The Human Extracellular Matrix Diseasome Reveals Genotype-Phenotype Associations with Clinical Implications for Age-Related Diseases.

The extracellular matrix (ECM) is earning an increasingly relevant role in many disease states and aging. The analysis of these disease states is possible with the GWAS and PheWAS methodologies, and through our analysis, we aimed to explore the relationships between polymorphisms in the compendium of ECM genes (i.e.

Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine.

The gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C.

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer.

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods.

Workshop during the Pacific Symposium of Biocomputing, Jan 3-7, 2019: Reading between the genes: interpreting non-coding DNA in high-throughput.

Identifying functional elements and predicting mechanistic insight from non-coding DNA and noncoding variation remains a challenge. Advances in genome-scale, high-throughput technology, however, have brought these answers closer within reach than ever, though there is still a need for new computational approaches to analysis and integration. This workshop aims to explore these resources and new computational methods applied to regulatory elements, chromatin interactions, non-protein-coding genes, and other non-coding DNA.

Putting benchmarks in their rightful place: The heart of computational biology.

Research in computational biology has given rise to a vast number of methods developed to solve scientific problems. For areas in which many approaches exist, researchers have a hard time deciding which tool to select to address a scientific challenge, as essentially all publications introducing a new method will claim better performance than all others. Not all of these claims can be correct.

Pathway networks generated from human disease phenome.

Background: Understanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to disease susceptibility have been identified, a large number are still unknown. In this paper, we propose a pathway-based approach to extend disease-variant associations and find new molecular connections between genetic mutations and diseases.

Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples.

The fight against cancer is hindered by its highly heterogeneous nature. Genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare somatic variants present only in a small fraction of lesions. Such rare somatic variants dominate the landscape of genomic mutations in cancer, yet efforts to correlate somatic mutations found in one or few individuals with functional roles have been largely unsuccessful.

Recruitment of Tiam1 to Semaphorin 4D Activates Rac and Enhances Proliferation, Invasion, and Metastasis in Oral Squamous Cell Carcinoma.

The semaphorins and the plexins are a family of large, cysteine-rich proteins originally identified as regulators of axon growth and lymphocyte activation that are now known to provide motility and positional information for a number of cell and tissue types. For example, our group and others have shown that some malignancies over express Semaphorin 4D (S4D), which acts through its receptor Plexin-B1 (PB1) on endothelial cells to attract blood vessels from the surrounding stroma for the purpose of supporting tumor growth. While plexins are the known functional receptors for the semaphorins, there is evidence that transmembrane semaphorins may transmit a signal themselves through their short cytoplasmic tail, a phenomenon known as 'reverse signaling.

Regulatory Single-Nucleotide Variant Predictor Increases Predictive Performance of Functional Regulatory Variants.

In silico methods for detecting functionally relevant genetic variants are important for identifying genetic markers of human inherited disease. Much research has focused on protein-coding variants since coding regions have well-defined physicochemical and functional properties. However, many bioinformatics tools are not applicable to variants outside coding regions.

Mac-1 Regulates IL-13 Activity in Macrophages by Directly Interacting with IL-13Rα1.

Mac-1 exhibits a unique inhibitory activity toward IL-13-induced JAK/STAT activation and thereby regulates macrophage to foam cell transformation. However, the underlying molecular mechanism is unknown. In this study, we report the identification of IL-13Rα1, a component of the IL-13 receptor (IL-13R), as a novel ligand of integrin Mac-1, using a co-evolution-based algorithm.

Small-angle X-ray scattering method to characterize molecular interactions: Proof of concept.

Characterizing biomolecular interactions is crucial to the understanding of biological processes. Existing characterization methods have low spatial resolution, poor specificity, and some lack the capability for deep tissue imaging. We describe a novel technique that relies on small-angle X-ray scattering signatures from high-contrast molecular probes that correlate with the presence of biomolecular interactions.

