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Multiple Sequence Alignment Methods

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Cover of 'Multiple Sequence Alignment Methods'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Dynamic Programming
  3. Altmetric Badge
    Chapter 2 Heuristic Alignment Methods
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    Chapter 3 Objective Functions
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    Chapter 4 Who watches the watchmen? An appraisal of benchmarks for multiple sequence alignment.
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    Chapter 5 BLAST and FASTA Similarity Searching for Multiple Sequence Alignment
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    Chapter 6 Multiple Sequence Alignment Methods
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    Chapter 7 T-coffee: tree-based consistency objective function for alignment evaluation.
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    Chapter 8 MAFFT: Iterative Refinement and Additional Methods
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    Chapter 9 Multiple Sequence Alignment Methods
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    Chapter 10 Phylogeny-aware alignment with PRANK.
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    Chapter 11 GramAlign: Fast alignment driven by grammar-based phylogeny
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    Chapter 12 Multiple Sequence Alignment with DIALIGN
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    Chapter 13 PicXAA: A Probabilistic Scheme for Finding the Maximum Expected Accuracy Alignment of Multiple Biological Sequences
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    Chapter 14 Multiple Protein Sequence Alignment with MSAProbs
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    Chapter 15 Large-Scale Multiple Sequence Alignment and Tree Estimation Using SATé
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    Chapter 16 PRALINE: A Versatile Multiple Sequence Alignment Toolkit
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    Chapter 17 PROMALS3D: Multiple Protein Sequence Alignment Enhanced with Evolutionary and Three-Dimensional Structural Information
  19. Altmetric Badge
    Chapter 18 MSACompro: Improving Multiple Protein Sequence Alignment by Predicted Structural Features
Attention for Chapter 10: Phylogeny-aware alignment with PRANK.
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Chapter title
Phylogeny-aware alignment with PRANK.
Chapter number 10
Book title
Multiple Sequence Alignment Methods
Published in
Methods in molecular biology, August 2013
DOI 10.1007/978-1-62703-646-7_10
Pubmed ID
Book ISBNs
978-1-62703-645-0, 978-1-62703-646-7
Authors

Ari Löytynoja, Löytynoja, Ari

Editors

David J Russell

Abstract

Evolutionary analyses require sequence alignments that correctly represent evolutionary homology. Evolutionary and structural homology are not the same and sequence alignments generated with methods designed for structural matching can be seriously misleading in comparative and phylogenetic analyses. The phylogeny-aware alignment algorithm implemented in the program PRANK has been shown to produce good alignments for evolutionary inferences. Unlike other alignment programs, PRANK makes use of phylogenetic information to distinguish alignment gaps caused by insertions or deletions and, thereafter, handles the two types of events differently. As a by-product of the correct handling of insertions and deletions, PRANK can provide the inferred ancestral sequences as a part of the output and mark the alignment gaps differently depending on their origin in insertion or deletion events. As the algorithm infers the evolutionary history of the sequences, PRANK can be sensitive to errors in the guide phylogeny and violations on the underlying assumptions about the origin and patterns of gaps. These issues are discussed in detail and practical advice for the use of PRANK in evolutionary analysis is provided. The PRANK software and other methods discussed here can be found from the program home page at http://code.google.com/p/prank-msa/.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 282 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Belgium 1 <1%
Austria 1 <1%
Unknown 279 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 75 27%
Researcher 50 18%
Student > Master 41 15%
Student > Bachelor 29 10%
Student > Doctoral Student 10 4%
Other 30 11%
Unknown 47 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 94 33%
Biochemistry, Genetics and Molecular Biology 84 30%
Immunology and Microbiology 17 6%
Computer Science 9 3%
Environmental Science 6 2%
Other 17 6%
Unknown 55 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 February 2021.
All research outputs
#13,396,317
of 22,731,677 outputs
Outputs from Methods in molecular biology
#3,598
of 13,086 outputs
Outputs of similar age
#104,895
of 199,045 outputs
Outputs of similar age from Methods in molecular biology
#15
of 58 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,086 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 70% of its peers.
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We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.