NSIT: Novel Sequence Identification Tool

Novel sequences are DNA sequences present in an individual''s genome but absent in the human reference assembly. They are predicted to be biologically important, both individual and population specific, and consistent with the known human migration paths. Recent works have shown that an average person harbors 2–5 Mb of such sequences and estimated that the human pan-genome contains as high as 19–40 Mb of novel sequences. To identify them in a de novo genome assembly, some existing sequence aligners have been used but no computational method has been specifically proposed for this task. In this work, we developed NSIT (Novel Sequence Identification Tool), a software that can accurately and efficiently identify novel sequences in an individual''s de novo whole genome assembly. We identified and characterized 1.1 Mb, 1.2 Mb, and 1.0 Mb of novel sequences in NA18507 (African), YH (Asian), and NA12878 (European) de novo genome assemblies, respectively. Our results show very high concordance with the previous work using the respective reference assembly. In addition, our results using the latest human reference assembly suggest that the amount of novel sequences per individual may not be as high as previously reported. We additionally developed a graphical viewer for comparisons of novel sequence contents. The viewer also helped in identifying sequence contamination; we found 130 kb of Epstein-Barr virus sequence in the previously published NA18507 novel sequences as well as 287 kb of zebrafish repeats in NA12878 de novo assembly. NSIT requires 2GB of RAM and 1.5–2 hrs on a commodity desktop. The program is applicable to input assemblies with varying contig/scaffold sizes, ranging from 100 bp to as high as 50 Mb. It works in both 32-bit and 64-bit systems and outperforms, by large margins, other fast sequence aligners previously applied to this task. To our knowledge, NSIT is the first software designed specifically for novel sequence identification in a de novo human genome assembly.     

Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a ‘one-stop’ analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes.     

Associating Genes and Protein Complexes with Disease via Network Propagation

A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE''s predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.     

The orphan disease networks

The low prevalence rate of orphan diseases (OD) requires special combined efforts to improve diagnosis, prevention, and discovery of novel therapeutic strategies. To identify and investigate relationships based on shared genes or shared functional features, we have conducted a bioinformatic-based global analysis of all orphan diseases with known disease-causing mutant genes. Starting with a bipartite network of known OD and OD-causing mutant genes and using the human protein interactome, we first construct and topologically analyze three networks: the orphan disease network, the orphan disease-causing mutant gene network, and the orphan disease-causing mutant gene interactome. Our results demonstrate that in contrast to the common disease-causing mutant genes that are predominantly nonessential, a majority of orphan disease-causing mutant genes are essential. In confirmation of this finding, we found that OD-causing mutant genes are topologically important in the protein interactome and are ubiquitously expressed. Additionally, functional enrichment analysis of those genes in which mutations cause ODs shows that a majority result in premature death or are lethal in the orthologous mouse gene knockout models. To address the limitations of traditional gene-based disease networks, we also construct and analyze OD networks on the basis of shared enriched features (biological processes, cellular components, pathways, phenotypes, and literature citations). Analyzing these functionally-linked OD networks, we identified several additional OD-OD relations that are both phenotypically similar and phenotypically diverse. Surprisingly, we observed that the wiring of the gene-based and other feature-based OD networks are largely different; this suggests that the relationship between ODs cannot be fully captured by the gene-based network alone.     

A Human-Specific De Novo Protein-Coding Gene Associated with Human Brain Functions

To understand whether any human-specific new genes may be associated with human brain functions, we computationally screened the genetic vulnerable factors identified through Genome-Wide Association Studies and linkage analyses of nicotine addiction and found one human-specific de novo protein-coding gene, FLJ33706 (alternative gene symbol C20orf203). Cross-species analysis revealed interesting evolutionary paths of how this gene had originated from noncoding DNA sequences: insertion of repeat elements especially Alu contributed to the formation of the first coding exon and six standard splice junctions on the branch leading to humans and chimpanzees, and two subsequent substitutions in the human lineage escaped two stop codons and created an open reading frame of 194 amino acids. We experimentally verified FLJ33706''s mRNA and protein expression in the brain. Real-Time PCR in multiple tissues demonstrated that FLJ33706 was most abundantly expressed in brain. Human polymorphism data suggested that FLJ33706 encodes a protein under purifying selection. A specifically designed antibody detected its protein expression across human cortex, cerebellum and midbrain. Immunohistochemistry study in normal human brain cortex revealed the localization of FLJ33706 protein in neurons. Elevated expressions of FLJ33706 were detected in Alzheimer''s brain samples, suggesting the role of this novel gene in human-specific pathogenesis of Alzheimer''s disease. FLJ33706 provided the strongest evidence so far that human-specific de novo genes can have protein-coding potential and differential protein expression, and be involved in human brain functions.     

PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data

Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely applied. Here, we present a combined algorithmic and heuristic framework for PUMA (Penalized Unified Multiple-locus Association) analysis that solves the problems of previously proposed methods including computational speed, poor performance on genome-scale simulated data, and identification of too many associations for real data to be biologically plausible. The framework includes a new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic model selection and testing methods for identification of robust associations. The PUMA framework implements the penalized maximum likelihood penalties previously proposed for GWAS analysis (i.e. Lasso, Adaptive Lasso, NEG, MCP), as well as a penalty that has not been previously applied to GWAS (i.e. LOG). Using simulations that closely mirror real GWAS data, we show that our framework has high performance and reliably increases power to detect weak associations, while existing PMR methods can perform worse than single marker testing in overall performance. To demonstrate the empirical value of PUMA, we analyzed GWAS data for type 1 diabetes, Crohns''s disease, and rheumatoid arthritis, three autoimmune diseases from the original Wellcome Trust Case Control Consortium. Our analysis replicates known associations for these diseases and we discover novel etiologically relevant susceptibility loci that are invisible to standard single marker tests, including six novel associations implicating genes involved in pancreatic function, insulin pathways and immune-cell function in type 1 diabetes; three novel associations implicating genes in pro- and anti-inflammatory pathways in Crohn''s disease; and one novel association implicating a gene involved in apoptosis pathways in rheumatoid arthritis. We provide software for applying our PUMA analysis framework.     

Identification of sequence variants in genetic disease-causing genes using targeted next-generation sequencing

Background

Identification of gene variants plays an important role in research on and diagnosis of genetic diseases. A combination of enrichment of targeted genes and next-generation sequencing (targeted DNA-HiSeq) results in both high efficiency and low cost for targeted sequencing of genes of interest.

Methodology/Principal Findings

To identify mutations associated with genetic diseases, we designed an array-based gene chip to capture all of the exons of 193 genes involved in 103 genetic diseases. To evaluate this technology, we selected 7 samples from seven patients with six different genetic diseases resulting from six disease-causing genes and 100 samples from normal human adults as controls. The data obtained showed that on average, 99.14% of 3,382 exons with more than 30-fold coverage were successfully detected using Targeted DNA-HiSeq technology, and we found six known variants in four disease-causing genes and two novel mutations in two other disease-causing genes (the STS gene for XLI and the FBN1 gene for MFS) as well as one exon deletion mutation in the DMD gene. These results were confirmed in their entirety using either the Sanger sequencing method or real-time PCR.

Conclusions/Significance

Targeted DNA-HiSeq combines next-generation sequencing with the capture of sequences from a relevant subset of high-interest genes. This method was tested by capturing sequences from a DNA library through hybridization to oligonucleotide probes specific for genetic disorder-related genes and was found to show high selectivity, improve the detection of mutations, enabling the discovery of novel variants, and provide additional indel data. Thus, targeted DNA-HiSeq can be used to analyze the gene variant profiles of monogenic diseases with high sensitivity, fidelity, throughput and speed.     

Anchored pseudo-de novo assembly of human genomes identifies extensive sequence variation from unmapped sequence reads

The human genome reference (HGR) completion marked the genomics era beginning, yet despite its utility universal application is limited by the small number of individuals used in its development. This is highlighted by the presence of high-quality sequence reads failing to map within the HGR. Sequences failing to map generally represent 2–5 % of total reads, which may harbor regions that would enhance our understanding of population variation, evolution, and disease. Alternatively, complete de novo assemblies can be created, but these effectively ignore the groundwork of the HGR. In an effort to find a middle ground, we developed a bioinformatic pipeline that maps paired-end reads to the HGR as separate single reads, exports unmappable reads, de novo assembles these reads per individual and then combines assemblies into a secondary reference assembly used for comparative analysis. Using 45 diverse 1000 Genomes Project individuals, we identified 351,361 contigs covering 195.5 Mb of sequence unincorporated in GRCh38. 30,879 contigs are represented in multiple individuals with ~40 % showing high sequence complexity. Genomic coordinates were generated for 99.9 %, with 52.5 % exhibiting high-quality mapping scores. Comparative genomic analyses with archaic humans and primates revealed significant sequence alignments and comparisons with model organism RefSeq gene datasets identified novel human genes. If incorporated, these sequences will expand the HGR, but more importantly our data highlight that with this method low coverage (~10–20×) next-generation sequencing can still be used to identify novel unmapped sequences to explore biological functions contributing to human phenotypic variation, disease and functionality for personal genomic medicine.     

