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Uncertainty in homology inferences: assessing and improving genomic sequence alignment
Gerton Lunter1, Andrea Rocco, Naila Mimouni
1MRC Functional Genetics Unit, Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3QX, United Kingdom. gerton.lunter@dpag.ox.ac.uk
Genome Research
|December 13, 2007
Summary
Sequence alignment errors are common in genomics, affecting over 15% of bases. A new Marginalized Posterior Decoding (MPD) algorithm significantly reduces these DNA alignment errors, improving accuracy.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Sequence alignment is fundamental to comparative genomics but remains a significant challenge.
- Statistical uncertainty in alignments is often overlooked, impacting downstream analyses.
- Existing whole-genome alignments contain substantial errors (>15% of bases).
Purpose of the Study:
- To investigate statistical uncertainty in pairwise genomic DNA alignments.
- To identify and categorize alignment errors and their biases.
- To develop and evaluate a novel algorithm for improved DNA sequence alignment.
Main Methods:
- Theoretical and simulation study of human-mouse divergence pairwise alignments.
- Development of the Marginalized Posterior Decoding (MPD) algorithm.
- Comparison of MPD against existing alignment algorithms, including Needleman-Wunsch.
Main Results:
- Over 15% of bases in current whole-genome alignments are incorrect.
- Three types of systematic alignment errors were identified.
- The MPD algorithm reduced misaligned bases by one-third compared to the best existing methods.
- MPD demonstrated robust improvements over the classic Needleman-Wunsch algorithm.
Conclusions:
- Probabilistic approaches are essential for improving DNA sequence alignment quality and quantifying uncertainty.
- Alignment errors are inevitable and must be considered in genomic research, especially for noncoding DNA.
- The MPD algorithm offers a more accurate and less biased method for sequence alignment.
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