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Distributed with the latest version of BLAST, this wrapper facilitates parallelization of the algorithm on modern hybrid architectures with many nodes and many cores within each node. [2] Protein: Burdyshaw CE, Sawyer S, Horton MD, Brook RG, Rekapalli B: 2017 CS-BLAST: Sequence-context specific BLAST, more sensitive than BLAST, FASTA, and SSEARCH.
PSI Protein Classifier is a program generalizing the results of both successive and independent iterations of the PSI-BLAST program. PSI Protein Classifier determines belonging of the found by PSI-BLAST proteins to the known families. The unclassified proteins are grouped according to similarity. PSI Protein Classifier allows to measure ...
PSI-blast based secondary structure PREDiction (PSIPRED) is a method used to investigate protein structure. It uses artificial neural network machine learning methods in its algorithm. [ 2 ] [ 3 ] [ 4 ] It is a server-side program, featuring a website serving as a front-end interface, which can predict a protein's secondary structure ( beta ...
The open-source software MMseqs is an alternative to BLAST/PSI-BLAST, which improves on current search tools over the full range of speed-sensitivity trade-off, achieving sensitivities better than PSI-BLAST at more than 400 times its speed. [27]
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A BLAST variant called MegaBLAST indexes 4 databases to speed up alignments. [ 9 ] BLAT can extend on multiple perfect and near-perfect matches (default is 2 perfect matches of length 11 for nucleotide searches and 3 perfect matches of length 4 for protein searches), while BLAST extends only when one or two matches occur close together.
CS-BLAST greatly improves alignment quality over the entire range of sequence identities and especially for difficult alignments in comparison to regular BLAST and PSI-BLAST. PSI-BLAST (Position-Specific Iterated BLAST) runs at about the same speed per iteration as regular BLAST, but is able to detect weaker sequence similarities that are still ...
It makes use of the BLAST [5] algorithm to identify similar sequences to then transfers existing functional annotation from yet characterised sequences to the novel one. The functional information is represented via the Gene Ontology (GO), a controlled vocabulary of functional attributes.