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Updated: Nov 17, 2025

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

A minimum reporting standard for multiple sequence alignments.

Thomas K F Wong^1,2, Subha Kalyaanamoorthy^1,3, Karen Meusemann^4,5,6

¹Land & Water, CSIRO, Canberra, ACT 2601, Australia.

NAR Genomics and Bioinformatics

|February 12, 2021

Summary

This summary is machine-generated.

We introduce a minimum reporting standard (MRS) to measure the completeness of multiple sequence alignments (MSAs). This standard, implemented in AliStat software, enhances transparency in molecular sequence data analysis.

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Area of Science:

Bioinformatics
Computational Biology
Molecular Biology

Background:

Multiple sequence alignments (MSAs) are crucial for analyzing molecular sequence data.
Current methods lack a standardized way to report MSA completeness.
Incomplete MSAs can affect downstream analyses.

Purpose of the Study:

To establish a minimum reporting standard (MRS) for quantifying MSA completeness.
To introduce four novel completeness metrics for MSAs.
To develop and implement the AliStat software to support the MRS.

Main Methods:

Development of four simple completeness metrics for MSAs.
Implementation of these metrics in the AliStat software.
Survey and analysis of existing published MSAs using the MRS.

Main Results:

The proposed MRS effectively quantifies MSA completeness.
AliStat software successfully implements the MRS metrics.
A survey revealed the benefits of applying the MRS to published MSAs, highlighting improved transparency.

Conclusions:

The developed MRS provides a much-needed standard for reporting MSA completeness.
AliStat facilitates the application of this standard, enhancing data transparency.
Adoption of the MRS will improve the reliability and reproducibility of molecular sequence data studies.