Welcome to OpenOmics’s documentation!

This Python package provide a series of tools to integrate and query the genomics, transcriptomics, proteomics, and clinical data (aka, the multi-omics data). With scalable data-frame manipulation tools, OpenOmics facilitates the common coding tasks when preparing data for RNA-seq bioinformatics analysis.

Features

• Provides a bioinformatics workflow to generate integrative results from multi-omics data.

• Facilitates integration of various bio-databases, multi-omics expression, genomics, and clinical data.

• Highly flexible to different data types and missing data.

• Provides researchers with means to consistently store and explore their experimental datasets.

• Enables scalable performance with parallel computing, while easily configurable to deploy on both single machine and a cluster.

Table of Content

Using OpenOmics Python API

• Installation
  • Stable release
  • From sources
• Getting started
  • Loading a single-omics dataframe
  • Creating a multi-omics dataset
  • Adding clinical data as sample attributes
  • Import an external database
  • Annotate your expression dataset with attributes
• Loading a multi-omics dataset
  • Load single omics expressions for MessengerRNA, MicroRNA, LncRNA
  • Create a MultiOmics dataset
  • Accessing clinical data
  • To match samples accross different multi-omics, use
• External annotation databases
  • Import GENCODE human release 32
  • Setting the cache download directory
• Creating biological interaction networks
• Preparing data for downstream analyses
  • To prepare the data for classification
  • Log2 transform the mRNA, microRNA, and lncRNA expression values
  • Classification of Cancer Stage

API Reference

• MultiOmics data
• Annotation interfaces
• Genomic databases
• Sequence databases
• Interaction databases
• Disease databases
• Ontology databases
• Utilities

MISC

• Frequently Asked Questions (FAQ)

Contributing and releases

• Contributing
• Release history