Next generation tools for the annotation of human SNPs

Interpretation of the consequences of mutations in protein kinases: combined use of bioinformatics and text mining

Protein kinases play a crucial role in a plethora of significant physiological functions and a number of mutations in this superfamily have been reported in the literature to disrupt protein structure and/or function. Computational and experimental research aims to discover the mechanistic connection between mutations in protein kinases and disease with the final aim of predicting the consequences of mutations on protein function and the subsequent phenotypic alterations. In this article, we will review the possibilities and limitations of current computational methods for the prediction of the pathogenicity of mutations in the protein kinase superfamily. In particular we will focus on the problem of benchmarking the predictions with independent gold standard datasets. We will propose a pipeline for the curation of mutations automatically extracted from the literature. Since many of these mutations are not included in the databases that are commonly used to train the computational methods to predict the pathogenicity of protein kinase mutations we propose them to build a valuable gold standard dataset in the benchmarking of a number of these predictors. Finally, we will discuss how text mining approaches constitute a powerful tool for the interpretation of the consequences of mutations in the context of disease genome analysis with particular focus on cancer.

Prioritization of pathogenic mutations in the protein kinase superfamily

BACKGROUND

Most of the many mutations described in human protein kinases are tolerated without significant disruption of the corresponding structures or molecular functions, while some of them have been associated to a variety of human diseases, including cancer. In the last decade, a plethora of computational methods to predict the effect of missense single-nucleotide variants (SNVs) have been developed. Still, current high-throughput sequencing efforts and the concomitant need for massive interpretation of protein sequence variants will demand for more efficient and/or accurate computational methods in the forthcoming years.

RESULTS

We present KinMut, a support vector machine (SVM) approach, to identify pathogenic mutations in the protein kinase superfamily. KinMut relays on a combination of sequence-derived features that describe mutations at different levels: (1) Gene level: membership to a specific group in Kinbase and the annotation with GO terms; (2) Domain level: annotated PFAM domains; and (3) Residue level: physicochemical features of amino acids, specificity determining positions, and functional annotations from SwissProt and FireDB. The system has been trained with the set of 3492 human kinase mutations in UniProt for which experimental validation of their pathogenic or neutral character exists. In addition, we discuss the relative importance of these independent properties and their combination for the development of a kinase-specific predictor. Finally, we compare KinMut with other state-of-the-art prediction methods.

CONCLUSIONS

Family-specific features appear among the most discriminative information sources, which allow us to produce accurate results in a reliable and very simple way with minimal supervision. Our study aims to broaden the knowledge on the mechanisms by which mutations in the human kinome contribute to disease with a particular focus in cancer. The classifier as well as further documentation is available at http://kinmut.bioinfo.cnio.es/.

VARIANT: Command Line, Web service and Web interface for fast and accurate functional characterization of variants found by Next-Generation Sequencing

The massive use of Next-Generation Sequencing (NGS) technologies is uncovering an unexpected amount of variability. The functional characterization of such variability, particularly in the most common form of variation found, the Single Nucleotide Variants (SNVs), has become a priority that needs to be addressed in a systematic way. VARIANT (VARIant ANalyis Tool) reports information on the variants found that include consequence type and annotations taken from different databases and repositories (SNPs and variants from dbSNP and 1000 genomes, and disease-related variants from the Genome-Wide Association Study (GWAS) catalog, Online Mendelian Inheritance in Man (OMIM), Catalog of Somatic Mutations in Cancer (COSMIC) mutations, etc). VARIANT also produces a rich variety of annotations that include information on the regulatory (transcription factor or miRNA-binding sites, etc.) or structural roles, or on the selective pressures on the sites affected by the variation. This information allows extending the conventional reports beyond the coding regions and expands the knowledge on the contribution of non-coding or synonymous variants to the phenotype studied. Contrarily to other tools, VARIANT uses a remote database and operates through efficient RESTful Web Services that optimize search and transaction operations. In this way, local problems of installation, update or disk size limitations are overcome without the need of sacrifice speed (thousands of variants are processed per minute). VARIANT is available at: http://variant.bioinfo.cipf.es.

