represented in the reference human genome (GRCh38). NRSs serve as useful catalogues of genetic variation for population and disease studies and while the majority consists of repetitive elements, a substantial fraction is made of non-repetitive, non-reference (NRNR) sequences. The presence of non-human sequences in these catalogues can inflate the number of "novel" human sequences, overestimate the genetic differentiation among populations, and jeopardize subsequent analyses that rely on these resources. We uncovered almost 2,000 contaminant sequences of microbial origin in NRNR sequences from recent human pan-genome studies. The contaminant contigs (3,501,302 bp) harbour genes totalling 4,720 predicted proteins (>40 aa). The major sources of contamination are related to Rhyzobiales, Burkholderiales, Pseudomonadales and Lactobacillales, which may have been associated with the original samples or introduced later during sequencing experiments. We additionally observed that the majority of human novel protein-coding genes described in one of the studies entirely overlap repetitive regions and are likely to be false positive predictions.We report here the list of contaminant sequences in three recent human pan-genome catalogues and discuss strategies to increase decontamination efficacy for current and future pan-genome studies.

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Section: Figmentioning

confidence: 99%

Microbial contaminants cataloged as novel human sequences in recent human pan-genomes

Manni

Zdobnov

2020

Preprint

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“…We also expected very little contamination in finished microbial genomes. Therefore, we created a control set of high-quality genomes consisting of 928 genomes from FDA-ARGOS, a curated set of complete microbial genomes [22], plus genomes for model organisms Saccharomyces cerevisiae, Danio rerio, Mus musculus, Drosophila melanogaster,…”

Section: Resultsmentioning

confidence: 99%

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

Steinegger

Salzberg

2020

Preprint

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Metagenomic sequencing allows researchers to investigate organisms sampled from their native environments by sequencing their DNA directly, and then quantifying the abundance and taxonomic composition of the organisms thus captured. However, these types of analyses are sensitive to contamination in public databases caused by incorrectly labeled reference sequences. Here we describe Conterminator, an efficient method to detect and remove incorrectly labelled sequences by an exhaustive all-against-all sequence comparison. Our analysis reports contamination in 114,035 sequences and 2767 species in the NCBI Reference Sequence Database (RefSeq), 2,161,746 sequences and 6795 species in the GenBank database, and 14,132 protein sequences in the NR non-redundant protein database. Conterminator uncovers contamination in sequences spanning the whole range from draft genomes to “complete” model organism genomes. Our method, which scales linearly with input size, was able to process 3.3 terabytes of genomic sequence data in 12 days on a single 32-core compute node. We believe that Conterminator can become an important tool to ensure the quality of reference databases with particular importance for downstream metagenomic analyses. Source code (GPLv3): https://github.com/martin-steinegger/conterminator

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“…It should be pointed out that currently fastv sorts the results according to the genome coverage and median k-mer hits, without considering the pathogenicity of each virus or microorganism. In future work, we will further link the microbial pathogen databases, like the FDA-ARGOS [38], to provide better functions for pathogen identification. awk -v num1=$percent -v num2=20 'BEGIN{print(num1>num2)?…”

Section: Discussionmentioning

confidence: 99%

A Computational Toolset for Rapid Identification of SARS-CoV-2, other Viruses, and Microorganisms from Sequencing Data

Chen

et al. 2020

Preprint

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In this paper, we present a toolset and related resources for rapid identification of viruses and microorganisms from short-read or long-read sequencing data. We present fastv as an ultra-fast tool to detect microbial sequences present in sequencing data, identify target microorganisms, and visualize coverage of microbial genomes. This tool is based on the k-mer mapping and extension method. K-mer sets are generated by UniqueKMER, another tool provided in this toolset.UniqueKMER can generate complete sets of unique k-mers for each genome within a large set of viral or microbial genomes. For convenience, unique k-mers for microorganisms and common viruses that afflict humans have been generated and are provided with the tools. As a lightweight tool, fastv accepts FASTQ data as input, and directly outputs the results in both HTML and JSON formats. Prior to the k-mer analysis, fastv automatically performs adapter trimming, quality pruning, base correction, and other pre-processing to ensure the accuracy of k-mer analysis. Specifically, fastv provides built-in support for rapid SARS-CoV-2 identification and typing. Experimental results showed that fastv achieved 100% sensitivity and 100% specificity for detecting SARS-CoV-2 from sequencing data; and can distinguish SARS-CoV-2 from SARS, MERS, and other coronaviruses. This toolset is available at: https://github.com/OpenGene/fastv. As part of the OpenGene projects, fastv and UniqueKMER are open-sourced through the MIT license.Fastv is available at https://github.com/OpenGene/fastv, and UniqueKMER is available at https://github.com/OpenGene/UniqueKMER. The pre-computed unique k-mer resources are also provided in these repositories. Key PointsThis tool presents a new tool fastv for rapid identification of SARS-Cov-2, other viruses and microorganisms. Another tool UniqueKMER is presented for generation of high-quality unique k-mers.Unique k-mer resources for tens of thousands of viruses and microorganisms have been precomputed, and uploaded to the tools' repositories. Supplementary DataA pipeline for alignment-based SARS-CoV-2 identification was provided in Supplementary file 1.

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FDA-ARGOS is a database with public quality-controlled reference genomes for diagnostic use and regulatory science

Cited by 100 publications

References 43 publications

Microbial contaminants cataloged as novel human sequences in recent human pan-genomes

Microbial contaminants cataloged as novel human sequences in recent human pan-genomes

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

A Computational Toolset for Rapid Identification of SARS-CoV-2, other Viruses, and Microorganisms from Sequencing Data

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