Please use this identifier to cite or link to this item: https://doi.org/10.1093/gbe/evs032
Title: Revealing mammalian evolutionary relationships by comparative analysis of gene clusters
Authors: Song, G.
Riemer, C.
Dickins, B.
Kim, H.L.
Zhang, L. 
Zhang, Y.
Hsu, C.-H.
Hardison, R.C.
Green, E.D.
Miller, W.
Keywords: Conversion
Evolutionary inference
Gene clusters
KIR
Orthology
Issue Date: 2012
Citation: Song, G., Riemer, C., Dickins, B., Kim, H.L., Zhang, L., Zhang, Y., Hsu, C.-H., Hardison, R.C., Green, E.D., Miller, W. (2012). Revealing mammalian evolutionary relationships by comparative analysis of gene clusters. Genome Biology and Evolution 4 (4) : 586-601. ScholarBank@NUS Repository. https://doi.org/10.1093/gbe/evs032
Abstract: Many software tools for comparative analysis of genomic sequence data have been released in recent decades. Despite this, it remains challenging to determine evolutionary relationships in gene clusters due to their complex histories involving duplications, deletions, inversions, and conversions. One concept describing these relationships is orthology. Orthologs derive from a common ancestor by speciation, in contrast to paralogs, which derive from duplication. Discriminating orthologs from paralogs is a necessary step in most multispecies sequence analyses, but doing so accurately is impeded by the occurrence of gene conversion events. We propose a refined method of orthology assignment based on two paradigms for interpreting its definition: by genomic context or by sequence content. X-orthology (based on context) traces orthology resulting from speciation and duplication only, while N-orthology (based on content) includes the influence of conversion events. We developed a computational method for automatically mapping both types of orthology on a per-nucleotide basis in gene cluster regions studied by comparative sequencing, and we make this mapping accessible by visualizing the output. All of these steps are incorporated into our newly extended CHAP 2 package. We evaluate our method using both simulated data and real gene clusters (including the well-characterized α-globin and β-globin clusters). We also illustrate use of CHAP 2 by analyzing four more loci: CCL (chemokine ligand), IFN (interferon), CYP2abf (part of cytochrome P450 family 2), and KIR (killer cell immunoglobulin-like receptors). These new methods facilitate and extend our understanding of evolution at these and other loci by adding automated accurate evolutionary inference to the biologist's toolkit. The CHAP 2 package is freely available from http://www.bx.psu.edu/miller-lab. © The Author(s) 2012.
Source Title: Genome Biology and Evolution
URI: http://scholarbank.nus.edu.sg/handle/10635/104622
ISSN: 17596653
DOI: 10.1093/gbe/evs032
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