SeqVis: Visualization of compositional heterogeneity in large alignments of nucleotides

Joshua W.K. Ho; Cameron E. Adams; Jie Bin Lew; Timothy J. Matthews; Chiu Chin Ng; Arash Shahabi-Sirjani; Leng Hong Tan; Yu Zhao; Simon Easteal; Susan R. Wilson; Lars S. Jermiin

doi:10.1093/bioinformatics/btl283

SeqVis: Visualization of compositional heterogeneity in large alignments of nucleotides

Joshua W.K. Ho, Cameron E. Adams, Jie Bin Lew, Timothy J. Matthews, Chiu Chin Ng, Arash Shahabi-Sirjani, Leng Hong Tan, Yu Zhao, Simon Easteal, Susan R. Wilson, Lars S. Jermiin^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

47 Citations (Scopus)

Abstract

Summary: Most phylogenetic methods assume that the sequences evolved under homogeneous, stationary and reversible conditions. Compositional heterogeneity in data intended for studies of phylogeny suggests that the data did not evolve under these conditions. SeqVis, a Java application for analysis of nucleotide content, reads sequence alignments in several formats and plots the nucleotide content in a tetrahedron. Once plotted, outliers can be identified, thus allowing for decisions on the applicability of the data for phylogenetic analysis.

Original language	English
Pages (from-to)	2162-2163
Number of pages	2
Journal	Bioinformatics
Volume	22
Issue number	17
DOIs	https://doi.org/10.1093/bioinformatics/btl283
Publication status	Published - 1 Sept 2006
Externally published	Yes

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title = "SeqVis: Visualization of compositional heterogeneity in large alignments of nucleotides",

abstract = "Summary: Most phylogenetic methods assume that the sequences evolved under homogeneous, stationary and reversible conditions. Compositional heterogeneity in data intended for studies of phylogeny suggests that the data did not evolve under these conditions. SeqVis, a Java application for analysis of nucleotide content, reads sequence alignments in several formats and plots the nucleotide content in a tetrahedron. Once plotted, outliers can be identified, thus allowing for decisions on the applicability of the data for phylogenetic analysis.",

author = "Ho, {Joshua W.K.} and Adams, {Cameron E.} and Lew, {Jie Bin} and Matthews, {Timothy J.} and Ng, {Chiu Chin} and Arash Shahabi-Sirjani and Tan, {Leng Hong} and Yu Zhao and Simon Easteal and Wilson, {Susan R.} and Jermiin, {Lars S.}",

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T2 - Visualization of compositional heterogeneity in large alignments of nucleotides

AU - Ho, Joshua W.K.

AU - Adams, Cameron E.

AU - Lew, Jie Bin

AU - Matthews, Timothy J.

AU - Ng, Chiu Chin

AU - Shahabi-Sirjani, Arash

AU - Tan, Leng Hong

AU - Zhao, Yu

AU - Easteal, Simon

AU - Wilson, Susan R.

AU - Jermiin, Lars S.

PY - 2006/9/1

Y1 - 2006/9/1

N2 - Summary: Most phylogenetic methods assume that the sequences evolved under homogeneous, stationary and reversible conditions. Compositional heterogeneity in data intended for studies of phylogeny suggests that the data did not evolve under these conditions. SeqVis, a Java application for analysis of nucleotide content, reads sequence alignments in several formats and plots the nucleotide content in a tetrahedron. Once plotted, outliers can be identified, thus allowing for decisions on the applicability of the data for phylogenetic analysis.

AB - Summary: Most phylogenetic methods assume that the sequences evolved under homogeneous, stationary and reversible conditions. Compositional heterogeneity in data intended for studies of phylogeny suggests that the data did not evolve under these conditions. SeqVis, a Java application for analysis of nucleotide content, reads sequence alignments in several formats and plots the nucleotide content in a tetrahedron. Once plotted, outliers can be identified, thus allowing for decisions on the applicability of the data for phylogenetic analysis.

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U2 - 10.1093/bioinformatics/btl283

DO - 10.1093/bioinformatics/btl283

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JO - Bioinformatics

JF - Bioinformatics

IS - 17

ER -

SeqVis: Visualization of compositional heterogeneity in large alignments of nucleotides

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