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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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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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Related Experiment Video

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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

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Identifying peaks in *-seq data using shape information.

Francesco Strino1, Michael Lappe2

  • 1Qiagen Aarhus, Silkeborgvej 2, Aarhus, 8000, DK, Denmark.

BMC Bioinformatics
|June 14, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a user-friendly, shape-based peak caller for ChIP-seq and other sequencing protocols. The tool requires minimal input and performs comparably to state-of-the-art algorithms, offering flexibility and ease of use.

Keywords:
CLC shape-based peak callerChIP-seqDNase-seqHotelling observerPeak calling

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Area of Science:

  • Genomics
  • Bioinformatics
  • Epigenetics

Background:

  • Peak calling is crucial for analyzing ChIP-seq and similar epigenetics data.
  • Existing peak callers can be difficult for non-bioinformaticians due to complex parameterization.
  • A new, user-friendly peak caller is presented for diverse sequencing protocols.

Purpose of the Study:

  • To introduce a novel, shape-based peak calling algorithm for sequencing data analysis.
  • To demonstrate the ease of use and broad applicability of the new peak caller.
  • To evaluate the performance of the new peak caller against existing state-of-the-art methods.

Main Methods:

  • Development of a shape-based peak calling algorithm adaptable to various sequencing protocols.
  • Implementation of the algorithm within CLC Genomics Workbench and CLC Genomics Server.
  • Comparative analysis using benchmark datasets and receiver-operator characteristic (ROC) plots.

Main Results:

  • The shape-based approach requires minimal user input and learns peak shapes from data.
  • The peak caller demonstrates applicability across different sequencing protocols, including DNase-seq.
  • Performance evaluation shows the CLC shape-based peak caller ranks well against established algorithms.

Conclusions:

  • The CLC shape-based peak caller offers a flexible and user-friendly alternative for sequencing data analysis.
  • The method achieves competitive performance compared to existing state-of-the-art peak callers.
  • This tool simplifies epigenetics data analysis for a wider range of users.