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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Reproducing the manual annotation of multiple sequence alignments using a SVM classifier.

Christian Blouin¹, Scott Perry, Allan Lavell

¹Department of Biochemistry and Molecular Biology, Dalhousie University, Sir Charles Tupper Medical Building, Halifax NS B3H 1X5, Canada. cblouin@cs.dal.ca

Bioinformatics (Oxford, England)

|September 23, 2009

Summary

This summary is machine-generated.

Manual editing of protein sequence alignments is time-consuming. This study introduces an automated method using a support vector machine (SVM) classifier to identify and remove non-homologous sites, improving reproducibility and efficiency in large-scale analyses.

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Published on: September 25, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate protein sequence alignment is crucial but challenging due to potential non-homologous sites.
Manual editing of alignments is labor-intensive, time-consuming, and lacks reproducibility.
Automated methods are needed to improve the efficiency and consistency of alignment refinement.

Purpose of the Study:

To develop an automated method for identifying and removing non-homologous sites in protein sequence alignments.
To enhance the reproducibility and scalability of alignment editing processes.
To reduce the manual effort required for refining multiple sequence alignments (MSAs).

Main Methods:

Implementation of a support vector machine (SVM) classifier.
Training the SVM on manually edited alignment data to classify sites as 'valid' or 'invalid'.
Development of the MANUEL software and a web-based application for automated alignment editing.

Main Results:

The SVM classifier achieved 95.0% accuracy in reproducing manual editing decisions.
Demonstrated the feasibility of retraining and extending the classifier with new MSA annotations.
Showed that near-optimal training can be achieved with as few as 1000 annotated sites.

Conclusions:

Automated alignment editing using SVM classifiers significantly improves reproducibility and efficiency.
The MANUEL software offers a practical solution for large-scale protein sequence alignment refinement.
This approach facilitates more reliable downstream analyses reliant on accurate sequence alignments.