Robust classification of protein variation using structural modelling and large-scale data integration

Existing methods for interpreting protein variation focus on annotating mutation pathogenicity rather than detailed interpretation of variant deleteriousness and frequently use only sequence-based or structure-based information. We present VIPUR, a computational framework that seamlessly integrates sequence analysis and structural modelling (using the Rosetta protein modelling suite) to identify and interpret deleterious protein variants. To train VIPUR, we collected 9477 protein variants with known effects on protein function from multiple organisms and curated structural models for each variant from crystal structures and homology models. VIPUR can be applied to mutations in any organism''s proteome with improved generalized accuracy (AUROC .83) and interpretability (AUPR .87) compared to other methods. We demonstrate that VIPUR''s predictions of deleteriousness match the biological phenotypes in ClinVar and provide a clear ranking of prediction confidence. We use VIPUR to interpret known mutations associated with inflammation and diabetes, demonstrating the structural diversity of disrupted functional sites and improved interpretation of mutations associated with human diseases. Lastly, we demonstrate VIPUR''s ability to highlight candidate variants associated with human diseases by applying VIPUR to de novo variants associated with autism spectrum disorders.     

A framework for exhaustively mapping functional missense variants

Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin‐like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.     

Progress in Detecting Genetic Alterations and Their Association with Human Disease

The completion of the Human Genome Project provided a reference sequence to which researchers could compare sequences from individual patients in the hope of identifying disease-causing mutations. However, this still necessitated candidate gene testing or a very limited screen of multiple genes using Sanger sequencing. With the advent of high-throughput Sanger sequencing, it became possible to screen hundreds of patients for alterations in hundreds of genes. This process was time consuming and limited to a few locations/institutions that had the space to house tens of sequencing equipment. The development of next generation sequencing revolutionized the process. It is now feasible to sequence the entire exome of multiple individuals in about 10 days. However, this meant that a massive amount of data needed to be filtered to identify the relevant alteration. This is presently the rate-limiting step in providing a convincing association between a genetic alteration and a human disorder.     

InSite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale

We propose InSite, a computational method that integrates high-throughput protein and sequence data to infer the specific binding regions of interacting protein pairs. We compared our predictions with binding sites in Protein Data Bank and found significantly more binding events occur at sites we predicted. Several regions containing disease-causing mutations or cancer polymorphisms in human are predicted to be binding for protein pairs related to the disease, which suggests novel mechanistic hypotheses for several diseases.     

A human phenome-interactome network of protein complexes implicated in genetic disorders

We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type 2 diabetes and coronary heart disease. Our publicly available draft of protein complexes associated with pathology comprises 506 complexes, which reveal functional relationships between disease-promoting genes that will inform future experimentation.     

Remarkably Little Variation in Proteins Encoded by the Y Chromosome's Single-Copy Genes, Implying Effective Purifying Selection

Y-linked single-nucleotide polymorphisms (SNPs) have served as powerful tools for reconstructing the worldwide genealogy of human Y chromosomes and for illuminating patrilineal relationships among modern human populations. However, there has been no systematic, worldwide survey of sequence variation within the protein-coding genes of the Y chromosome. Here we report and analyze coding sequence variation among the 16 single-copy “X-degenerate” genes of the Y chromosome. We examined variation in these genes in 105 men representing worldwide diversity, resequencing in each man an average of 27 kb of coding DNA, 40 kb of intronic DNA, and, for comparison, 15 kb of DNA in single-copy Y-chromosomal pseudogenes. There is remarkably little variation in X-degenerate protein sequences: two chromosomes drawn at random differ on average by a single amino acid, with half of these differences arising from a single, conservative Asp→Glu mutation that occurred ∼50,000 years ago. Further analysis showed that nucleotide diversity and the proportion of variant sites are significantly lower for nonsynonymous sites than for synonymous sites, introns, or pseudogenes. These differences imply that natural selection has operated effectively in preserving the amino acid sequences of the Y chromosome''s X-degenerate proteins during the last ∼100,000 years of human history. Thus our findings are at odds with prominent accounts of the human Y chromosome''s imminent demise.