ArticleEvolutionary bioinformatics online2016
EvalMSA: A Program to Evaluate Multiple Sequence Alignments and Detect Outliers.
Article in Evolutionary bioinformatics online, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Seqrutinator: scrutiny of large protein superfamily sequence datasets for the identification and elimination of non-functional homologues.Genome biology · 2024Article
- Structure modelling of odorant receptor fromComputational and structural biotechnology journal · 2023Article
- Using the Evolutionary History of Proteins to Engineer Insertion-Deletion Mutants from Robust, Ancestral Templates Using Graphical Representation of Ancestral Sequence Predictions (GRASP).Methods in molecular biology (Clifton, N.J.) · 2022Article
- Article
- LMAP_S: Lightweight Multigene Alignment and Phylogeny eStimation.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present EvalMSA, a software tool for evaluating and detecting outliers in multiple sequence alignments (MSAs). This tool allows the identification of divergent sequences in MSAs by scoring the contribution of each row in the alignment to its quality using a sum-of-pair-based method and additional analyses. Our main goal is to provide users with objective data in order to take informed decisions about the relevance and/or pertinence of including/retaining a particular sequence in an MSA. EvalMSA is written in standard Perl and also uses some routines from the statistical language R. Therefore, it is necessary to install the R-base package in order to get full functionality. Binary packages are freely available from http://sourceforge.net/projects/evalmsa/for Linux and Windows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.