Biology subjects

Steinegger, M.

Publications and source records attributed to Steinegger, M..

67 records · Page 4Linked to original sources

bioRxiv2021

ColabFold - Making protein folding accessible to all

ColabFold offers accelerated protein structure and complex predictions by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFolds 40 - 60x faster search and optimized model use allows predicting close to a thousand structures per day on a server with one GPU. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at github.com/sokrypton/ColabFold. Its novel environmental databases are available at colabfold.mmseqs.com Contactmilot.mirdita@mpibpc.mpg.de, so@fas.harvard.edu, martin.steinegger@snu.ac.kr

bioinformatics↗

bioRxiv2021

AGNOSTOS-DB: a resource to unlock the uncharted regions of the coding sequence space

Genomes and metagenomes contain a considerable percentage of genes of unknown function, which are often excluded from downstream analyses limiting our understanding of the studied biological systems. To address this challenge, we developed AGNOSTOS, a combined database-computational workflow resource that unifies the known and unknown coding sequence space of genomes and metagenomes. Here, we present AGNOSTOS-DB, an extensive database of high-quality gene clusters enriched with functional, ecological and phylogenetic information. Moreover, AGNOSTOS allows integrating new data into existing AGNOSTOS-DBs, maximizing the information retrievable for the genes of unknown function. As a proof of concept, we provide a seed database that integrates the predicted genes from marine and human metagenomes, as well as from Bacteria, Archaea, Eukarya and giant viruses environmental and cultivar genomes. The seed database comprises 6,572,081 gene clusters connecting 342 million genes and represents a comprehensive and scalable resource for the inclusion and exploration of the unknown fraction of genomes and metagenomes.

microbiology↗

bioRxiv2021

PhyloCSF++: A fast and user-friendly implementation of PhyloCSF with annotation tools

SummaryPhyloCSF++ is an efficient and parallelized C++ implementation of the popular PhyloCSF method to distinguish protein-coding and non-coding regions in a genome based on multiple sequence alignments. It can score alignments or produce browser tracks for entire genomes in the wig file format. Additionally, PhyloCSF++ annotates coding sequences in GFF/GTF files using precomputed tracks or computes and scores multiple sequence alignments on the fly with MMseqs2. AvailabilityPhyloCSF++ is released under the AGPLv3 license. Binaries and source code are available at https://github.com/cpockrandt/PhyloCSFpp. The software can be installed through bioconda. A variety of tracks can be accessed through ftp://ftp.ccb.jhu.edu/pub/software/phylocsfpp/. Contactpockrandt@jhu.edu, martin.steinegger@snu.ac.kr

bioinformatics↗

bioRxiv2021

PredictProtein - Predicting Protein Structure and Function for 29 Years

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProteins infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. AvailabilityFreely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlab

bioinformatics↗

bioRxiv2020

Fast and sensitive taxonomic assignment to metagenomic contigs

MMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contigs taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2-18x faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments. AvailabilityMMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https://mmseqs.com. Contacteli.levy.karin@gmail.com

bioinformatics↗

bioRxiv2020

Light into the darkness: Unifying the known and unknown coding sequence space in microbiome analyses

Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40%-60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we propose a conceptual framework and a computational workflow that bridge the known-unknown gap in genomes and metagenomes. We showcase our approach by exploring 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes. We quantify the extent of the unknown fraction, its diversity, and its relevance across multiple biomes. Furthermore, we provide a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria, being a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data.

microbiology↗

bioRxiv2020

Highly multiplexed oligonucleotide probe-ligation testing enables efficient extraction-free SARS-CoV-2 detection and viral genotyping

The emergence of SARS-CoV-2 has caused the current COVID-19 pandemic with catastrophic societal impact. Because many individuals shed virus for days before symptom onset, and many show mild or no symptoms, an emergent and unprecedented need exists for development and deployment of sensitive and high throughput molecular diagnostic tests. RNA-mediated oligonucleotide Annealing Selection and Ligation with next generation DNA sequencing (RASL-seq) is a highly multiplexed technology for targeted analysis of polyadenylated mRNA, which incorporates sample barcoding for massively parallel analyses. Here we present a more generalized method, capture RASL-seq (“cRASL-seq”), which enables analysis of any targeted pathogen-(and/or host-) associated RNA molecules. cRASL-seq enables highly sensitive (down to ∼1-100 pfu/ml or cfu/ml) and highly multiplexed (up to ∼10,000 target sequences) detection of pathogens. Importantly, cRASL-seq analysis of COVID-19 patient nasopharyngeal (NP) swab specimens does not involve nucleic acid extraction or reverse transcription, steps that have caused testing bottlenecks associated with other assays. Our simplified workflow additionally enables the direct and efficient genotyping of selected, informative SARS-CoV-2 polymorphisms across the entire genome, which can be used for enhanced characterization of transmission chains at population scale and detection of viral clades with higher or lower virulence. Given its extremely low per-sample cost, simple and automatable protocol and analytics, probe panel modularity, and massive scalability, we propose that cRASL-seq testing is a powerful new surveillance technology with the potential to help mitigate the current pandemic and prevent similar public health crises.Competing Interest StatementJ.J.C. and H.B.L. are listed as inventors on a patent describing the cRASL-seq method. H.B.L. has founded a company to license and commercialize oligonucleotide probe ligation related technologies.View Full Text

