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RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants.

ABSTRACT: Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

SUBMITTER: Lin H

PROVIDER: S-EPMC6883696 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants.

Lin Hai H Hargreaves Katherine A KA Li Rudong R Reiter Jill L JL Wang Yue Y Mort Matthew M Cooper David N DN Zhou Yaoqi Y Zhang Chi C Eadon Michael T MT Dolan M Eileen ME Ipe Joseph J Skaar Todd C TC Liu Yunlong Y

Genome biology 20191128 1

Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional ...[more]

PMID: 31779641

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RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Project description:Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure and evolutionary conservation features. RegSNPs-intron shows excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of regSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

2019-09-30 | GSE138130 | GEO

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Project description:RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

| PRJNA574846 | ENA

SNVHMM: predicting single nucleotide variants from next generation sequencing.

Project description:BackgroundThe rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Single nucleotide variants (SNVs) inferred from next generation sequencing are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNVs detection capability in the regulatory regions of the genome. Post probabilistic based methods are efficient for detection of SNVs in high coverage regions or sequencing data with high depth. However, for data with low sequencing depth, the efficiency of such algorithms remains poor and needs to be improved.ResultsA new tool SNVHMM basing on a discrete hidden Markov model (HMM) was developed to infer the genotype for each position on the genome. We incorporated the mapping quality of each read and the corresponding base quality on the reads into the emission probability of HMM. The context information of the whole observation as well as its confidence were completely utilized to infer the genotype for each position on the genome in study. Therefore, more probability power can be gained over the Bayes based methods, which is very useful for SNVs detection for data with low sequencing depth. Moreover, our model was verified by testing against two sets of lobular breast tumor and Myelodysplastic Syndromes (MDS) data each. Comparing against a recently published SNVs calling algorithm SNVMix2, our model improved the performance of SNVMix2 largely when the sequencing depth is low and also outperformed SNVMix2 when SNVMix2 is well trained by large datasets.ConclusionsSNVHMM can detect SNVs from NGS cancer data efficiently even if the sequence depth is very low. The training data size can be very small for SNVHMM to work. SNVHMM incorporated the base quality and mapping quality of all observed bases and reads, and also provides the option for users to choose the confidence of the observation for SNVs prediction.

| S-EPMC3718670 | biostudies-literature

RareVar: A Framework for Detecting Low-Frequency Single-Nucleotide Variants.

Project description:Accurate identification of low-frequency somatic point mutations in tumor samples has important clinical utilities. Although high-throughput sequencing technology enables capturing such variants while sequencing primary tumor samples, our ability for accurate detection is compromised when the variant frequency is close to the sequencer error rate. Most current experimental and bioinformatic strategies target mutations with ?5% allele frequency, which limits our ability to understand the cancer etiology and tumor evolution. We present an experimental and computational modeling framework, RareVar, to reliably identify low-frequency single-nucleotide variants from high-throughput sequencing data under standard experimental protocols. RareVar protocol includes a benchmark design by pooling DNAs from already sequenced individuals at various concentrations to target variants at desired frequencies, 0.5%-3% in our case. By applying a generalized, linear model-based, position-specific error model, followed by machine-learning-based variant calibration, our approach outperforms existing methods. Our method can be applied on most capture and sequencing platforms without modifying the experimental protocol.

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IntSplice2: Prediction of the Splicing Effects of Intronic Single-Nucleotide Variants Using LightGBM Modeling.

Project description:Prediction of the effect of a single-nucleotide variant (SNV) in an intronic region on aberrant pre-mRNA splicing is challenging except for an SNV affecting the canonical GU/AG splice sites (ss). To predict pathogenicity of SNVs at intronic positions -50 (Int-50) to -3 (Int-3) close to the 3' ss, we developed light gradient boosting machine (LightGBM)-based IntSplice2 models using pathogenic SNVs in the human gene mutation database (HGMD) and ClinVar and common SNVs in dbSNP with 0.01 ≤ minor allelic frequency (MAF) < 0.50. The LightGBM models were generated using features representing splicing cis-elements. The average recall/sensitivity and specificity of IntSplice2 by fivefold cross-validation (CV) of the training dataset were 0.764 and 0.884, respectively. The recall/sensitivity of IntSplice2 was lower than the average recall/sensitivity of 0.800 of IntSplice that we previously made with support vector machine (SVM) modeling for the same intronic positions. In contrast, the specificity of IntSplice2 was higher than the average specificity of 0.849 of IntSplice. For benchmarking (BM) of IntSplice2 with IntSplice, we made a test dataset that was not used to train IntSplice. After excluding the test dataset from the training dataset, we generated IntSplice2-BM and compared it with IntSplice using the test dataset. IntSplice2-BM was superior to IntSplice in all of the seven statistical measures of accuracy, precision, recall/sensitivity, specificity, F1 score, negative predictive value (NPV), and matthews correlation coefficient (MCC). We made the IntSplice2 web service at https://www.med.nagoya-u.ac.jp/neurogenetics/IntSplice2.

| S-EPMC8326971 | biostudies-literature

SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors.

