Cerebral cavernous malformations (CCMs) are low-flow vascular malformations in the brain associated with recurrent hemorrhage and seizures. Despite accumulating evidence demonstrating the role of lncRNAs in cerebrovascular disorders, their identification in CCMs pathology remains unknown. The objective of this study ...
Read More »LSTrAP – efficiently combining RNA sequencing data into co-expression networks
Since experimental elucidation of gene function is often laborious, various in silico methods have been developed to predict gene function of uncharacterized genes. Since functionally related genes are often expressed in the same tissues, conditions and developmental stages (co-expressed), functional ...
Read More »Predicting Gene Function and Protein-Protein Interactions with RNA-Seq Co-expression Data
This presentation is by Hyojin Lee, an undergraduate student from Princeton University. Hyojin describes her summer research project with the BD2K-LINCS DCIC in the Ma’ayan Lab at the Icahn School of Medicine at Mount Sinai. http://lincs-dcic.org/summer-research… http://icahn.mssm.edu/labs/maayan http://amp.pharm.mssm.edu/archs4/ https://github.com/MaayanLab/predicti… Abstract ...
Read More »Researchers use single-cell RNA-Seq to peer into the black box of co-expression networks
Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and biochemical mechanisms that are active in normal and disease processes. However, co-expression analysis is often treated as a black box with results being ...
Read More »(Post) Graduate Course ‘The Power of RNA-Seq’
Date: February 10th-12th, 2016 Location: Room PC95 (first floor), RADIX building (107), Wageningen Campus, Droevendaalsesteeg 1, Wageningen, the Netherlands Directions: directions (check also Map Wageningen UR Campus) Language: English Group size: maximum of 35 participants Credits: 0.8 ECTS Registration and ...
Read More »MAST – a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. Researchers at the Fred Hutchinson Cancer Research Center propose a two-part, generalized linear model for ...
Read More »GeneFriends – a human RNA-seq-based gene and transcript co-expression database
Co-expression networks have proven effective at assigning putative functions to genes based on the functional annotation of their co-expressed partners, in candidate gene prioritization studies and in improving our understanding of regulatory networks. The growing number of genome resequencing efforts ...
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