Human genomic disease variants: a neutral evolutionary explanation

Many perspectives on the role of evolution in human health include nonempirical assumptions concerning the adaptive evolutionary origins of human diseases. Evolutionary analyses of the increasing wealth of clinical and population genomic data have begun to challenge these presumptions. In order to systematically evaluate such claims, the time has come to build a common framework for an empirical and intellectual unification of evolution and modern medicine. We review the emerging evidence and provide a supporting conceptual framework that establishes the classical neutral theory of molecular evolution (NTME) as the basis for evaluating disease- associated genomic variations in health and medicine. For over a decade, the NTME has already explained the origins and distribution of variants implicated in diseases and has illuminated the power of evolutionary thinking in genomic medicine. We suggest that a majority of disease variants in modern populations will have neutral evolutionary origins (previously neutral), with a relatively smaller fraction exhibiting adaptive evolutionary origins (previously adaptive). This pattern is expected to hold true for common as well as rare disease variants. Ultimately, a neutral evolutionary perspective will provide medicine with an informative and actionable framework that enables objective clinical assessment beyond convenient tendencies to invoke past adaptive events in human history as a root cause of human disease.

Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis

Cystic fibrosis transmembrane conductance regulator (CFTR) mutation is associated with a phenotypic spectrum that includes cystic fibrosis (CF). The disease liability of some common CFTR mutations is known, but rare mutations are seen in too few patients to categorize unequivocally, making genetic diagnosis difficult. Computational methods can predict the impact of mutation, but prediction specificity is often below that required for clinical utility. Here, we present a novel supervised learning approach for predicting CF from CFTR missense mutation. The algorithm begins by constructing custom multiple sequence alignments called phenotype-optimized sequence ensembles (POSEs). POSEs are constructed iteratively, by selecting sequences that optimize predictive performance on a training set of CFTR mutations of known clinical significance. Next, we predict CF disease liability from a different set of CFTR mutations (test-set mutations). This approach achieves improved prediction performance relative to popular methods recently assessed using the same test-set mutations. Of clinical significance, our method achieves 94% prediction specificity. Because databases such as HGMD and locus-specific mutation databases are growing rapidly, methods that automatically tailor their predictions for a specific phenotype may be of immediate utility. If the performance achieved here generalizes to other systems, the approach could be an excellent tool to help establish genetic diagnoses.

MSV3d: database of human MisSense Variants mapped to 3D protein structure

The elucidation of the complex relationships linking genotypic and phenotypic variations to protein structure is a major challenge in the post-genomic era. We present MSV3d (Database of human MisSense Variants mapped to 3D protein structure), a new database that contains detailed annotation of missense variants of all human proteins (20 199 proteins). The multi-level characterization includes details of the physico-chemical changes induced by amino acid modification, as well as information related to the conservation of the mutated residue and its position relative to functional features in the available or predicted 3D model. Major releases of the database are automatically generated and updated regularly in line with the dbSNP (database of Single Nucleotide Polymorphism) and SwissVar releases, by exploiting the extensive Décrypthon computational grid resources. The database (http://decrypthon.igbmc.fr/msv3d) is easily accessible through a simple web interface coupled to a powerful query engine and a standard web service. The content is completely or partially downloadable in XML or flat file formats.

