ArticleFrontiers in genetics2017
Analytical Biases Associated with GC-Content in Molecular Evolution.
Article in Frontiers in genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 86 citations in OpenAlex.
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- The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder.bioRxiv : the preprint server for biology · 2024Article
- GC Content Across Insect Genomes: Phylogenetic Patterns, Causes and Consequences.Journal of molecular evolution · 2024Article
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- An insight into the role of the N-terminal domain ofFrontiers in molecular biosciences · 2024Article
- Revised time estimation of the ancestral human chromosome 2 fusion.BMC genomics · 2022Article
- Nucleotide Usage Biases Distort Inferences of the Species Tree.Genome biology and evolution · 2022Article
- T-G-A Deficiency Pattern in Protein-Coding Genes and Its Potential Reason.Frontiers in microbiology · 2022Article
- Comparing Ultraconserved Elements and Exons for Phylogenomic Analyses of Middle American Cichlids: When Data Agree to Disagree.Genome biology and evolution · 2021Article
- Phylogenetic Permulations: A Statistically Rigorous Approach to Measure Confidence in Associations in a Phylogenetic Context.Molecular biology and evolution · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular evolution is being revolutionized by high-throughput sequencing allowing an increased amount of genome-wide data available for multiple species. While base composition summarized by GC-content is one of the first metrics measured in genomes, its genomic distribution is a frequently neglected feature in downstream analyses based on DNA sequence comparisons. Here, we show how base composition heterogeneity among loci and taxa can bias common molecular evolution analyses such as phylogenetic tree reconstruction, detection of natural selection and estimation of codon usage. We then discuss the biological, technical and methodological causes of these GC-associated biases and suggest approaches to overcome them.
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