PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants

Nucleic Acids Research, Journal Year: 2017, Volume and Issue: 45(W1), P. W247 - W252

Published: April 24, 2017

One of the major challenges in human genetics is to identify functional effects coding and non-coding single nucleotide variants (SNVs). In past, several methods have been developed disease-related amino acid changes but only few tools are able score impact variants. Among most popular algorithms, CADD FATHMM predict effect SNVs regions combining sequence conservation with features derived from ENCODE project data. Thus, run or locally, installation process requires download a large set pre-calculated information. To facilitate variant annotation we develop PhD-SNPg, new easy-to-install lightweight machine learning method that depends on sequence-based features. Despite this, PhD-SNPg performs similarly better than more complex methods. This makes ideal for quick SNV interpretation, as benchmark tool development. Availability: accessible at http://snps.biofold.org/phd-snpg.

Language: Английский

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High-throughput identification of human SNPs affecting regulatory element activity

Nature Genetics, Journal Year: 2019, Volume and Issue: 51(7), P. 1160 - 1169

Published: June 28, 2019

Language: Английский

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Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

Pharmacogenomics, Journal Year: 2018, Volume and Issue: 19(7), P. 629 - 650

Published: April 26, 2018

This Perspective provides examples of current and future applications deep learning in pharmacogenomics, including: identification novel regulatory variants located noncoding domains the genome their function as applied to pharmacoepigenomics; patient stratification from medical records; mechanistic prediction drug response, targets interactions. Deep encapsulates a family machine algorithms that has transformed many important subfields artificial intelligence over last decade, demonstrated breakthrough performance improvements on wide range tasks biomedicine. We anticipate future, will be widely used predict personalized response optimize medication selection dosing, using knowledge extracted large complex molecular, epidemiological, clinical demographic datasets.

Language: Английский

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Predicting functional variants in enhancer and promoter elements using RegulomeDB

Human Mutation, Journal Year: 2019, Volume and Issue: 40(9), P. 1292 - 1298

Published: June 22, 2019

Here we present a computational model, Score of Unified Regulatory Features (SURF), that predicts functional variants in enhancer and promoter elements. SURF is trained on data from massively parallel reporter assays the effect expression levels. It achieved top performance Fifth Critical Assessment Genome Interpretation “Regulation Saturation” challenge. We also show features queried through RegulomeDB, which are direct annotations genomics data, help improve prediction accuracy beyond transfer learning DNA sequence-based deep models. Some most important include DNase footprints, especially when coupled with complementary ChIP-seq data. Furthermore, found our model good predicting allele-specific transcription factor binding events. As an extension to current scoring system expect prioritize regulatory regions, thus understanding noncoding regions lead disease.

Language: Английский

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Editing the genome of hiPSC with CRISPR/Cas9: disease models

Mammalian Genome, Journal Year: 2017, Volume and Issue: 28(7-8), P. 348 - 364

Published: March 16, 2017

The advent of human-induced pluripotent stem cell (hiPSC) technology has provided a unique opportunity to establish cellular models disease from individual patients, and study the effects underlying genetic aberrations upon multiple different types, many which would not normally be accessible. Combining this with recent advances in genome editing techniques such as clustered regularly interspaced short palindromic repeat (CRISPR) system an ability repair putative causative alleles patient lines, or introduce into healthy "WT" line. This enabled analysis isogenic pairs that differ single change, allows thorough assessment molecular phenotypes result abnormality. Importantly, establishes true lesion, is often impossible ascertain human studies alone. These lines can used only understand consequences mutations, but also perform high throughput pharmacological screens both pathological mechanisms develop novel therapeutic agents prevent treat diseases. In future, optimising developing manipulation technologies may facilitate provision gene therapies, intervene ultimately cure debilitating disorders.

