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Related Concept Videos

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Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Updated: May 9, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts.

Liang Sun1, Haitao Luo, Dechao Bu

  • 1Bioinformatics Research Group, Advanced Computing Research Laboratory, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Laboratory of Bioinformatics and Non-coding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.

Nucleic Acids Research
|July 30, 2013
PubMed
Summary
This summary is machine-generated.

A new tool, the Coding-Non-Coding Index (CNCI), accurately distinguishes protein-coding from non-coding transcripts using nucleotide triplets. This method works across species and on incomplete sequences, aiding transcript annotation.

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Last Updated: May 9, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

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Published on: July 12, 2022

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp
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Published on: June 20, 2018

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution
10:45

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Classifying protein-coding and non-coding transcripts is challenging, particularly for species with limited genomic annotation.
  • High-throughput sequencing generates vast amounts of transcript data requiring robust analytical tools.

Purpose of the Study:

  • To develop and evaluate a novel computational tool, the Coding-Non-Coding Index (CNCI), for distinguishing protein-coding from non-coding transcripts.
  • To assess the efficacy of CNCI in classifying incomplete transcripts and sense-antisense pairs.
  • To apply CNCI for cross-species transcript classification and cataloging long non-coding RNAs.

Main Methods:

  • Developed the CNCI tool based on profiling adjoining nucleotide triplets (k-mers) within transcript sequences.
  • Evaluated CNCI's performance on transcriptomes from various species, including vertebrates, invertebrates, and plants.
  • Utilized CNCI to identify and catalog long non-coding RNAs in orangutan.

Main Results:

  • CNCI effectively distinguishes protein-coding from non-coding sequences independent of existing annotations.
  • The tool demonstrated high accuracy in classifying transcripts assembled from whole-transcriptome sequencing data.
  • CNCI successfully identified gene evolutionary divergence and provided a novel long non-coding RNA catalog for orangutan.

Conclusions:

  • CNCI is a powerful and accurate signature tool for classifying protein-coding and non-coding transcripts, even with incomplete data.
  • The method is applicable across diverse species, aiding in genomic annotation and evolutionary studies.
  • CNCI facilitates the discovery of novel non-coding RNAs, expanding our understanding of the transcriptome.