CoChat
Sign Up
Cited by 229Genomics and Phylogenetic StudiesFractal and DNA sequence analysisAlgorithms and Data Compression

Multiple sequence alignment modeling: Methods and applications

Open access
Chatzou, Maria, Magis, Cedrik, Chang, Jia-Ming, Kemena, Carsten, Bussotti, Giovanni, Erb, Ionas, Notredame, Cedric·2016·Briefings in Bioinformatics
DOI 10.1093/bib/bbv099
Open this paper in CoChat — free accountOpen PDF

Abstract

This review provides an overview on the development of Multiple sequence alignment (MSA) methods and their main applications. It is focused on progress made over the past decade. The three first sections review recent algorithmic developments for protein, RNA/DNA and genomic alignments. The fourth section deals with benchmarks and explores the relationship between empirical and simulated data, along with the impact on method developments. The last part of the review gives an overview on available MSA local reliability estimators and their dependence on various algorithmic properties of available methods.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/bib/bbv099

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The quality of the multiple sequence alignment algorithm has been evaluated based on alignment accuracy, biological relevance, and statistical significance, through metrics such as sum-of-pairs score, total column score, and consensus identity.

    “its quality has been evaluated based on alignment accuracy, biological relevance, and statistical significance, through metrics or scoring schemes such as sum-of-pairs score (scores the sum of pairs of aligned residues), total column score (scores based on the similarity of residues to a consensus sequence), and consensus identity (scores based on concepts of information theory like entropy and mu”

    From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [25]

    The ‘Benchmarking multiple aligners accuracy’ section (page unavailable) supports evaluating aligners for alignment accuracy against reference alignments. ‘Commonly used algorithms’ (page unavailable) describes sum-of-pairs as an alignment scoring function, not as the stated evaluation metric. The retrieved passages do not support the claim’s specific list of evaluation criteria or its inclusion of total column score and consensus identity. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Tools like LASTZ and MULTIZ have been designed to tackle the challenges of aligning divergent genomes, enabling the alignment of divergent genomes.

    “However, tools like LASTZ and MULTIZ have been designed to tackle these challenges, enabling the alignment of divergent genomes [25,69].”

    From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [25]

    The ‘Multiply aligning non-transcribed sequences’ section identifies poor conservation and genome rearrangements as alignment challenges, and ‘Multiple genome alignments’ describes methods for addressing rearrangements. Our copy does not identify LASTZ or MULTIZ or say that those tools enable alignment of divergent genomes, so the tool-specific claim is unsupported. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 100

  1. Van Noorden R, Maher B, Nuzzo R. The top 100 papers. Nature 2014;514:550-3.
  2. Thompson J, Higgins D, Gibson T. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 1994;22:4673-90.
  3. Thompson JD, Linard B, Lecompte O, et al. A comprehensive benchmark study of multiple sequence alignment methods: current challenges and future perspectives. PLoS One 2011;6:e18093.
  4. Kemena C, Notredame C. Upcoming challenges for multiple sequence alignment methods in the high-throughput era. Bioinformatics 2009;25:2455-65.
  5. Edgar RC, Batzoglou S. Multiple sequence alignment. Curr Opin Struct Biol 2006;16:368-73.
  6. Notredame C, Higgins DG. SAGA: sequence alignment by genetic algorithm. Nucleic Acids Res 1996;24:1515-24.
  7. Hogeweg P, Hesper B. The alignment of sets of sequences and the construction of phylogenetic trees: an integrated method. J Mol Evol 1984;20:175-86.
  8. Notredame C, Higgins DG, Heringa J. T-Coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 2000;302:205-17.
  9. Do CB, Mahabhashyam MS, Brudno M, et al. ProbCons: prob- abilistic consistency-based multiple sequence alignment. Genome Res 2005;15:330-40.
  10. Wallace IM, O'Sullivan O, Higgins DG. Evaluation of iterative alignment algorithms for multiple alignment. Bioinformatics 2005;21:1408-14.
  11. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 2004;32:1792-7.
  12. Katoh K, Misawa K, Kuma K, et al. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 2002;30:3059-66.
  13. Sievers F, Wilm A, Dineen D, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 2011;7:539.
  14. Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 1987;4:406-25.
  15. Murtagh F. Complexities of hierarchic clustering algorithms: state of the art. Comput Stat Q 1984;1:101-13.
  16. Wheeler TJ, Kececioglu JD. Multiple alignment by aligning alignments. Bioinformatics 2007; 23:i559-68.
  17. Morgenstern B, Frech K, Dress A, et al. DIALIGN: finding local similarities by multiple sequence alignment. Bioinformatics 1998;14:290-4.
  18. Kececioglu JD. The maximum weight trace problem in multiple sequence alignment. Lect Notes Comput Sci 1983;684:106-19.
  19. Kececioglu JD, Lenhof HP, Mehlhorn K, et al. A polyhedral ap- proach to sequence alignment problems. Discret Appl Math 2000;104:143-86.
  20. Paten B, Herrero J, Beal K, et al. Sequence progressive align- ment, a framework for practical large-scale probabilistic consistency alignment. Bioinformatics 2009;25:295-301.
  21. Durbin R, Eddy S, Krogh A, et al. Probabilistic models of pro- teins and nucleic acids. Biol Seq Anal 1998;14:164-73.
  22. Liu Y, Schmidt B, Maskell DL. MSAProbs: multiple sequence alignment based on pair hidden Markov models and parti- tion function posterior probabilities. Bioinformatics 2010;26:1958-64.
  23. Di Tommaso P, Orobitg M, Guirado F, et al. Cloud-Coffee: im- plementation of a parallel consistency-based multiple align- ment algorithm in the T-Coffee package and its benchmarking on the Amazon Elastic-Cloud. Bioinformatics 2010;26:1903-4.
  24. Rausch T, Emde AK, Weese D, et al. Segment-based multiple sequence alignment. Bioinformatics 2008;24:i187-92.
  25. Breen MS, Kemena C, Vlasov PK, et al. Epistasis as the pri- mary factor in molecular evolution. Nature 2012;490:535-8.
  26. Mirarab S, Nguyen N, Guo S, et al. PASTA: ultra-large mul- tiple sequence alignment for Nucleotide and Amino-acid se- quences. J Comput Biol 2015;22:377-86.
  27. Liu K, Raghavan S, Nelesen S, et al. Rapid and accurate large- scale coestimation of sequence alignments and phylogen- etic trees. Science 2009;324:1561-4.
  28. Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 2004;5:113.
  29. Blackshields G, Sievers F, Shi W, et al. Sequence embedding for fast construction of guide trees for multiple sequence alignment. Algorithms Mol Biol 2010;5:21.
  30. Lo ¨ytynoja A, Goldman N. An algorithm for progressive mul- tiple alignment of sequences with insertions. Proc Natl Acad Sci USA 2005;102:10557-62.
  31. Morrison DA. Why would phylogeneticists ignore computer- ized sequence alignment? Syst Biol 2009; 58:150-8.
  32. Blackburne BP, Whelan S. Class of multiple sequence align- ment algorithm affects genomic analysis. Mol Biol Evol 2013;30:642-53.
  33. Markova-Raina P, Petrov D. High sensitivity to aligner and high rate of false positives in the estimates of positive selec- tion in the 12 Drosophila genomes. Genome Res 2011;21:863-74.
  34. Chang JM, Tommaso P, Notredame C. TCS: a new multiple sequence alignment reliability measure to estimate align- ment accuracy and improve phylogenetic tree reconstruc- tion. Mol Biol Evol 2014;31:1625-37.
  35. Rost B. Twilight zone of protein sequence alignments. Protein Eng 1999;12:85-94.
  36. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999;292:195-202.
  37. Pei J, Kim BH, Grishin NV. PROMALS3D: a tool for multiple protein sequence and structure alignments. Nucleic Acids Res 2008;36:2295-300.
  38. O'Sullivan O, Suhre K, Abergel C, et al. 3DCoffee: combining protein sequences and structures within multiple sequence alignments. J Mol Biol 2004;340:385-95.
  39. Armougom F, Moretti S, Poirot O, et al. Expresso: automatic in- corporation of structural information in multiple sequence alignments using 3D-Coffee. Nucleic Acids Res 2006;34:W604-8.
  40. Capriotti E, Marti-Renom MA. Quantifying the relationship between sequence and three-dimensional structure conser- vation in RNA. BMC Bioinformatics 2010;11:322.
  41. Sankoff D. Simultaneous solution of the RNA folding, align- ment and protosequence problems. SIAM J Appl Math 1985;45:810-25.
  42. Dowell RD, Eddy SR. Efficient pairwise RNA structure predic- tion and alignment using sequence alignment constraints. BMC Bioinformatics 2006;7:400.
  43. Mathews DH, Turner DH. Dynalign: an algorithm for finding the secondary structure common to two RNA sequences. J Mol Biol 2002;317:191-203.
  44. Mathews DH. Predicting a set of minimal free energy RNA secondary structures common to two sequences. Bioinformatics 2005;21:2246-53.
  45. Holmes I. Accelerated probabilistic inference of RNA struc- ture evolution. BMC Bioinformatics 2005;6:73.
  46. Gorodkin J, Heyer LJ, Stormo GD. Finding the most signifi- cant common sequence and structure motifs in a set of RNA sequences. Nucleic Acids Res 1997;25:3724-32.
  47. Havgaard JH, Lyngso RB, Stormo GD, et al. Pairwise local structural alignment of RNA sequences with sequence simi- larity less than 40%. Bioinformatics 2005;21:1815-24.
  48. Tabei Y, Kiryu H, Kin T, et al. A fast structural multiple align- ment method for long RNA sequences. BMC Bioinformatics 2008;9:33.
  49. Tabei Y, Tsuda K, Kin T, et al. SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed- length stem fragments. Bioinformatics 2006;22:1723-9.
  50. McCaskill JS. The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 1990;29:1105-19.
  51. Kiryu H, Tabei Y, Kin T, et al. Murlet: a practical multiple alignment tool for structural RNA sequences. Bioinformatics 2007;23:1588-98.
  52. Siebert S, Backofen R. MARNA: multiple alignment and con- sensus structure prediction of RNAs based on sequence structure comparisons. Bioinformatics 2005;21:3352-9.
  53. Hofacker IL, Bernhart SHF, Stadler PF. Alignment of RNA base pairing probability matrices. Bioinformatics 2004;20:2222-7.
  54. Will S, Reiche K, Hofacker IL, et al. Inferring noncoding RNA families and classes by means of genome-scale structure- based clustering. PLoS Comput Biol 2007;3:e65.
  55. Torarinsson E, Havgaard JH, Gorodkin J. Multiple structural alignment and clustering of RNA sequences. Bioinformatics 2007;23:926-32.
  56. Will S, Joshi T, Hofacker IL, et al. LocARNA-P: accurate boundary prediction and improved detection of structural RNAs. RNA 2012;18:900-14.
  57. Sorescu DA, Mo ¨hl M, Mann M, et al. CARNA-alignment of RNA structure ensembles. Nucleic Acids Res 2012;40:W49-53.
  58. Do CB, Foo CS, Batzoglou S. A max-margin model for effi- cient simultaneous alignment and folding of RNA se- quences. Bioinformatics 2008;24:i68-76.
  59. Harmanci AO, Sharma G, Mathews DH. Efficient pairwise RNA structure prediction using probabilistic alignment con- straints in Dynalign. BMC Bioinformatics 2007;8:130.
  60. Will S, Otto C, Miladi M, et al. SPARSE: quadratic time simul- taneous alignment and folding of RNAs without sequence- based heuristics. Bioinformatics 2015;31:2489-96.
  61. Dalli D, Wilm A, Mainz I, et al. STRAL: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time. Bioinformatics 2006;22:1593-9.
  62. Bauer M, Klau GW, Reinert K. Accurate multiple sequence- structure alignment of RNA sequences using combinatorial optimization. BMC Bioinformatics 2007;8:271.
  63. Xu X, Ji Y, Stormo GD. RNA Sampler: a new sampling based algorithm for common RNA secondary structure prediction and structural alignment. Bioinformatics 2007;23:1883-91.
  64. Reeder J, Giegerich R. Consensus shapes: an alternative to the Sankoff algorithm for RNA consensus structure predic- tion. Bioinformatics 2005;21:3516-23.
  65. Wilm A, Higgins DG, Notredame C. R-Coffee: a method for multiple alignment of non-coding RNA. Nucleic Acids Res 2008;36:e52.
  66. Bernhart SH, Hofacker IL, Stadler PF. Local RNA base pairing probabilities in large sequences. Bioinformatics 2006;22:614-15.
  67. Katoh K, Toh H. Improved accuracy of multiple ncRNA align- ment by incorporating structural information into a MAFFT- based framework. BMC Bioinformatics 2008;9:212.
  68. Capriotti E, Marti-Renom MA. RNA structure alignment by a unit-vector approach. Bioinformatics 2008;24:i112-18.
  69. Ferre `F, Ponty Y, Lorenz WA, et al. DIAL: a web server for the pairwise alignment of two RNA three-dimensional struc- tures using nucleotide, dihedral angle and base-pairing sim- ilarities. Nucleic Acids Res 2007;35:W659-68.
  70. Rahrig RR, Leontis NB, Zirbel CL. R3D Align: global pairwise alignment of RNA 3D structures using local superpositions. Bioinformatics 2010;26:2689-97.
  71. Chang Y-F, Huang YL, Lu CL. SARSA: a web tool for structural alignment of RNA using a structural alphabet. Nucleic Acids Res. 2008;36:W19-24.
  72. Bauer RA, Rother K, Moor P, et al. Fast structural alignment of Biomolecules using a Hash table, N-Grams and string de- scriptors. Algorithms 2009;2:692-709.
  73. Kemena C, Bussotti G, Capriotti E, et al. Using tertiary struc- ture for the computation of highly accurate multiple RNA alignments with the SARA-Coffee package. Bioinformatics 2013;29:1112-19.
  74. Seibel PN, Mu ¨ller T, Dandekar T, et al. 4SALE-a tool for syn- chronous RNA sequence and secondary structure alignment and editing. BMC Bioinformatics 2006;7:498.
  75. Lu ¨ck R, Gra ¨f S, Steger G. ConStruct: a tool for thermodynamic controlled prediction of conserved secondary structure. Nucleic Acids Res 1999;27:4208-17.
  76. Jeon YS, Chung H, Park S, et al. jPHYDIT: a JAVA-based inte- grated environment for molecular phylogeny of ribosomal RNA sequences. Bioinformatics 2005;21:3171-3.
  77. Griffiths-Jones S. RALEE-RNA ALignment editor in Emacs. Bioinformatics 2005;21:257-9.
  78. Andersen ES, Lind-Thomsen A, Knudsen B, et al. Semiautomated improvement of RNA alignments. RNA 2007;13:1850-9.
  79. Derrien T, Johnson R, Bussotti G, et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene struc- ture, evolution, and expression. Genome Res 2012;22:1775-89.
  80. Bussotti G, Raineri E, Erb I, et al. BlastR-fast and accurate database searches for non-coding RNAs. Nucleic Acids Res 2011;39:6886-95.
  81. Lindgreen S, Gardner PP, Krogh A. MASTR: multiple align- ment and structure prediction of non-coding RNAs using simulated annealing. Bioinformatics 2007;23:3304-11.
  82. Orom UA, Derrien T, Beringer M, et al. Long noncoding RNAs with enhancer-like function in human cells. Cell 2010;143:46-58.
  83. Tilgner H, Nikolaou C, Althammer S, et al. Nucleosome pos- itioning as a determinant of exon recognition. Nat Struct Mol Biol 2009;16:996-1001.
  84. Sankoff D, Blanchette M. Multiple genome rearrangement and breakpoint phylogeny. J Comput Biol 1998;5:555-70.
  85. Dewey CN. Aligning multiple whole genomes with Mercator and MAVID. Methods Mol Biol 2007;395:221-36.
  86. Angiuoli S V, Salzberg SL. Mugsy: fast multiple alignment of closely related whole genomes. Bioinformatics 2011;27:334-42.
  87. Brudno M, Do CB, Cooper GM, et al. LAGAN and Multi- LAGAN: efficient tools for large-scale multiple alignment of genomic DNA. Genome Res 2003;13:721-31.
  88. Blanchette M, Kent WJ, Riemer C, et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res 2004;14:708-15.
  89. Paten B, Herrero J, Beal K, et al. Enredo and Pecan: genome- wide mammalian consistency-based multiple alignment with paralogs. Genome Res 2008;18:1814-28.
  90. Raphael B, Zhi D, Tang H, et al. A novel method for multiple alignment of sequences with repeated and shuffled elem- ents. Genome Res 2004;14:2336-46.
  91. Paten B, Earl D, Nguyen N, et al. Cactus: algorithms for gen- ome multiple sequence alignment. Genome Res 2011;21:1512-28.
  92. Kehr B, Trappe K, Holtgrewe M, et al. Genome alignment with graph data structures: a comparison. BMC Bioinformatics 2014;15:99.
  93. Earl D, Nguyen N, Hickey G, et al. Alignathon: a competitive assessment of whole-genome alignment methods. Genome Res 2014;24:2077-89.
  94. Kim J, Ma J. PSAR: measuring multiple sequence alignment reliability by probabilistic sampling. Nucleic Acids Res 2011;39:6359-68.
  95. Su J, Teichmann SA, Down TA. Assessing computational methods of cis-regulatory module prediction. PLoS Comput Biol 2010;6:e1001020.
  96. Aerts S. Computational strategies for the genome-wide identification of cis-regulatory elements and transcriptional targets. Curr Top Dev Biol 2012;98:121-45. 100. Berezikov E, Guryev V, Plasterk RHA, et al. CONREAL: con- served regulatory elements anchored alignment algorithm for identification of transcription factor binding sites by phylogenetic footprinting. Genome Res 2004;14:170-8. 101. Sinha S, He X. MORPH: probabilistic alignment combined with hidden Markov models of cis-regulatory modules. PLoS Comput Biol 2007;3:e216 102.
  97. Satija R, Pachter L, Hein J. Combining statistical alignment and phylogenetic footprinting to detect regulatory elements. Bioinformatics 2008;24:1236-42. 103. Satija R, Nova ´k A, Miklo ´s I, et al. BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC. BMC Evol Biol 2009;9:217. 104. Majoros WH, Ohler U. Modeling the evolution of regulatory elements by simultaneous detection and alignment with phylogenetic pair HMMs. PLoS Comput Biol 2010;6:e1001037. 105. Erb I, Gonzalez-Vallinas JR, Bussotti G, et al. Use of ChIP-Seq data for the design of a multiple promoter-alignment method. Nucleic Acids Res 2012;40:e52. 106. Ettwiller L, Paten B, Souren M, et al. The discovery, position- ing and verification of a set of transcription-associated motifs in vertebrates. Genome Biol 2005;6:R104. 107. Notredame C, Holm L, Higgins DG. COFFEE: an objective function for multiple sequence alignments. Bioinformatics 1998;14:407-22. 108. Edgar RC. Quality measures for protein alignment bench- marks. Nucleic Acids Res 2010;38:2145-53. 109. Iantorno S, Gori K, Goldman N, et al. Who watches the watchmen? An appraisal of benchmarks for multiple se- quence alignment. Methods Mol Biol 2014;1079:59-73. 110. Bahr A, Thompson JD, Thierry JC, et al. BAliBASE (Benchmark Alignment dataBASE): enhancements for repeats, trans- membrane sequences and circular permutations. Nucleic Acids Res 2001;29:323-6. 111. Van Walle I, Lasters I, Wyns L. SABmark-a benchmark for sequence alignment that covers the entire known fold space. Bioinformatics 2005;21:1267-8. 112. Sievers F, Dineen D, Wilm A, et al. Making automated mul- tiple alignments of very large numbers of protein se- quences. Bioinformatics 2013;29:989-95. 113. Yilmaz P, Parfrey LW, Yarza P, et al. The SILVA and 'All- species Living Tree Project (LTP)' taxonomic frameworks. Nucleic Acids Res 2014;42:D643-8. 114. Lackner P, Koppensteiner WA, Sippl MJ, et al. ProSup: a refined tool for protein structure alignment. Protein Eng 2000;13:745-52. 115. Armougom F, Moretti S, Keduas V, et al. The iRMSD: a local measure of sequence alignment accuracy using structural information. Bioinformatics 2006;22:e35-9. 116. Gardner PP, Wilm A, Washietl S. A benchmark of multiple sequence alignment programs upon structural RNAs. Nucleic Acids Res 2005;33:2433-9. 117. Ogden TH, Rosenberg MS. Multiple sequence alignment ac- curacy and phylogenetic inference. Syst Biol 2006;55:314-28. 118. Stoye J, Evers D, Meyer F. Rose: generating sequence fami- lies. Bioinformatics 1998;14:157-63. 119. Strope CL, Abel K, Scott SD, et al. Biological sequence simula- tion for testing complex evolutionary hypotheses: indel- Seq-Gen version 2.0. Mol Biol Evol 2009;26:2581-93. 120. Cartwright RA. DNA assembly with gaps (Dawg): simulating sequence evolution. Bioinformatics 2005;21(Suppl 3):iii31-8. 121.
  98. Fletcher W, Yang Z. INDELible: a flexible simulator of biolo- gical sequence evolution. Mol Biol Evol 2009;26:1879-88. 122. Thompson JD, Plewniak F, Poch O. BAliBASE: a benchmark alignment database for the evaluation of multiple align- ment programs. Bioinformatics 1999;15:87-8. 123. Wallace IM, Blackshields G, Higgins DG. Multiple sequence alignments. Curr Opin Struct Biol 2005;15:261-6. 124. Hall BG. Comparison of the accuracies of several phylogen- etic methods using protein and DNA sequences. Mol Biol Evol 2005;22:792-802. 125. Rosenberg MS. Multiple sequence alignment accuracy and evolutionary distance estimation. BMC Bioinformatics 2005;6:278. 126. Wang L-S, Leebens-Mack J, Kerr Wall P, et al. The impact of multiple protein sequence alignment on phylogenetic estima- tion. IEEE/ACM Trans Comput Biol Bioinform 2011;8:1108-19. 127. Dessimoz C, Gil M. Phylogenetic assessment of alignments reveals neglected tree signal in gaps. Genome Biol 2010;11:R37. 128. O'Sullivan O, Zehnder M, Higgins D, et al. APDB: a novel meas- ure for benchmarking sequence alignment methods without reference alignments. Bioinformatics 2003;19(Suppl 1):i215-21. 129. Lin K, Kleinjung J, Taylor WR, et al. Testing homology with Contact Accepted mutatiOn (CAO): a contact-based Markov model of protein evolution. Comput Biol Chem 2003;27:93-102. 130.
  99. Kemena C, Taly JF, Kleinjung J, et al. STRIKE: evaluation of protein MSAs using a single 3D structure. Bioinformatics 2011;27:3385-3391. 131. Hickson RE, Simon C, Perrey SW. The performance of several multiple-sequence alignment programs in relation to secondary-structure features for an rRNA sequence. Mol Biol Evol 2000;17:530-9. 132. Talavera G, Castresana J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol 2007;56:564-577. 133. Pei J, Grishin N V. AL2CO: calculation of positional conserva- tion in a protein sequence alignment. Bioinformatics 2001;17:700-12. 134. Landan G, Graur D. Heads or tails: a simple reliability check for multiple sequence alignments. Mol Biol Evol 2007;24:1380-3. 135.
  100. Penn O, Privman E, Landan G, et al. An alignment confidence score capturing robustness to guide tree uncertainty. Mol Biol Evol 2010;27:1759-67. 136. Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T. TrimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 2009;25:1972-3. 137. Thompson JD, Thierry JC, Poch O. RASCAL: rapid scanning and correction of multiple sequence alignments. Bioinformatics 2003;19:1155-61. 138. Mevissen HT, Vingron M. Quantifying the local reliability of a sequence alignment. Protein Eng 1996;9:127-32. 139. Sela I, Ashkenazy H, Katoh K, et al. GUIDANCE2: accurate de- tection of unreliable alignment regions accounting for the uncertainty of multiple parameters. Nucleic Acids Res 2015;43:W7-14.

Cited by

Sorted by citations

Authors
Chatzou, M., Magis, C., Chang, J.-M., Kemena, C., Bussotti, G., Erb, I., & Notredame, C.
Affiliations
Centre National de la Recherche Scientifique (FR)
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2015-11-27
Volume
17
Issue
6
Pages
1009-1023
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbv099
OpenAlex
W2258865027
Cited by
229
References
1
Citations by year
2026: 18 · 2025: 25 · 2024: 38 · 2023: 30 · 2022: 28 · 2021: 25 · 2020: 22 · 2019: 17 · 2018: 8 · 2017: 11 · 2016: 6 · 2012: 1
FWCI
0.81
Type
journal article
Language
en
Open access
Locations
  • Briefings in Bioinformatics (publishedVersion)
  • PubMed (publishedVersion)
  • HighWire Press Open Archive (submittedVersion)
Funders
Ministerio de Economía y Competitividad (BFU2014-55062-P, BFU2014)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
  • Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
Keywords
multiple sequence alignment, protein sequence alignment, genome alignment, alignment algorithms, benchmarking, simulated data, alignment accuracy
MeSH terms
Algorithms, DNA, Proteins, Reproducibility of Results, Sequence Alignment, Genomics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review