Bioinformatics Tools for the Cloud

Brought to you by: ddsommer

Last Update: 2015-08-07

Browse Code

Get an email when there's a new version of Bioinformatics Tools for the Cloud

The project is for building a suite of bioinformatics tools that run on the Cloud. The tools will mostly be targeted at MapReduce applications for genome assembly and genome annotation.

Project Activity

See All Activity >

Follow Bioinformatics Tools for the Cloud

Bioinformatics Tools for the Cloud Web Site

Other Useful Business Software

Ship Agents Faster

Transform your applications and workflows into powerful agentic systems at global scale.

Gemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.

Get Started Free

Rate This Project

User Reviews

Be the first to post a review of Bioinformatics Tools for the Cloud!

Additional Project Details

Registered

2009-10-20

Report inappropriate content

Ship Agents Faster

Transform your applications and workflows into powerful agentic systems at global scale.

Get Started Free

Recommended Projects

SIBsim4
SIBsim4: an extensive rewrite of the sim4 program to improve execution speed and provide more informative output. Written in C, mainly targeted at Linux and Unix. Keywords: bioinformatics, genome, transcriptome, EST, splicing, exon, intron.
GenomeRunner
Annotation and enrichment of Next-Gen sequencing data
CONTIGuator
CONTIGuator is a Python script for Linux environments whose purpose is to speed-up the bacterial genome assembly process and to obtain a first insight of the genome structure using the well-known artemis comparison tool (ACT).
EvidentialGene
Evidence Directed Gene Construction for Eukaryotes
ChIP-Seq
The ChIP-Seq software provides methods for the analysis of ChIP-seq data and other types of mass genome annotation data. The most common analysis tasks include positional correlation analysis, peak detection, and genome partitioning into signal-rich and signal-depleted regions.