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Background Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. Methods We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. Results GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. Conclusions GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevor’s performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.

The need for improved algorithmic support for variant prioritization and disease-gene identification in per- sonal genomes data is widely acknowledged. We previously presented the Variant Annotation, Analysis, and Search Tool (VAAST), which employs an aggregative variant association test that combines both amino acid substitution (AAS) and allele frequencies. Here we describe and benchmark VAAST 2.0, which uses a novel conservation-controlled AAS matrix (CASM), to incorporate information about phylogenetic conservation. We show that the CASM approach improves VAAST's variant prioritization accuracy compared to its previous implementation, and compared to SIFT, PolyPhen-2, and MutationTaster. We also show that VAAST 2.0 outperforms KBAC, WSS, SKAT, and variable threshold (VT) using published case-control datasets for Crohn disease (NOD2), hypertriglyceridemia (LPL), and breast cancer (CHEK2). VAAST 2.0 also improves search accuracy on simulated datasets across a wide range of allele frequencies, population-attributable disease risks, and al- lelic heterogeneity, factors that compromise the accuracies of other aggregative variant association tests. We also demonstrate that, although most aggregative variant association tests are designed for common genetic diseases, these tests can be easily adopted as rare Mendelian disease-gene finders with a simple ranking-by-statistical-significance protocol, and the performance compares very favorably to state-of-art filtering approaches. The latter, despite their popularity, have suboptimal performance especially with the increasing case sample size.

High-throughput sequencing of related individuals has become an important tool for studying human disease. However, owing to technical complexity and lack of available tools, most pedigree-based sequencing studies rely on an ad hoc combination of suboptimal analyses. Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throughput sequence data in pedigrees. pVAAST uses a sequence-based model to perform variant and gene-based linkage analysis. Linkage information is then combined with functional prediction and rare variant case-control association information in a unified statistical framework. pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive and de novo inheritance patterns. The approach is robust to incomplete penetrance and locus heterogeneity and is applicable to a wide variety of genetic traits. pVAAST maintains high power across studies of monogenic, high-penetrance phenotypes in a single pedigree to highly polygenic, common phenotypes involving hundreds of pedigrees. © 2014 Nature America, Inc.

Genetic testing is routinely used for second-tier confirmation of newborn sequencing results to rule out false positives and to confirm diagnoses in newborns undergoing inpatient and outpatient care. We developed a targeted next-generation sequencing panel coupled with a variant processing pipeline and demonstrated utility and performance benchmarks across multiple newborn disease presentations in a retrospective clinical study.The test utilizes an in silico gene filter that focuses directly on 126 genes related to newborn screening diseases and is applied to the exome or a next-generation sequencing panel called NBDx. NBDx targets the 126 genes and additional newborn-specific disorders. It integrates DNA isolation from minimally invasive biological specimens, targeted next-generation screening, and rapid characterization of genetic variation.We report a rapid parallel processing of 8 to 20 cases within 105 hours with high coverage on our NBDx panel. Analytical sensitivity of 99.8% was observed across known mutation hotspots. Concordance calls with or without clinical summaries were 94% and 75%, respectively.Rapid, automated targeted next-generation sequencing and analysis are practical in newborns for second-tier confirmation and neonatal intensive care unit diagnoses, laying a foundation for future primary DNA-based molecular screening of additional disorders and improving existing molecular testing options for newborns.

Computational methods for automated genome annotation are critical to our community's ability to make full use of the large volume of genomic sequence being generated and released. To explore the accuracy of these automated feature prediction tools in the genomes of higher organisms, we evaluated their performance on a large, well-characterized sequence contig from the Adh region ofDrosophila melanogaster. This experiment, known as the Genome Annotation Assessment Project (GASP), was launched in May 1999. Twelve groups, applying state-of-the-art tools, contributed predictions for features including gene structure, protein homologies, promoter sites, and repeat elements. We evaluated these predictions using two standards, one based on previously unreleased high-quality full-length cDNA sequences and a second based on the set of annotations generated as part of an in-depth study of the region by a group ofDrosophila experts. Although these standard sets only approximate the unknown distribution of features in this region, we believe that when taken in context the results of an evaluation based on them are meaningful. The results were presented as a tutorial at the conference on Intelligent Systems in Molecular Biology (ISMB-99) in August 1999. Over 95% of the coding nucleotides in the region were correctly identified by the majority of the gene finders, and the correct intron/exon structures were predicted for >40% of the genes. Homology-based annotation techniques recognized and associated functions with almost half of the genes in the region; the remainder were only identified by the ab initio techniques. This experiment also presents the first assessment of promoter prediction techniques for a significant number of genes in a large contiguous region. We discovered that the promoter predictors' high false-positive rates make their predictions difficult to use. Integrating gene finding and cDNA/EST alignments with promoter predictions decreases the number of false-positive classifications but discovers less than one-third of the promoters in the region. We believe that by establishing standards for evaluating genomic annotations and by assessing the performance of existing automated genome annotation tools, this experiment establishes a baseline that contributes to the value of ongoing large-scale annotation projects and should guide further research in genome informatics.

