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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...
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...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Related Experiment Video

Updated: May 10, 2026

Cost-Efficient Transcriptomic-Based Drug Screening
06:40

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

A novel min-cost flow method for estimating transcript expression with RNA-Seq.

Alexandru I Tomescu1, Anna Kuosmanen, Romeo Rizzi

  • 1Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Finland. tomescu@cs.helsinki.fi.

BMC Bioinformatics
|June 6, 2013
PubMed
Summary
This summary is machine-generated.

A new network flow method accurately identifies and quantifies RNA sequencing (RNA-Seq) data isoforms. This approach offers a general solution for transcript identification and is competitive with existing tools.

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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene transcription and alternative splicing produce diverse RNA sequences (isoforms).
  • RNA sequencing (RNA-Seq) enables high-throughput isoform identification and quantification from short reads.
  • Developing accurate computational methods for isoform analysis remains a significant challenge.

Purpose of the Study:

  • To introduce a novel method for RNA-Seq isoform identification and quantification.
  • To leverage network flow algorithms for solving the multiassembly problem in transcriptomics.
  • To provide a general framework applicable to various isoform quantification models.

Main Methods:

  • Developed a method based on minimum-cost network flows to model isoform identification.
  • Translated the isoform quantification problem into a min-cost flow problem on an offset flow network.
  • Utilized polynomial-time solvable network flow algorithms for optimal transcript prediction.

Main Results:

  • The proposed network flow method effectively identifies and quantifies RNA isoforms.
  • The approach encompasses previous methods under a least sum of squares model.
  • The method demonstrates competitive prediction accuracy compared to established tools like Cufflinks and IsoLasso.

Conclusions:

  • A general and effective network flow-based method for RNA-Seq isoform analysis has been presented.
  • The tool, Traph (Transcripts in Graphs), offers a competitive solution for isoform identification and quantification.
  • The method is computationally efficient and broadly applicable to transcriptomic data analysis.