Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies

Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization

Among an assortment of genetic variations, Missense are major ones which a small subset of them may led to the upset of the protein function and ultimately end in human diseases. Various machine learning methods were declared to differentiate deleterious and benign missense variants by means of a large number of features, including structure, sequence, interaction networks, gene disease associations as well as phenotypes. However, development of a reliable and accurate algorithm for merging heterogeneous information is highly needed as it could be captured all information of complex interactions on network that genes participate in. In this study we proposed a new method based on the non-negative matrix tri-factorization clustering method. We outlined two versions of the proposed method: two-source and three-source algorithms. Two-source algorithm aggregates individual deleteriousness prediction methods and PPI network, and three-source algorithm incorporates gene disease associations into the other sources already mentioned. Four benchmark datasets were employed for internally and externally validation of both algorithms of our predictor. The results at all datasets confirmed that, our method outperforms most state of the art variant prediction tools. Two key features of our variant effect prediction method are worth mentioning. Firstly, despite the fact that the incorporation of gene disease information at three-source algorithm can improve prediction performance by comparison with two-source algorithm, our method did not hinder by type 2 circularity error unlike some recent ensemble-based prediction methods. Type 2 circularity error occurs when the predictor annotates variants on the basis of the genes located on. Secondly, the performance of our predictor is superior over other ensemble-based methods for variants positioned on genes in which we do not have enough information about their pathogenicity.

From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data

In many cases, the unprecedented availability of data provided by high-throughput sequencing has shifted the bottleneck from a data availability issue to a data interpretation issue, thus delaying the promised breakthroughs in genetics and precision medicine, for what concerns Human genetics, and phenotype prediction to improve plant adaptation to climate change and resistance to bioagressors, for what concerns plant sciences. In this paper, we propose a novel Genome Interpretation paradigm, which aims at directly modeling the genotype-to-phenotype relationship, and we focus on A. thaliana since it is the best studied model organism in plant genetics. Our model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data. We show that 75 of these phenotypes are predicted with a Pearson correlation ≥0.4, and are mostly related to flowering traits. We show that our end-to-end NN approach achieves better performances and larger phenotype coverage than models predicting single phenotypes from the GWAS-derived known associated genes. Galiana is also fully interpretable, thanks to the Saliency Maps gradient-based approaches. We followed this interpretation approach to identify 36 novel genes that are likely to be associated with flowering traits, finding evidence for 6 of them in the existing literature.

Analysis of CYP2J2 mutations in the Chinese Uyghur population

Genetic characteristics of CYP2J2 in different populations may be helpful to explore interethnic variability in drug response and disease susceptibility. There is no information about the genetic profile of CYP2J2 in the Chinese Uyghur population. We used PCR and first-generation sequencing technology to investigate CYP2J2 mutations in 100 unrelated healthy Chinese Uyghurs. The chi-square test was used to compare genotyping data of CYP2J2 in the Chinese Uyghur population with other ethnic groups. The SIFT and PolyPhen-2 online tools were used to predict the protein function of the novel nonsynonymous mutations in CYP2J2. CADD software was used to predict pathogenicity of the mutations. We found twenty-eight mutations in CYP2J2, five new mutations, three alleles (*1, *7 and *8), and three genotypes (*1/*1, *1/*7 and *1/*8) in the Chinese Uyghur population. The allele frequencies of CYP2J2 *1, *7 and *8 were 96%, 3.45%, and 0.5%, respectively. Interethnic comparison found that subgenotype *1 in Uyghur was significantly higher than in Taiwanese and African Americans, and *7 frequency in Uyghur was slightly lower than that in Taiwanese and African Americans (P<0.05); *8 was only found in Chinese Uyghur and Korean populations, with frequencies of 0.5% and 0.8%, respectively. Furthermore, the protein prediction results revealed that the five nonsynonymous mutations could influence protein structure and function. The observations of this study give rise to useful information on CYP2J2 mutations in Chinese Uyghurs, which may support future important clinical implications for the use of medications metabolized by CYP2J2.