pathology↗

bioRxiv2020

Metagenomic association analysis of gut symbiont Lactobacillus reuteri without host-specific genome isolation

Lactobacillus reuteri is a model symbiont colonizing the guts of vertebrates used for studies on host adaptation of the gut symbiont. Previous studies investigated host-specific phylogenetic and functional properties by isolating its genomic sequence. This dependency on genome isolation is a significant bottleneck. Here we propose a method to study the association between L. reuteri and its hosts directly from metagenomic reads without strain isolation by using pan-genomes. We characterized the host-specificity of L. reuteri in metagenomic samples not only in the previously studied organisms (mice and pigs) but additionally in dogs. For each sample, two types of profiles were generated: (1) genome-based strain type abundance profiles and (2) gene composition profiles. Our profiles showed host-association of L. reuteri in both phylogenetic and functional aspects without depending on the host-specific genome isolation. We could observe not only the presence of host-specific lineages but also the dominant lineages associated with the different hosts. Furthermore, we show that metagenome-assembled genomes provide detailed insights into the host-specificity of L. reuteri. We could infer evolutionary trajectories of host-associative L. reuteri strains in the metagenomic samples by placing the metagenome-assembled genomes into a phylogenetic tree and identify novel host-specific genes which were unannotated in existing pan-genome databases. Our pan-genomic approach drops the need for time-consuming and expensive host-specific genome isolation while producing consistent results with previous host-association findings in mice and pigs. Additionally, we could predict associations that have not yet been studied in dogs.

microbiology↗

bioRxiv2020

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

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

bioinformatics↗

bioRxiv2019

HH-suite3 for fast remote homology detection and deep protein annotation

BackgroundHH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous sequences. ResultsWe developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. This accelerated HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ~10x faster than PSI-BLAST and ~20x faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over servers in a cluster using OpenMP and message passing interface (MPI). The free, open-source, GNU GPL(v3)-licensed software is available at https://github.com/soedinglab/hh-suite. ConclusionThe added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.

bioinformatics↗

bioRxiv2018

MMseqs2 desktop and local web server app for fast, interactive sequence searches

The MMseqs2 desktop and web server app facilitates interactive sequence searches through custom protein sequence and profile databases on personal workstations. By eliminating MMseqs2s runtime overhead, we reduced response times to a few seconds at sensitivities close to BLAST.\n\nAvailability and implementationThe app is easy to install for non-experts. Source code, prebuilt desktop app packages for Windows, macOS and Linux, Docker images for the web server application, and a demo web server are available at https://search.mmseqs.com.\n\nContactmartin.steinegger@mpibpc.mpg.de or soeding@mpibpc.mpg.de

bioinformatics↗

bioRxiv2018

Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold

The open-source de-novo Protein-level assembler Plass (https://plass.mmseqs.org) assembles six-frame-translated sequencing reads into protein sequences. It recovers 2 to 10 times more protein sequences from complex metagenomes and can assemble huge datasets. We assembled two redundancy-filtered reference protein catalogs, 2 billion sequences from 640 soil samples (SRC) and 292 million sequences from 775 marine eukaryotic metatranscriptomes (MERC), the largest free collections of protein sequences.

bioinformatics↗

bioRxiv2017

Linclust: clustering protein sequences in linear time

Metagenomic datasets contain billions of protein sequences that could greatly enhance large-scale functional annotation and structure prediction. Utilizing this enormous resource would require reducing its redundancy by similarity clustering. However, clustering hundreds of million of sequences is impractical using current algorithms because their runtimes scale as the input set size N times the number of clusters K, which is typically of similar order as N, resulting in runtimes that increase almost quadratically with N. We developed Linclust, the first clustering algorithm whose runtime scales as N, independent of K. It can also cluster datasets several times larger than the available main memory. We cluster 1.6 billion metagenomic sequence fragments in 10 hours on a single server to 50% sequence identity, > 1000 times faster than has been possible before. Linclust will help to unlock the great wealth contained in metagenomic and genomic sequence databases. (Open-source software and Metaclust database: https://mmseqs.org/).

bioinformatics↗