Trinity: Transcriptome Assembly for Genetic and Functional Analysis of Cancer

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Award Type: 
U24
Award Year: 
2013
PI Name(s): 
Brian Haas
Institution(s): 
Broad Institute
Status: 
Active
Description: 

The cancer transcriptome is shaped by genetic changes, variation in gene transcription, mRNA processing, editing and stability, and the cancer microbiome. Deciphering this variation and understanding its implications on tumorigenesis requires sophisticated computational analyses. Most RNA-Seq analyses rely on methods that first map short reads to a reference genome, and then compare them to annotated transcripts or assemble them. However, this strategy can be limited when the cancer genome is substantially different than the reference or for detecting sequences from the cancer microbiome. ‘Assembly first' (de novo) methods that combine reads into transcripts without any mapping are a compelling alternative. The assembled transcriptome can then be used to identify mutations, splicing patterns, expression levels, tumor-associated microbes, and – if collected from single cells – characterize tumor heterogeneity. There is thus an enormous need for computationally efficient, accurate and user friendly tools for transcriptome reconstruction and analysis in cancer. Here, we will enhance and maintain Trinity, a leading software for de novo RNA-Seq assembly, for the cancer domain. Working with a network of cancer researchers on Driving Cancer Projects, we will tailor analytic modules for critical tasks in cancer biology, including mutation detection (SNPs, fusion transcripts, alternative splicing) and identification of foreign microbial transcripts, extending discoveries beyond the limits of a reference genome. We will continue to update the Trinity software to enhance its core algorithm, leveragimg new sequencing technologies as they arise, and extending support of post-assembly annotation and expression analyses. We will enhance the Trinity software for different computational environments, including user-friendly interfaces to high performance computing infrastructure freely available to researchers, through a web-based front-end to large-scale national compute resources. Finally, we will grow the Trinity cancer user community, using online and in-person training and support.

Reporter URL:  https://reporter.nih.gov/project-details/10469550