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Related Experiment Video

Updated: Jun 19, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Assessing the discordance of multiple sequence alignments.

Amol Prakash¹, Martin Tompa

¹Biomarker Research Initiative in Mass Spectrometry Center, Thermo, 790 Memorial Drive, Suite 201, Cambridge, MA 02139, USA. amol.prakash@thermofisher.com

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|October 31, 2009

Summary

This summary is machine-generated.

StatSigMA is a new tool to detect unrelated sequences in multiple sequence alignments, crucial for computational biology. This helps ensure the reliability of genomic and protein analyses.

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Multiple sequence alignments are vital in computational biology for tasks like comparative genomics and protein functional annotation.
Assessing the reliability of multiple sequence alignments is challenging due to computational complexity and numerous available tools.
Contamination with unrelated sequences can compromise the accuracy of biological sequence analyses.

Purpose of the Study:

To introduce StatSigMA, a novel tool designed to identify unrelated sequences within multiple sequence alignments.
To provide a method for assessing the statistical significance of multiple sequence alignments.
To enhance the reliability of computational biology workflows.

Main Methods:

StatSigMA analyzes nucleotide or amino acid sequence alignments.
The tool assesses the statistical significance of alignments to detect potential contamination.
Methods focus on distinguishing homologous from nonhomologous sequences.

Main Results:

StatSigMA can effectively identify alignments contaminated with unrelated sequences.
The tool demonstrates utility in distinguishing homologous from nonhomologous sequences.
StatSigMA aids in comparing the quality of alignments generated by different multiple alignment tools.

Conclusions:

StatSigMA is a valuable tool for improving the reliability of multiple sequence alignments.
The accurate assessment of alignment quality is critical for downstream applications in genomics and proteomics.
StatSigMA offers a robust solution for data quality control in computational biology.