ChiTaRS is a database (ChiTaRS-5.0) of about 111,582 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-5.0, 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 23,167 human cancer breakpoints in different cancer types. The database includes unique information correlating chimeric breakpoints with 3D chromatin contact maps, generated from public datasets of chromosome conformation capture techniques (Hi-C). In this update, we have added curated information on druggable fusion targets matched with chimeric breakpoints, which are applicable to precision medicine in cancers. The introduction of a new section that lists chimeric RNAs in various cell-lines is another salient feature.
Finally, using text-mining techniques, novel chimeras in Alzheimer’s disease, schizophrenia, dyslexia and other diseases were collected in ChiTaRS. Thus, this improved version is an extensive catalogue of chimeras from multiple species. It extends our understanding of the evolution of chimeric transcripts in eukaryotes and contributes to the analysis of 3D genome conformational changes and the functional role of chimeras in the etiopathogenesis of cancers and other complex diseases.
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.
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.
The Database of Protein-protein interActions of Stress-response genes in subTerranean and fossORial AnimaLs (PASTORAL)
PASTORAL is a database that has been developed to catalog and identify protein-protein interactions of stress-response genes in subterranean and fossorial animals with Nano-Spalax Galili as a model organism. It is a unique database of protein-protein interactions (PPI) of stress-response genes in subterranean and fossorial animals.
In addition to this, PASTORAL can also be used to search for relevant stress-response genes and their role in specific environmental conditions, identify their corresponding protein-protein interactions, orthologs, codon usage preferences and network-related features based on protein-protein interactions.
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.
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.