Phylogenetic Methods in the Genomic Era: Challenges in Multiple Sequence Alignment and Phylogenetics for Genome-Scale Data
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".
Deadline for manuscript submissions: closed (10 January 2022) | Viewed by 9803
Special Issue Editors
Interests: comparative bioinformatics; phylognetics; multiple sequence alignment; biological sequences; algorithms
Interests: bioinformatics; sequence alignment; alignment uncertainty; molecular evolution
Interests: phylogeny and comparative analysis; molecular evolution; bioinformatics software; distributed computing
Special Issue Information
Dear Colleagues,
Genome sequencing projects have become routine due to the drastically lower cost of sequencing. Grand-scale genome sequencing projects dedicating a systematic approach to targeting well-recognized taxonomic groups have started to appear, such as the Bird 10,000 Genomes (B10K) Project (Zhang 2015), the Global Ant Genome Alliance (GAGA) Project (Boomsma et al. 2017), and the Genome 10K (Genome 10K Community of Scientists 2009). These mega sequencing projects are changing the analytical landscape for systematics, posing new challenges to phylogeneticists and algorithmists for developing better ways to accommodate big data. Essentially, the tool development for phylogenetics and multiple sequence alignment (MSA) has been stimulated by the ever-rapidly-growing genomic data. Researchers have begun addressing some aspects of the challenges from a wide variety of angles. Lemoine and colleagues proposed a revised version of Felsenstein’s phylogenetic bootstrap based on gradual “transfer” distance to adjust for lower support due to bigger datasets (Lemoine et al. 2018). Morel and colleagues developed a tool, ParGenes, for massively parallel model selection and phylogenetic tree inference on thousands of genes (Morel et al. 2019). Chatzou and colleagues have shown that large-scale progressive multiple alignment methods are unstable, and could produce significantly different output when changing sequence input order (Chatzou et al. 2018). Sievers and Higgins have developed an improved version of ClustalW, Clustal Omega, to accommodate large-scale MSAs (Sievers and Higgins 2018). To highlight the importance of this exciting moment for phylogenetic method development and evolutionary data inference in facing the big data era, this Special Issue welcomes contributions of methods and metrics addressing challenges from sequence alignment to tree reconstruction in phylogenomics.
Dr. Cedric Notredame
Dr. Jia-Ming Chang
Dr. Minh Bui
Dr. Ding He
Guest Editors
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Keywords
- Phylogenomics
- Phylogenetic method development
- Alignment uncertainty
- Tree reconstruction
- Evolutionary data inference
- Multiple sequence alignment (MSA)
- Big datasets
- Bioinformatics
- Sequencing projects
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