A5009891693- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- RNA and protein synthesis mechanisms
- Genomics and Rare Diseases
- Molecular Biology Techniques and Applications
- Gene expression and cancer classification
- Microbial Metabolic Engineering and Bioproduction
- CRISPR and Genetic Engineering
- Cell Image Analysis Techniques
- Heat shock proteins research
- Single-cell and spatial transcriptomics
- Cancer Genomics and Diagnostics
- Nanoplatforms for cancer theranostics
- Genetic Associations and Epidemiology
- RNA modifications and cancer
- Endoplasmic Reticulum Stress and Disease
- interferon and immune responses
- Computational Drug Discovery Methods
- RNA Research and Splicing
Ben-Gurion University of the Negev
2021-2024
Abstract The sensitivity of the protein-folding environment to chaperone disruption can be highly tissue-specific. Yet, organization system across physiological human tissues has received little attention. Through computational analyses large-scale tissue transcriptomes, we unveil that is composed core elements are uniformly expressed tissues, and variable differentially fit with tissue-specific requirements. We demonstrate via a proteomic analysis muscle-specific signature functional...
DifferentialNet is a novel database that provides users with differential interactome analysis of human tissues (http://netbio.bgu.ac.il/diffnet/). Users query by protein, and retrieve its protein-protein interactions (PPIs) per tissue via an interactive graphical interface. To compute PPIs, we integrated available data experimentally detected PPIs RNA-sequencing profiles tens gathered the Genotype-Tissue Expression consortium (GTEx) Human Protein Atlas (HPA). We associated each PPI score...
Significance Bioinformatic analysis revealed that approximately 40% of human messenger RNAs contain upstream open reading frames (uORFs) in their 5′ untranslated regions. Some these sequences are translated, but the function encoded peptides remains unknown. Our study a uORF encoding for peptide exhibiting kinase inhibitory activity. This uORF, PKC family member, possess typical pseudosubstrate motif, which autoinhibits catalytic activity all PKCs. Using mouse models and cells, we show this...
Differential network analysis, designed to highlight changes between conditions, is an important paradigm in biology. However, differential analysis methods have been typically compare two conditions and were rarely applied multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation lacking.Here, we present a framework assessing the ability of human tissue interactomes tissue-selective processes disorders. For this, created benchmark 6499...
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts answer this question were limited testing a few candidate mechanisms. To at larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), machine learning approach predict that underlie diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features inferred from heterogeneous omics datasets....
Abstract Motivation The distinct functionalities of human tissues and cell types underlie complex phenotype–genotype relationships, yet often remain elusive. Harnessing the multitude bulk single-cell transcriptomes while focusing on processes can help reveal these functionalities. Results Tissue-Process Activity (TiPA) method aims to identify that are preferentially active or under-expressed in specific contexts, by comparing expression levels process genes between contexts. We tested TiPA...
Abstract The distinct functions and phenotypes of human tissues cells derive from the activity biological processes that varies in a context-dependent manner. Here, we present Process Activity (ProAct) webserver estimates preferential tissues, cells, other contexts. Users can upload differential gene expression matrix measured across contexts or use built-in 34 tissues. Per context, ProAct associates ontology (GO) with estimated scores, which are inferred input matrix. visualizes these...
ABSTRACT Motivation Differential network analysis, designed to highlight interaction changes between conditions, is an important paradigm in biology. However, analysis methods have been typically compare few were rarely applied protein networks (interactomes). Moreover, large-scale benchmarks for their evaluation lacking. Results Here, we assess five by applying them 34 human tissues interactomes. For this, created a manually-curated benchmark of 6,499 tissue-specific, gene ontology...
Abstract About 40% of human mRNAs contain upstream open reading frames (uORFs) in their 5’-untranslated regions. Some these uORF sequences thought to attenuate scanning ribosomes or lead mRNA degradation were recently shown be translated, although the function most encoded peptides remains unknown. Our recent study was first reveal a uORF-encoded peptide exhibiting kinase inhibitory activity (Jayaram DR, et al. PNAS (2021)). This (uORF2), protein C (PKC) family member (PKCeta) main ORF,...
Abstract Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic are typically oblivious tissue contexts. Here we report machine-learning framework, denoted “Tissue Risk Assessment Causality by Expression for variants” (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/ ), offers two advancements. First, TRACEvar predicts specific tissues. This was achieved creating...
ABSTRACT Genetic studies of Mendelian and rare diseases face the critical challenges identifying pathogenic gene variants their modes-of-action. Previous efforts rarely utilized tissue-selective manifestation these for elucidation. Here we introduce an interpretable machine learning (ML) platform that utilizes heterogeneous large-scale tissue-aware datasets human genes, rigorously, concurrently quantitatively assesses hundreds candidate mechanisms per disease. The resulting ML is applicable...