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Updated: Apr 10, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Ultra-large alignments using phylogeny-aware profiles.

Nam-Phuong D Nguyen¹, Siavash Mirarab², Keerthana Kumar³

¹Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana, 61801, Illinois, USA. namphuon@illinois.edu.

|June 17, 2015

Summary

This summary is machine-generated.

UPP is a new multiple sequence alignment tool that uses an ensemble of hidden Markov models. It accurately aligns large biological datasets, including fragmentary sequences, for evolutionary and homology studies.

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Last Updated: Apr 10, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

Bioinformatics
Computational Biology
Evolutionary Biology

Background:

Accurate multiple sequence alignment (MSA) is crucial for biological research, including evolutionary history estimation and remote homology detection.
Large datasets and fragmentary sequences pose significant challenges to existing MSA methods, limiting their accuracy and applicability.

Purpose of the Study:

To develop a novel and highly accurate method for multiple sequence alignment of large biological datasets.
To address the challenges posed by fragmentary sequences in large-scale sequence analysis.

Main Methods:

Introduction of UPP (Unsupervised Phylogenetic Placement), a novel multiple sequence alignment method.
Implementation of a new machine learning technique: the ensemble of hidden Markov models.

Main Results:

UPP demonstrates high accuracy in aligning both nucleotide and amino acid sequences.
The method performs effectively on ultra-large datasets and datasets containing fragmentary sequences.
UPP provides reliable alignments essential for downstream phylogenetic and homology analyses.

Conclusions:

UPP offers a significant advancement in multiple sequence alignment for large-scale biological data.
The ensemble of hidden Markov models provides a robust approach for handling complex sequence datasets.
UPP is a valuable tool for researchers in evolutionary biology and bioinformatics, available at https://github.com/smirarab/sepp.