Database URL:http://decrypthon.igbmc.fr/msv3d

Deleterious GRM1 mutations in schizophrenia

We analysed a phenotypically well-characterised sample of 450 schziophrenia patients and 605 controls for rare non-synonymous single nucleotide polymorphisms (nsSNPs) in the GRM1 gene, their functional effects and family segregation. GRM1 encodes the metabotropic glutamate receptor 1 (mGluR1), whose documented role as a modulator of neuronal signalling and synaptic plasticity makes it a plausible schizophrenia candidate. In a recent study, this gene was shown to harbour a cluster of deleterious nsSNPs within a functionally important domain of the receptor, in patients with schizophrenia and bipolar disorder. Our Sanger sequencing of the GRM1 coding regions detected equal numbers of nsSNPs in cases and controls, however the two groups differed in terms of the potential effects of the variants on receptor function: 6/6 case-specific and only 1/6 control-specific nsSNPs were predicted to be deleterious. Our in-vitro experimental follow-up of the case-specific mutants showed that 4/6 led to significantly reduced inositol phosphate production, indicating impaired function of the major mGluR1 signalling pathway; 1/6 had reduced cell membrane expression; inconclusive results were obtained in 1/6. Family segregation analysis indicated that these deleterious nsSNPs were inherited. Interestingly, four of the families were affected by multiple neuropsychiatric conditions, not limited to schizophrenia, and the mutations were detected in relatives with schizophrenia, depression and anxiety, drug and alcohol dependence, and epilepsy. Our findings suggest a possible mGluR1 contribution to diverse psychiatric conditions, supporting the modulatory role of the receptor in such conditions as proposed previously on the basis of in vitro experiments and animal studies.

AnnTools: a comprehensive and versatile annotation toolkit for genomic variants

AnnTools is a versatile bioinformatics application designed for comprehensive annotation of a full spectrum of human genome variation: novel and known single-nucleotide substitutions (SNP/SNV), short insertions/deletions (INDEL) and structural variants/copy number variation (SV/CNV). The variants are interpreted by interrogating data compiled from 15 constantly updated sources. In addition to detailed functional characterization of the coding variants, AnnTools searches for overlaps with regulatory elements, disease/trait associated loci, known segmental duplications and artifact prone regions, thereby offering an integrated and comprehensive analysis of genomic data. The tool conveniently accepts user-provided tracks for custom annotation and offers flexibility in input data formats. The output is generated in the universal Variant Call Format. High annotation speed makes AnnTools suitable for high-throughput sequencing facilities, while a low-memory footprint and modest CPU requirements allow it to operate on a personal computer. The application is freely available for public use; the package includes installation scripts and a set of helper tools.

AVAILABILITY

http://anntools.sourceforge.net/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bioinformatics for personal genome interpretation

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.

dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions

With the advance of sequencing technologies, whole exome sequencing has increasingly been used to identify mutations that cause human diseases, especially rare Mendelian diseases. Among the analysis steps, functional prediction (of being deleterious) plays an important role in filtering or prioritizing nonsynonymous SNP (NS) for further analysis. Unfortunately, different prediction algorithms use different information and each has its own strength and weakness. It has been suggested that investigators should use predictions from multiple algorithms instead of relying on a single one. However, querying predictions from different databases/Web-servers for different algorithms is both tedious and time consuming, especially when dealing with a huge number of NSs identified by exome sequencing. To facilitate the process, we developed dbNSFP (database for nonsynonymous SNPs' functional predictions). It compiles prediction scores from four new and popular algorithms (SIFT, Polyphen2, LRT, and MutationTaster), along with a conservation score (PhyloP) and other related information, for every potential NS in the human genome (a total of 75,931,005). It is the first integrated database of functional predictions from multiple algorithms for the comprehensive collection of human NSs. dbNSFP is freely available for download at http://sites.google.com/site/jpopgen/dbNSFP.

Next-generation sequencing for cancer diagnostics: a practical perspective

Next-generation sequencing (NGS) is arguably one of the most significant technological advances in the biological sciences of the last 30 years. The second generation sequencing platforms have advanced rapidly to the point that several genomes can now be sequenced simultaneously in a single instrument run in under two weeks. Targeted DNA enrichment methods allow even higher genome throughput at a reduced cost per sample. Medical research has embraced the technology and the cancer field is at the forefront of these efforts given the genetic aspects of the disease. World-wide efforts to catalogue mutations in multiple cancer types are underway and this is likely to lead to new discoveries that will be translated to new diagnostic, prognostic and therapeutic targets. NGS is now maturing to the point where it is being considered by many laboratories for routine diagnostic use. The sensitivity, speed and reduced cost per sample make it a highly attractive platform compared to other sequencing modalities. Moreover, as we identify more genetic determinants of cancer there is a greater need to adopt multi-gene assays that can quickly and reliably sequence complete genes from individual patient samples. Whilst widespread and routine use of whole genome sequencing is likely to be a few years away, there are immediate opportunities to implement NGS for clinical use. Here we review the technology, methods and applications that can be immediately considered and some of the challenges that lie ahead.

