# Trinity: Transcriptome assembly for genetic and functional analysis of cancer

> **NIH NIH U24** · BROAD INSTITUTE, INC. · 2020 · $153,096

## Abstract

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, and being
able to tackle analyses of bulk RNA-Seq as well as transcriptomes of individual tumor cells. 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, fusion transcripts, 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, executed millions of times by
thousands of rsearchers, over 4k literature citations, and now includes a host of modules for downstream
analyses, contributed by the Trinity development team or contributed by 3rd party developers. Here, we will
continue to enhance and maintain Trinity and further develop our Trinity Cancer Transcriptome Analysis Tookit
(CTAT) as leading tool suite for bulk and single-cell 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, leveraging new sequencing
technologies and integrating genome data with genome-free assembly (Aim 2). We will integrate Trinity CTAT
into the NCI cloud computing platform via FireCloud for scalable cancer transcriptome data processing and
analyses (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 in diverse modalities.

## Key facts

- **NIH application ID:** 10151318
- **Project number:** 3U24CA180922-08S1
- **Recipient organization:** BROAD INSTITUTE, INC.
- **Principal Investigator:** Eric Banks
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $153,096
- **Award type:** 3
- **Project period:** 2013-09-17 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10151318

## Citation

> US National Institutes of Health, RePORTER application 10151318, Trinity: Transcriptome assembly for genetic and functional analysis of cancer (3U24CA180922-08S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10151318. Licensed CC0.

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