A hidden Markov model-based gene-finding system calledGenie was applied to the genomic Adh region inDrosophila melanogaster as a part of the Genome Annotation Assessment Project (GASP). Predictions from three versions of theGenie gene-finding system were submitted, one based on statistical properties of coding genes, a second included EST alignment information, and a third that integrated protein sequence homology information. All three programs were trained on the providedDrosophila training data. In addition, promoter assignments from an integrated neural network were submitted. The gene assignments overlapped >90% of the 222 annotated genes and 26 possibly novel genes were predicted, of which some might be overpredictions. The system correctly identified the exon boundaries of 70% of the exons in cDNA-confirmed genes and 77% of the exons with the addition of EST sequence alignments. The best of the three Geniesubmissions predicted 19 of the annotated 43 gene structures entirely correct (44%). In the promoter category, only 30% of the transcription start sites could be detected, but by integrating this program as a sensor into Genie the false-positive rate could be dropped to 1/16,786 (0.006%). The results of the experiment on the long contiguous genomic sequence revealed some problems concerning gene assembly in Genie. The results were used to improve the system. We show that Genie is a robust hidden Markov model system that allows for a generalized integration of information from different sources such as signal sensors (splice sites, start codon, etc.), content sensors (exons, introns, intergenic) and alignments of mRNA, EST, and peptide sequences. The assessment showed that Genie could effectively be used for the annotation of complete genomes from higher organisms.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License.-- et al., [Background]: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. [Results]: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. [Conclusions]: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups., This work was supported by funds provided through the Gene Partnership and the Manton Center for Orphan Disease Research at Boston Children’s Hospital and the Center for Biomedical Informatics at Harvard Medical School and by generous donations in-kind of genomic sequencing services by Life Technologies (Carlsbad, CA, USA) and Complete Genomics (Mountain View, CA, USA).

Aims: Next-generation sequencing is being implemented in the clinical laboratory environment for the purposes of candidate causal variant discovery in patients affected with a variety of genetic disorders. The successful implementation of this technology for diagnosing genetic disorders requires a rapid, user-friendly method to annotate variants and generate short lists of clinically relevant variants of interest. This report describes Omicia’s Opal platform, a new software tool designed for variant discovery and interpretation in a clinical laboratory environment. The software allows clinical scientists to process, analyze, interpret and report on personal genome files. Materials & Methods: To demonstrate the software, the authors describe the interactive use of the system for the rapid discovery of disease-causing variants using three cases. Results & Conclusion: Here, the authors show the features of the Opal system and their use in uncovering variants of clinical significance.

Full sequencing of individual human genomes has greatly expanded our understanding of human genetic variation and population history. Here, we present a systematic analysis of 50 human genomes from 11 diverse global populations sequenced at high coverage. Our sample includes 12 individuals who have admixed ancestry and who have varying degrees of recent (within the last 500 years) African, Native American, and European ancestry. We found over 21 million single-nucleotide variants that contribute to a 1.75-fold range in nucleotide heterozygosity across diverse human genomes. This heterozygosity ranged from a high of one heterozygous site per kilobase in west African genomes to a low of 0.57 heterozygous sites per kilobase in segments inferred to have diploid Native American ancestry from the genomes of Mexican and Puerto Rican individuals. We show evidence of all three continental ancestries in the genomes of Mexican, Puerto Rican, and African American populations, and the genome-wide statistics are highly consistent across individuals from a population once ancestry proportions have been accounted for. Using a generalized linear model, we identified subtle variations across populations in the proportion of neutral versus deleterious variation and found that genome-wide statistics vary in admixed populations even once ancestry proportions have been factored in. We further infer that multiple periods of gene flow shaped the diversity of admixed populations in the Americas—70% of the European ancestry in today’s African Americans dates back to European gene flow happening only 7–8 generations ago.

VAAST (the Variant Annotation, Analysis & Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds on existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-to-use fashion. VAAST can score both coding and noncoding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology. Here we demonstrate its ability to identify damaged genes using small cohorts (n = 3) of unrelated individuals, wherein no two share the same deleterious variants, and for common, multigenic diseases using as few as 150 cases.

