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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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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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RACE - Rapid Amplification of cDNA Ends02:35

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Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
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Ribosome Profiling

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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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Identification of Circular RNAs using RNA Sequencing
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Identification of Circular RNAs using RNA Sequencing

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TRUFA: A User-Friendly Web Server for de novo RNA-seq Analysis Using Cluster Computing.

Etienne Kornobis1, Luis Cabellos2, Fernando Aguilar2

  • 1Departamento de biodiversidad y biología evolutiva, Museo Nacional de Ciencias Naturales MNCN (CSIC), Madrid, Spain.

Evolutionary Bioinformatics Online
|June 10, 2015
PubMed
Summary
This summary is machine-generated.

TRUFA is a new, user-friendly bioinformatics platform for transcriptome analysis (RNA-seq). It simplifies complex tasks like transcript assembly and annotation, making RNA-seq data accessible to more researchers.

Keywords:
RNA-seqannotationde novo assemblyexpression quantificationread cleaningtranscriptomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) for transcriptome analysis (RNA-seq) is increasingly accessible.
  • Many biological disciplines require RNA-seq data analysis.
  • Bioinformatics expertise and computational resources are often limiting factors for researchers.

Purpose of the Study:

  • To present TRUFA (TRanscriptome User-Friendly Analysis), an open informatics platform.
  • To provide a web-based interface for de novo RNA-seq analysis and comparative transcriptomics.
  • To enable researchers without extensive bioinformatics knowledge to perform complex transcriptome analyses.

Main Methods:

  • TRUFA offers a comprehensive pipeline for raw read cleaning, transcript assembly, annotation, and expression quantification.
  • The platform is highly parallelized and utilizes high-performance computing resources for intensive analyses.
  • TRUFA was validated using four previously published RNA-seq datasets.

Main Results:

  • TRUFA produced globally similar results compared to original studies for validation datasets.
  • The platform demonstrated superior performance in analyzing a specific green tea dataset.
  • TRUFA enables fast, robust, and user-friendly RNA-seq data analysis.

Conclusions:

  • TRUFA democratizes RNA-seq data analysis by providing an accessible and efficient platform.
  • The tool addresses the gap in bioinformatics expertise and computational resources for researchers.
  • TRUFA facilitates deeper biological insights from transcriptome data across various disciplines.