If you wish to obtain the source code for any of the below applications please contact cbrc.info@kaust.edu.sa.
- Microbial Knowledge Exploration Portal (DESM) - allows for discovery, analysis and exploration of information from a number of topic-specific microbial knowledgebase.
Associated publication - Salhi, A., Essack, M., Radovanovic, A., Marchand, B., Bougouffa, S., Antunes, A., … Bajic, V. B. (2015). DESM: portal for microbial knowledge exploration systems. Nucleic Acids Research, 44(D1), D624–D633. https://doi.org/10.1093/nar/gkv1147 - Biofuel Producer Screen (BioPS) - an in silico tool that can be used to screen and evaluate the cyanobacterial strains natural potential for FFA production based on their proteomes.
Associated publication - Motwalli, O., Essack, M., Salhi, A., Hanks, J., Mijakovic, I., & Bajic, V. B. (2018). BioPS: System for screening and assessment of biofuel-production potential of cyanobacteria. PLOS ONE, 13(8), e0202002. https://doi.org/10.1371/journal.pone.0202002 -
bTSSfinder - predicts putative prompters for five classes of sigma factors in E. coli (σ70, σ38, σ32, σ28 and σ24) and in Cyanobacteria (σA, σC, σH, σG and σF)
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DMAP: Metagenomics analysis and comparison platform - enables the integration of annotations for the exploration and analysis of microbial metagenomes
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Dragon Explorer of Enzymes and Compounds Of Industrial Importance (DEECOII) - highlights industrially important biological entities (enzymes, microorganisms or chemical compounds)
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Dragon Explorer of Osmoprotection Pathways (DEOP) - allows exploration of in silico-curated information related to the production of osmoprotectants captured from 1,160 species and includes 834 pathways, 3,529 genes, 4,899 enzymes, 2,907 compounds (143 osmoprotectant compounds) and 4,052 reactions (1,889 reactions directly affecting an osmoprotectant).
Associated publication - Shahmuradov, I. A., Mohamad Razali, R., Bougouffa, S., Radovanovic, A., & Bajic, V. B. (2016). bTSSfinder: a novel tool for the prediction of promoters in cyanobacteria andEscherichia coli. Bioinformatics, btw629. https://doi.org/10.1093/bioinformatics/btw629 -
Dragon Desert Masker (DDM) - Transcription Initiation Desert Identification.
Associated publication - Schaefer, U., Kodzius, R., Kai, C., Kawai, J., Carninci, P., Hayashizaki, Y., & Bajic, V. B. (2010). High Sensitivity TSS Prediction: Estimates of Locations Where TSS Cannot Occur. PLoS ONE, 5(11), e13934. https://doi.org/10.1371/journal.pone.0013934 -
Dragon Motif Finder V7 (DMF) - This is a simple ab-initio motif finding tool that allows the processing of relatively large sequence sets in a relatively short amount of time on the web.
Associated publication - Marchand, B., Bajic, V. B., & Kaushik, D. K. (2011). Highly scalableab initiogenomic motif identification. Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis on - SC ’11. https://doi.org/10.1145/2063384.2063459
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Dragon PPRE Spotter - This predicts the DNA binding sites of peroxisome proliferatior-activated receptors (PPARs) with extremely high accuracy that is at least order of magnitude better than the next best system for the same purpose.
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Memory-efficient genome assemblers on stand-alone and cloud infrastructures (MEGA).
Associated publication - Kleftogiannis, D., Kalnis, P., & Bajic, V. B. (2013). Comparing Memory-Efficient Genome Assemblers on Stand-Alone and Cloud Infrastructures. PLoS ONE, 8(9), e75505. https://doi.org/10.1371/journal.pone.0075505 -
Readscan - This is a highly scalable parallel program to identify non-host sequences (of potential pathogen origin) and estimate their genome relative abundance in high-throughput sequence datasets.
Associated Publication - Naeem, Rashid, M., & Pain, A. (2012). READSCAN: a fast and scalable pathogen discovery program with accurate genome relative abundance estimation. Bioinformatics, 29(3), 391–392. https://doi.org/10.1093/bioinformatics/bts684 -
LigandRFs: predict protein-ligand binding sites - This is a random forest-based approach to predict protein-ligand binding sites.