The Missing Expression Level-Evolutionary Rate Anticorrelation in Viruses Does Not Support Protein Function as a Main Constraint on Sequence Evolution

One of the central goals in molecular evolutionary biology is to determine the sources of variation in the rate of sequence evolution among proteins. Gene expression level is widely accepted as the primary determinant of protein evolutionary rate, because it scales with the extent of selective constraints imposed on a protein, leading to the well-known negative correlation between expression level and protein evolutionary rate (the E-R anticorrelation). Selective constraints have been hypothesized to entail the maintenance of protein function, the avoidance of cytotoxicity caused by protein misfolding or nonspecific protein-protein interactions, or both. However, empirical tests evaluating the relative importance of these hypotheses remain scarce, likely due to the nontrivial difficulties in distinguishing the effect of a deleterious mutation on a protein's function versus its cytotoxicity. We realized that examining the sequence evolution of viral proteins could overcome this hurdle. It is because purifying selection against mutations in a viral protein that result in cytotoxicity per se is likely relaxed, whereas purifying selection against mutations that impair viral protein function persists. Multiple analyses of SARS-CoV-2 and nine other virus species revealed a complete absence of any E-R anticorrelation. As a control, the E-R anticorrelation does exist in human endogenous retroviruses where purifying selection against cytotoxicity is present. Taken together, these observations do not support the maintenance of protein function as the main constraint on protein sequence evolution in cellular organisms.

Computational analysis of hereditary spastic paraplegia mutations in the kinesin motor domains of KIF1A and KIF5A

Hereditary spastic paraplegias (HSPs) are a genetically heterogeneous collection of neurodegenerative disorders categorized by progressive lower-limb spasticity and frailty. The complex HSP forms are characterized by various neurological features including progressive spastic weakness, urinary sphincter dysfunction, extra pyramidal signs and intellectual disability (ID). The kinesin superfamily proteins (KIFs) are microtubule-dependent molecular motors involved in intracellular transport. Kinesins directionally transport membrane vesicles, protein complexes, and mRNAs along neurites, thus playing important roles in neuronal development and function. Recent genetic studies have identified kinesin mutations in patients with HSPs. In this study, we used the computational approaches to investigate the 40 missense mutations associated with HSP and ID in KIF1A and KIF5A. We performed homology modeling to construct the structures of kinesin–microtubule binding domain and kinesin–tubulin complex. We applied structure-based energy calculation methods to determine the effects of missense mutations on protein stability and protein–protein interaction. The results revealed that the most of disease-causing mutations could change the folding free energy of kinesin motor domain and the binding free energy of kinesin–tubulin complex. We found that E253K associated with ID in KIF1A decrease the protein stability of kinesin motor domains. We showed that the HSP mutations located in kinesin–tubulin complex interface, such as K253N and R280C in KIF5A, can destabilize the kinesin–tubulin complex. The computational analysis provides useful information for understanding the roles of kinesin mutations in the development of ID and HSPs.

CHG: A Systematically Integrated Database of Cancer Hallmark Genes

Background

The analysis of cancer diversity based on a logical framework of hallmarks has greatly improved our understanding of the occurrence, development and metastasis of various cancers.

Methods

We designed Cancer Hallmark Genes (CHG) database which focuses on integrating hallmark genes in a systematic, standard way and annotates the potential roles of the hallmark genes in cancer processes. Following the conceptual criteria description of hallmark function the keywords for each hallmark were manually selected from the literature. Candidate hallmark genes collected were derived from 301 pathways of KEGG database by Lucene and manually corrected.

Results

Based on the variation data, we finally identified the hallmark genes of various types of cancer and constructed CHG. And we also analyzed the relationships among hallmarks and potential characteristics and relationships of hallmark genes based on the topological structures of their networks. We manually confirm the hallmark gene identified by CHG based on literature and database. We also predicted the prognosis of breast cancer, glioblastoma multiforme and kidney papillary cell carcinoma patients based on CHG data.

Conclusions

In summary, CHG, which was constructed based on a hallmark feature set, provides a new perspective for analyzing the diversity and development of cancers.

eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics

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.

Whole exome sequencing identifies novel candidate mutations in a Chinese family with left ventricular noncompaction

