PPI collection : Collection of PPIs predicted by us and from literature
Each PPI is annotated with the source in the 3th column in data and PPI list in webpage.
"regulation_leaves": regulatory PPI from (1).
"regulation_kernel": regulatory PPI from (2).
"kinase": kinase-substrate PPI from (3).
"functional": predicted functional PPI.
"physical": predicted physical PPI.
The 4th column is for the confident score if predicted otherwise empty.
The 5th column is the tag which is 1/0 representing that protein/gene names of interactors are in 1th and 2th columns.
experimentally determined PPIs : experimentally determined PPIs from public databases (UniProt(2015.10.01), BioGrid(2015.9.30), DIP(2015.7.1), IntAct(2015.10.20) and MINT(2012.10.26)) and text mining.
all prediction : all predicted functional and physical PPIs.
high-confident prediction : high-confident predicted PPIs.
high and medium-confident prediction : high and medium-confident predictied PPIs.
Pathway information : the pathway annotations from maizeCyc database(http://maizecyc.maizegdb.org/)(4).
GO information : the GO annotations from agriGO database(http://bioinfo.cau.edu.cn/agriGO/)(5).
Domain information : he domains of maize proteins are annotated by sequence search service provided by Pfam database(6).
TF information : the family information of regulatory factor obtained from the ProFITS database(http://bioinfo.cau.edu.cn/ProFITS/) and literature(7).
Reference:
1. Yu, C.P., Chen, S.C., Chang, Y.M., Liu, W.Y., Lin, H.H., Lin, J.J., Chen, H.J., Lu, Y.J., Wu, Y.H., Lu, M.Y. et al. (2015) Transcriptome dynamics of developing maize leaves and genomewide prediction of cis elements and their cognate transcription factors. Proc Natl Acad Sci U S A, 112, E2477-2486.
2. Zhan, J.P., Thakare, D., Ma, C., Lloyd, A., Nixon, N.M., Arakaki, A.M., Burnett, W.J., Logan, K.O., Wang, D.F., Wang, X.F. et al. (2015) RNA Sequencing of Laser-Capture Microdissected Compartments of the Maize Kernel Identifies Regulatory Modules Associated with Endosperm Cell Differentiation. Plant Cell, 27, 513-531.
3. Walley, J.W., Shen, Z., Sartor, R., Wu, K.J., Osborn, J., Smith, L.G. and Briggs, S.P. (2013) Reconstruction of protein networks from an atlas of maize seed proteotypes. Proc Natl Acad Sci U S A, 110, E4808-4817.
4. Monaco, M.K., Sen, T.Z., Dharmawardhana, P.D., Ren, L., Schaeffer, M., Naithani, S., Amarasinghe, V., Thomason, J., Harper, L., Gardiner, J. et al. (2013) Maize Metabolic Network Construction and Transcriptome Analysis. Plant Genome, 6.
5. Du, Z., Zhou, X., Ling, Y., Zhang, Z. and Su, Z. (2010) agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res, 38, W64-70.
6. Coggill, P., Finn, R.D. and Bateman, A. (2008) Identifying protein domains with the Pfam database. Curr Protoc Bioinformatics. Journal of theoretical biology, Chapter 2, Unit 2 5.
7. Burdo, B., Gray, J., Goetting-Minesky, M.P., Wittler, B., Hunt, M., Li, T., Velliquette, D., Thomas, J., Gentzel, I., dos Santos Brito, M. et al. (2014) The Maize TFome--development of a transcription factor open reading frame collection for functional genomics. Plant J, 80, 356-366.