FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer

Fu, Yao; Liu, Zhu; Lou, Shaoke; Bedford, Jason; Mu, Xinmeng Jasmine; Yip, Kevin Y.; Khurana, Ekta; Gerstein, Mark

doi:10.1186/preaccept-1739683221127290

Cited by 157 publications

(263 citation statements)

References 46 publications

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“…A total of 11,679 out of 25,765 variants were not linked to clinically characterized genes and formed a separate panel (see Table 2 for an overview, which includes the number of pathogenic variants in each panel). In addition, we assessed the performance of GAVIN in compared to 12 common in silico tools for pathogenicity prediction: MSC (using two different settings), CADD (using three different thresholds), SIFT [5], PolyPhen2 [15], PROVEAN [16], Condel [17], PON-P2 [18], PredictSNP2 [19], FATHMM-MKL [20], GWAVA [21], FunSeq [22], and DANN [23].…”

Section: Performance Benchmarkmentioning

confidence: 99%

GAVIN - Gene-Aware Variant INterpretation for medical sequencing

Velde

Boer

Diemen

et al. 2016

Preprint

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We present Gene-Aware Variant INterpretation (GAVIN), a new method that accurately classifies variants for clinical diagnostic purposes. Classifications are based on gene-specific calibrations of allele frequencies from the ExAC database, likely variant impact using SnpEff, and estimated deleteriousness based on CADD scores for >3000 genes. In a benchmark on 18 clinical gene sets, we achieve a sensitivity of 91.4% and a specificity of 76.9%. This accuracy is unmatched by 12 other tools. We provide GAVIN as an online MOLGENIS service to annotate VCF files and as an open source executable for use in bioinformatic pipelines. It can be found at http://molgenis.org/gavin.

show abstract

Section: Performance Benchmarkmentioning

confidence: 99%

GAVIN - Gene-Aware Variant INterpretation for medical sequencing

Velde

Boer

Diemen

et al. 2016

Preprint

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“…They were regarded as transcription noise in the human genome, due to their lack of capability of protein translation. Over the previous decade, an increasing amount of evidence has indicated that lncRNAs have a variety of roles in numerous physiological processes (19)(20)(21)(22)(23)(24)(25). Despite a lack of capability of encoding proteins, lncRNAs may function through regulating gene expression at various levels, including chromatin architecture, transcription, RNA splicing, and protein translation and turnover (26,27).…”

Section: Introductionmentioning

confidence: 99%

“…Implementation of CADD as a support vector machine has successfully differentiated 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants (18). Fu et al (19) developed a computational framework, FunSeq2, which processed large-scale genomics (including 1000 Genomes and ENCODE data) and cancer resources, and combined a high-throughput variant prioritization pipeline to annotate and prioritize somatic alterations, particularly regulatory non-coding mutations.…”

Section: Introductionmentioning

confidence: 99%

Functional annotation of noncoding variants and prioritization of cancer-associated lncRNAs in lung cancer

2016

Oncology Letters

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Abstract. Multiple computational tools have been widely applied to the detection of coding driver mutations in cancer; however, the prioritization of pathogenic non-coding variants remains a difficult and demanding task. The present study was performed to distinguish non-coding disease-causing mutations from neutral ones, and to prioritize potential cancer-associated long non-coding RNAs (lncRNAs) with a logistic regression model in lung cancer. A logistic regression model was constructed, combining 19,153 disease-associated ClinVar and Human Gene Mutation Database pathogenic variants as the response variable and non-coding features as the predictor variable. Validation of the model was conducted with genome-wide association study (GWAS) disease-or trait-associated single nucleotide polymorphisms (SNPs) and recurrent somatic mutations. High scoring regions were characterized with respect to their distribution in various features and gene classes; potential cancer-associated lncRNA candidates were prioritized, combining the fraction of high-scoring regions and average score predicted by the logistic regression model. H3K79me2 was the most negative factor that contributed to the model, while conserved regions were most positively informative to the model. The area under the receiver operating characteristic curve of the model was 0.89. The model assigned a significantly higher score to GWAS SNPs and recurrent somatic mutations compared with neutral SNPs (mean, 5.9012 vs. 5.5238; P<0.001, Mann-Whitney U test) and non-recurrent mutations (mean, 5.4677 vs. 5.2277, P<0.001, Mann-Whitney U test), respectively. It was observed that regions, including splicing sites and untranslated regions, and gene classes, including cancer genes and cancer-associated lncRNAs, had an increased enrichment of high-scoring regions. In total, 2,679 cancer-associated lncRNAs were determined and characterized. A total of 104 of these lncRNAs were differentially expressed between lung cancer and normal specimens. The logistic regression model is a useful and efficient scoring system to prioritize non-coding pathogenic variants and lncRNAs, and may provide the basis for detecting non-coding driver lncRNAs in lung cancer.