A new methodology to associate SNPs with human diseases according to their pathway related context

Genome-wide association studies (GWAS) with hundreds of żthousands of single nucleotide polymorphisms (SNPs) are popular strategies to reveal the genetic basis of human complex diseases. Despite many successes of GWAS, it is well recognized that new analytical approaches have to be integrated to achieve their full potential. Starting with a list of SNPs, found to be associated with disease in GWAS, here we propose a novel methodology to devise functionally important KEGG pathways through the identification of genes within these pathways, where these genes are obtained from SNP analysis. Our methodology is based on functionalization of important SNPs to identify effected genes and disease related pathways. We have tested our methodology on WTCCC Rheumatoid Arthritis (RA) dataset and identified: i) previously known RA related KEGG pathways (e.g., Toll-like receptor signaling, Jak-STAT signaling, Antigen processing, Leukocyte transendothelial migration and MAPK signaling pathways); ii) additional KEGG pathways (e.g., Pathways in cancer, Neurotrophin signaling, Chemokine signaling pathways) as associated with RA. Furthermore, these newly found pathways included genes which are targets of RA-specific drugs. Even though GWAS analysis identifies 14 out of 83 of those drug target genes; newly found functionally important KEGG pathways led to the discovery of 25 out of 83 genes, known to be used as drug targets for the treatment of RA. Among the previously known pathways, we identified additional genes associated with RA (e.g. Antigen processing and presentation, Tight junction). Importantly, within these pathways, the associations between some of these additionally found genes, such as HLA-C, HLA-G, PRKCQ, PRKCZ, TAP1, TAP2 and RA were verified by either OMIM database or by literature retrieved from the NCBI PubMed module. With the whole-genome sequencing on the horizon, we show that the full potential of GWAS can be achieved by integrating pathway and network-oriented analysis and prior knowledge from functional properties of a SNP.

Identifying functional single nucleotide polymorphisms in the human CArGome

Regulatory SNPs (rSNPs) reside primarily within the nonprotein coding genome and are thought to disturb normal patterns of gene expression by altering DNA binding of transcription factors. Nevertheless, despite the explosive rise in SNP association studies, there is little information as to the function of rSNPs in human disease. Serum response factor (SRF) is a widely expressed DNA-binding transcription factor that has variable affinity to at least 1,216 permutations of a 10 bp transcription factor binding site (TFBS) known as the CArG box. We developed a robust in silico bioinformatics screening method to evaluate sequences around RefSeq genes for conserved CArG boxes. Utilizing a predetermined phastCons threshold score, we identified 8,252 strand-specific CArGs within an 8 kb window around the transcription start site of 5,213 genes, including all previously defined SRF target genes. We then interrogated this CArG dataset for the presence of previously annotated common polymorphisms. We found a total of 118 unique CArG boxes harboring a SNP within the 10 bp CArG sequence and 1,130 CArG boxes with SNPs located just outside the CArG element. Gel shift and luciferase reporter assays validated SRF binding and functional activity of several new CArG boxes. Importantly, SNPs within or just outside the CArG box often resulted in altered SRF binding and activity. Collectively, these findings demonstrate a powerful approach to computationally define rSNPs in the human CArGome and provide a foundation for similar analyses of other TFBS. Such information may find utility in genetic association studies of human disease where little insight is known regarding the functionality of rSNPs.

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.

