nhmmer: DNA homology search with profile HMMs

By Travis J. Wheeler, Sean R. Eddy. Bioinform., 2013

795 citations recorded by the source corpus.

Summary: Sequence database searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. We present a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome. Availability: nhmmer is a part of the new HMMER3.1 release. Source code and documentation can be downloaded from http://hmmer.org. HMMER3.1 is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. Contact: wheelert@janelia.hhmi.org

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9f3b8e44b6aee9085a3f00ca45fafce16ae2c661

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Citations in the corpus

The SIB Swiss Institute of Bioinformatics’ resources: focus on curated databases (2015, background): “1 (24) using each individual element to search whole genome assemblies of all other species.”
Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf (2023, background): “Unfortunately, with the continuous increase in database sizes, the nhmmer (Wheeler & Eddy 2013) search for homologues became too demanding, and the previous pipeline was unable to handle nucleic acid queries.”
RepeatModeler2: automated genomic discovery of transposable element families (2019, background): “The latter can be used for generating profile Hidden Markov Models (Wheeler and Eddy 2013) and preserves the provenance of the family’s representative sequences.”
Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models (2014, background): “Profile HMMs provide a formal probabilistic framework for sequence comparison [1-3], leveraging the information contained in a sequence alignment to improve detection of distantly related sequences [4,5].”
De Novo RNA Tertiary Structure Prediction at Atomic Resolution Using Geometric Potentials from Deep Learning (2022, methodology): “Briefly, rMSA constructs an MSA for a query sequence by iteratively searching multiple nucleic acid sequence databases, including Rfam 3 , RNAcentral 2 , and the nt database 29 using blastn 30 , nhmmer 31 , and cmsearch 31 (Figure S4).”
The Dfam community resource of transposable element families, sequence models, and genome annotations (2020, background): “Pro�le HMMs [4, 5] yield sensitivity gains in part by modeling the position-specic residue and indel (insertion and deletion) variability found in family MSAs.”
RepeatModeler2 for automated genomic discovery of transposable element families (2020, background): “The latter can be used for generating profile HMMs (37) and preserves the provenance of the family ’ s representative sequences.”
Fish community assessment with eDNA metabarcoding: effects of sampling design and bioinformatic filtering (2017, methodology): “USEARCH analysis for OTUs on the combined pools of ampliconspecific sequences and subsequent HMMER modeling (to remove non-vertebrate OTUs) for each of the three markers resulted in detection of 32 OTUs from the 16S fragment, 42 OTUs from the 12S fragment, and 29 OTUs from the CytB fragment (Supplem”