Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA-seq03:21

RNA-seq

12.7K
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...
12.7K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

13.9K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
13.9K
Next-generation Sequencing03:00

Next-generation Sequencing

102.3K
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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
102.3K
Sanger Sequencing01:57

Sanger Sequencing

781.3K
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...
781.3K
Ribosome Profiling02:24

Ribosome Profiling

4.4K
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...
4.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

On the translational potential of atlases in precision oncology.

Translational cancer research·2025
Same author

Paclitaxel neurotoxicity is triggered by epidermal EG5-dependent microtubule fasciculation and X-ROS formation.

Research square·2025
Same author

Can we cure antiphospholipid syndrome?

Current opinion in immunology·2025
Same author

Correction: The landscape of BRAF transcript and protein variants in human cancer.

Molecular cancer·2025
Same author

Health Professionals' Preferences for Next-Generation Sequencing in the Diagnosis of Suspected Genetic Disorders in the Paediatric Population.

Journal of personalized medicine·2025
Same author

Prioritizing Context-Dependent Cancer Gene Signatures in Networks.

Cancers·2025
Same journal

From Pixels to Patterns: A Multidimensional Framework to Decode Cytoskeletal Organization.

Computational and structural biotechnology journal·2026
Same journal

A Large Concept Model for Mechanistic Simulation of Disease Trajectories: A Hypothesis-Generating Exemplar for Pediatric Acute Lymphoblastic Leukemia.

Computational and structural biotechnology journal·2026
Same journal

Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.

Computational and structural biotechnology journal·2026
Same journal

High-Throughput Prediction of Protein-Protein Interactions Uncovers Hidden Molecular Networks in Biosynthetic Gene Clusters.

Computational and structural biotechnology journal·2026
Same journal

A Region-Aware Structured Framework Improves Prediction of Gene Expression from DNA Methylation.

Computational and structural biotechnology journal·2026
Same journal

Ensemble Machine Learning Approaches Predict Survival in Lower-Grade Glioma Based on Glycosphingolipid Gene Expression and Metabolic Modeling.

Computational and structural biotechnology journal·2026
See all related articles

Related Experiment Video

Updated: Apr 20, 2026

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
05:12

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

1.5K

RNA-Seq Data: A Complexity Journey.

Enrico Capobianco1

  • 1Center for Computational Science, University of Miami, Miami, FL, USA.

Computational and Structural Biotechnology Journal
|November 20, 2014
PubMed
Summary
This summary is machine-generated.

Deciphering complex transcriptome data remains challenging. Novel inference approaches are needed to accurately identify, quantify, and analyze gene and non-coding RNA expression from RNA-Seq data.

Keywords:
ComplexityInverse problemsNetworksRNA-SeqTranscriptome profiling

More Related Videos

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

556
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.7K

Related Experiment Videos

Last Updated: Apr 20, 2026

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms
05:12

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

1.5K
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

556
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.7K

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Extensive transcription across eukaryotic genomes has been reported, sparking excitement.
  • Concerns exist regarding the methods used to detect pervasive transcription and its functionality.
  • The ENCODE project shifted focus towards functional validation of transcribed elements.

Purpose of the Study:

  • To highlight challenges in deciphering complex transcriptome data.
  • To emphasize the need for novel inference approaches for accurate analysis.
  • To address uncertainties in gene and non-coding RNA identification and quantification.

Main Methods:

  • Review of current understanding of transcriptome complexity.
  • Analysis of limitations in RNA-Seq data interpretation.
  • Identification of direct and inverse problems in transcriptome analysis.

Main Results:

  • Significant challenges persist in transcriptome data analysis.
  • Current methods lead to uncertainty in gene and non-coding RNA identification and quantification.
  • A gap exists in robust inference approaches for complex transcriptomes.

Conclusions:

  • Accurate identification, quantification, and differential expression analysis of genes and non-coding RNAs require advanced methods.
  • Novel inference approaches are crucial for overcoming transcriptome data complexity.
  • Further research is needed to develop and validate new computational strategies.