Systems Analysis Implicates WAVE2 Complex in the Pathogenesis of Developmental Left-Sided Obstructive Heart Defects.

Genetic variants are the primary driver of congenital heart disease (CHD) pathogenesis. However, our ability to identify causative variants is limited. To identify causal CHD genes that are associated with specific molecular functions, the study used prior knowledge to filter de novo variants from 2,881 probands with sporadic severe CHD.

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets.

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success.

Pharos: Collating protein information to shed light on the druggable genome.

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd.

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO).

The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins.

Genomics, epigenomics, transcriptomics, proteomics and metabolomics efforts rapidly generate a plethora of data on the activity and levels of biomolecules within mammalian cells. At the same time, curation projects that organize knowledge from the biomedical literature into online databases are expanding. Hence, there is a wealth of information about genes, proteins and their associations, with an urgent need for data integration to achieve better knowledge extraction and data reuse.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download.

L1000CDS: LINCS L1000 characteristic direction signatures search engine.

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS.

Reprint of "Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction".

With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction.

Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction.

With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction.

GEO2Enrichr: browser extension and server app to extract gene sets from GEO and analyze them for biological functions.

Motivation: Identification of differentially expressed genes is an important step in extracting knowledge from gene expression profiling studies. The raw expression data from microarray and other high-throughput technologies is deposited into the Gene Expression Omnibus (GEO) and served as Simple Omnibus Format in Text (SOFT) files. However, to extract and analyze differentially expressed genes from GEO requires significant computational skills.

Lean Big Data integration in systems biology and systems pharmacology.

Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs.

Drug/Cell-line Browser: interactive canvas visualization of cancer drug/cell-line viability assay datasets.

Summary: Recently, several high profile studies collected cell viability data from panels of cancer cell lines treated with many drugs applied at different concentrations. Such drug sensitivity data for cancer cell lines provide suggestive treatments for different types and subtypes of cancer. Visualization of these datasets can reveal patterns that may not be obvious by examining the data without such efforts.

Coupled agent-based and finite-element models for predicting scar structure following myocardial infarction.

Following myocardial infarction, damaged muscle is gradually replaced by collagenous scar tissue. The structural and mechanical properties of the scar are critical determinants of heart function, as well as the risk of serious post-infarction complications such as infarct rupture, infarct expansion, and progression to dilated heart failure. A number of therapeutic approaches currently under development aim to alter infarct mechanics in order to reduce complications, such as implantation of mechanical restraint devices, polymer injection, and peri-infarct pacing.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data.

Mechanical boundary conditions bias fibroblast invasion in a collagen-fibrin wound model.

Because fibroblasts deposit the collagen matrix that determines the mechanical integrity of scar tissue, altering fibroblast invasion could alter wound healing outcomes. Anisotropic mechanical boundary conditions (restraint, stretch, or tension) could affect the rate of fibroblast invasion, but their importance relative to the prototypical drivers of fibroblast infiltration during wound healing--cell and chemokine concentration gradients--is unknown. We tested whether anisotropic mechanical boundary conditions affected the directionality and speed of fibroblasts migrating into a three-dimensional model wound, which could simultaneously expose fibroblasts to mechanical, structural, steric, and chemical guidance cues.

Mechanical regulation of fibroblast migration and collagen remodelling in healing myocardial infarcts.

Effective management of healing and remodelling after myocardial infarction is an important problem in modern cardiology practice. We have recently shown that the level of infarct anisotropy is a critical determinant of heart function following a large anterior infarction, which suggests that therapeutic gains may be realized by controlling infarct anisotropy. However, factors regulating infarct anisotropy are not well understood.

Regional mechanics determine collagen fiber structure in healing myocardial infarcts.

Following myocardial infarction, the mechanical properties of the healing infarct are an important determinant of heart function and the risk of progression to heart failure. In particular, mechanical anisotropy (having different mechanical properties in different directions) in the healing infarct can preserve pump function of the heart. Based on reports of different collagen structures and mechanical properties in various animal models, we hypothesized that differences in infarct size, shape, and/or location produce different patterns of mechanical stretch that guide evolving collagen fiber structure.

Methods for photocrosslinking alginate hydrogel scaffolds with high cell viability.

Methods for seeding high-viability (>85%) three-dimensional (3D) alginate-chondrocyte hydrogel scaffolds are presented that employ photocrosslinking of methacrylate-modified alginate with the photoinitiator VA-086. Comparison with results from several other photoinitiators, including Irgacure 2959, highlights the role of solvent, ultraviolet exposure, and photoinitiator cytotoxicity on process viability of bovine chondrocytes in two-dimensional culture. The radicals generated from VA-086 photodissociation are shown to be noncytotoxic at w/v concentrations up to 1.