Heiko Horn

Bio: Heiko Horn is an academic researcher from Broad Institute. The author has contributed to research in topics: Augmented browsing & Gene. The author has an hindex of 21, co-authored 43 publications receiving 13075 citations. Previous affiliations of Heiko Horn include European Bioinformatics Institute & University of Copenhagen.

A global reference for human genetic variation.

Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

A global reference for human genetic variation

Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

A scored human protein–protein interaction network to catalyze genomic interpretation

Abstract: Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap, or InWeb_IM) with severalfold more interactions (>500,000) and better functional biological relevance than comparable resources. We illustrate that InWeb_InBioMap enables functional interpretation of >4,700 cancer genomes and genes involved in autism.

KinomeXplorer: an integrated platform for kinome biology studies

Abstract: or even impossible to be captured by cellular or in vivo experiments alone. Furthermore, it is difficult to design kinase perturbation experiments, because the kinome-wide selectivity and specificity of many kinase inhibitors is unknown3,4. As a result, knowledge is lacking on which of the ~540 human kinases phosphorylate a given site: of the 42,914 phosphorylation sites currently annotated in the Phospho.ELM database5, only ~20% have been linked to a kinase. Technological advances in mass spectrometry–based phosphoproteomics have accelerated the ability to identify phosphorylation sites but not to determine which kinases phosphorylate them. To systematically identify these dynamic interactions, computational methods to guide experiments must be deployed. We have shown that combining computational algorithms with quantitative mass spectrometry is a powerful approach to validate kinase-substrate relationships6. Notably, we have shown that kinase specificity can be described in terms of two main contributing elements: the recognition motif of the individual kinase (for example, X-S/T-Q-X for the ATM kinase) and proteins that can be functionally associated with it (i.e., not just proteins that directly interact with the kinase). The network context of kinases is crucial, as exemplified by the discovery that the phenotypic role of the JNK kinase depends entirely on the state of the cellular signaling networks before its activation7. In other words, it is crucial to assess the protein networks embedding kinases and how these are dynamically modulated (for example, through time or perturbations) to predict cell behavior8. KinomeXplorer (Fig. 1) provides workflows that enable researchers to efficiently analyze phosphorylationd e p e n d e nt i n t e r a c t i o n n e t w o r k s (Supplementary Fig. 1) and aids them in designing follow-up perturbation experiments. The platform includes improved versions of NetworKIN (an algorithm that integrates cellular context information and motif-based predictions)6 and NetPhorest (a phylogenetic tree–based algorithm to classify phosphorylation sites in terms of kinases and phosphobinding domains)9, conferring increased prediction accuracy through a novel Bayesian scoring scheme, broader kinome coverage, new phosphatome coverage and a redesigned unifying web interface. The framework also integrates the new KinomeSelector tool, which enables the user to select an optimal kinase panel to functionally perturb the predicted phosphorylation signaling networks. We re-engineered the NetworKIN algorithm to improve its performance and usability (Supplementary Note). To calculate the NetworKIN score, we combined the NetPhorest probability and the STRING-derived proximity score using KinomeXplorer: an integrated platform for kinome biology studies

Analysis of protein-coding genetic variation in 60,706 humans

Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.

Abstract: A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era

The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.

Abstract: A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

The UK Biobank resource with deep phenotyping and genomic data

Abstract: The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.