Genome-wide prediction of splice-modifying SNPs in human genes using a new analysis pipeline called AASsites

Background

Some single nucleotide polymorphisms (SNPs) are known to modify the risk of developing certain diseases or the reaction to drugs. Due to next generation sequencing methods the number of known human SNPs has grown. Not all SNPs lead to a modified protein, which may be the origin of a disease. Therefore, the recognition of functional SNPs is needed. Because most SNP annotation tools look for SNPs which lead to an amino acid exchange or a premature stop, we designed a new tool called AASsites which searches for SNPs which modify splicing.

Results

AASsites uses several gene prediction programs and open reading frame prediction to compare the wild type (wt) and the variant gene sequence. The results of the comparison are combined by a handmade rule system to classify a change in splicing as “likely, probable, unlikely”. Having received good results from tests with SNPs known for changing the splicing pattern we checked 80,000 SNPs from the human genome which are located near splice sites for their ability to change the splicing pattern of the gene and hereby result in a different protein. We identified 301 “likely” and 985 “probable” classified SNPs with such characteristics. Within this set 33 SNPs are described in the ssSNP Target database to cause modified splicing.

Conclusions

With AASsites single SNPs can be checked for those causing splice modifications. Screening 80,000 known human SNPs we detected about 1,200 SNPs which probably modify splicing. AASsites is available at http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar using any web browser.

CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer

Summary: Thousands of cancer exomes are currently being sequenced, yielding millions of non-synonymous single nucleotide variants (SNVs) of possible relevance to disease etiology. Here, we provide a software toolkit to prioritize SNVs based on their predicted contribution to tumorigenesis. It includes a database of precomputed, predictive features covering all positions in the annotated human exome and can be used either stand-alone or as part of a larger variant discovery pipeline.

Availability and Implementation: MySQL database, source code and binaries freely available for academic/government use at http://wiki.chasmsoftware.org, Source in Python and C++. Requires 32 or 64-bit Linux system (tested on Fedora Core 8,10,11 and Ubuntu 10), 2.5*≤ Python <3.0*, MySQL server >5.0, 60 GB available hard disk space (50 MB for software and data files, 40 GB for MySQL database dump when uncompressed), 2 GB of RAM.

Contact:karchin@jhu.edu

Supplementary Information:Supplementary data are available at Bioinformatics online.

Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed

Multiple algorithms are used to predict the impact of missense mutations on protein structure and function using algorithm-generated sequence alignments or manually curated alignments. We compared the accuracy with native alignment of SIFT, Align-GVGD, PolyPhen-2, and Xvar when generating functionality predictions of well-characterized missense mutations (n = 267) within the BRCA1, MSH2, MLH1, and TP53 genes. We also evaluated the impact of the alignment employed on predictions from these algorithms (except Xvar) when supplied the same four alignments including alignments automatically generated by (1) SIFT, (2) Polyphen-2, (3) Uniprot, and (4) a manually curated alignment tuned for Align-GVGD. Alignments differ in sequence composition and evolutionary depth. Data-based receiver operating characteristic curves employing the native alignment for each algorithm result in area under the curve of 78-79% for all four algorithms. Predictions from the PolyPhen-2 algorithm were least dependent on the alignment employed. In contrast, Align-GVGD predicts all variants neutral when provided alignments with a large number of sequences. Of note, algorithms make different predictions of variants even when provided the same alignment and do not necessarily perform best using their own alignment. Thus, researchers should consider optimizing both the algorithm and sequence alignment employed in missense prediction.

Bioinformatics challenges for personalized medicine

MOTIVATION

Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.

RESULTS

This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.

CONTACT

russ.altman@stanford.edu

The structural impact of cancer-associated missense mutations in oncogenes and tumor suppressors

BACKGROUND

Current large-scale cancer sequencing projects have identified large numbers of somatic mutations covering an increasing number of different cancer tissues and patients. However, the characterization of these mutations at the structural and functional level remains a challenge.