Left ventricular noncompaction (LVNC) is an inherited cardiomyopathy involving numerous genes. To identify novel candidate causal mutations, a whole exome sequencing study was performed on a Chinese LVNC family. Exons of the most prevalent pathogenic genes of LVNC (myosin heavy chain 7 and actin, α‑cardiac muscle 1) were sequenced, although no mutations were identified. Following this, Burrows‑Wheeler Aligner, PICARD and Genome Analysis Toolkit (v.2.8) were used to analyze the exome sequencing data. Non‑silent single nucleotide variants (SNVs) that were identified in patients with LVNC, although not in the healthy individual, were investigated further using SNV prioritization via the integration of genomic data (SPRING) based on P‑values. Co‑expressed gene enrichment analysis was performed using Genotype Tissue Expression (GTEx) data in order to investigate the potential roles of the genes containing SNVs in the myocardium. In the Chinese LVNC family, seven novel SNVs were identified that were only present in patients with LVNC and annotated by SPRING with P<0.05. Among these SNVs, hemicentin 1 [c. thymine (T) 9776 cytosine (C)], tolloid like 2 [c. cytosine (C) 2615 thymine (T)], fms related tyrosine kinase 3 [c. guanine (G) 976 adenine (A)] and nucleotide binding protein like [c. guanine (G) 91 thymine (T)] were located in conserved regions and annotated as deleterious by PolyPhen2, LRT and MutationTaster database analyses. Based on GTEx data, it was revealed that NUBPL was co‑expressed with almost all previously established LVNC pathogenic genes. Furthermore, the results of the present study demonstrated that genes co‑expressed with NUBPL were additionally enriched in the Notch signaling pathway. In addition, the results revealed numerous novel mutations that may be causal SNVs for the development of LVNC in the family involved in the present study.

Polymorphisms of drug-metabolizing enzyme CYP2E1 in Chinese Uygur population

Pharmacogenetics is the genetic basis of pharmacokinetics, genetic testing, and clinical management in diseases. Evaluation about genetic alterations of drug metabolizing enzymes in human genome contributes toward understanding the interindividual and interethnic variability for clinical response to potential toxicants. CYP2E1 gene encodes a drug-metabolizing enzyme that metabolizes mostly small, polar molecules, including toxic laboratory chemicals. The aim of this study was to investigate CYP2E1 polymorphisms and gene profile in a Chinese Uygur population. Frequencies for the CYP2E1 mutated alleles and genotypes were screened in 100 unrelated random healthy Uygur volunteers. PCR and direct sequencing revealed a total of 32 polymorphisms, of which 5 novel mutations were presented. Rs 943975 was the most common single nucleotide polymorphism (SNP). The allele frequencies of CYP2E11A, 4, 7A, and 7C were 65.5, 2, 19.5, and 13%, respectively. The most common genotype combinations were CYP2C191A/1A (43%) and 1A/7C (24%). Functional prediction for 2 nonsynonymous mutations G173S and V179I was performed using MutationTaster, sorting intolerant from tolerant, and PolyPhen-2. The observations of the present study give rise to useful information on CYP2E1 polymorphisms in Chinese Uygur individuals. The results suggest important clinical implications for the use of medications metabolized by CYP2E1 among Uygurs.

Pharmacogenetics: An Important Part of Drug Development with A Focus on Its Application

Since the human genome project in 2003, the view of personalized medicine to improve diagnosis and cure diseases at the molecular level became more real. Sequencing the human genome brought some benefits in medicine such as early detection of diseases with a genetic predisposition, treating patients with rare diseases, the design of gene therapy and the understanding of pharmacogenetics in the metabolism of drugs. This review explains the concepts of pharmacogenetics, polymorphisms, mutations, variations, and alleles, and how this information has helped us better understand the metabolism of drugs. Multiple resources are presented to promote reducing the gap between scientists, physicians, and patients in understanding the use and benefits of pharmacogenetics. Some of the most common clinical examples of genetic variants and how pharmacogenetics was used to determine treatment options for patients having these variants were discussed. Finally, we evaluated some of the challenges of implementing pharmacogenetics in a clinical setting and proposed actions to be taken to make pharmacogenetics a standard diagnostic tool in personalized medicine.

Mimvec: a deep learning approach for analyzing the human phenome

Background

The human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using the traditional term frequency-inverse document frequency (TF-IDF) formulation. This framework, though intuitive, not only ignores semantic relationships between words but also tends to produce high-dimensional vectors, and hence lacks the ability to precisely capture intrinsic semantic characteristics of biomedical documents. To overcome these limitations, we propose a framework called mimvec to analyze the human phenome by making use of the state-of-the-art deep learning technique in natural language processing.

Results

We converted 24,061 records in the Online Mendelian Inheritance in Man (OMIM) database to low-dimensional vectors using our method. We demonstrated that the vector presentation not only effectively enabled classification of phenotype records against gene ones, but also succeeded in discriminating diseases of different inheritance styles and different mechanisms. We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. With a joint analysis of this phenome with multiple genomic data, we showed that phenotype overlap indeed implied genotype overlap. We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones.