Associated Publication - Chen, Huang, J. Z., & Gao, X. (2014). LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone. BMC Bioinformatics, 15(S15). https://doi.org/10.1186/1471-2105-15-s15-s4 -
DES-TOMATO - a unique and one-of-a-kind knowledge exploration system focused on tomato species.
Associated Publication - Salhi, Negrão, S., Essack, M., Morton, M. J. L., Bougouffa, S., Razali, R., Radovanovic, A., Marchand, B., Kulmanov, M., Hoehndorf, R., Tester, M., & Bajic, V. B. (2017). DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-05448-0
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DES-ncRNA - focuses on human miRNAs and lncRNAs. DES-ncRNA belongs to the next generation of knowledge-mining resources
Associated Publication - Salhi, Essack, M., Alam, T., Bajic, V. P., Ma, L., Radovanovic, A., Marchand, B., Schmeier, S., Zhang, Z., & Bajic, V. B. (2017). DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining. RNA Biology, 14(7), 963–971. https://doi.org/10.1080/15476286.2017.1312243
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Dragon Text Mining PWMs Generator (DTMPG)
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DRAF - a transcription factor (TF) binding site (TFBS) prediction tool
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DragonWFS - a parallel GA wrapper feature selection tool for optimizing the performance of different classification methods.
Associated Publication - Soufan, Kleftogiannis, D., Kalnis, P., & Bajic, V. B. (2015). DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm. PLOS ONE, 10(2), e0117988. https://doi.org/10.1371/journal.pone.0117988
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FSFE - identifying enhancer signatures based on state-of-the-art computational techniques
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OmniGA - a framework for the optimization of omnivariate decision trees (ODTs) based on a parallel genetic algorithm (GA)
- DEEP - a general computational framework for predicting enhancers (NAR)
Associated publication - Kleftogiannis, Kalnis, P., & Bajic, V. B. (2014). DEEP: a general computational framework for predicting enhancers. Nucleic Acids Research, 43(1), e6–e6. https://doi.org/10.1093/nar/gku1058
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Dragon Database of Genes Implicated in Prostate Cancer (DDPC) - a centralized resource for researchers to support functional characterization and analysis of molecular processes related to prostate cancer
Associated Publication - Maqungo, Kaur, M., Kwofie, S. K., Radovanovic, A., Schaefer, U., Schmeier, S., Oppon, E., Christoffels, A., & Bajic, V. B. (2010). DDPC: Dragon Database of Genes associated with Prostate Cancer. Nucleic Acids Research, 39(Database), D980–D985. https://doi.org/10.1093/nar/gkq849
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Dragon Database for Methylated Genes and Diseases (DDMGD)
Associated Publication - Raies, Mansour, H., Incitti, R., & Bajic, V. B. (2014). DDMGD: the database of text-mined associations between genes methylated in diseases from different species. Nucleic Acids Research, 43(D1), D879–D886. https://doi.org/10.1093/nar/gku1168
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Dragon Database on Polymorphic Regulation of Human miRNA Genes (dPORE - miRNA) - integrates information from promoter regions of human miRNA genes, SNPs, and predicted TFBSs in the promoter regions.
Associated Publication - Schmeier, Schaefer, U., MacPherson, C. R., & Bajic, V. B. (2011). dPORE-miRNA: Polymorphic Regulation of MicroRNA Genes. PLoS ONE, 6(2), e16657. https://doi.org/10.1371/journal.pone.0016657
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Dragon ENhancers database (DENdb) -Database of integrated human enhancers
Associated Publication - Ashoor, Kleftogiannis, D., Radovanovic, A., & Bajic, V. B. (2015). DENdb: database of integrated human enhancers. Database, 2015, bav085. https://doi.org/10.1093/database/bav085
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Dragon Exploration System for Toxicants and Fertility (DESTAF) - a specialized, public resource specifically aimed at researchers investigating reproductive toxicity
Associated Publication - Dawe, Radovanovic, A., Kaur, M., Sagar, S., Seshadri, S. V., Schaefer, U., Kamau, A. A., Christoffels, A., & Bajic, V. B. (2012). DESTAF: A database of text-mined associations for reproductive toxins potentially affecting human fertility. Reproductive Toxicology, 33(1), 99–105. https://doi.org/10.1016/j.reprotox.2011.12.007