# Informatics Platform for Mammalian Gene Regulation at Isoform-level

> **NIH NIH R01** · STATE UNIVERSITY NEW YORK STONY BROOK · 2020 · $343,649

## Abstract

SUMMARY
With each successive discovery in genetics, the true dynamic complexity of the human genome has become
increasingly apparent, requiring relatively consistent updates to the technical definition of the word “gene”. It is
now understood that the notion of “one gene makes one protein that functions in one signaling pathway” in
human cells is overly simplistic, because majority of the human genes produce multiple functional products
(transcript variants and protein isoforms), through alternative transcription and/or alternative splicing.
Therefore, our central hypothesis is that the isoform-level gene products – “transcript variants” and “protein
isoforms” are the basic functional units in a mammalian cell, and accordingly, the informatics platforms for
managing and analyzing gene regulation data both in normal and disease cells should adopt “gene isoform
centric” rather than “gene centric” approaches. Towards the goal of broadly impacting gene regulation and
functional studies at gene isoform-level, we have been developing novel algorithms for analyses of genome-
wide transcriptome (RNA-seq and exon-array) and protein-DNA binding (ChIP-seq) data, and for extending the
gene-level orthology mapping to exon- and transcript-level mapping between the orthologous human and
mouse genes. By applying these novel algorithms on public datasets, we have observed significant expression
differences between different sample groups (e.g., developmental stages, cancer subtypes, normal vs cancer)
for numerous genes at the isoform-level but not at the overall gene-level, and experimentally validated the
`significant' isoforms using RT-qPCR in independent bio-specimens. While the application of these algorithms
has led to the development of new methods for diagnosis of glioblastoma or a sub-type thereof, the isoform-
level transcriptome analyses results also led to some challenging questions – for example – How are the
alternative promoters of a gene show switch-like opposing patterns of activity (while one promoter is up- the
other is down-regulated in one condition vs the other), and how are different splice-variants of a gene show
opposing expression patterns in cancer versus normal tissue samples? We currently lack informatics methods
to address these challenging questions. Therefore, we propose to develop novel statistical methods (1) for
integrative cluster analysis of isoform-level gene expression information from exon-array and RNA-seq
platforms, (2) for identification of differential transcript/isoform usage in heterogeneous cancer samples, and
(3) for identification of alternative transcription/splicing quantitative trait locus (sQTL) in tumor adjusted by
somatic genetic and epigenetic changes. And, (4) the novel predictions from these algorithms will be
experimentally validated by performing Chromatin immunoprecipitation (ChIP), dual-luciferase reporter assay
and CRISPR/Cas9 genome editing in U87 and A172 cells. The novel bioinformatics methods devel...

## Key facts

- **NIH application ID:** 10273985
- **Project number:** 7R01LM011297-09
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** RAMANA V DAVULURI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $343,649
- **Award type:** 7
- **Project period:** 2020-11-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10273985

## Citation

> US National Institutes of Health, RePORTER application 10273985, Informatics Platform for Mammalian Gene Regulation at Isoform-level (7R01LM011297-09). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10273985. Licensed CC0.

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