Conclusions

Our method is capable of not only capturing semantic relationships between words in biomedical records but also alleviating the dimensional disaster accompanying the traditional TF-IDF framework. With the approaching of precision medicine, there will be abundant electronic records of medicine and health awaiting for deep analysis, and we expect to see a wide spectrum of applications borrowing the idea of our method in the near future.

Variant Ranker: a web-tool to rank genomic data according to functional significance

BACKGROUND

The increasing volume and complexity of high-throughput genomic data make analysis and prioritization of variants difficult for researchers with limited bioinformatics skills. Variant Ranker allows researchers to rank identified variants and determine the most confident variants for experimental validation.

RESULTS

We describe Variant Ranker, a user-friendly simple web-based tool for ranking, filtering and annotation of coding and non-coding variants. Variant Ranker facilitates the identification of causal variants based on novelty, effect and annotation information. The algorithm implements and aggregates multiple prediction algorithm scores, conservation scores, allelic frequencies, clinical information and additional open-source annotations using accessible databases via ANNOVAR. The available information for a variant is transformed into user-specified weights, which are in turn encoded into the ranking algorithm. Through its different modules, users can (i) rank a list of variants (ii) perform genotype filtering for case-control samples (iii) filter large amounts of high-throughput data based on user custom filter requirements and apply different models of inheritance (iv) perform downstream functional enrichment analysis through network visualization. Using networks, users can identify clusters of genes that belong to multiple ontology categories (like pathways, gene ontology, disease categories) and therefore expedite scientific discoveries. We demonstrate the utility of Variant Ranker to identify causal genes using real and synthetic datasets. Our results indicate that Variant Ranker exhibits excellent performance by correctly identifying and ranking the candidate genes CONCLUSIONS: Variant Ranker is a freely available web server on http://paschou-lab.mbg.duth.gr/Software.html . This tool will enable users to prioritise potentially causal variants and is applicable to a wide range of sequencing data.

Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data

The emergence of exome sequencing in recent years has enabled rapid and cost-effective detection of genetic variants in coding regions and offers a great opportunity to combine sequencing experiments with subsequent computational analysis for dissecting genetic basis of human inherited diseases. However, this strategy, though successful in practice, still faces such challenges as limited sample size and substantial number or diversity of candidate variants. To overcome these obstacles, researchers have been concentrated in the development of advanced computational methods and have recently achieved great progress for analysing single nucleotide variant. Nevertheless, it still remains unclear on how to analyse indels, another type of genetic variant that accounts for substantial proportion of known disease-causing variants. In this paper, we proposed an integrative method to effectively identify disease-causing indels from exome sequencing data. Specifically, we put forward a statistical method to combine five functional prediction scores, four genic association scores and a genic intolerance score to produce an integrated p-value, which could then be used for prioritizing candidate indels. We performed extensive simulation studies and demonstrated that our method achieved high accuracy in uncovering disease-causing indels. Our software is available at http://bioinfo.au.tsinghua.edu.cn/jianglab/IndelPrioritizer/.

PERCH: A Unified Framework for Disease Gene Prioritization

To interpret genetic variants discovered from next-generation sequencing, integration of heterogeneous information is vital for success. This article describes a framework named PERCH (Polymorphism Evaluation, Ranking, and Classification for a Heritable trait), available at http://BJFengLab.org/. It can prioritize disease genes by quantitatively unifying a new deleteriousness measure called BayesDel, an improved assessment of the biological relevance of genes to the disease, a modified linkage analysis, a novel rare-variant association test, and a converted variant call quality score. It supports data that contain various combinations of extended pedigrees, trios, and case-controls, and allows for a reduced penetrance, an elevated phenocopy rate, liability classes, and covariates. BayesDel is more accurate than PolyPhen2, SIFT, FATHMM, LRT, Mutation Taster, Mutation Assessor, PhyloP, GERP++, SiPhy, CADD, MetaLR, and MetaSVM. The overall approach is faster and more powerful than the existing quantitative method pVAAST, as shown by the simulations of challenging situations in finding the missing heritability of a complex disease. This framework can also classify variants of unknown significance (variants of uncertain significance) by quantitatively integrating allele frequencies, deleteriousness, association, and co-segregation. PERCH is a versatile tool for gene prioritization in gene discovery research and variant classification in clinical genetic testing.

