Introduction

Version: 0.1.1
Authors: William Montgomery, Gareth Halladay, Amber Scott, Anela Tosevska, Frank Burkholder, Adam Richards, Andrew Gaines
Web site: https://biof-git.colorado.edu/hackathon/summarizing_rnaseq
Documentation: ?????
Copyright: This document has been placed in the public domain.
License: is released under the MIT License

Purpose

RNA-Seq technologies have revolutionized the biological sciences. One challenging aspect of these data is that there is so much information contained within a given experiment that results sections do little more than scratch the surface. Results change with time, because model get changed or updated and things like annotations are continually changing.

The first objective here is to create an interactive results summary environment. All version of the results can be displayed and iterative updates should be made easy. The backbone of this objective will be with Flask and AWS. This ties into the second objective where we want to create an environment that encourages the comparison of models. Sklearn has become one of the easiest toolkits to carry out predictive analytics and it will be at the center of our solution for this objective.

Methods

Examples

Lets turn these two analyses into something shiny and new.

• https://ajrichards.github.io/public/pieris-supplement/index.html
• https://ajrichards.github.io/public/aad/index.html htsint (High-Throughput Sequencing INTegrate) is a Python package used to create gene sets for the study of high-throughput sequencing data. The goal is to create functional modules through the integration of heterogeneous types of data. These functional modules are primarily based on the Gene Ontology, but as the package matures additional sources of data will be incorporated. The functional modules produced can be subsequently tested for significance in terms of differential expression in RNA-Seq or microarray studies using gene set enrichment analysis.
• BLAST mapping
• Gene Ontology queries
• Heatmaps for differential expression analysis
• Creation of gene sets for gene set enrichment analysis
• Visualization of gene sets

Software stack

For more details visit the documentation:

• PostgreSQL
• SQLAlchemy
• htsint
• DESeq2
• sklearn
• Flask

Useful References

• https://www.ncbi.nlm.nih.gov/pubmed/26399714
• http://www.biorxiv.org/content/early/2016/12/02/091280.article-info