Language: Английский

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Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine

Biomolecules, Journal Year: 2019, Volume and Issue: 10(1), P. 62 - 62

Published: Dec. 30, 2019

To clarify the mechanisms of diseases, such as cancer, studies analyzing genetic mutations have been actively conducted for a long time, and large number achievements already reported. Indeed, genomic medicine is considered core discipline precision medicine, currently, clinical application cutting-edge aimed at improving prevention, diagnosis treatment wide range diseases promoted. However, although Human Genome Project was completed in 2003 large-scale analyses since accomplished worldwide with development next-generation sequencing (NGS), explaining mechanism disease onset only using variation has recognized difficult. Meanwhile, importance epigenetics, which describes inheritance by other than DNA sequence, recently attracted attention, and, particular, many reported involvement epigenetic deregulation human cancer. So far, given that tend to be independently, physiological relationships between genetics epigenetics remain almost unknown. Since this situation may disadvantage developing integrated understanding appears now critical. Importantly, current progress artificial intelligence (AI) technologies, machine learning deep learning, remarkable enables multimodal big omics data. In regard, it important develop platform can conduct analysis medical data AI accelerate realization medicine. review, we discuss genome-wide multiomics era

Language: Английский

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Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome

The Journal of Clinical Endocrinology & Metabolism, Journal Year: 2019, Volume and Issue: 104(9), P. 3835 - 3850

Published: April 30, 2019

Polycystic ovary syndrome (PCOS) is among the most common endocrine disorders of premenopausal women, affecting 5% to15% this population depending on diagnostic criteria applied. It characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. PCOS highly heritable, but only a small proportion heritability can be accounted for genetic susceptibility variants identified to date.

Language: Английский

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Detecting DNA and RNA and Differentiating Single-Nucleotide Variations via Field-Effect Transistors

Nano Letters, Journal Year: 2020, Volume and Issue: 20(8), P. 5982 - 5990

Published: July 24, 2020

We detect short oligonucleotides and distinguish between sequences that differ by a single base, using label-free, electronic field-effect transistors (FETs). Our sensing platform utilizes ultrathin-film indium oxide FETs chemically functionalized with single-stranded DNA (ssDNA). The ssDNA-functionalized semiconducting channels in fully complementary differentiate these from those having different types locations of base-pair mismatches. Changes charge associated surface-bound ssDNA vs double-stranded (dsDNA) alter FET channel conductance to enable detection due differences duplex stability. illustrate the capability ssDNA-FETs RNA nucleotide variations. development implementation biosensors rapidly sensitively present new opportunities fields disease diagnostics precision medicine.

Language: Английский

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A transcription factor collective defines the HSN serotonergic neuron regulatory landscape

eLife, Journal Year: 2018, Volume and Issue: 7

Published: March 19, 2018

Cell differentiation is controlled by individual transcription factors (TFs) that together activate a selection of enhancers in specific cell types. How these combinations TFs identify and their target sequences remains poorly understood. Here, we the cis-regulatory transcriptional code controls serotonergic HSN neurons Caenorhabditis elegans. Activation transcriptome directly orchestrated collective six TFs. Binding site clusters for this TF form regulatory signature sufficient de novo identification neuron functional enhancers. Among C. elegans neurons, most closely resembles mouse neurons. Mouse orthologs also regulate can functionally substitute worm counterparts which suggests deep homology. Our results rules governing landscape critically important neuronal type two species separated over 700 million years.

Language: Английский

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Genome‐wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral

Molecular Ecology, Journal Year: 2020, Volume and Issue: 29(12), P. 2176 - 2188

Published: May 26, 2020

Marine heat waves are increasing in magnitude, duration, and frequency as a result of climate change the principal global driver mortality reef-building corals. Resilience-based genetic management may increase coral tolerance, but it is unclear how temperature responses regulated at genome level thus corals adapt to warming naturally or through selective breeding. Here we combine phenotypic, pedigree, genomic marker data from colonies sourced warm reef on Great Barrier Reef reproductively crossed with conspecific cooler produce combinations purebreds warm-cool hybrid larvae juveniles. Interpopulation breeding created significantly greater diversity across compared between populations maintained key regions associated tolerance fitness. High-density genome-wide scans single nucleotide polymorphisms (SNPs) identified alleles larval families reared 27.5°C (87-2,224 loci), including loci putatively proteins involved stress (cell membrane formation, metabolism, immune responses). Underlying genetics these explained 43% PCoA multilocus variation survival, growth, bleaching 31°C juvenile stage. Genetic contribution total fitness traits (narrow-sense heritability) was high for survival not growth juveniles, heritability being higher relative 27.5°C. While based only limited number crosses, mechanistic understanding presented here demonstrates that allele frequencies affected by one generation breeding, information assessments intervention feasibility modelling futures.

Language: Английский

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