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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Published on: February 18, 2022

sORF finder: a program package to identify small open reading frames with high coding potential.

Kousuke Hanada¹, Kenji Akiyama, Tetsuya Sakurai

¹Plant Science Center, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. kohanada@psc.riken.jp

Bioinformatics (Oxford, England)

|December 17, 2009

Summary

This summary is machine-generated.

sORF finder identifies small open reading frames (sORFs) with high coding potential. It analyzes nucleotide bias and amino acid substitution rates to find functional sORFs.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Small open reading frames (sORFs) play crucial roles in gene regulation and protein synthesis.
Identifying functional sORFs is challenging due to their small size and potential for pseudogenes.

Purpose of the Study:

To develop and present sORF finder, a novel computational tool for identifying small open reading frames (sORFs) with high coding potential.
To provide a robust method for evaluating the functional constraint of sORFs at both nucleotide and amino acid levels.

Main Methods:

Utilizes nucleotide composition bias analysis to distinguish coding from non-coding sequences.
Evaluates synonymous and non-synonymous substitution rates to assess functional constraint at the amino acid level.
Implements a program package for efficient and accurate sORF identification.

Main Results:

sORF finder successfully identifies sORFs exhibiting characteristics of high coding potential.
The tool differentiates potential coding sORFs based on sequence composition and evolutionary constraints.
Demonstrates the utility of analyzing nucleotide bias and substitution rates for sORF discovery.

Conclusions:

sORF finder is an effective program package for identifying small open reading frames (sORFs) with significant coding potential.
The application aids in the discovery of functional sORFs by assessing nucleotide composition and amino acid-level functional constraints.
Provides a valuable resource for researchers investigating sORF biology and function.