Project description:MotivationNext-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer. NGS produces millions of short sequence reads that, once aligned to a reference genome sequence, can be interpreted for the presence of SNVs. Although tools exist for SNV discovery from NGS data, none are specifically suited to work with data from tumors, where altered ploidy and tumor cellularity impact the statistical expectations of SNV discovery.ResultsWe developed three implementations of a probabilistic Binomial mixture model, called SNVMix, designed to infer SNVs from NGS data from tumors to address this problem. The first models allelic counts as observations and infers SNVs and model parameters using an expectation maximization (EM) algorithm and is therefore capable of adjusting to deviation of allelic frequencies inherent in genomically unstable tumor genomes. The second models nucleotide and mapping qualities of the reads by probabilistically weighting the contribution of a read/nucleotide to the inference of a SNV based on the confidence we have in the base call and the read alignment. The third combines filtering out low-quality data in addition to probabilistic weighting of the qualities. We quantitatively evaluated these approaches on 16 ovarian cancer RNASeq datasets with matched genotyping arrays and a human breast cancer genome sequenced to >40x (haploid) coverage with ground truth data and show systematically that the SNVMix models outperform competing approaches.AvailabilitySoftware and data are available at http://compbio.bccrc.caContactsshah@bccrc.ca SUPPLEMANTARY INFORMATION: Supplementary data are available at Bioinformatics online.

| S-EPMC2832826 | biostudies-literature

A mechanism for a single nucleotide intron shift.

Project description:Spliceosomal introns can occupy nearby rather than identical positions in orthologous genes (intron sliding or shifting). Stwintrons are complex intervening sequences, where an 'internal' intron interrupts one of the sequences essential for splicing, generating after its excision, a newly formed canonical intron defined as 'external'. In one experimentally demonstrated configuration, two alternatively excised internal introns, overlapping by one G, disrupt respectively the donor and the acceptor sequence of an external intron, leading to mRNAs encoding identical proteins. In a gene encoding a DHA1 antiporter in Pezizomycotina, we find a variety of predicted intron configurations interrupting the DNA stretch encoding a conserved peptidic sequence. Some sport a stwintron where the internal intron interrupts the donor of the external intron (experimentally confirmed for Aspergillus nidulans). In others, we found and demonstrate (for Trichoderma reesei) alternative, overlapping internal introns. Discordant canonical introns, one nt apart, are present in yet other species, exactly as predicted by the alternative loss of either of the internal introns at the DNA level from an alternatively spliced stwintron. An evolutionary pathway of 1 nt intron shift, involving an alternatively spliced stwintron intermediate is proposed on the basis of the experimental and genomic data presented.

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Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies.

Project description:Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ~22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.

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Evaluating the impact of single nucleotide variants on transcription factor binding.

Project description:Diseases and phenotypes caused by disrupted transcription factor (TF) binding are being identified, but progress is hampered by our limited capacity to predict such functional alterations. Improving predictions may be dependent on expanding the set of bona fide TF binding alterations. Allele-specific binding (ASB) events, where TFs preferentially bind to one of the two alleles at heterozygous sites, reveal the impact of sequence variations in altered TF binding. Here, we present the largest ASB compilation to our knowledge, 10 765 ASB events retrieved from 45 ENCODE ChIP-Seq data sets. Our analysis showed that ASB events were frequently associated with motif alterations of the ChIP'ed TF and potential partner TFs, allelic difference of DNase I hypersensitivity and allelic difference of histone modifications. For TF dimers bound symmetrically to DNA, ASB data revealed that central positions of the TF binding motifs were disproportionately important for binding. Lastly, the impact of variation on TF binding was predicted by a classification model incorporating all the investigated features of ASB events. Classification models using only DNase I hypersensitivity and sequence data exhibited predictive accuracy approaching the models with substantially more features. Taken together, the combination of ASB data and the classification model represents an important step toward elucidating regulatory variants across the human genome.

| S-EPMC5137422 | biostudies-literature

A common intronic single nucleotide variant modifies PKD1 expression level.

Project description:Autosomal dominant polycystic kidney disease (ADPKD), caused by mutations in PKD1 and PKD2 (PKD1/2), has unexplained phenotypic variability likely affected by environmental and other genetic factors. Approximately 10% of individuals with ADPKD phenotype have no causal mutation detected, possibly due to unrecognized risk variants of PKD1/2. This study was designed to identify risk variants of PKD genes through population genetic analyses. We used Wright's F-statistics (Fst) to evaluate common single nucleotide variants (SNVs) potentially favored by positive natural selection in PKD1 from 1000 Genomes Project (1KG) and genotyped 388 subjects from the Rogosin Institute ADPKD Data Repository. The variants with >90th percentile Fst scores underwent further investigation by in silico analysis and molecular genetics analyses. We identified a deep intronic SNV, rs3874648G> A, located in a conserved binding site of the splicing regulator Tra2-β in PKD1 intron 30. Reverse-transcription PCR (RT-PCR) of peripheral blood leukocytes (PBL) from an ADPKD patient homozygous for rs3874648-A identified an atypical PKD1 splice form. Functional analyses demonstrated that rs3874648-A allele increased Tra2-β binding affinity and activated a cryptic acceptor splice-site, causing a frameshift that introduced a premature stop codon in mRNA, thereby decreasing PKD1 full-length transcript level. PKD1 transcript levels were lower in PBL from rs3874648-G/A carriers than in rs3874648-G/G homozygotes in a small cohort of normal individuals and patients with PKD2 inactivating mutations. Our findings indicate that rs3874648G > A is a PKD1 expression modifier attenuating PKD1 expression through Tra2-β, while the derived G allele advantageously maintains PKD1 expression and is predominant in all subpopulations.

| S-EPMC10947153 | biostudies-literature

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