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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Related Experiment Video

Updated: Apr 5, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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deepBase: a database for deeply annotating and mining deep sequencing data.

Jian-Hua Yang1, Peng Shao, Hui Zhou

  • 1Key Laboratory of Gene Engineering of the Ministry of Education, State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou 510275, PR China.

Nucleic Acids Research
|December 8, 2009
PubMed
Summary
This summary is machine-generated.

We developed deepBase, a novel database for discovering and annotating small RNAs from transcriptomic data. This resource aids researchers in exploring vast next-generation sequencing datasets for non-coding RNA identification.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • High-throughput next-generation sequencing (NGS) generates massive transcriptomic data.
  • Analyzing these large datasets for small RNA discovery presents significant challenges.

Purpose of the Study:

  • To introduce deepBase, a novel database for comprehensive annotation and discovery of small RNAs.
  • To facilitate the exploration of small RNAs within diverse transcriptomic datasets.

Main Methods:

  • Collected deep sequencing data from 185 small RNA libraries across seven species.
  • Utilized bioinformatics tools like miRDeep and snoSeeker for prediction.
  • Mapped approximately 14.6 million unique reads to over 284 million genomic loci.

Main Results:

  • Identified millions of unique ncRNA-associated small RNAs (nasRNAs), promoter-associated small RNAs (pasRNAs), exon-associated small RNAs (easRNAs), and repeat-associated small RNAs (rasRNAs).
  • Predicted 2038 miRNA and 1889 snoRNA candidates.
  • Organized mapped reads into approximately 1.2 million RNA clusters.

Conclusions:

  • deepBase provides an integrated, interactive platform for comparative analysis of small RNAs.
  • The database offers search functionalities and related information to aid further research.