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

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...
Sanger Sequencing01:57

Sanger Sequencing

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...
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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...
Leaky Scanning02:28

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...
Next-generation Sequencing03:00

Next-generation Sequencing

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.
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...

You might also read

Related Articles

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

Sort by
Same author

African ancestry-enriched variants in the GATM gene are associated with elevated serum creatinine levels.

Genome medicine·2026
Same author

High-resolution map of chromatin accessibility - insights into the focused binding of a large number of transcription factors.

Epigenetics & chromatin·2026
Same author

Pastrami: a fast and efficient algorithm for fine-scale genetic ancestry inference.

NAR genomics and bioinformatics·2025
Same author

Cancer histone mutations impact protein binding and DNA repair with possible links to genomic instability.

Nucleic acids research·2025
Same author

PTEN status on gonadotropin-releasing hormone (GnRH) metabolite, GnRH-(1-5), effects in endometrial cancer cell lines migration, & transcriptomic analysis of basal cell line and tumor gene expressions†.

Biology of reproduction·2025
Same author

The Consortium for Genomic Diversity, Ancestry, and Health in Colombia (CÓDIGO): building local capacity in genomics and bioinformatics.

Communications biology·2025

Related Experiment Video

Updated: Jul 20, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Scanning sequences after Gibbs sampling to find multiple occurrences of functional elements.

Kannan Tharakaraman1, Leonardo Mariño-Ramírez, Sergey L Sheetlin

  • 1Computational Biology Branch, National Center for Biotechnology Information National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, MSC 6075 Bethesda, MD 20894-6075, USA. tharakar@ncbi.nlm.nih.gov

BMC Bioinformatics
|September 12, 2006
PubMed
Summary

The A-GLAM program now rapidly identifies multiple DNA regulatory elements in sequences. An enhanced scanning step improves de novo motif discovery, overcoming limitations of traditional Gibbs sampling for complex promoter regions.

More Related Videos

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp
10:44

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Published on: June 20, 2018

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Related Experiment Videos

Last Updated: Jul 20, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
09:06

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp
10:44

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

Published on: June 20, 2018

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple instances of DNA regulatory elements are common within target promoters.
  • Traditional de novo motif discovery programs using Gibbs sampling can be slow to find all element instances.

Purpose of the Study:

  • To improve the A-GLAM program for efficient de novo discovery of DNA regulatory elements.
  • To enhance the A-GLAM program's capability to locate multiple instances of regulatory elements within DNA sequences.

Main Methods:

  • Implemented an optional "scanning step" in A-GLAM following Gibbs sampling.
  • The scanning step utilizes a position-specific scoring matrix (PSSM) and resembles iterative PSI-BLAST.
  • Employs Bayesian calculus for iterative PSSM updates based on subsequence E-values.

Main Results:

  • The enhanced scanning step rapidly locates additional regulatory element instances after initial Gibbs sampling.
  • A-GLAM reports predicted elements with associated E-values for statistical evaluation.
  • The improved A-GLAM successfully predicted multiple instances of regulatory motifs in experimental datasets.

Conclusions:

  • The enhanced A-GLAM program effectively identifies multiple DNA regulatory elements.
  • This improvement significantly accelerates the de novo discovery of regulatory motifs in sequence sets.
  • The updated A-GLAM provides statistically evaluated predictions of regulatory elements.