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Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

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Published on: September 13, 2018

Characterization of multiple sequence alignment errors using complete-likelihood score and position-shift map.

Kiyoshi Ezawa^1,2

¹Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, 820-8502, Japan. kezawa.ezawa3@gmail.com.

BMC Bioinformatics

|March 20, 2016

Summary

This summary is machine-generated.

New tools, the complete-likelihood score and position-shift map, help identify errors in multiple sequence alignments (MSAs). These methods reveal that current aligners often make errors, even when producing seemingly optimal results.

Keywords:

Error Insertion/deletion (indel)Likelihood MSA space exploration Multiple sequence alignment (MSA)Power-law Probability distribution Stochastic evolutionary model Visualization

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Area of Science:

Computational Biology
Bioinformatics
Evolutionary Biology

Background:

Multiple sequence alignment (MSA) is fundamental for homology-based sequence analysis.
Characterizing errors in state-of-the-art aligners is crucial for improving MSA accuracy.
This study introduces the complete-likelihood score and position-shift map to analyze MSA errors.

Purpose of the Study:

To develop and apply novel tools for characterizing errors in multiple sequence alignments (MSAs).
To evaluate the performance of leading MSA aligners (MAFFT and Prank) using these new analytical methods.
To understand the nature and extent of alignment errors in simulated datasets.

Main Methods:

Developed the complete-likelihood score to assess MSA quality based on evolutionary models.
Introduced the position-shift map for visualizing and disentangling MSA reconstruction errors.
Simulated MSAs with varying divergence levels under a defined evolutionary model.
Reconstructed MSAs using MAFFT and Prank, then analyzed errors with the new tools.

Main Results:

A high percentage (40-99%) of gapped segments in reconstructed MSAs contained errors.
In many erroneous cases, aligners produced MSAs with complete-likelihood scores equal to or higher than true MSAs.
Position-shift map analyses revealed that true MSAs are in close proximity to reconstructed MSAs for low/moderate divergences, but not for high divergences.

Conclusions:

Improving MSA accuracy requires strategies tailored to specific aligner types.
Relying on a single optimal MSA may be insufficient; exploring a distribution of likely MSAs is important.