We describe the genome sequencing of an anonymous individual of African origin using a novel ligation-based sequencing assay that enables a unique form of error correction that improves the raw accuracy of the aligned reads to >99.9%, allowing us to accurately call SNPs with as few as two reads per allele. We collected several billion mate-paired reads yielding ∼18× haploid coverage of aligned sequence and close to 300× clone coverage. Over 98% of the reference genome is covered with at least one uniquely placed read, and 99.65% is spanned by at least one uniquely placed mate-paired clone. We identify over 3.8 million SNPs, 19% of which are novel. Mate-paired data are used to physically resolve haplotype phases of nearly two-thirds of the genotypes obtained and produce phased segments of up to 215 kb. We detect 226,529 intra-read indels, 5590 indels between mate-paired reads, 91 inversions, and four gene fusions. We use a novel approach for detecting indels between mate-paired reads that are smaller than the standard deviation of the insert size of the library and discover deletions in common with those detected with our intra-read approach. Dozens of mutations previously described in OMIM and hundreds of nonsynonymous single-nucleotide and structural variants in genes previously implicated in disease are identified in this individual. There is more genetic variation in the human genome still to be uncovered, and we provide guidance for future surveys in populations and cancer biopsies.

The millions of mutations and polymorphisms that occur in human populations are potential predictors of disease, of our reactions to drugs, of predisposition to microbial infections, and of age-related conditions such as impaired brain and cardiovascular functions. However, predicting the phenotypic consequences and eventual clinical significance of a sequence variant is not an easy task. Computational approaches have found perturbation of conserved amino acids to be a useful criterion for identifying variants likely to have phenotypic consequences. To our knowledge, however, no study to date has explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying polymorphisms with likely phenotypic consequences. In order to investigate the potential of this approach, we have assembled a unique collection of known disease-causing variants from OMIM and the Human Genome Mutation Database (HGMD) and used them to identify and characterize pairs of sequence variants that occur at homologous positions within paralogous human proteins. Our analyses demonstrate that the locations of variants are correlated in paralogous proteins. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, information about variant-pairs can be used to identify potentially disease-causing variants, extend existing procedures for polymorphism prioritization, and provide a suite of candidates for further diagnostic and therapeutic purposes., Author Summary There exists a superabundance of human sequence variations. Testing every sequence variant for association with human disease is often infeasible, as studies must be very large—and hence expensive—to overcome the statistical penalties used to control for multiple tests. A common alternative is to assay only a subset of sequence variants for which there are prior reasons to believe they may be disease-causing. Sequence variants that change conserved amino acids, for example, are often disease-causing. As an adjunct to this approach, we have explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying disease-causing DNA sequence variations. We find that DNA sequence variants co-occur at aligned amino acid pairs more frequently than expected by chance, suggesting that similar functional constraints on paralogous proteins result in coordinated distributions of variants along their lengths. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. These facts provide new avenues for the identification of disease-causing sequence variations.

According to the most recent estimates, the number of human genes is possibly--but not certainly--between 20,000 and 25,000. To contribute strategies to reduce this uncertainty, several groups working on computational gene prediction met recently at the Welcome Trust Sanger Institute with the goal to test and compare predictive methods of genome annotation.

Computational methods for automated genome annotation are critical to understanding and interpreting the bewildering mass of genomic sequence data presently being generated and released. A neural network model of the structural and compositional properties of a eukaryotic core promoter region has been developed and its application for analysis of the Drosophila melanogaster genome is presented. The model uses a time-delay architecture, a special case of a feed-forward neural network. The structure of this model allows for variable spacing between functional binding sites, which is known to play a key role in the transcription initiation process. Application of this model to a test set of core promoters not only gave better discrimination of potential promoter sites than previous statistical or neural network models, but also revealed indirectly subtle properties of the transcription initiation signal. When tested in the Adh region of 2.9 Mbases of the Drosophila genome, the neural network for promoter prediction (NNPP) program that incorporates the time-delay neural network model gives a recognition rate of 75% (69/92) with a false positive rate of 1/547 bases. The present work can be regarded as one of the first intensive studies that applies novel gene regulation technologies to the identification of the complex gene regulation sites in the genome of Drosophila melanogaster.

The Genome Annotation Assessment Project tested current methods of gene identification, including a critical assessment of the accuracy of different methods. Two new databases have provided new resources for gene annotation: these are the InterPro database of protein domains and motifs, and the Gene Ontology database for terms that describe the molecular functions and biological roles of gene products. Efforts in genome annotation are most often based upon advances in computer systems that are specifically designed to deal with the tremendous amounts of data being generated by current sequencing projects. These efforts in analysis are being linked to new ways of visualizing computationally annotated genomes.

