A High Performance Computing Cluster (TIGSS HPCC) has been established in 2012 specifically tailored for Bioinformatics / Computational Biology research. The compute cluster is located in CSC building.
We also provide Bioinformatics consultation and computational analyses of high-throughput data, not limited to Next-generation sequencing data. We follow best practices as specified in many peer reviewed journals and protocol articles. We document every step of the analyses so that you will be able to replicate the analyses if you choose to do so. The following is the simple price structure for data analyses:
- Initial Bioinformatics Consultation / Discussions of Project Design: FREE
- Small assistance / Custom analysis steps / Preliminary analyses ( less than 2 hours): FREE
- Comprehensive data analyses (Internal): $50 / hr
- Comprehensive data analyses (External Academic / Federal): $75 / hr
- Comprehensive data analyses (External Commercial): $100 / hr
Since many tools and open source software produces different outputs in different data formats, please contact us regarding your requirements.
Contact for more information.
lncRNApipe is a reference annotation based automated pipeline to identify non-coding RNAs, both known and novel from RNA-Seq reads. It
Multi-parental recombinant inbred populations, such as the Collaborative Cross (CC) mouse genetic reference population, are increasingly being used for analysis
The TIGSS High Performance Computing cluster hosts a broad range of tools such as BWA, ABySS, SOAPdenovo, Velvet, TopHat, Cufflinks,
The TIGSS High Performance Compute Cluster (HPCC) is tailored for bioinformatics and computational Biology applications, sequence assembly, alignment and analysis
Provides expansive suite of tools for interpretation of omic data including upstream regulators and downstream effects, cross-experiment comparison of pathways
Access to Texas A&M Institute for Genome Sciences and Society (TIGSS) Computational Resources is provided to all participants. By default
Comprehensive suite of tools for analysis of next-gen sequencing data including resequencing data, workflow management, read mapping, de novo assembly,