Hybrid curation of gene-mutation relations combining automated extraction and crowdsourcing.

Background: This article describes capture of biological information using a hybrid approach that combines natural language processing to extract biological entities and crowdsourcing with annotators recruited via Amazon Mechanical Turk to judge correctness of candidate biological relations. These techniques were applied to extract gene- mutation relations from biomedical abstracts with the goal of supporting production scale capture of gene-mutation-disease findings as an open source resource for personalized medicine.

Results: The hybrid system could be configured to provide good performance for gene-mutation extraction (precision ∼82%; recall ∼70% against an expert-generated gold standard) at a cost of $0.

Domain landscapes of somatic mutations in cancer.

The fight against cancer has been hindered by its highly heterogeneous nature. Recent genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare cancer somatic mutations present only in a small fraction of lesions. For instance, the genome of a colorectal cancer in one patient can have somewhere between 50 to 100 somatic mutations, but might share only 2 or 3 mutated genes with colorectal tumor genomes from other patients.

Towards precision medicine: advances in computational approaches for the analysis of human variants.

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases.

A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations.

Background: The body of disease mutations with known phenotypic relevance continues to increase and is expected to do so even faster with the advent of new experimental techniques such as whole-genome sequencing coupled with disease association studies. However, genomic association studies are limited by the molecular complexity of the phenotype being studied and the population size needed to have adequate statistical power. One way to circumvent this problem, which is critical for the study of rare diseases, is to study the molecular patterns emerging from functional studies of existing disease mutations.

Modeling cell heterogeneity: from single-cell variations to mixed cells.

Emerging technologies such as single cell gene expression analysis and single cell genome sequencing provide an unprecedented opportunity to quantitatively probe biological interactions at the single cell level. This new level of insight has begun to reveal a more accurate picture of cellular behavior, and to highlight the importance of understanding cellular variation in a wide range of biological contexts. The aim of this workshop is to bring together researchers working on identifying and modeling cell heterogeneity that arises by a variety of mechanisms, including but not limited to cell-to-cell noise, cell-state switches and cell differentiation, heterogeneity in immune responses, cancer evolution, and heterogeneity in disease progression.

Text and data mining for biomedical discovery.

The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction.

Chapter 4: Protein interactions and disease.

Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems.

Domain landscapes of somatic mutations in cancer.

Background: Large-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highlighting genes mutated in a large percentage of tumor samples as those likely to contain driver mutations. However, this gene-based approach usually does not consider the position of the mutation within the gene or the functional context the position of the mutation provides.

A mutation-centric approach to identifying pharmacogenomic relations in text.

Objectives: To explore the notion of mutation-centric pharmacogenomic relation extraction and to evaluate our approach against reference pharmacogenomic relations.

Methods: From a corpus of MEDLINE abstracts relevant to genetic variation, we identify co-occurrences between drug mentions extracted using MetaMap and RxNorm, and genetic variants extracted by EMU. The recall of our approach is evaluated against reference relations curated manually in PharmGKB.

Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants.

Objective: Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome.

Materials And Methods: An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism.

Results: Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.

Incorporating molecular and functional context into the analysis and prioritization of human variants associated with cancer.

Background And Objective: With recent breakthroughs in high-throughput sequencing, identifying deleterious mutations is one of the key challenges for personalized medicine. At the gene and protein level, it has proven difficult to determine the impact of previously unknown variants. A statistical method has been developed to assess the significance of disease mutation clusters on protein domains by incorporating domain functional annotations to assist in the functional characterization of novel variants.

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.

Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature.

Motivation: A major goal of biomedical research in personalized medicine is to find relationships between mutations and their corresponding disease phenotypes. However, most of the disease-related mutational data are currently buried in the biomedical literature in textual form and lack the necessary structure to allow easy retrieval and visualization. We introduce a high-throughput computational method for the identification of relevant disease mutations in PubMed abstracts applied to prostate (PCa) and breast cancer (BCa) mutations.