Integrative Variation Analysis Reveals that a Complex Genotype May Specify Phenotype in Siblings with Syndromic Autism Spectrum Disorder

It has been proposed that copy number variations (CNVs) are associated with increased risk of autism spectrum disorder (ASD) and, in conjunction with other genetic changes, contribute to the heterogeneity of ASD phenotypes. Array comparative genomic hybridization (aCGH) and exome sequencing, together with systems genetics and network analyses, are being used as tools for the study of complex disorders of unknown etiology, especially those characterized by significant genetic and phenotypic heterogeneity. Therefore, to characterize the complex genotype-phenotype relationship, we performed aCGH and sequenced the exomes of two affected siblings with ASD symptoms, dysmorphic features, and intellectual disability, searching for de novo CNVs, as well as for de novo and rare inherited point variations—single nucleotide variants (SNVs) or small insertions and deletions (indels)—with probable functional impacts. With aCGH, we identified, in both siblings, a duplication in the 4p16.3 region and a deletion at 8p23.3, inherited by a paternal balanced translocation, t(4, 8) (p16; p23). Exome variant analysis found a total of 316 variants, of which 102 were shared by both siblings, 128 were in the male sibling exome data, and 86 were in the female exome data. Our integrative network analysis showed that the siblings’ shared translocation could explain their similar syndromic phenotype, including overgrowth, macrocephaly, and intellectual disability. However, exome data aggregate genes to those already connected from their translocation, which are important to the robustness of the network and contribute to the understanding of the broader spectrum of psychiatric symptoms. This study shows the importance of using an integrative approach to explore genotype-phenotype variability.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

Global inference of disease-causing single nucleotide variants from exome sequencing data

Background

Whole exome sequencing (WES) has recently emerged as an effective approach for identifying genetic variants underlying human diseases. However, considerable time and labour is needed for careful investigation of candidate variants. Although filtration based on population frequencies and functional prediction scores could effectively remove common and neutral variants, hundreds or even thousands of rare deleterious variants still remain. In addition, current WES platforms also provide variant information in flanking noncoding regions, such as promoters, introns and splice sites. Despite of being recognized to harbour causal variants, these regions are usually ignored by current analysis pipelines.

Results

We present a novel computational method, called Glints, to overcome the above limitations. Glints is capable of identifying disease-causing SNVs in both coding and flanking noncoding regions from exome sequencing data. The principle behind Glints is that disease-causing variants should manifest their effect at both variant and gene levels. Specifically, Glints integrates 14 types of functional scores, including predictions for both coding and noncoding variants, and 9 types of association scores, which help identifying disease relevant genes. We conducted a large-scale simulation studies based on 1000 Genomes Project data and demonstrated the effectiveness of our method in both coding and flanking noncoding regions. We also applied Glints in two real exome sequencing and demonstrated its effectiveness for uncovering disease-causing SNVs. Both standalone software and web server are available at our website http://bioinfo.au.tsinghua.edu.cn/jianglab/glints.

Conclusions

Glints is effective for uncovering disease-causing SNVs in coding and flanking noncoding regions, which is supported by both simulation and real case studies. Glints is expected to be a useful tool for human genetics research based on exome sequencing data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1325-x) contains supplementary material, which is available to authorized users.

IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering

Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for individuals. We developed an application software called IGSA that leverages a powerful analytical capacity in gene sets enrichment and samples clustering. IGSA calculates gene sets expression scores for each sample and takes an accumulating clustering strategy to let the samples gather into the set according to the progress of disease from mild to severe. We focus on gastric, pancreatic and ovarian cancer data sets for the performance of IGSA. We also compared the results of IGSA in KEGG pathways enrichment with David, GSEA, SPIA, ssGSEA and analyzed the results of IGSA clustering and different similarity measurement methods. Notably, IGSA is proved to be more sensitive and specific in finding significant pathways, and can indicate related changes in pathways with the severity of disease. In addition, IGSA provides with significant gene sets profile for each sample.

KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily

Background

The association between aberrant signal processing by protein kinases and human diseases such as cancer was established long time ago. However, understanding the link between sequence variants in the protein kinase superfamily and the mechanistic complex traits at the molecular level remains challenging: cells tolerate most genomic alterations and only a minor fraction disrupt molecular function sufficiently and drive disease.

