Description: Jaspar is a database of transcription factor binding profiles, widely used in bioinformatics to study genetic regulation. This database provides information on the DNA sequences to which transcription factors bind, which are key proteins in the regulation of gene expression. Jaspar is based on a binding profile approach, where the binding preferences of different transcription factors are represented through position weight matrices. These matrices allow researchers to predict how and where transcription factors will bind in the genome, which is crucial for understanding the mechanisms of genetic regulation. The database includes experimental data from various species, making it a valuable tool for comparative and evolutionary studies. Additionally, Jaspar is open access, facilitating its use by the scientific community and promoting collaboration and information sharing in the fields of molecular biology and genetics. Its user-friendly interface and search tools allow users to quickly find the information they need, making it accessible to both experienced researchers and those new to the field of bioinformatics.
History: Jaspar was created in 2004 by a team of researchers led by Dr. H. J. Bussemaker at Columbia University. Since its launch, it has significantly evolved, incorporating data from multiple species and enhancing its analysis tools. Over the years, Jaspar has been regularly updated to include new binding profiles and improve data quality, becoming an essential resource for the scientific community.
Uses: Jaspar is primarily used to predict transcription factor binding to DNA sequences, helping researchers understand gene expression regulation. It is also employed in evolutionary studies to compare binding profiles across different species and in research on genetic diseases, where the regulation of specific genes may be altered.
Examples: An example of the use of Jaspar is in cancer studies, where researchers can identify how specific transcription factors affect the expression of genes related to tumor growth. Another example is its application in developmental biology, where the regulation of genes during embryonic development is studied.