Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Examining sequence alignments using a model-based approach
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology. (Whelan Lab)ORCID iD: 0000-0003-3056-3173
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Multiple sequence alignment (MSA) is a commonly performed procedure required for a number of evolutionary and comparative analyses. The common two-step process of sequence alignment followed by statistical phylogenetic inference depends on MSA quality. MSA is computationally difficult and as a result in many cases sequence alignments contain regions of spurious homologies. These errors in the alignment affect downstream results, so choosing an accurate MSA is critical.  Researchers often face the problem of choosing an aligner out of many multiple sequence alignment methods (MSAMs). This choice is often based on the results of benchmarks with various popular methods claiming high accuracy scores. These methods compete to obtain the highest scores in the commonly used sum-of-pairs benchmark—which accounts for a fraction of the true homologies recovered—ignoring the fraction of introduced false positive homologies. Furthermore, these benchmarks do not account for the fact that some homologies are more difficult to recover than the others. We take a probabilistic model-based approach to examine the quality of pairwise homologies returned by four popular MSAMs. We use pair-hidden Markov models to break down alignment columns into pairs and obtain distributions of pairwise posterior scores for these aligners. Basing our results on a structural benchmark and a simulation study, we find that MSAMs appear to return a sample from a confidence set defined by high posterior probabilities. Furthermore, we find that the reference alignment contains low pairwise posterior portions of pairwise homologies which cannot be expected to be recovered by any MSAM. Finally, we look at several possible test statistics, with and without the need for reference alignments, and ultimately suggest using positive predictive value (PPV) and mean posterior probability for MSA evaluation.

Keywords [en]
Sequence alignment, alignment accuracy, alignment uncertainty, pair hidden Markov models
National Category
Evolutionary Biology
Identifiers
URN: urn:nbn:se:uu:diva-360840OAI: oai:DiVA.org:uu-360840DiVA, id: diva2:1249413
Available from: 2018-09-19 Created: 2018-09-19 Last updated: 2018-09-21
In thesis
1. Evolutionary Approaches to Sequence Alignment
Open this publication in new window or tab >>Evolutionary Approaches to Sequence Alignment
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Molecular evolutionary biology allows us to look into the past by analyzing sequences of amino acids or nucleotides. These analyses can be very complex, often involving advanced statistical models of sequence evolution to construct phylogenetic trees, study the patterns of natural selection and perform a number of other evolutionary studies. In many cases, these evolutionary studies require a prerequisite of multiple sequence alignment (MSA) - a technique, which aims at grouping the characters that share a common ancestor, or homology, into columns. This information regarding shared homology is needed by statistical models to describe the process of substitutions in order to perform evolutionary inference. Sequence alignment, however, is difficult and MSAs often contain whole regions of wrongly aligned characters, which impact downstream analyses.

In this thesis I use two broad groups of approaches to avoid errors in the alignment. The first group addresses the analysis methods without sequence alignment by explicitly modelling the processes of substitutions, and insertions and deletions (indels) between pairs of sequences using pair hidden Markov models. I describe an accurate tree inference method that uses a neighbor joining clustering approach to construct a tree from a matrix of model-based evolutionary distances.

Next, I develop a pairwise method of modelling how natural selection acts on substitutions and indels. I further show the relationship between the constraints acting on these two evolutionary forces to show that natural selection affects them in a similar way.

The second group of approaches deals with errors in existing alignments. I use a statistical model-based approach to evaluate the quality of multiple sequence alignments.

First, I provide a graph-based tool for removing wrongly aligned pairs of residues by splitting them apart. This approach tends to produce better results when compared to standard column-based filtering.

Second, I provide a way to compare MSAs using a probabilistic framework. I propose new ways of scoring of sequence alignments and show that popular methods produce similar results.

The overall purpose of this work is to facilitate more accurate evolutionary analyses by addressing the problem of sequence alignment in a statistically rigorous manner.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 57
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1723
Keywords
molecular evolution, multiple sequence alignment, pair hidden Markov models
National Category
Evolutionary Biology
Research subject
Biology with specialization in Evolutionary Genetics
Identifiers
urn:nbn:se:uu:diva-360871 (URN)978-91-513-0445-8 (ISBN)
Public defence
2018-11-09, Ekmansalen, EBC, Norrbyvägen 14, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2018-10-17 Created: 2018-09-19 Last updated: 2018-11-19

Open Access in DiVA

No full text in DiVA

Search in DiVA

By author/editor
Bogusz, MarcinWhelan, Simon
By organisation
Evolutionary Biology
Evolutionary Biology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 145 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf