Alignment Uncertainty and Genomic Analysis

By Karen M Wong, Marc A. Suchard, John P. Huelsenbeck. Science, 2008

400 citations recorded by the source corpus.

Subfields: Food Science, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a25bdf49864dcf0c39924d50ce2e368aa0d7ce4d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

1. Simon Whelan
2. Lior S. Pachter
3. Benjamin P. Blackburne

In Molecular Biology

1. Kazutaka Katoh
2. Daron M. Standley
3. Tal Pupko
4. Haim Ashkenazy
5. Benjamin P. Blackburne
6. Ari Löytynoja
7. Simon Whelan
8. Hervé Philippe

Citations in the corpus

• Phylogenetic assessment of alignments reveals neglected tree signal in gaps (2010, background): “This measure has been shown [46] to strongly correlate (Spearman's rank correlation r s = 0.92, P < 0.0001) with Bayesian-inferred alignment variability.”
• webPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser (2010, background): “Commonly used alignment programs do not consider phylogeny in their placement of gaps, however, and create systematic errors with insertion-deletion events, affecting downstream evolutionary analyses [1,2].”
• Class of multiple sequence alignment algorithm affects genomic analysis. (2013, methodology): “Related research has shown that the detection of adaptive evolution (positive selection) is particularly susceptible to alignment error both in empirical studies (Wong et al. 2008; Markova-Raina and Petrov 2011) and simulation studies (Fletcher and Yang 2010).”
• Alignment-free sequence comparison: benefits, applications, and tools (2017, background): “Moreover, the scoring system is not consensual between applications, and many reports have shown that small changes in the input parameters can greatly affect the alignment [30].”
• Fast Statistical Alignment (2009, background): “Discordance among programs is frequently noted [5], but the different architectures of individual programs, and in some cases the lack of open software, makes it difficult to explore novel combinations of existing ideas for improving alignments.”
• GUIDANCE: a web server for assessing alignment confidence scores (2010, background): “The removal of the unconfidently-aligned sequences is necessary to avoid artifacts that they would have otherwise caused in downstream analyses such as inference of positive selection (17,26).”
• Recent developments in the MAFFT multiple sequence alignment program (2008, background): “It should also be noted that different alignments sometimes result in quite different trees in phylogenetic analyses [34–36].”
• Pitfalls in supermatrix phylogenomics (2017, positive): “The importance of a correct alignment in phylogenetic inference has long been pointed out (Morrison & Ellis 1997; Ogden & Rosenberg 2006; Talavera & Castresana 2007; Wong et al. 2008) and numerous efforts have been made to improve multiple sequence alignment software (Higgins et al.”
• T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension (2011, background): “This possibility is important in a period where concerns are growing on the non-neutrality of the alignment methods towards subsequent modeling (15).”
• MACSE: Multiple Alignment of Coding SEquences Accounting for Frameshifts and Stop Codons (2011, background): “In all these studies, the initial MSA can strongly impact conclusions and biological interpretations [5].”