RESULTS

We present results from an analysis of the structural impact of frequent missense cancer mutations using an automated method. We find that inactivation of tumor suppressors in cancer correlates frequently with destabilizing mutations preferably in the core of the protein, while enhanced activity of oncogenes is often linked to specific mutations at functional sites. Furthermore, our results show that this alteration of oncogenic activity is often associated with mutations at ATP or GTP binding sites.

CONCLUSIONS

With our findings we can confirm and statistically validate the hypotheses for the gain-of-function and loss-of-function mechanisms of oncogenes and tumor suppressors, respectively. We show that the distinct mutational patterns can potentially be used to pre-classify newly identified cancer-associated genes with yet unknown function.

Using functional annotation for the empirical determination of Bayes Factors for genome-wide association study analysis

A genome wide association study (GWAS) typically results in a few highly significant ‘hits’ and a much larger set of suggestive signals (‘near-hits’). The latter group are expected to be a mixture of true and false associations. One promising strategy to help separate these is to use functional annotations for prioritisation of variants for follow-up. A key task is to determine which annotations might prove most valuable. We address this question by examining the functional annotations of previously published GWAS hits. We explore three annotation categories: non-synonymous SNPs (nsSNPs), promoter SNPs and cis expression quantitative trait loci (eQTLs) in open chromatin regions. We demonstrate that GWAS hit SNPs are enriched for these three functional categories, and that it would be appropriate to provide a higher weighting for such SNPs when performing Bayesian association analyses. For GWAS studies, our analyses suggest the use of a Bayes Factor of about 4 for cis eQTL SNPs within regions of open chromatin, 3 for nsSNPs and 2 for promoter SNPs.

Data integration workflow for search of disease driving genes and genetic variants

Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/

Performance of mutation pathogenicity prediction methods on missense variants

Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans. The number of SNPs identified in the human genome is growing rapidly, but attaining experimental knowledge about the possible disease association of variants is laborious and time-consuming. Several computational methods have been developed for the classification of SNPs according to their predicted pathogenicity. In this study, we have evaluated the performance of nine widely used pathogenicity prediction methods available on the Internet. The evaluated methods were MutPred, nsSNPAnalyzer, Panther, PhD-SNP, PolyPhen, PolyPhen2, SIFT, SNAP, and SNPs&GO. The methods were tested with a set of over 40,000 pathogenic and neutral variants. We also assessed whether the type of original or substituting amino acid residue, the structural class of the protein, or the structural environment of the amino acid substitution, had an effect on the prediction performance. The performances of the programs ranged from poor (MCC 0.19) to reasonably good (MCC 0.65), and the results from the programs correlated poorly. The overall best performing methods in this study were SNPs&GO and MutPred, with accuracies reaching 0.82 and 0.81, respectively.

Statistical analysis strategies for association studies involving rare variants

The limitations of genome-wide association (GWA) studies that focus on the phenotypic influence of common genetic variants have motivated human geneticists to consider the contribution of rare variants to phenotypic expression. The increasing availability of high-throughput sequencing technologies has enabled studies of rare variants but these methods will not be sufficient for their success as appropriate analytical methods are also needed. We consider data analysis approaches to testing associations between a phenotype and collections of rare variants in a defined genomic region or set of regions. Ultimately, although a wide variety of analytical approaches exist, more work is needed to refine them and determine their properties and power in different contexts.

In silico functional profiling of human disease-associated and polymorphic amino acid substitutions

