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Related Concept Videos

Ribosome Profiling02:24

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.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

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Related Experiment Video

Updated: May 19, 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

A modified statistically optimal null filter method for recognizing protein-coding regions.

Lei Zhang1, Fengchun Tian, Shiyuan Wang

  • 1College of Communication Engineering, Chongqing University, Chongqing 400044, China. leizhang@cqu.edu.cn

Genomics, Proteomics & Bioinformatics
|August 25, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new filter method for identifying protein-coding regions in DNA. The enhanced model achieves high accuracy in gene prediction, improving speed and precision for biological signal processing.

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Last Updated: May 19, 2026

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

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Published on: July 12, 2022

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
08:23

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

Published on: February 18, 2022

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
13:00

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA

Published on: December 2, 2009

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Signal Processing

Background:

  • Accurate protein-coding gene prediction is crucial for understanding genomic DNA.
  • Existing methods face challenges in precisely identifying coding regions within uncharacterized sequences.

Purpose of the Study:

  • To develop a modified filter method for enhanced protein-coding region recognition.
  • To improve the accuracy and speed of gene prediction in genomic sequences.

Main Methods:

  • Utilized a statistically optimal null filter (SONF) theory for a modified filter approach.
  • Implemented a square deviation gain (SDG) metric for coding region identification.
  • Designed an SDG amplification model with Class I and Class II enhancement to suppress non-coding regions.

Main Results:

  • The modified model achieved high performance metrics: 91.4% sensitivity, 96% specificity, and 93.7% precision.
  • Evaluated at the nucleotide level using benchmark datasets.
  • Demonstrated superior performance compared to existing gene prediction methods.

Conclusions:

  • The proposed model offers a potentially valuable tool for the gene finding field.
  • This method enhances the precision and speed of identifying protein-coding regions.
  • The approach shows promise for advancing biological signal processing in genomics.