dbDNV: a resource of duplicated gene nucleotide variants in human genome

Ho, Meng‐Ru; Chen, Chun-Houh; Lin, Wen-chang

doi:10.1093/nar/gkq1197

Cited by 12 publications

(11 citation statements)

References 24 publications

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“…Finally, we included scaffolds not mapped to particular chromosomes, and removed potentially duplicated regions that were missing from the reference genome. These procedures have been demonstrated to reduce false genetic variation observed in RNA sequencing data ( Peng et al 2012 ), as paralogous variants could be mistaken for STR variation when an incomplete reference genome is used ( Ho et al 2011 ; Bass et al 2012 ; Peng et al 2012 ).…”

Section: Discussionmentioning

confidence: 99%

Reverse Transcription Errors and RNA–DNA Differences at Short Tandem Repeats

et al. 2016

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Transcript variation has important implications for organismal function in health and disease. Most transcriptome studies focus on assessing variation in gene expression levels and isoform representation. Variation at the level of transcript sequence is caused by RNA editing and transcription errors, and leads to nongenetically encoded transcript variants, or RNA–DNA differences (RDDs). Such variation has been understudied, in part because its detection is obscured by reverse transcription (RT) and sequencing errors. It has only been evaluated for intertranscript base substitution differences. Here, we investigated transcript sequence variation for short tandem repeats (STRs). We developed the first maximum-likelihood estimator (MLE) to infer RT error and RDD rates, taking next generation sequencing error rates into account. Using the MLE, we empirically evaluated RT error and RDD rates for STRs in a large-scale DNA and RNA replicated sequencing experiment conducted in a primate species. The RT error rates increased exponentially with STR length and were biased toward expansions. The RDD rates were approximately 1 order of magnitude lower than the RT error rates. The RT error rates estimated with the MLE from a primate data set were concordant with those estimated with an independent method, barcoded RNA sequencing, from a Caenorhabditis elegans data set. Our results have important implications for medical genomics, as STR allelic variation is associated with >40 diseases. STR nonallelic transcript variation can also contribute to disease phenotype. The MLE and empirical rates presented here can be used to evaluate the probability of disease-associated transcripts arising due to RDD.

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Section: Discussionmentioning

confidence: 99%

Reverse Transcription Errors and RNA–DNA Differences at Short Tandem Repeats

et al. 2016

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“…Directly related to this, the presence of CNVs in a sample can cause other problems for short-read mapping and downstream genotype inference. For example, heterozygous gene deletions (hemizygotes) can masquerade as homozygotes for a given SNP or coding allele, whereas paralogous sequence variants between close gene duplicates can result in artifactual heterozygote calls (24,25).…”

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confidence: 99%

Comment on “A Database of Human Immune Receptor Alleles Recovered from Population Sequencing Data”

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Jackson

et al. 2017

The Journal of Immunology

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“…eDiVA‐Score is built by training a random forest (RF) model using the R “randomForest” package with 1000 binary classification trees (Breiman, ; Hastie, Tibshirani, & Friedman, ) and five‐fold cross validation. Eleven features were selected to train the RF model: (a) the maximum minor allele frequency (MAF) of 1000Genomes and GnomAD databases; (b) four conservation measures (conservation in primates and mammals using the PhastCons (Hubisz et al, ) and PhyloP (Pollard, Hubisz, Rosenbloom, & Siepel, ); (c) four functional impact predictors: Condel (González‐Pérez & López‐Bigas, ), Phred‐scaled CADD score (Kircher et al, ), Eigen (Ionita‐Laza et al, ), and Mutation Assessor (Reva et al, ); (d) the likelihood to be in a segmental duplication, which correlates with false‐positive variant calls (Ho, Tsai, Chen, & Lin, ); and (e) an in‐house estimator of systematic sequencing errors called ABB‐score (Muyas et al, ). Note that Condel, Eigen and CADD are combination scores integrating several features also included in eDiVA‐score, namely evolutionary conservation (PhastCons and PhyloP in mammals and primates) and Mutation Assessor scores.…”

Section: Methodsmentioning

confidence: 99%

eDiVA—Classification and prioritization of pathogenic variants for clinical diagnostics

et al. 2019

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Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole‐exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20–30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single‐nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent–child trios. eDiVA combines next‐generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning‐based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state‐of‐the‐art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.

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dbDNV: a resource of duplicated gene nucleotide variants in human genome

Cited by 12 publications

References 24 publications

Reverse Transcription Errors and RNA–DNA Differences at Short Tandem Repeats

Reverse Transcription Errors and RNA–DNA Differences at Short Tandem Repeats

Comment on “A Database of Human Immune Receptor Alleles Recovered from Population Sequencing Data”

eDiVA—Classification and prioritization of pathogenic variants for clinical diagnostics

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