An important challenge in translational bioinformatics is to understand how genetic variation gives rise to molecular changes at the protein level that can precipitate both monogenic and complex disease. To this end, we compiled datasets of human disease-associated amino acid substitutions (AAS) in the contexts of inherited monogenic disease, complex disease, functional polymorphisms with no known disease association, and somatic mutations in cancer, and compared them with respect to predicted functional sites in proteins. Using the sequence homology-based tool SIFT to estimate the proportion of deleterious AAS in each dataset, only complex disease AAS were found to be indistinguishable from neutral polymorphic AAS. Investigation of monogenic disease AAS predicted to be nondeleterious by SIFT were characterized by a significant enrichment for inherited AAS within solvent accessible residues, regions of intrinsic protein disorder, and an association with the loss or gain of various posttranslational modifications. Sites of structural and/or functional interest were therefore surmised to constitute useful additional features with which to identify the molecular disruptions caused by deleterious AAS. A range of bioinformatic tools, designed to predict structural and functional sites in protein sequences, were then employed to demonstrate that intrinsic biases exist in terms of the distribution of different types of human AAS with respect to specific structural, functional and pathological features. Our Web tool, designed to potentiate the functional profiling of novel AAS, has been made available at http://profile.mutdb.org/.

A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions

There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprung's disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package.

It has been demonstrated that both rare and common variants are involved in complex disease etiology. Until recently it was only possible to perform large scale analysis of common variants. With the development of next-generation sequencing technologies, detection and mapping of rare variants have been made possible. However, methods used to analyze common variants are not powerful for the analysis of rare variants. To address the problems of rare variant analysis working in a novel framework, the kernel-based adaptive cluster (KBAC) method was developed to perform gene/locus based analysis. The KBAC combines variant classification and association testing in a coherent framework. Through simulations motivated by population genetic and disease data, it is demonstrated that the KBAC has superior power to other rare variant analysis methods, especially in the presence of variant misclassification and gene interaction. Using data from the Dallas Heart Study, the KBAC method was applied to test for associations between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified. The KBAC method is implemented in a user-friendly R package.

Genes, mutations, and human inherited disease at the dawn of the age of personalized genomics

The number of reported germline mutations in human nuclear genes, either underlying or associated with inherited disease, has now exceeded 100,000 in more than 3,700 different genes. The availability of these data has both revolutionized the study of the morbid anatomy of the human genome and facilitated "personalized genomics." With approximately 300 new "inherited disease genes" (and approximately 10,000 new mutations) being identified annually, it is pertinent to ask how many "inherited disease genes" there are in the human genome, how many mutations reside within them, and where such lesions are likely to be located? To address these questions, it is necessary not only to reconsider how we define human genes but also to explore notions of gene "essentiality" and "dispensability."Answers to these questions are now emerging from recent novel insights into genome structure and function and through complete genome sequence information derived from multiple individual human genomes. However, a change in focus toward screening functional genomic elements as opposed to genes sensu stricto will be required if we are to capitalize fully on recent technical and conceptual advances and identify new types of disease-associated mutation within noncoding regions remote from the genes whose function they disrupt.

Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor

SUMMARY

A tool to predict the effect that newly discovered genomic variants have on known transcripts is indispensible in prioritizing and categorizing such variants. In Ensembl, a web-based tool (the SNP Effect Predictor) and API interface can now functionally annotate variants in all Ensembl and Ensembl Genomes supported species.

AVAILABILITY

The Ensembl SNP Effect Predictor can be accessed via the Ensembl website at http://www.ensembl.org/. The Ensembl API (http://www.ensembl.org/info/docs/api/api_installation.html for installation instructions) is open source software.

Varietas: a functional variation database portal

Current high-throughput technologies for investigating genomic variation in large population based samples produce data on a scale of millions of variations. Browsing through these results and identifying relevant functional variations is a major hurdle in these genome-wide association studies. In order to help researchers locate the most promising associations, we have developed a web-based database portal called Varietas. Varietas can be used for retrieving information concerning genomic variations such as single-nucleotide polymorphisms (SNPs), copy number variants and insertions/deletions, while enabling users to annotate large number of variations in a batch like manner and to find information about related genes, phenotypes and diseases. Varietas also links out to various external genomic databases, allowing users to quickly browse through a set of variations and follow the most promising leads. Varietas periodically integrates data from the major SNP and genome databases, including Ensembl genome database, NCBI dbSNP database, The Genomic Association Database and SNPedia.

