sideRETRO: a pipeline for identifying somatic and dimorphic insertions of processed pseudogenes or retrocopies

ABSTRACTRetrocopies or processed pseudogenes are gene copies resulting from mRNA retrotransposition. These gene duplicates can be fixed, somatically inserted or dimorphic in the genome. However, knowledge regarding unfixed retrocopies (retroCNVs) is still limited, and the development of computational tools for effectively identifying and genotyping them is an urgent need. Here, we present sideRETRO, a pipeline dedicated not only to detecting retroCNVs in whole-genome or whole-exome sequencing data but also to revealing their insertion sites, zygosity, and genomic context and classifying them as somatic or dimorphic events. We show that sideRETRO can identify novel retroCNVs and genotype them (93.2% accuracy), in addition to identifying dimorphic retroCNVs in whole-genome and whole-exome data. Therefore, sideRETRO fills a gap in the literature and presents an efficient and straightforward algorithm to accelerate the study of retroCNVs.AvailabilitysideRETRO is available at https://github.com/galantelab/sideRETRO

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sideRETRO: a pipeline for identifying somatic and polymorphic insertions of processed pseudogenes or retrocopies

Bioinformatics ◽

10.1093/bioinformatics/btaa689 ◽

2020 ◽

Author(s):

Thiago L A Miller ◽

Fernanda Orpinelli Rego ◽

José Leonel L Buzzo ◽

Pedro A F Galante

Keyword(s):

Supplementary Information ◽

Whole Genome ◽

Sequencing Data ◽

Genomic Context ◽

Gene Copies ◽

Whole Exome ◽

Whole Exome Sequencing Data ◽

Processed Pseudogenes ◽

Bona Fide ◽

Insertion Sites

Abstract Motivation Retrocopies or processed pseudogenes are gene copies resulting from mRNA retrotransposition. These gene duplicates can be fixed, somatically inserted or polymorphic in the genome. However, knowledge regarding unfixed retrocopies (retroCNVs) is still limited, and the development of computational tools for effectively identifying and genotyping them is an urgent need. Results Here, we present sideRETRO, a pipeline dedicated not only to detecting retroCNVs in whole-genome or whole-exome sequencing data but also to revealing their insertion sites, zygosity and genomic context and classifying them as somatic or polymorphic events. We show that sideRETRO can identify novel retroCNVs and genotype them, in addition to finding polymorphic retroCNVs in whole-genome and whole-exome data. Therefore, sideRETRO fills a gap in the literature and presents an efficient and straightforward algorithm to accelerate the study of bona fide retroCNVs. Availability and implementation sideRETRO is available at https://github.com/galantelab/sideRETRO Supplementary information Supplementary data are available at Bioinformatics online.

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SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences from Reference Genomes

10.1101/824128 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yue Xing ◽

Alan R. Dabney ◽

Xiao Li ◽

Guosong Wang ◽

Clare A. Gill ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Copy Number Variants ◽

Whole Genome ◽

Sequencing Data ◽

Software Applications ◽

Exome Sequencing Data ◽

Whole Exome ◽

Whole Exome Sequencing Data

AbstractCopy number variants are insertions and deletions of 1 kb or larger in a genome that play an important role in phenotypic changes and human disease. Many software applications have been developed to detect copy number variants using either whole-genome sequencing or whole-exome sequencing data. However, there is poor agreement in the results from these applications. Simulated datasets containing copy number variants allow comprehensive comparisons of the operating characteristics of existing and novel copy number variant detection methods. Several software applications have been developed to simulate copy number variants and other structural variants in whole-genome sequencing data. However, none of the applications reliably simulate copy number variants in whole-exome sequencing data. We have developed and tested SECNVs (Simulator of Exome Copy Number Variants), a fast, robust and customizable software application for simulating copy number variants and whole-exome sequences from a reference genome. SECNVs is easy to install, implements a wide range of commands to customize simulations, can output multiple samples at once, and incorporates a pipeline to output rearranged genomes, short reads and BAM files in a single command. Variants generated by SECNVs are detected with high sensitivity and precision by tools commonly used to detect copy number variants. SECNVs is publicly available at https://github.com/YJulyXing/SECNVs.

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Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btu651 ◽

2014 ◽

Vol 30 (24) ◽

pp. 3532-3540 ◽

Cited By ~ 73

Author(s):

Layla Oesper ◽

Gryte Satas ◽

Benjamin J. Raphael

Keyword(s):

Exome Sequencing ◽

Whole Exome Sequencing ◽

Tumor Heterogeneity ◽

Whole Genome ◽

Sequencing Data ◽

Exome Sequencing Data ◽

Whole Exome ◽

Whole Exome Sequencing Data

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Biallelic novel mutations of the COL27A1 gene in a patient with Steel syndrome

Human Genome Variation ◽

10.1038/s41439-021-00149-7 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Jong Seop Kim ◽

Hyoungseok Jeon ◽

Hyeran Lee ◽

Jung Min Ko ◽

Yonghwan Kim ◽

...

Keyword(s):

Hip Dysplasia ◽

Large Deletion ◽

Compound Heterozygous ◽

Radial Head Dislocation ◽

Sequencing Data ◽

Novel Mutations ◽

Exome Sequencing Data ◽

Whole Exome ◽

Whole Exome Sequencing Data ◽

Carpal Coalition

AbstractAn 11-year-old Korean boy presented with short stature, hip dysplasia, radial head dislocation, carpal coalition, genu valgum, and fixed patellar dislocation and was clinically diagnosed with Steel syndrome. Scrutinizing the trio whole-exome sequencing data revealed novel compound heterozygous mutations of COL27A1 (c.[4229_4233dup]; [3718_5436del], p.[Gly1412Argfs*157];[Gly1240_Lys1812del]) in the proband, which were inherited from heterozygous parents. The maternal mutation was a large deletion encompassing exons 38–60, which was challenging to detect.

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ETumorMetastasis: A Network-based Algorithm Predicts Clinical Outcomes Using Whole-exome Sequencing Data of Cancer Patients

Genomics Proteomics & Bioinformatics ◽

10.1016/j.gpb.2020.06.009 ◽

2021 ◽

Cited By ~ 1

Author(s):

Jean-Sébastien Milanese ◽

Chabane Tibiche ◽

Naif Zaman ◽

Jinfeng Zou ◽

Pengyong Han ◽

...

Keyword(s):

Exome Sequencing ◽

Cancer Patients ◽

Clinical Outcomes ◽

Whole Exome Sequencing ◽

Sequencing Data ◽

Exome Sequencing Data ◽

Whole Exome ◽

Whole Exome Sequencing Data

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A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

International Journal of Genomics ◽

10.1155/2016/7983236 ◽

2016 ◽

Vol 2016 ◽

pp. 1-16 ◽

Cited By ~ 16

Author(s):

Jennifer D. Hintzsche ◽

William A. Robinson ◽

Aik Choon Tan

Keyword(s):

Exome Sequencing ◽

Whole Exome Sequencing ◽

Sequencing Data ◽

Disease Treatment ◽

Computational Tools ◽

Whole Exome ◽

Data Production ◽

Whole Exome Sequencing Data ◽

Computationally Intensive ◽

Generation Technology

Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.

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