ChiTaRS is a database (ChiTaRS-3.1) of about 42,000 chimeric transcripts in humans, mice, fruit flies, zebrafish, cows, rats, pig and yeast. It was developed by Dr. Milana Frenkel-Morgenstern and Dr. Alessandro Gorohovski at the Structural Biology and Biocomputing Programme Lab in Spanish National Cancer Research Centre (CNIO), Madrid, Spain under the supervision of Prof. Alfonso Valencia.
In the current version, ChiTaRS-3.1, we extended the experimental data evidence as well as included a novel type of the sense-antisense chimeric transcripts of the same gene confirmed experimentally by RT-PCR, qPCR, RNA-sequencing and mass-spec peptides. In addition, we collected about 11,500 human cancer breakpoints in different cancer types.
Currently, work on the ChiTaRS database improvements is carried out at the Cancer Genomics and BioComputing Lab in Bar-Ilan University. Read more about ChiTaRS here.
ChiPPI Webserver and Database
Using a methodology that treats discreet protein domains as binding sites for specific domains of partner proteins, we have cataloged the partner proteins for about 29,000 fusion proteins. We have developed ChiPPI (Chimeric Protein-Protein-Interactions) which compares the protein domains in fusion proteins to the domains present in both parental proteins. Read more about the ChiPPI Webserver here.
AnnotatorPPI Webserver and Database
The AnnotatorPPI server is webserver that provides an automatic annotation of uploaded FASTA sequences of the clones of interest and build the protein-protein interaction networks for every sequence provided. After “Bulk Annotation” is done each entry is associated with a gene sequence, detailed functional annotation, and links to Ensembl, Entrez, GeneCards, InterPro and UniProt databases.
The input for the server is one or more DNA sequences in the FASTA format. The server produces sequence-to-sequence comparison between the user sequences and all the sequences in NCBI using automatic BLAT script. The output is a list of genes found in the database and the associated E-values. Read more about the AnnotatorPPI Webserver and Database here.
Database of Fusion Proteins and Interactions
Here, we attempt to identify as well as catalog physical interactions between pairs or groups of proteins using text mining. Protein-protein interactions are important for studying intracellular signaling pathways, modeling protein structures as well as other processes. Identification of protein-protein interactions for fusion proteins still lacks useful information and resources. ProtFus catalogs the list of some possible mentions of interactions of fusion proteins from text using a Natural Language Processing method. Publicly available information from biomedical research is readily accessible through the internet and is becoming a powerful resource for predictive protein-protein interactions and protein docking.