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“…Variants are scored by combining inter-and intra-species conservation, loss-and gain-of-function events for transcription-factor binding, enhancer-gene linkages and network centrality, and per-element recurrence across samples (13). Kircher et al (11) contrasted the annotations of fixed or nearly-fixed derived alleles in humans with those of simulated variants and developed combined annotation-dependent depletion (CADD).…”

Section: Introductionmentioning

confidence: 99%

“…Fu et al (13) reported a computational framework, FunSeq2, that combines an adjustable data context integrating large-scale genomics, such as 1000 Genomes and ENCODE data, and cancer resources with a weighted scoring system. Variants are scored by combining inter-and intra-species conservation, loss-and gain-of-function events for transcription-factor binding, enhancer-gene linkages and network centrality, and per-element recurrence across samples (13).…”

Section: Introductionmentioning

confidence: 99%

Prioritization of non-coding disease-causing variants and long non-coding RNAs in liver cancer

et al. 2016

Oncology Letters

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Abstract. There are multiple bioinformatics tools available for the detection of coding driver mutations in cancers. However, the prioritization of pathogenic non-coding variants remains a challenging and demanding task. The present study was performed to discriminate non-coding disease-causing mutations and prioritize potential cancer-implicated long non-coding RNAs (lncRNAs) in liver cancer using a logistic regression model. A logistic regression model was constructed by combining 19,153 disease-associated ClinVar and human gene mutation database pathogenic variants as the response variable and non-coding features as the predictor variable. Genome-wide association study (GWAS) disease or trait-associated variants and recurrent somatic mutations were used to validate the model. Non-coding gene features with the highest fractions of load were characterized and potential cancer-associated lncRNA candidates were prioritized by combining the fraction of high-scoring regions and average score predicted by the logistic regression model. H3K9me3 and conserved regions were the most negatively and positively informative for the model, respectively. The area under the receiver operating characteristic curve of the model was 0.92. The average score of GWAS disease-associated variants was significantly increased compared with neutral single nucleotide polymorphisms (5.8642 vs. 5.4707; P<0.001), the average score of recurrent somatic mutations of liver cancer was significantly increased compared with non-recurrent somatic mutations (5.4101 vs. 5.2768; P=0.0125). The present study found regions in lncRNAs and introns/untranslated regions of protein coding genes where mutations are most likely to be damaging. In total, 847 lncRNAs were filtered out from the background. Characterization of this subset of lncRNAs showed that these lncRNAs are more conservative, less mutated and more highly expressed compared with other control lncRNAs. In addition, 23 of these lncRNAs were differentially expressed between 12 pairs of liver cancer and adjacent normal specimens. The logistic regression model is a useful tool to prioritize non-coding pathogenic variants and lncRNAs, and paves the way for the detection of non-coding driver lncRNAs in liver cancer.

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FunSeq2: A framework for prioritizing noncoding regulatory variants in cancer

Cited by 157 publications

References 46 publications

GAVIN - Gene-Aware Variant INterpretation for medical sequencing

GAVIN - Gene-Aware Variant INterpretation for medical sequencing

Functional annotation of noncoding variants and prioritization of cancer-associated lncRNAs in lung cancer

Prioritization of non-coding disease-causing variants and long non-coding RNAs in liver cancer

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