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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. 
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RACE - Rapid Amplification of cDNA Ends

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DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Updated: May 22, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

SRMA: an R package for resequencing array data analysis.

Nianxiang Zhang1, Yan Xu, Martin O'Hely

  • 1Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA.

Bioinformatics (Oxford, England)
|May 15, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces Sequence Robust Multi-Array Analysis (SRMA), an R package for analyzing resequencing microarray data. SRMA accurately detects rare DNA variations and structural changes with high sensitivity and low error rates.

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Introductory Analysis and Validation of CUT&RUN Sequencing Data
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Introductory Analysis and Validation of CUT&RUN Sequencing Data

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Last Updated: May 22, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Sequencing by hybridization to oligonucleotides is a fast, cost-effective method for targeted DNA sequencing.
  • Resequencing microarrays enable rapid sequencing of numerous human genes.
  • Challenges like cross-hybridization and variable probe affinity limit current microarray accuracy.

Purpose of the Study:

  • To develop an R package for analyzing resequencing microarray data.
  • To implement a novel statistical algorithm for enhanced rare variant detection.
  • To improve the accuracy and sensitivity of DNA variation identification from microarray data.

Main Methods:

  • Development of an R package integrating the Sequence Robust Multi-Array Analysis (SRMA) algorithm.
  • Implementation of five modules: quality control, data normalization, single and multi-array analysis, and output analysis.
  • Utilizing SRMA for high-sensitivity detection of rare DNA variations.

Main Results:

  • The SRMA algorithm achieves high sensitivity (FNR < 5%) and accuracy (FPR < 1x10⁻⁵) for rare variant detection.
  • The SRMA R package provides an efficient workflow for resequencing microarray data analysis.
  • Accurate identification of single nucleotide variations and structural changes, including gene deletions.

Conclusions:

  • The SRMA R package offers a robust solution for resequencing microarray data analysis.
  • This tool significantly enhances the ability to detect rare DNA variations with high confidence.
  • SRMA facilitates broader applications of resequencing microarray technology in genetic research.