Results

KinMutRF is a novel random-forest method to automatically identify pathogenic variants in human kinases. Twenty six decision trees implemented as a random forest ponder a battery of features that characterize the variants: a) at the gene level, including membership to a Kinbase group and Gene Ontology terms; b) at the PFAM domain level; and c) at the residue level, the types of amino acids involved, changes in biochemical properties, functional annotations from UniProt, Phospho.ELM and FireDB. KinMutRF identifies disease-associated variants satisfactorily (Acc: 0.88, Prec:0.82, Rec:0.75, F-score:0.78, MCC:0.68) when trained and cross-validated with the 3689 human kinase variants from UniProt that have been annotated as neutral or pathogenic. All unclassified variants were excluded from the training set. Furthermore, KinMutRF is discussed with respect to two independent kinase-specific sets of mutations no included in the training and testing, Kin-Driver (643 variants) and Pon-BTK (1495 variants). Moreover, we provide predictions for the 848 protein kinase variants in UniProt that remained unclassified.

A public implementation of KinMutRF, including documentation and examples, is available online (http://kinmut2.bioinfo.cnio.es). The source code for local installation is released under a GPL version 3 license, and can be downloaded from https://github.com/Rbbt-Workflows/KinMut2.

Conclusions

KinMutRF is capable of classifying kinase variation with good performance. Predictions by KinMutRF compare favorably in a benchmark with other state-of-the-art methods (i.e. SIFT, Polyphen-2, MutationAssesor, MutationTaster, LRT, CADD, FATHMM, and VEST). Kinase-specific features rank as the most elucidatory in terms of information gain and are likely the improvement in prediction performance. This advocates for the development of family-specific classifiers able to exploit the discriminatory power of features unique to individual protein families.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2723-1) contains supplementary material, which is available to authorized users.

dbWGFP: a database and web server of human whole-genome single nucleotide variants and their functional predictions

The recent advancement of the next generation sequencing technology has enabled the fast and low-cost detection of all genetic variants spreading across the entire human genome, making the application of whole-genome sequencing a tendency in the study of disease-causing genetic variants. Nevertheless, there still lacks a repository that collects predictions of functionally damaging effects of human genetic variants, though it has been well recognized that such predictions play a central role in the analysis of whole-genome sequencing data. To fill this gap, we developed a database named dbWGFP (a database and web server of human whole-genome single nucleotide variants and their functional predictions) that contains functional predictions and annotations of nearly 8.58 billion possible human whole-genome single nucleotide variants. Specifically, this database integrates 48 functional predictions calculated by 17 popular computational methods and 44 valuable annotations obtained from various data sources. Standalone software, user-friendly query services and free downloads of this database are available at http://bioinfo.au.tsinghua.edu.cn/dbwgfp. dbWGFP provides a valuable resource for the analysis of whole-genome sequencing, exome sequencing and SNP array data, thereby complementing existing data sources and computational resources in deciphering genetic bases of human inherited diseases.

An Atlas of the Human Kinome Reveals the Mutational Landscape Underlying Dysregulated Phosphorylation Cascades in Cancer

Kinase inhibitors are used widely to treat various cancers, but adaptive reprogramming of kinase cascades and activation of feedback loop mechanisms often contribute to therapeutic resistance. Determining comprehensive, accurate maps of kinase circuits may therefore help elucidate mechanisms of response and resistance to kinase inhibitor therapies. In this study, we identified and validated phosphorylatable target sites across human cell and tissue types to generate PhosphoAtlas, a map of 1,733 functionally interconnected proteins comprising the human phospho-reactome. A systematic curation approach was used to distill protein phosphorylation data cross-referenced from 38 public resources. We demonstrated how a catalog of 2,617 stringently verified heptameric peptide regions at the catalytic interface of kinases and substrates could expose mutations that recurrently perturb specific phospho-hubs. In silico mapping of 2,896 nonsynonymous tumor variants identified from thousands of tumor tissues also revealed that normal and aberrant catalytic interactions co-occur frequently, showing how tumors systematically hijack, as well as spare, particular subnetworks. Overall, our work provides an important new resource for interrogating the human tumor kinome to strategically identify therapeutically actionable kinase networks that drive tumorigenesis. Cancer Res; 76(7); 1733-45. ©2016 AACR.

Exonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools

The identification of a causal mutation is essential for molecular diagnosis and clinical management of many genetic disorders. However, even if next-generation exome sequencing has greatly improved the detection of nucleotide changes, the biological interpretation of most exonic variants remains challenging. Moreover, particular attention is typically given to protein-coding changes often neglecting the potential impact of exonic variants on RNA splicing. Here, we used the exon 10 of MLH1, a gene implicated in hereditary cancer, as a model system to assess the prevalence of RNA splicing mutations among all single-nucleotide variants identified in a given exon. We performed comprehensive minigene assays and analyzed patient's RNA when available. Our study revealed a staggering number of splicing mutations in MLH1 exon 10 (77% of the 22 analyzed variants), including mutations directly affecting splice sites and, particularly, mutations altering potential splicing regulatory elements (ESRs). We then used this thoroughly characterized dataset, together with experimental data derived from previous studies on BRCA1, BRCA2, CFTR and NF1, to evaluate the predictive power of 3 in silico approaches recently described as promising tools for pinpointing ESR-mutations. Our results indicate that ΔtESRseq and ΔHZEI-based approaches not only discriminate which variants affect splicing, but also predict the direction and severity of the induced splicing defects. In contrast, the ΔΨ-based approach did not show a compelling predictive power. Our data indicates that exonic splicing mutations are more prevalent than currently appreciated and that they can now be predicted by using bioinformatics methods. These findings have implications for all genetically-caused diseases.

Genomics of Immune Diseases and New Therapies

Genomic DNA sequencing technologies have been one of the great advances of the 21st century, having decreased in cost by seven orders of magnitude and opening up new fields of investigation throughout research and clinical medicine. Genomics coupled with biochemical investigation has allowed the molecular definition of a growing number of new genetic diseases that reveal new concepts of immune regulation. Also, defining the genetic pathogenesis of these diseases has led to improved diagnosis, prognosis, genetic counseling, and, most importantly, new therapies. We highlight the investigational journey from patient phenotype to treatment using the newly defined XMEN disease, caused by the genetic loss of the MAGT1 magnesium transporter, as an example. This disease illustrates how genomics yields new fundamental immunoregulatory insights as well as how research genomics is integrated into clinical immunology. At the end, we discuss two other recently described diseases, CHAI/LATAIE (CTLA-4 deficiency) and PASLI (PI3K dysregulation), as additional examples of the journey from unknown immunological diseases to new precision medicine treatments using genomics.

Prioritization Of Nonsynonymous Single Nucleotide Variants For Exome Sequencing Studies Via Integrative Learning On Multiple Genomic Data

The rapid advancement of next generation sequencing technology has greatly accelerated the progress for understanding human inherited diseases via such innovations as exome sequencing. Nevertheless, the identification of causative variants from sequencing data remains a great challenge. Traditional statistical genetics approaches such as linkage analysis and association studies have limited power in analyzing exome sequencing data, while relying on simply filtration strategies and predicted functional implications of mutations to pinpoint pathogenic variants are prone to produce false positives. To overcome these limitations, we herein propose a supervised learning approach, termed snvForest, to prioritize candidate nonsynonymous single nucleotide variants for a specific type of disease by integrating 11 functional scores at the variant level and 8 association scores at the gene level. We conduct a series of large-scale in silico validation experiments, demonstrating the effectiveness of snvForest across 2,511 diseases of different inheritance styles and the superiority of our approach over two state-of-the-art methods. We further apply snvForest to three real exome sequencing data sets of epileptic encephalophathies and intellectual disability to show the ability of our approach to identify causative de novo mutations for these complex diseases. The online service and standalone software of snvForest are found at http://bioinfo.au.tsinghua.edu.cn/jianglab/snvforest.

An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions

SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.

Determinants of the rate of protein sequence evolution

The rate and mechanism of protein sequence evolution have been central questions in evolutionary biology since the 1960s. Although the rate of protein sequence evolution depends primarily on the level of functional constraint, exactly what determines functional constraint has remained unclear. The increasing availability of genomic data has enabled much needed empirical examinations on the nature of functional constraint. These studies found that the evolutionary rate of a protein is predominantly influenced by its expression level rather than functional importance. A combination of theoretical and empirical analyses has identified multiple mechanisms behind these observations and demonstrated a prominent role in protein evolution of selection against errors in molecular and cellular processes.

