ArticleAlgorithms for molecular biology : AMB2014
Probabilistic approaches to alignment with tandem repeats.
Article in Algorithms for molecular biology : AMB, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Noncanonical chromosomal-end-specific telomeric repeat arrays in naturally telomerase-negative yeasts.Nucleic acids research · 2026Article
- Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications.Molecular biology and evolution · 2024Review
- ULTRA-Effective Labeling of Repetitive Genomic Sequence.bioRxiv : the preprint server for biology · 2024Article
- ULTRA-effective labeling of tandem repeats in genomic sequence.Bioinformatics advances · 2024Article
- Variable-order sequence modeling improves bacterial strain discrimination for Ion Torrent DNA reads.BMC bioinformatics · 2017Article
- Split-alignment of genomes finds orthologies more accurately.Genome biology · 2015Article
- ULTRA: A Model Based Tool to Detect Tandem Repeats.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and BiomedicineArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundShort tandem repeats are ubiquitous in genomic sequences and due to their complex evolutionary history pose a challenge for sequence alignment tools.
resultsTo better account for the presence of tandem repeats in pairwise sequence alignments, we propose a simple tractable pair hidden Markov model that explicitly models their presence. Using the framework of gain functions, we design several optimization criteria for decoding this model and describe resulting decoding algorithms, ranging from the traditional Viterbi and posterior decoding to block-based decoding algorithms tailored to our model. We compare the accuracy of individual decoding algorithms on simulated and real data and find that our approach is superior to the classical three-state pair HMM.
conclusionsOur study illustrates versatility of pair hidden Markov models coupled with appropriate decoding criteria as a modeling tool for capturing complex sequence features.
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Registered trials
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