The fly Drosophila melanogaster is one of the most intensively studied organisms in biology and serves as a model system for the investigation of many developmental and cellular processes common to higher eukaryotes, including humans. We have determined the nucleotide sequence of nearly all of the ∼120-megabase euchromatic portion of the Drosophila genome using a whole-genome shotgun sequencing strategy supported by extensive clone-based sequence and a high-quality bacterial artificial chromosome physical map. Efforts are under way to close the remaining gaps; however, the sequence is of sufficient accuracy and contiguity to be declared substantially complete and to support an initial analysis of genome structure and preliminary gene annotation and interpretation. The genome encodes ∼13,600 genes, somewhat fewer than the smaller Caenorhabditis elegans genome, but with comparable functional diversity.

A contiguous sequence of nearly 3 Mb from the genome of Drosophila melanogaster has been sequenced from a series of overlapping P1 and BAC clones. This region covers 69 chromosome polytene bands on chromosome arm 2L, including the genetically well-characterized "Adh region." A computational analysis of the sequence predicts 218 protein-coding genes, 11 tRNAs, and 17 transposable element sequences. At least 38 of the protein-coding genes are arranged in clusters of from 2 to 6 closely related genes, suggesting extensive tandem duplication. The gene density is one protein-coding gene every 13 kb; the transposable element density is one element every 171 kb. Of 73 genes in this region identified by genetic analysis, 49 have been located on the sequence; P-element insertions have been mapped to 43 genes. Ninety-five (44%) of the known and predicted genes match a Drosophila EST, and 144 (66%) have clear similarities to proteins in other organisms. Genes known to have mutant phenotypes are more likely to be represented in cDNA libraries, and far more likely to have products similar to proteins of other organisms, than are genes with no known mutant phenotype. Over 650 chromosome aberration breakpoints map to this chromosome region, and their nonrandom distribution on the genetic map reflects variation in gene spacing on the DNA. This is the first large-scale analysis of the genome of D. melanogaster at the sequence level. In addition to the direct results obtained, this analysis has allowed us to develop and test methods that will be needed to interpret the complete sequence of the genome of this species. Before beginning a Hunt, it is wise to ask someone what you are looking for before you begin looking for it. Milne 1926

MOTIVATION: We describe a new content-based approach for the detection of promoter regions of eukaryotic protein encoding genes. Our system is based on three interpolated Markov chains (IMCs) of different order which are trained on coding, non-coding and promoter sequences. It was recently shown that the interpolation of Markov chains leads to stable parameters and improves on the results in microbial gene finding (Salzberg et al., Nucleic Acids Res., 26, 544-548, 1998). Here, we present new methods for an automated estimation of optimal interpolation parameters and show how the IMCs can be applied to detect promoters in contiguous DNA sequences. Our interpolation approach can also be employed to obtain a reliable scoring function for human coding DNA regions, and the trained models can easily be incorporated in the general framework for gene recognition systems. RESULTS: A 5-fold cross-validation evaluation of our IMC approach on a representative sequence set yielded a mean correlation coefficient of 0.84 (promoter versus coding sequences) and 0.53 (promoter versus non-coding sequences). Applied to the task of eukaryotic promoter region identification in genomic DNA sequences, our classifier identifies 50% of the promoter regions in the sequences used in the most recent review and comparison by Fickett and Hatzigeorgiou ( Genome Res., 7, 861-878, 1997), while having a false-positive rate of 1/849 bp.

Background: We present the results of EGASP, a community experiment to assess the state-ofthe-art in genome annotation within the ENCODE regions, which span 1% of the human genome sequence. The experiment had two major goals: the assessment of the accuracy of computational methods to predict protein coding genes; and the overall assessment of the completeness of the current human genome annotations as represented in the ENCODE regions. For the computational prediction assessment, eighteen groups contributed gene predictions. We evaluated these submissions against each other based on a ‘reference set’ of annotations generated as part of the GENCODE project. These annotations were not available to the prediction groups prior to the submission deadline, so that their predictions were blind and an external advisory committee could perform a fair assessment. Results: The best methods had at least one gene transcript correctly predicted for close to 70% of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into account alternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotide level, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programs relying on mRNA and protein sequences were the most accurate in reproducing the manually curated annotations. Experimental validation shows that only a very small percentage (3.2%) of

Here we describe the Genome Variation Format (GVF) and the 10Gen dataset. GVF, an extension of Generic Feature Format version 3 (GFF3), is a simple tab-delimited format for DNA variant files, which uses Sequence Ontology to describe genome variation data. The 10Gen dataset, ten human genomes in GVF format, is freely available for community analysis from the Sequence Ontology website and from an Amazon elastic block storage (EBS) snapshot for use in Amazon's EC2 cloud computing environment.