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

By Martin Steinegger, Milot Mirdita, Johannes Söding. 2018

409 citations recorded by the source corpus.

The open-source de novo protein-level assembler, Plass (https://plass.mmseqs.com), assembles six-frame-translated sequencing reads into protein sequences. It recovers 2–10 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 (soil reference protein catalog) and 292 million sequences from 775 marine eukaryotic metatranscriptomes (marine eukaryotic reference catalog), the largest free collections of protein sequences. The protein-level assembler can assemble protein catalogs from raw metagenomic sequencing data, enabling large-scale metagenomics studies.

Subfields: Ecology, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Evolutionary-scale prediction of atomic level protein structure with a language model (2022, background): “[24] Martin Steinegger, Milot Mirdita, and Johannes SÈ oding.”
Language models enable zero-shot prediction of the effects of mutations on protein function (2021, background): “For scalability, learning from sequences is important: while there are no central databases of high-throughput functional measurements, and few compilations exist, billions of sequences are available to learn from in sequence databases [47, 48].”
Masked Inverse Folding with Sequence Transfer for Protein Representation Learning (2023, background): “…(Devlin et al., 2018), uses increasingly-large transformer (Vaswani et al., 2017) models to reconstruct masked and mutated protein sequences taken from databases such as UniProt (UniProt Consortium, 2021), UniRef (Suzek et al., 2015), and BFD (Steinegger et al., 2019; Steinegger & Söding, 2018).”
AlphaFold2-aware protein-DNA binding site prediction using graph transformer (2022, methodology): “Following the tutorial at https://github.com/deepmind/alphafold, we downloaded the model parameters and genetic databases including UniRef90 [46], MGnify [47], BFD [48], Uniclust30 [49], PDB70 [50] and PDB [44] to implement AlphaFold2 in the Tianhe-2 supercomputer.”