Funding

● Genomics-driven personalized immunotherapy in ovarian cancer, Funding source: Connecticut Innovations (CI)/Connecticut Bioscience Innovation Fund (CBIF), PI: Pramod Srivastava, Co-PIs: Sahar Al Seesi, Molly Brewer, Ion Mandoiu, and Susan Tannenbaum. $500K, 07/2016-06/2018.

● Azure Microsoft Cloud Grant , Funding Source: Microsoft, PI: Abdel Raouf , Co-PIs: Sahar Al Seesi and Sarah Tasneem, $20K, -07/2016 – 08/2017.

● Gene differential expression analysis for RNA-Seq data on the cloud. Funding source: NASA through Connecticut Space Grant Consortium. PI: Sahar Al Seesi, Co-PIs Amal Abdel Raouf and Sarah Tasneem. $15K, 05/2015-08/2016.

● Spark grant: Oncoimmune, a tumor-specific immunotherapy for the treatment of stage III/IV ovarian cancer. Funding source: University of Connecticut Health Center. PI: Pramod Srivastava, Co-PIs: Sahar Al Seesi , Fei Duan , Angela Kueck, and Ion Mandoiu. $30K, 2013.

Software

IsoEM2

IsoEM2 infers isoform and gene expression levels from high-throughput transcriptome sequencing (RNA-Seq) data. IsoEM2 uses an Expectation-Maximization (EM) algorithm based on a probabilistic model that takes into account the fragment length distribution, with mean/standard deviations specified by the user or automatically inferred when using paired-end reads. The current version (IsoEM2) generates bootstrap-based confidence intervals for the TPM/FPKM estimates and is distributed along with the IsoDE2 package for performing bootstrap-based differential expression analysis.

IsoDE2

IsoDE2 (distributes as part of the IsoEM2 package) performs differential gene and isoform expression analysis for RNA-Seq data both with and without replicates. IsoDE is based on bootstrapping, to compensate for lack of replicates. IsoDE2 relies on IsoEM2, an accurate expectation-maximization algorithm for gene/isoform level estimation that performs fast in-memory bootstrapping.

GeNeo

GeNeo is a suite of tools for predicting neo-epitopes from matched normal and tumor human exome sequencing data coupled with tumor transcriptome sequencing to identify the epitopes expressed in the tumor. One of the main tools in this suite is a somatic variant calling pipeline that makes use of cross sequencing platforms data, multiple somatic callers, and SNV validation steps using targeted single cell sequencing. Epitope calling is done through a tool that uses NetMHC 4.0. The tools are accessible through easy to use graphical user interfaces available on the Galaxy platform.

Epi-Seq

Epi-Seq is a multi-step bioinformatics analysis pipeline that starts from the raw RNA-Seq tumor reads, and produces a set of predicted tumor-specific expressed epitopes. It integrates several bioinformatics tools, including SNVQ for calling SNVs from RNA-Seq and NetMHC 3.0 for epitope prediction.