# Topological and functional characterization of human translation efficiency covariation network

## TL;DR

This study builds a network to analyze how translation efficiency of genes varies together, revealing new insights into gene regulation.

## Contribution

The paper introduces a novel translation efficiency covariation network for human genes.

## Key findings

- The TEC network shows modular structure, small-world characteristics, and rich-club organization.
- Genes central in the TEC network, like PKM, are often peripheral in RNA co-expression networks.
- Tissue-specific TEC networks reveal context-dependent translation patterns.

## Abstract

Gene co-expression networks based on RNA abundance have identified genes with shared biological functions, common regulatory elements, and physical interactions among their protein products. Although thousands of ribosome profiling datasets are publicly available, they have not been leveraged to construct networks to characterize translation efficiency covariation (TEC) to quantify how translation of different transcripts co-varies across conditions.

We construct and analyze a human TEC network, revealing topological and functional properties distinct from RNA co-expression networks. The TEC network displays modular structure, small-world characteristics, and rich-club organization but differs substantially in node connectivity and neighborhood composition. Comparative analyses show that genes such as PKM, which are central in the TEC network due to their role in translational regulation, are peripheral in RNA co-expression networks. Tissue-specific TEC networks further uncover context-dependent translation patterns. These results suggest that TEC networks provide a complementary framework for understanding gene regulation.

The code for this study is archived on https://zenodo.org/records/17275939 and publicly available at https://github.com/CenikLab/TEC-Network-Analyses. The associated data can be accessed at https://zenodo.org/records/17275970.