Literature DB >> 24170395

Who watches the watchmen? An appraisal of benchmarks for multiple sequence alignment.

Stefano Iantorno1, Kevin Gori, Nick Goldman, Manuel Gil, Christophe Dessimoz.   

Abstract

Multiple sequence alignment (MSA) is a fundamental and ubiquitous technique in bioinformatics used to infer related residues among biological sequences. Thus alignment accuracy is crucial to a vast range of analyses, often in ways difficult to assess in those analyses. To compare the performance of different aligners and help detect systematic errors in alignments, a number of benchmarking strategies have been pursued. Here we present an overview of the main strategies-based on simulation, consistency, protein structure, and phylogeny-and discuss their different advantages and associated risks. We outline a set of desirable characteristics for effective benchmarking, and evaluate each strategy in light of them. We conclude that there is currently no universally applicable means of benchmarking MSA, and that developers and users of alignment tools should base their choice of benchmark depending on the context of application-with a keen awareness of the assumptions underlying each benchmarking strategy.

Mesh:

Year:  2014        PMID: 24170395     DOI: 10.1007/978-1-62703-646-7_4

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  22 in total

1.  Simple chained guide trees give poorer multiple sequence alignments than inferred trees in simulation and phylogenetic benchmarks.

Authors:  Ge Tan; Manuel Gil; Ari P Löytynoja; Nick Goldman; Christophe Dessimoz
Journal:  Proc Natl Acad Sci U S A       Date:  2015-01-06       Impact factor: 11.205

2.  Evaluating Statistical Multiple Sequence Alignment in Comparison to Other Alignment Methods on Protein Data Sets.

Authors:  Michael Nute; Ehsan Saleh; Tandy Warnow
Journal:  Syst Biol       Date:  2019-05-01       Impact factor: 15.683

3.  Simple chained guide trees give high-quality protein multiple sequence alignments.

Authors:  Kieran Boyce; Fabian Sievers; Desmond G Higgins
Journal:  Proc Natl Acad Sci U S A       Date:  2014-07-07       Impact factor: 11.205

4.  Studying RNA Homology and Conservation with Infernal: From Single Sequences to RNA Families.

Authors:  Lars Barquist; Sarah W Burge; Paul P Gardner
Journal:  Curr Protoc Bioinformatics       Date:  2016-06-20

5.  Inferring horizontal gene transfer.

Authors:  Matt Ravenhall; Nives Škunca; Florent Lassalle; Christophe Dessimoz
Journal:  PLoS Comput Biol       Date:  2015-05-28       Impact factor: 4.475

6.  Current Methods for Automated Filtering of Multiple Sequence Alignments Frequently Worsen Single-Gene Phylogenetic Inference.

Authors:  Ge Tan; Matthieu Muffato; Christian Ledergerber; Javier Herrero; Nick Goldman; Manuel Gil; Christophe Dessimoz
Journal:  Syst Biol       Date:  2015-06-01       Impact factor: 15.683

7.  YOC, A new strategy for pairwise alignment of collinear genomes.

Authors:  Raluca Uricaru; Célia Michotey; Hélène Chiapello; Eric Rivals
Journal:  BMC Bioinformatics       Date:  2015-04-02       Impact factor: 3.169

8.  GUIDANCE2: accurate detection of unreliable alignment regions accounting for the uncertainty of multiple parameters.

Authors:  Itamar Sela; Haim Ashkenazy; Kazutaka Katoh; Tal Pupko
Journal:  Nucleic Acids Res       Date:  2015-04-16       Impact factor: 16.971

9.  Evaluating the accuracy and efficiency of multiple sequence alignment methods.

Authors:  Muhammad Tariq Pervez; Masroor Ellahi Babar; Asif Nadeem; Muhammad Aslam; Ali Raza Awan; Naeem Aslam; Tanveer Hussain; Nasir Naveed; Salman Qadri; Usman Waheed; Muhammad Shoaib
Journal:  Evol Bioinform Online       Date:  2014-12-07       Impact factor: 1.625

10.  DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment.

Authors:  Erik S Wright
Journal:  BMC Bioinformatics       Date:  2015-10-06       Impact factor: 3.169

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