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Updated: Jun 29, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

TPMA: A two pointers meta-alignment tool to ensemble different multiple nucleic acid sequence alignments.

Yixiao Zhai^1,2,3, Jiannan Chao^1,3, Yizheng Wang^1,3

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.

Plos Computational Biology

|April 1, 2024

Summary

This summary is machine-generated.

Two Pointers Meta-Alignment (TPMA) integrates nucleic acid sequence alignments, merging locally optimal results into a globally superior alignment. TPMA offers improved accuracy and efficiency over existing tools like M-Coffee.

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Last Updated: Jun 29, 2025

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate multiple sequence alignment (MSA) is crucial for biological sequence analysis.
No single MSA tool consistently excels across all datasets, necessitating the use of multiple tools.
Existing methods for combining alignments can be time-consuming and memory-intensive.

Purpose of the Study:

Introduce Two Pointers Meta-Alignment (TPMA), a novel tool for integrating nucleic acid sequence alignments.
Develop a method to merge locally optimal alignment regions into a globally optimal alignment.
Improve the accuracy and efficiency of multiple sequence alignment.

Main Methods:

TPMA partitions initial alignments into blocks using two pointers based on identical sequence fragments.
Blocks with high sum of pairs (SP) scores are selected and concatenated.
The performance of TPMA was evaluated against M-Coffee using simulated and real datasets.

Main Results:

TPMA consistently outperformed M-Coffee in aSP, Q, and total column (TC) scores on most datasets.
TPMA demonstrated significantly lower running time and memory consumption compared to M-Coffee.
Comprehensive assessment of MSA tools led to proposed combination strategies for efficient large-scale dataset integration.

Conclusions:

TPMA provides a superior method for integrating nucleic acid sequence alignments.
TPMA offers a more accurate and computationally efficient alternative to existing meta-alignment tools.
The study provides practical strategies for tool selection and data integration in MSA.