Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells. From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data. Abstract RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS).
These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner—a multi-level bioinformatics protocol and pipeline—has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks.
To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. Copyright: © 2015 Li et al. Introduction Methods Fig 1. 1. 2. Human Disease Modeling Reveals Integrated Transcriptional and Epigenetic Mechanisms of NOTCH1 Haploinsufficiency. To view the full text, please login as a subscribed user or purchase a subscription.
Click here to view the full text on ScienceDirect. Figure 1 Transcriptional Mechanisms in EC Differentiation and Response to Shear Stress (A) Stages of EC differentiation analyzed. (B) Unique signature of EC differentiation stages by RNA-seq. (C) Top stage-predictive TFs identified by random forest classifier.
(D) Left: expression of TFs whose motifs were tested in the corresponding rows on the right. (E) Left: diagram of static-specific pro-inflammatory genes (pink). In (B–D): n = 5. Cytoscape: An Open Source Platform for Complex Network Analysis and Visualization. Sign In. Pathway enrichment - Gene Network. Wisdom of Crowds for Robust Gene Network Inference. Gene Coexpression Networks. Welcome to the Weighted Gene Co-Expression Network Page.
Network analysis software. Library / Module For Network Analysis. CoExpress. A Tool for an Effective Co-Expression Analysis of Large Microarray Data Sets Co-expression (CE) analysis of microarray data may provide interesting insights in understanding the gene and transcript level regulations in biological samples.
It allows gene-networks reconstruction, disease pattern recognition, inferring of causal genes, etc. However, due to high computational costs and memory limitations, there is still a need in effective and user-friendly tools for the analysis of CE. Here we propose a stand-alone software tool CoExpress for the interactive CE analysis of microarray data. The software is a user-friendly and allows on-the-fly study of CE, including: expression data preprocessing, possibility for customized preprocessiong usin R; filtering; advanced interactive analysis of the expression profiles; building and visualization of CE matrix using correlation or mutual information metrics; comparing the detected co-expressions with predicted targets (for miRNA:mRNA interaction).
Any Tutorial For Co-Expression(Network) Analysis? Genetic programs in human and mouse early embryos revealed by single-cell RNA[thinsp]sequencing. Primary authors These authors contributed equally to this work.Zhigang Xue & Kevin Huang Affiliations Translational Center for Stem Cell Research, Tongji Hospital, Department of Regenerative Medicine, Tongji University School of Medicine, Shanghai 200065, China Zhigang Xue, Yun Feng, Zhenshan Liu, Qiao Zeng, Liming Cheng & Yi E.
Sun Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California 90095, USA Kevin Huang, Chaochao Cai, Steve Horvath & Guoping Fan State Key Laboratory of Reproductive Medicine, Center of Clinical Reproductive Medicine, First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China Lingbo Cai, Chun-yan Jiang & Jia-yin Liu Contributions Z.X., K.H., J.L. and G.F. designed the study. Competing financial interests The authors declare no competing financial interests. Tutorials for WGCNA R package. Peter Langfelder and Steve Horvath Dept. of Human Genetics, UC Los Ageles (PL, SH), Dept. of Biostatistics, UC Los Ageles (SH) Peter (dot) Langfelder (at) gmail (dot) com, SHorvath (at) mednet (dot) ucla (dot) edu This page provides a set of tutorials for the WGCNA package.
We illustrate various aspects of data input, network construction, module detection, relating modules and genes to external information etc. Before going through the tutorials, please make sure you have installed (the newest version of) the WGCNA package and all packages it depends on. We provide three introductory tutorials (I - III), each split into smaller sections for easier reading, and we link to more advanced tutorials that describe research analyses in which we used WGCNA. The first tutorial guides the reader through an analysis of a single empirical gene expression data set. The tutorials on this page were last updated on June 6, 2014.
WGCNA background and glossary I. Data description and download R Tutorial II.