Database URL:http://kokki.uku.fi/bioinformatics/varietas/

ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a 'variants reduction' protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/.

Advances in translational bioinformatics: computational approaches for the hunting of disease genes

Over a 100 years ago, William Bateson provided, through his observations of the transmission of alkaptonuria in first cousin offspring, evidence of the application of Mendelian genetics to certain human traits and diseases. His work was corroborated by Archibald Garrod (Archibald AE. The incidence of alkaptonuria: a study in chemical individuality. Lancert 1902;ii:1616-20) and William Farabee (Farabee WC. Inheritance of digital malformations in man. In: Papers of the Peabody Museum of American Archaeology and Ethnology. Cambridge, Mass: Harvard University, 1905; 65-78), who recorded the familial tendencies of inheritance of malformations of human hands and feet. These were the pioneers of the hunt for disease genes that would continue through the century and result in the discovery of hundreds of genes that can be associated with different diseases. Despite many ground-breaking discoveries during the last century, we are far from having a complete understanding of the intricate network of molecular processes involved in diseases, and we are still searching for the cures for most complex diseases. In the last few years, new genome sequencing and other high-throughput experimental techniques have generated vast amounts of molecular and clinical data that contain crucial information with the potential of leading to the next major biomedical discoveries. The need to mine, visualize and integrate these data has motivated the development of several informatics approaches that can broadly be grouped in the research area of 'translational bioinformatics'. This review highlights the latest advances in the field of translational bioinformatics, focusing on the advances of computational techniques to search for and classify disease genes.

Genomics software: The view from 10,000 feet

The rate of change in genomics, and 'omics generally, shows no signs of slowing down. Related analysis software is struggling to keep apace. This paper provides a brief review of the field.

Automated inference of molecular mechanisms of disease from amino acid substitutions

MOTIVATION

Advances in high-throughput genotyping and next generation sequencing have generated a vast amount of human genetic variation data. Single nucleotide substitutions within protein coding regions are of particular importance owing to their potential to give rise to amino acid substitutions that affect protein structure and function which may ultimately lead to a disease state. Over the last decade, a number of computational methods have been developed to predict whether such amino acid substitutions result in an altered phenotype. Although these methods are useful in practice, and accurate for their intended purpose, they are not well suited for providing probabilistic estimates of the underlying disease mechanism.

RESULTS

We have developed a new computational model, MutPred, that is based upon protein sequence, and which models changes of structural features and functional sites between wild-type and mutant sequences. These changes, expressed as probabilities of gain or loss of structure and function, can provide insight into the specific molecular mechanism responsible for the disease state. MutPred also builds on the established SIFT method but offers improved classification accuracy with respect to human disease mutations. Given conservative thresholds on the predicted disruption of molecular function, we propose that MutPred can generate accurate and reliable hypotheses on the molecular basis of disease for approximately 11% of known inherited disease-causing mutations. We also note that the proportion of changes of functionally relevant residues in the sets of cancer-associated somatic mutations is higher than for the inherited lesions in the Human Gene Mutation Database which are instead predicted to be characterized by disruptions of protein structure.

AVAILABILITY

http://mutdb.org/mutpred

CONTACT

predrag@indiana.edu; smooney@buckinstitute.org.

Analytical methods for inferring functional effects of single base pair substitutions in human cancers

Cancer is a genetic disease that results from a variety of genomic alterations. Identification of some of these causal genetic events has enabled the development of targeted therapeutics and spurred efforts to discover the key genes that drive cancer formation. Rapidly improving sequencing and genotyping technology continues to generate increasingly large datasets that require analytical methods to identify functional alterations that deserve additional investigation. This review examines statistical and computational approaches for the identification of functional changes among sets of single-nucleotide substitutions. Frequency-based methods identify the most highly mutated genes in large-scale cancer sequencing efforts while bioinformatics approaches are effective for independent evaluation of both non-synonymous mutations and polymorphisms. We also review current knowledge and tools that can be utilized for analysis of alterations in non-protein-coding genomic sequence.