The SickKids Genome Clinic: developing and evaluating a pediatric model for individualized genomic medicine

Our increasing knowledge of how genomic variants affect human health and the falling costs of whole-genome sequencing are driving the development of individualized genomic medicine. This new clinical paradigm uses knowledge of an individual's genomic variants to anticipate, diagnose and manage disease. While individualized genetic medicine offers the promise of transformative change in health care, it forces us to reconsider existing ethical, scientific and clinical paradigms. The potential benefits of pre-symptomatic identification of at-risk individuals, improved diagnostics, individualized therapy, accurate prognosis and avoidance of adverse drug reactions coexist with the potential risks of uninterpretable results, psychological harm, outmoded counseling models and increased health care costs. Here we review the challenges, opportunities and limits of integrating genomic analysis into pediatric clinical practice and describe a model for implementing individualized genomic medicine. Our multidisciplinary team of bioinformaticians, health economists, health services and policy researchers, ethicists, geneticists, genetic counselors and clinicians has designed a 'Genome Clinic' research project that addresses multiple challenges in pediatric genomic medicine--ranging from development of bioinformatics tools for the clinical assessment of genomic variants and the discovery of disease genes to health policy inquiries, assessment of clinical care models, patient preference and the ethics of consent.

An integrative computational approach for prioritization of genomic variants

An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.

Current trend of annotating single nucleotide variation in humans--A case study on SNVrap

As high throughput methods, such as whole genome genotyping arrays, whole exome sequencing (WES) and whole genome sequencing (WGS), have detected huge amounts of genetic variants associated with human diseases, function annotation of these variants is an indispensable step in understanding disease etiology. Large-scale functional genomics projects, such as The ENCODE Project and Roadmap Epigenomics Project, provide genome-wide profiling of functional elements across different human cell types and tissues. With the urgent demands for identification of disease-causal variants, comprehensive and easy-to-use annotation tool is highly in demand. Here we review and discuss current progress and trend of the variant annotation field. Furthermore, we introduce a comprehensive web portal for annotating human genetic variants. We use gene-based features and the latest functional genomics datasets to annotate single nucleotide variation (SNVs) in human, at whole genome scale. We further apply several function prediction algorithms to annotate SNVs that might affect different biological processes, including transcriptional gene regulation, alternative splicing, post-transcriptional regulation, translation and post-translational modifications. The SNVrap web portal is freely available at http://jjwanglab.org/snvrap.

Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing

The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, pre-processing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.

Human germline and pan-cancer variomes and their distinct functional profiles

Identification of non-synonymous single nucleotide variations (nsSNVs) has exponentially increased due to advances in Next-Generation Sequencing technologies. The functional impacts of these variations have been difficult to ascertain because the corresponding knowledge about sequence functional sites is quite fragmented. It is clear that mapping of variations to sequence functional features can help us better understand the pathophysiological role of variations. In this study, we investigated the effect of nsSNVs on more than 17 common types of post-translational modification (PTM) sites, active sites and binding sites. Out of 1 705 285 distinct nsSNVs on 259 216 functional sites we identified 38 549 variations that significantly affect 10 major functional sites. Furthermore, we found distinct patterns of site disruptions due to germline and somatic nsSNVs. Pan-cancer analysis across 12 different cancer types led to the identification of 51 genes with 106 nsSNV affected functional sites found in 3 or more cancer types. 13 of the 51 genes overlap with previously identified Significantly Mutated Genes (Nature. 2013 Oct 17;502(7471)). 62 mutations in these 13 genes affecting functional sites such as DNA, ATP binding and various PTM sites occur across several cancers and can be prioritized for additional validation and investigations.

Correlating information contents of gene ontology terms to infer semantic similarity of gene products

Successful applications of the gene ontology to the inference of functional relationships between gene products in recent years have raised the need for computational methods to automatically calculate semantic similarity between gene products based on semantic similarity of gene ontology terms. Nevertheless, existing methods, though having been widely used in a variety of applications, may significantly overestimate semantic similarity between genes that are actually not functionally related, thereby yielding misleading results in applications. To overcome this limitation, we propose to represent a gene product as a vector that is composed of information contents of gene ontology terms annotated for the gene product, and we suggest calculating similarity between two gene products as the relatedness of their corresponding vectors using three measures: Pearson's correlation coefficient, cosine similarity, and the Jaccard index. We focus on the biological process domain of the gene ontology and annotations of yeast proteins to study the effectiveness of the proposed measures. Results show that semantic similarity scores calculated using the proposed measures are more consistent with known biological knowledge than those derived using a list of existing methods, suggesting the effectiveness of our method in characterizing functional relationships between gene products.