Yang Y et al. (2019), WGDdetector: a pipeline for detecting whole gen...

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BMC Bioinformatics 2019 Feb 13;201:75. doi: 10.1186/s12859-019-2670-3.

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WGDdetector: a pipeline for detecting whole genome duplication events using the genome or transcriptome annotations.

Yang Y , Li Y , Chen Q , Sun Y , Lu Z .

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BACKGROUND: With the availability of well-assembled genomes of a growing number of organisms, identifying the bioinformatic basis of whole genome duplication (WGD) is a growing field of genomics. The most extant software for detecting footprints of WGDs has been restricted to a well-assembled genome. However, the massive poor quality genomes and the more accessible transcriptomes have been largely ignored, and in theoretically they are also likely to contribute to detect WGD using dS based method. Here, to resolve these problems, we have designed a universal and simple technical tool WGDdetector for detecting WGDs using either genome or transcriptome annotations in different organisms based on the widely used dS based method. RESULTS: We have constructed WGDdetector pipeline that integrates all analyses including gene family constructing, dS estimating and phasing, and outputting the dS values of each paralogs pairs processed with only one command. We further chose four species (Arabidopsis thaliana, Juglans regia, Populus trichocarpa and Xenopus laevis) representing herb, wood and animal, to test its practicability. Our final results showed a high degree of accuracy with the previous studies using both genome and transcriptome data. CONCLUSION: WGDdetector is not only reliable and stable for genome data, but also a new way to using the transcriptome data to obtain the correct dS distribution for detecting WGD. The source code is freely available, and is implemented in Windows and Linux operation system.

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References [+] :

Arrigo, Rarely successful polyploids and their legacy in plant genomes. 2012, Pubmed

	Fig. 1. Workflow in WGDdetector. The input files only including the protein and CDS files. The proteins were used in the similarity searching and gene family constructing. The CDSs were used to calculating dS values based on the proteins constructed gene family information. The further sub-gene family building and dS phasing were implemented with the Perl scripts and the R software
	Fig. 2. Comparison of the dS distributions within A. thaliana and P. trichocarpa. The y axis is the density values and the x axis represent the dS values. The four species were marked at the top of each sub picture. The software and corresponding dataset were listed at the right of each sub picture