ArticleBioinformatics advances2024
Sensitive and error-tolerant annotation of protein-coding DNA with BATH.
Article in Bioinformatics advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Discovery and protein language model-guided design of hyperactive transposases.Nature biotechnology · 2026Article
- TRB proteins in moss reveal their evolutionarily conserved roles in plant development and telomere maintenance.The Plant journal : for cell and molecular biology · 2025Article
- NEAR: neural embeddings for amino acid relationships.Bioinformatics (Oxford, England) · 2025Article
- NEAR: Neural Embeddings for Amino acid Relationships.bioRxiv : the preprint server for biology · 2025Article
- VIBES: a workflow for annotating and visualizing viral sequences integrated into bacterial genomes.NAR genomics and bioinformatics · 2024Article
- nail: software for high-speed, high-sensitivity protein sequence annotation.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Summary: We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based translated sequence annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long-read sequencing data and in the context of pseudogenes. Availability and implementation: The software is available at https://github.com/TravisWheelerLab/BATH.
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Registered trials
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