D
David Dineen
Researcher at University College Dublin
Publications - 5
Citations - 12599
David Dineen is an academic researcher from University College Dublin. The author has contributed to research in topics: Multiple sequence alignment & Tree (data structure). The author has an hindex of 4, co-authored 5 publications receiving 10266 citations. Previous affiliations of David Dineen include University of California, Berkeley.
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Journal ArticleDOI
Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega
Fabian Sievers,Andreas Wilm,David Dineen,Toby J. Gibson,Kevin Karplus,Weizhong Li,Rodrigo Lopez,Hamish McWilliam,Michael Remmert,Johannes Söding,Julie D. Thompson,Desmond G. Higgins +11 more
TL;DR: A new program called Clustal Omega is described, which can align virtually any number of protein sequences quickly and that delivers accurate alignments, and which outperforms other packages in terms of execution time and quality.
Journal ArticleDOI
Making automated multiple alignments of very large numbers of protein sequences
TL;DR: It is found that the accuracy of such alignments decreases markedly as the number of sequences grows, and the availability of high quality curated alignments will have to complement algorithmic and/or software developments in the long-term.
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The PhyloFacts FAT-CAT web server: ortholog identification and function prediction using fast approximate tree classification
TL;DR: Results on a data set of microbial, plant and animal proteins demonstrate FAT-CAT’s high precision at separating orthologs and paralogs and robustness to promiscuous domains and the precision of ortholog identification based on subtree hidden Markov model scoring.
Journal ArticleDOI
High DNA melting temperature predicts transcription start site location in human and mouse
TL;DR: Looking in detail at melting temperature, which measures the temperature, at which two strands of DNA separate, it is found that peaks in melting temperature correspond closely to experimentally determined transcription start sites in human and mouse chromosomes.
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Ensemble approach combining multiple methods improves human transcription start site prediction
TL;DR: The heterogeneity of current prediction sets is demonstrated, and a two-level classifier ('Profisi Ensemble') is constructed using predictions from 7 programs, along with 2 other data sources, to achieve a 14% increase over the current state of the art, as benchmarked by a third-party tool.