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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
RNA Structure01:23

RNA Structure

Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
RNA Structure01:19

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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
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RNA Structure01:23

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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...

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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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PicXAA-R: efficient structural alignment of multiple RNA sequences using a greedy approach.

Sayed Mohammad Ebrahim Sahraeian1, Byung-Jun Yoon

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. msahraeian@tamu.edu

BMC Bioinformatics
|February 24, 2011
PubMed
Summary

PicXAA-R is a novel algorithm for the structural alignment of multiple non-coding RNAs (ncRNAs). This method efficiently aligns ncRNAs by considering folding and local similarities, offering fast and accurate results.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Non-coding RNAs (ncRNAs) play crucial roles in biological processes, necessitating accurate structural alignment.
  • Existing multiple ncRNA alignment methods, often progressive, can propagate errors and degrade alignment quality.
  • Non-progressive approaches, like the protein alignment tool PicXAA, offer potential for improved accuracy.

Purpose of the Study:

  • To develop an efficient and accurate algorithm for multiple non-coding RNA (ncRNA) structural alignment.
  • To extend the non-progressive alignment strategy of PicXAA to handle ncRNA sequences.
  • To address the limitations of progressive alignment methods in ncRNA structural analysis.

Main Methods:

  • PicXAA-R utilizes a greedy approach for structural alignment of ncRNAs.
  • It incorporates both intra-sequence folding information and inter-sequence local similarities.
  • Probabilistic consistency transformations enhance base-pairing and alignment probabilities, using a graph-based scheme.

Main Results:

  • PicXAA-R demonstrates high efficiency and accuracy in structural alignment of multiple ncRNAs.
  • The algorithm excels with datasets exhibiting locally similar sequences.
  • Experimental results confirm its speed and consistent alignment quality across diverse datasets.

Conclusions:

  • PicXAA-R is a leading algorithm for fast and accurate multiple ncRNA structural alignment.
  • It effectively handles local sequence similarities, improving alignment outcomes.
  • The freely available source code facilitates further research and application.