# Developing Ribolog: A toolbox for comprehensive analysis of ribosome profiling data

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $25,286

## Abstract

ABSTRACT
Ribosome profiling technology provides quantitative insights into translational regulation at a genomic scale, a
mechanism that plays a crucial role in several important biological processes from embryonic development to
carcinogenesis. Despite advances in ribosome profiling data analysis methods, a number of challenges remain
to be addressed including tests with small sample sizes and read count biases due to ribosome stalling. I
propose to develop a logistic-regression-based method called “Ribolog” to model ribosome profiling data in
which individual sequencing reads are units of observation and translation efficiency is calculated as the odds
of observing “RPF” vs. “RNA” reads. The logistic regression model has several distinct advantages over the
methods based on negative binomial modeling of RNA-seq and Ribo-seq read counts: (i) It neither assumes
equality of mean and variance nor does it require estimation of dispersion. (ii) It has much higher statistical
power than count-based methods because in this model, statistical sample size equals the number of reads,
not the number of replicates. (iii) It works with single sample per condition (unreplicated datasets); therefore, it
is applicable to clinical or single cell data. (iv) It is easily adaptable for experiments with synthetic spike-in
standards. (v) In replicated datasets, it enables empirical significance testing and calculation of novel
informative QC measures. (vi) It can accommodate complex experimental designs involving multiple samples
and covariates in one model; and is not limited to pairwise comparisons. Our preliminary results applying
Ribolog to a dataset comprising two non-metastatic and two corresponding metastatic cell lines indicate that
this method is indeed highly powerful and 80-90% reproducible among biological replicates. Additionally, we
provide modules for stalling bias correction, meta-analysis, model selection, experimental design and quality
control. Combining Ribolog with other analytical methods – some developed previously in our lab – we
construct a multiomic framework to integrate Ribo-seq data with RNA-seq, tRNA profiling, genetic variation,
miRNA, codon optimality etc. to identify the driving causes of translation dynamics and contribute to the next
generation of multi-layered genotype-to-phenotype maps. The method will be implemented in R and made
available to the scientific community as an open-access package. Given my expertise in statistics, my
continued training in experimental biology, access to state-of-the-art datasets, and support from multiple labs
with expertise in computational and experimental studies of translation and broader genomic topics and
technologies, I am uniquely situated to tackle this problem. In addition to providing novel insights into the
biology of translational control and benefiting the community, this project will enable me to extend my training
in a number of exciting areas that are most relevant to my career devel...

## Key facts

- **NIH application ID:** 10213543
- **Project number:** 3F32GM133118-01S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Hosseinali Asgharian
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $25,286
- **Award type:** 3
- **Project period:** 2019-08-01 → 2020-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10213543

## Citation

> US National Institutes of Health, RePORTER application 10213543, Developing Ribolog: A toolbox for comprehensive analysis of ribosome profiling data (3F32GM133118-01S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10213543. Licensed CC0.

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