Biology subjects

Liberzon, A.

Publications and source records attributed to Liberzon, A..

3 recordsLinked to original sources

bioRxiv2017

A unified web platform for network-based analyses of genomic data

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.

genomics↗

bioRxiv2017

A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.\n\nHIGHLIGHTSO_LIA new gene expression profiling method, L1000, dramatically lowers cost\nC_LIO_LIThe Connectivity Map database now includes 1.3 million publicly accessible L1000 perturbational profiles\nC_LIO_LIThis expanded Connectivity Map facilitates discovery of small molecule mechanism of action and functional annotation of genetic variants\nC_LIO_LIThe work establishes feasibility and utility of a truly comprehensive Connectivity Map\nC_LI

genomics↗

bioRxiv2016

GSKB: A gene set database for pathway analysis in mouse

Interpretation of high-throughput genomics data based on biological pathways constitutes a constant challenge, partly because of the lack of supporting pathway database. In this study, we created a functional genomics knowledgebase in mouse, which includes 33,261 pathways and gene sets compiled from 40 sources such as Gene Ontology, KEGG, GeneSetDB, PANTHER, microRNA and transcription factor target genes, etc. In addition, we also manually collected and curated 8,747 lists of differentially expressed genes from 2,526 published gene expression studies to enable the detection of similarity to previously reported gene expression signatures. These two types of data constitute a Gene Set Knowledgebase (GSKB), which can be readily used by various pathway analysis software such as gene set enrichment analysis (GSEA). Using our knowledgebase, we were able to detect the correct microRNA (miR-29) pathway that was suppressed using antisense oligonucleotides and confirmed its role in inhibiting fibrogenesis, which might involve upregulation of transcription factor SMAD3. The knowledgebase can be queried as a source of published gene lists for further meta-analysis. Through meta-analysis of 56 published gene lists related to retina cells, we revealed two fundamentally different types of gene expression changes. One is related to stress and inflammatory response blamed for causing blindness in many diseases; the other associated with visual perception by normal retina cells. GSKB is available online at http://ge-lab.org/gs/, and also as a Bioconductor package (gskb, https://bioconductor.org/packages/gskb/). This database enables in-depth interpretation of mouse genomics data both in terms of known pathways and the context of thousands of published expression signatures.

bioinformatics↗