The Vision

CicerTransDB is a database of Chickpea Transcription Factors. It was created to facilitate users with a platform for unified and comprehensive study of chickpea transcription factors.

The Process

CicerTransDB was created through genome wide cataloguing of domains in chickpea proteins. The data was then searched for dna binding domains as listed in PlantTFDB (Jin et al., 2014) and one of our published paper (Wardhan et al., 2012). The whole primary list was then manually classified into 47 groups comprising final list of CicerTransDB.

Further data like motifs, gene ontology, homologues were searched and added to the database which was provided to the user at frontend using PHP/MySQL.


CicerTransDB is a unified portal for various applications. It not only provides facility to extract gene, protein and promoter sequences of transcription factors but also its associated features like motifs in promoter region, homologues in TAIR, PlantTFDB, Gene Ontology and localization. This makes gathering information easier for researcher.

It also holds many one click interfaces to connect to other servers for cross validation like InterPro (domain), PlantPan and PlantCare (motif) among others. Many similar one click tools for novel analysis are also included in construction of website, for example Phyre (3D structure prediction) and NetPhos (Phosphorylation), TMHMM (Transmembrane domain). For better understanding, maps for protein domains and chromosome maps for each transcription factor family have also been provided.

Advanced user can use the webAPI included in wiki section for queries and pipelines. Altogether, features provided in this website enable fast and detailed information access about chickpea transcription factors. CicerTransDB lets users perform several tasks without getting into the error prone manual copy pasting sequences to a list of sites. At the same time it allows advanced users to use the data in their own pipeline through dynamic webAPI.


We are grateful to NIPGR for funding this research. The data was obtained from Chickpea Genome Annotation v 1.0 (Varshney et al., 2013). The classification system was based on information provided by PlantTFDB.

The protein domains for genome-wide analysis were analysed through Pfam. Proteins were annotated by blast against NCBInr database. Promoters were analysed through PLACE database for motifs. Gene ontologies were assigned using Blast2GO program. Codes for sequence submission to third party tools were taken from respective websites. (see References)


  • Gayali S, Acharya S, Lande NV, Pandey A, Chakraborty S, Chakraborty N (2016) CicerTransDB 1.0: a resource for expression and functional study of chickpea transcription factors. BMC Plant Biol 16: 169


  • Conesa, A., Gotz, S., Garcia-Gomez, J. M., et al. (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics, 21, 3674–3676. doi: 10.1093/bioinformatics/bti610

  • Finn, R. D., Bateman, A., Clements, J., et al. (2014) Pfam: the protein families database. Nucleic Acids Res., 42, D222–D230. doi: 10.1093/nar/gkt1223

  • Jin J, Zhang H, Kong L, et al. (2014) PlantTFDB 3.0: A portal for the functional and evolutionary study of plant transcription factors. Nucleic Acids Res. 42:D1182-D1187. doi: 10.1093/nar/gkt1016

  • Varshney RK, Song C, Saxena RK, et al. (2013) Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat Biotechnol 31:240-246. doi: 10.1038/nbt.2491

  • Wardhan V, Jahan K, Gupta S, et al. (2012) Overexpression of CaTLP1, a putative transcription factor in chickpea (Cicer arietinum L.), promotes stress tolerance. Plant Mol Biol 79:479-493. doi: 10.1007/s11103-012-9925-y

External tools

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  • Chang, W.-C., Lee, T.-Y., Huang, H.-D., et al. (2008) PlantPAN: Plant promoter analysis navigator, for identifying combinatorial cis-regulatory elements with distance constraint in plant gene groups. BMC Genomics, 9, 561. doi: 10.1186/1471-2164-9-561
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  • Hofmann K. and Stoffel,W. (1993) TMbase-a database of membrane spanning proteins segments. Biol. Chem. Hoppe Seyler, 347, 166. doi: 10.1093/nar/gkg020
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  • Kelley, L. A., Mezulis, S., Yates, C. M., et al. (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc., 10, 845–858, doi: 10.1038/nprot.2015.053
  • Krogh, A., Larsson, B., von Heijne, G., et al. (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol., 305, 567–580. doi: 10.1006/jmbi.2000.4315
  • Laskowski, R. A. (2001) PDBsum: summaries and analyses of PDB structures. Nucleic Acids Res., 29, 221–222. doi: 10.1093/nar/29.1.221
  • Leinonen R, Akhtar R , Birney E , Bower L , Cerdeno-Tárraga A , Cheng Y , Cleland I , Faruque N , Goodgame N , Gibson R , Hoad G , Jang M , Pakseresht N , Plaister S , Radhakrishnan R , Reddy K , Sobhany S , Ten Hoopen P , Vaughan R , Zalunin V , Cochrane G (2011) The European Nucleotide Archive. Nucleic Acids Res.,39,D28-31. doi: 10.1093/nar/gkq967
  • Lewis, T. E., Sillitoe, I., Andreeva, A., et al. (2013) Genome3D: A UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains. Nucleic Acids Res., 41, D499-507. doi: 10.1093/nar/gks1266
  • Li, J., Dai, X., Liu, T., et al. (2012) LegumeIP: An integrative database for comparative genomics and transcriptomics of model legumes Nucleic Acids Res., 40,D1221-D1229. doi: 10.1093/nar/gkr939
  • Lin, J. and Hu, J. (2013) SeqNLS: Nuclear Localization Signal Prediction Based on Frequent Pattern Mining and Linear Motif Scoring. PLoS One, 8, e76864, doi: 10.1371/journal.pone.0076864
  • Rombauts, S., Déhais, P., Van Montagu, M., et al. (1999) PlantCARE, a plant cis-acting regulatory element database. Nucleic Acids Res. 27, 295–296. doi: 10.1093/nar/27.1.295
  • Wang M. and Marin A. 2006. Characterization and Prediction of Alternative Splice Sites. Gene, 366,219-227. doi: 10.1016/j.gene.2005.07.015
  • Zsuzsanna Dosztányi, Bálint Mészáros and István Simon. 2009. ANCHOR: web server for predicting protein binding regions in disordered proteins. Bioinformatics,25, 2745-2746. doi:10.1093/bioinformatics/btp518


  • Liability:
    For documents and software available from this server, NIPGR does not warrant or assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed.
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    Some CicerTransDB Web pages may provide links to other Internet sites for the convenience of users. NIPGR is not responsible for the availability or content of these external sites, nor does NIPGR endorse, warrant, or guarantee the products, services, or information described or offered at these other Internet sites. Users cannot assume that the external sites will abide by the same Privacy Policy to which NIPGR adheres. It is the responsibility of the user to examine the copyright and licensing restrictions of linked pages and to secure all necessary permissions.
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