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Multi-species Conserved Sequences

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An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Multiple Sequence Alignment for Large Heterogeneous Datasets Using SATé, PASTA, and UPP.

Tandy Warnow¹, Siavash Mirarab²

¹Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA. warnow@illinois.edu.

Methods in Molecular Biology (Clifton, N.J.)

|December 8, 2020

Summary

Estimating large multiple sequence alignments is difficult. New software, PASTA and UPP, create highly accurate alignments and phylogenetic trees for massive datasets, even with fragmentary sequences.

Keywords:

Ensembles of Hidden Markov Models Multiple sequence alignment PASTA SATé UPP

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Area of Science:

Bioinformatics
Computational Biology
Phylogenetics

Background:

Accurate multiple sequence alignment (MSA) is crucial for biological research.
Estimating MSAs for very large datasets (millions of sequences) presents significant computational challenges.
Existing methods often struggle with scalability and accuracy on ultra-large datasets.

Purpose of the Study:

To introduce and evaluate two novel software packages, PASTA and UPP, for constructing MSAs on large and ultra-large biological datasets.
To assess the accuracy of alignments and resulting phylogenetic trees generated by PASTA and UPP.
To compare the performance of PASTA and UPP against other methods, particularly for datasets with fragmentary sequences.

Main Methods:

Development of two distinct software packages: PASTA and UPP, designed for scalable MSA construction.
Benchmarking alignment and tree-building accuracy using datasets up to 1,000,000 sequences.
Comparative analysis of PASTA and UPP performance against established methods, considering sequence completeness (full-length vs. fragmentary).

Main Results:

Both PASTA and UPP successfully generated highly accurate MSAs for datasets of 1,000,000 sequences.
Phylogenetic trees inferred from these alignments demonstrated high accuracy.
PASTA achieved superior tree accuracy with full-length sequences, while UPP offered improved accuracy over PASTA and other methods when dealing with numerous fragmentary sequences.

Conclusions:

PASTA and UPP represent significant advancements in handling large-scale MSA estimation.
The choice between PASTA and UPP depends on the characteristics of the input data, specifically the proportion of fragmentary sequences.
These open-source tools provide valuable solutions for phylogenetic analysis of massive sequence datasets.