RNA-Seq studies indicate that the cancer transcriptome are 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 differerit 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. Trinity, first released in mid-2011 and freely available as Open Source, is the leading software for de novo RNA-Seq assembly, with over 16,000 downloads, 177 literature citations, and a host of modules for downstream analyses, contributed by 3rd party developers. While widely-adopted in the general research community, Trinity (and any de novo RNA-Seq assembly) is only now emerging in the cancer domain. Here, we will enhance and maintain Trinity as a leading tool for cancer transcriptomics. We will tailor analytic modules for critical tasks in cancer biology, working with a network of cancer researchers on Driving Cancer Projects (Aim 1). We will continue to update the Trinity software to enhance the core algorithm, leverage new sequencing technologies as they arise, and incorporate additional 3rd party tools (Aim 2). We will enhance the Trinity software for different computational environments, including user-friendly interfaces to high performance computing infrastructure freely available to any NCI-funded researcher (Aim 3). We will grow the Trinity cancer user community, using online and in- person training and support (Aim 4), to allow any cancer researcher to leverage it.
The RNA in cancer cells can be altered in its sequence, level and structure, and understanding these alterations can help us diagnose disease and find new drug targets. To find these variations, we need to concatenate together short sequences measured experimentally. Here, we enhance and maintain the Trinity software for this purpose, and build computational resources and training materials to help cancer researchers use it.
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