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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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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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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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RNA Coding Potential Prediction Using Alignment-Free Logistic Regression Model.

Ying Li1, Liguo Wang2,3

  • 1Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA.

Methods in Molecular Biology (Clifton, N.J.)
|December 16, 2020
PubMed
Summary
This summary is machine-generated.

The Coding-Potential Assessment Tool (CPAT) accurately distinguishes protein-coding from noncoding RNAs using sequence-based linguistic features. This tool provides probabilities for RNA protein-coding likelihood, aiding genomic research.

Keywords:
LincRNALncRNALogistic regressionNoncoding RNAPredictionProtein coding

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Distinguishing protein-coding from noncoding RNAs is crucial for understanding gene function and regulation.
  • Traditional methods often rely on experimental validation, which can be time-consuming and resource-intensive.

Purpose of the Study:

  • To introduce the Coding-Potential Assessment Tool (CPAT), a computational method for classifying RNA sequences.
  • To provide a rapid and accurate classification of RNA protein-coding potential using linguistic sequence features.

Main Methods:

  • CPAT utilizes a logistic regression model trained on linguistic features derived from RNA sequences.
  • The tool accepts nucleotide sequences or genomic coordinates as input.
  • Prebuilt models are available for human, mouse, zebrafish, and fly genomes.

Main Results:

  • CPAT accurately and quickly distinguishes between protein-coding and noncoding RNAs.
  • The output is a probability score (0-1) indicating the likelihood of protein coding.
  • CPAT can be accessed online or installed locally.

Conclusions:

  • CPAT offers an efficient computational approach for assessing RNA protein-coding potential.
  • The tool facilitates large-scale analysis of RNA function across different species.
  • Users can train custom models for novel genomes, expanding CPAT's applicability.