HMMER web server: 2018 update

By Simon C. Potter, Aurélien Luciani, Sean R. Eddy, Young M. Park, Rodrigo Lopez, Robert D. Finn. 2018

1,259 citations recorded by the source corpus.

Abstract The HMMER webserver [http://www.ebi.ac.uk/Tools/hmmer] is a free-to-use service which provides fast searches against widely used sequence databases and profile hidden Markov model (HMM) libraries using the HMMER software suite (http://hmmer.org). The results of a sequence search may be summarized in a number of ways, allowing users to view and filter the significant hits by domain architecture or taxonomy. For large scale usage, we provide an application programmatic interface (API) which has been expanded in scope, such that all result presentations are available via both HTML and API. Furthermore, we have refactored our JavaScript visualization library to provide standalone components for different result representations. These consume the aforementioned API and can be integrated into third-party websites. The range of databases that can be searched against has been expanded, adding four sequence datasets (12 in total) and one profile HMM library (6 in total). To help users explore the biological context of their results, and to discover new data resources, search results are now supplemented with cross references to other EMBL-EBI databases.

Subfields: Molecular Biology, Spectroscopy, ML Systems & Hardware, Computational Biology & Bioinformatics

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Citations in the corpus

Learning inverse folding from millions of predicted structures (2022, methodology): “We searched all HMMs associated with the validation and test splits against the UniRef50 sequences using default parameters in hmmsearch (Potter et al., 2018) and excluded all hits.”
ProteinNet: a standardized data set for machine learning of protein structure (2019, methodology): “In addition to raw MSAs, PSSMs were derived using Easel [24] in a weighted fashion so that similar sequences collectively contributed less to PSSM probabilities than diverse sequences.”
The EMBL-EBI search and sequence analysis tools APIs in 2019 (2019, methodology): “The Perl and Python clients include a feature that allow sequence search analysis (e.g. NCBI BLAST+, FASTA and HMMER3) to be submitted in batches, from an input that is composed by multiple sequences in fasta format (using the optional flag ‘– multifasta’).”
NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning (2022, background): “MSAs can be generated using different tools (6–11), which vary in their precision and speed of execution.”
NMR-assisted protein structure prediction with MELDxMD (2019, methodology): “8 In addition, we calculated our own evolutionary information based on metagenomic libraries using jackhmmer 9 and GREMLIN.”
InterPro in 2019: improving coverage, classification and access to protein sequence annotations (2018, methodology): “Wehave also re-used the taxonomy browser component originally developed for the HMMER website (35).”
Universal Transforming Geometric Network (2019, methodology): “Then, we normalize the PSSM values to between 0 and 1 and concatenate it with the one-hot encoding.”
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints (2018, methodology): “A representative target sequence was found for each family using hmmsearch [42] to search the UniRef90 database with the Pfam HMM and taking the closest subsequence match by E-value.”