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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Eukaryotic Transcription Activators02:42

Eukaryotic Transcription Activators

Transcription activators are proteins that promote the transcription of genes from DNA to RNA. In most cases, these proteins contain two separate domains ‒ a domain that binds to DNA and a domain for activating transcription; however, in some cases, a single domain is responsible for both binding and activation of transcription, as seen in the glucocorticoid receptor and MyoD.
The binding domains are capable of recognizing and interacting with regulatory sequences on the DNA. These domains are...

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Related Experiment Video

Updated: May 13, 2026

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow
07:48

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow

Published on: April 1, 2022

LASAGNA: a novel algorithm for transcription factor binding site alignment.

Chih Lee1, Chun-Hsi Huang

  • 1Department of Computer Science and Engineering, University of Connecticut, Fairfield Road, Storrs, CT 06269, USA.

BMC Bioinformatics
|March 26, 2013
PubMed
Summary
This summary is machine-generated.

A new algorithm, LASAGNA, effectively aligns variable-length transcription factor binding sites (TFBSs), outperforming existing methods. This TFBS alignment tool improves DNA sequence analysis and motif discovery for biological research.

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Related Experiment Videos

Last Updated: May 13, 2026

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow
07:48

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow

Published on: April 1, 2022

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
12:24

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

Published on: July 2, 2010

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcription factor (TF) binding site (TFBS) identification is crucial in DNA sequence analysis.
  • Existing tools often rely on position-specific scoring matrices (PSSMs) derived from aligned TFBSs.
  • A significant portion of TFs lack available PSSMs due to challenges in aligning variable-length TFBSs.

Purpose of the Study:

  • To develop a novel algorithm for aligning variable-length TFBSs.
  • To improve the accuracy and efficiency of TFBS identification and PSSM construction.
  • To provide a user-friendly tool for TFBS search and visualization.

Main Methods:

  • Designed LASAGNA, an alignment algorithm accounting for TFBS lengths and position dependence.
  • Compared LASAGNA against ClustalW2 and MEME for TFBS alignment.
  • Evaluated a LASAGNA-dependent PSSM method against alignment-free TFBS search methods.
  • Developed LASAGNA-ChIP for ChIP-seq data analysis.

Main Results:

  • LASAGNA significantly outperformed ClustalW2 and MEME in aligning TFBSs across 189 TFs from 5 species.
  • The LASAGNA-based PSSM method demonstrated higher precision at fixed recall rates compared to alignment-free methods.
  • LASAGNA-ChIP showed comparable performance to MEME in motif discovery for ChIP-seq data.

Conclusions:

  • LASAGNA is a simple and effective algorithm for aligning variable-length binding sites.
  • The LASAGNA algorithm has been integrated into the LASAGNA-Search webtool for TFBS analysis.
  • LASAGNA-Search provides precomputed PSSM models for numerous TFs, facilitating research.