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Updated: Jun 9, 2026
06:38
High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
Published on: February 7, 2019
Probabilistic approaches to transcription factor binding site prediction
Stefan Posch1, Jan Grau, André Gohr
1Institute of Computer Science, Martin Luther University, Halle-Wittenberg, Germany.
Methods in Molecular Biology (Clifton, N.J.)
|September 10, 2010
Summary
Jstacs is a Java framework for analyzing DNA sequences. It enables flexible combination of statistical models and learning principles for accurate transcription factor binding site prediction.
Area of Science:
- Bioinformatics and Computational Biology
- Genomics and Molecular Biology
Background:
- Numerous computational tools exist for predicting transcription factor binding sites (TFBS).
- Existing TFBS prediction programs often lack user flexibility in selecting statistical models and learning principles.
Purpose of the Study:
- To introduce Jstacs, a modular Java framework for sequence analysis.
- To enable users to freely combine diverse statistical models and learning principles for TFBS recognition.
Main Methods:
- Development of an object-oriented Java framework (Jstacs).
- Implementation of a modular design allowing combination of various statistical models.
- Integration of different machine learning principles for parameter estimation.
Main Results:
- Jstacs provides a flexible platform for TFBS prediction.
- The framework facilitates custom model and learning principle selection.
Conclusions:
- Jstacs enhances the adaptability and precision of transcription factor binding site prediction.
- The framework empowers researchers to tailor analyses to specific biological questions.
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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...
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...
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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...
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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...