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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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EvalMSA: A Program to Evaluate Multiple Sequence Alignments and Detect Outliers.

Alvaro Chiner-Oms1, Fernando González-Candelas1

  • 1Joint Research Unit "Infection and Public Health" FISABIO, Cavanilles Institute for Biodiversity and Evolutionary Biology, University of Valencia, Paterna, Valencia, Spain.; CIBER in Epidemiology and Public Health, Madrid, Spain.

Evolutionary Bioinformatics Online
|December 7, 2016
PubMed
Summary
This summary is machine-generated.

EvalMSA is a new software tool that helps researchers evaluate multiple sequence alignments (MSAs). It identifies divergent sequences, providing objective data for informed decisions on sequence inclusion in MSAs.

Keywords:
gappinessmultiple sequence alignmentoutlier sequence

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Area of Science:

  • Bioinformatics
  • Computational Biology

Background:

  • Multiple sequence alignments (MSAs) are fundamental in bioinformatics for studying protein and nucleotide sequence evolution.
  • Assessing the quality and identifying outlier sequences within MSAs is crucial for accurate downstream analyses.
  • Existing methods may lack objective quantitative measures for evaluating sequence contributions to alignment quality.

Purpose of the Study:

  • To introduce EvalMSA, a novel software tool designed for the comprehensive evaluation and outlier detection in MSAs.
  • To provide users with objective metrics for assessing the quality and relevance of individual sequences within an MSA.
  • To facilitate informed decisions regarding the inclusion or exclusion of sequences in MSAs.

Main Methods:

  • EvalMSA employs a sum-of-pair-based scoring method to quantify the contribution of each sequence (row) to the overall MSA quality.
  • The tool integrates additional analytical routines to provide a robust evaluation of sequence data.
  • It is implemented in standard Perl, with dependencies on the R statistical language package for full functionality.

Main Results:

  • EvalMSA successfully identifies divergent and potentially erroneous sequences within MSAs.
  • The software provides quantitative scores reflecting each sequence's impact on alignment quality.
  • Objective data is generated to support decisions on sequence relevance and retention.

Conclusions:

  • EvalMSA offers a valuable tool for improving the reliability of MSAs by enabling objective quality assessment.
  • The software aids researchers in making data-driven decisions about sequence inclusion, enhancing the accuracy of evolutionary and functional studies.
  • EvalMSA is freely available for Linux and Windows, promoting its accessibility in the bioinformatics community.