PlaNet: Plant network and modeling database
Plant Network and modeling database (PlaNet) is a resource of metabolic pathways in cassava and its molecular regulation such as transcriptional and translational regulation. For the first version, the useful information of carbon assimilation pathways in cassava (Manihot esculenta Crantz) reconstructed from comparative genomics approach using multi-plant templates is available here. The cassava metabolic network is composed of genes, enzymes, biochemical compounds for each biochemical reaction. Due to the complex of living organism, its phenotype is not only relied on metabolic regulation but other regulations are also important, namely transcriptional, post-transcriptional, translational and post-translational regulation. Therefore, more useful information will be further explored for the next version of PlaNet including transcriptional regulatory network (TRN), regulation of non-coding RNA, and protein-protein interaction (PPI) network, respectively.
Saithong, T., Rongsirikul, O., Kalapanulak, S., Chiewchankaset, P., Siriwat, W., Netrphan, S., Suksangpanomrung, M., Meechai, A., and Cheevadhanarak, S. (2013) Starch Biosynthesis in Cassava: a Genome-based Pathway Reconstruction and Its Exploitation in Data Integration, BMC Systems Biology, Vol. 7, No. 1, pp. 75-92.
MePPI-In: Genome-wide prediction of protein-protein interaction network of cassava using interolog-based method
MePPI-In, Protein-protein interaction (PPI) network of cassava, was constructed based on interolog method. The predicted PPIs of cassava were inferred according to the information of seven plants: Arabidopsis (235,215 of PPIs in Arabidopsis used for this study), rice (76,829 of PPIs in rice used for this study), potato (42 of PPIs in potato used for this study), maize (25 of PPIs in maize used for this study), castor bean (10 of PPIs in castor bean used for this study), soybean (10 of PPIs in soybean used for this study) and poplar (8 of PPIs in poplar used for this study). MePPI-In is the first genome-wide PPI network of cassava that proposed up to 90,173 interactions of 7,209 proteins.
MePPI-U: Integrative interactome network inferring the dynamic protein-protein interaction in cassava
Protein-protein interaction (PPI) network of cassava, was constructed based on interolog and domain methods. This cassava PPI network is an update version of MePPI-In (Thanasomboon et al, 2017). The MePPI-U consisted of 3,638,916 interactions of 24,590 proteins covering 60 percent of proteins in cassava genome.
Thanasomboon R, Kalapanulak S, Netrphan S, Saithong T (2020) Exploring dynamic protein-protein interactions in cassava through the integrative interactome network, Scientific Reports, 10(1):6510.
MeRecon: Carbon assimilation pathway of cassava
The MeRecon pathway was reconstructed by using comparative genomics approach based upon six plant templates: Arabidopsis, rice, maize, castor bean, potato, and turnip. It consists of eight sub-metabolisms: Calvin cycle, sucrose biosynthesis, starch biosynthesis, respiration, amino acid biosynthesis, cell wall biosynthesis, fatty acid biosynthesis, and nucleotide biosynthesis sub-metabolisms visualized by SmartDraw. The reconstructed pathway maps can be download as below.
Siriwat W, Kalapanulak S, Suksangpanomrung M, Saithong T (2018) Unlocking conserved and diverged metabolic characteristics in cassava carbon assimilation via comparative genomics approach, Scientific Reports, 8(1): 16593.
ph-MeRecon: The compartmentalized network of primary carbon metabolism in photosynthetic tissues of cassava
The ph-MeRecon (photosynthetic-Manihot esculenta Metabolic Pathway Reconstruction) was constructed by integrating the genomic information and biochemical reactions of cassava, Arabidopsis, and rice into the genome-based pathway of primary metabolism in cassava, MeRecon (Siriwat, 2012). It comprises 461 metabolites, 550 reactions, and 1,037 metabolic genes. Enzymatic genes on the network were validated using RNA-expression data, and the reactions and pathways were compartmentalized into cytoplasm, chloroplast, mitochondria, and peroxisome.
Punyasu N, Kalapanulak S, Siriwat W, Saithong T (2019) Development of a compartmentalized model for insight into the structured metabolic pathway of carbon metabolism in cassava leaves. Australian Journal of Crop Science 13(04): 605-615.
Me-miRNA: Cassava microRNAs collection
A collection of landscape microRNAs and their target genes in cassava (Me-miRNA) based on cassava genome version 4.1
Yawichai A, Kalapanulak S, Thammarongtham C, Saithong T (2019) Genome-Wide Identification of Putative MicroRNAs in Cassava (Manihot esculenta Crantz) and Their Functional Landscape in Cellular Regulation. BioMed Research International 2019: 2019846.
Me-lncRNAs: Long non-coding RNA network
The Me-lncRNAs was constructed by using cassava genome version 6.1 (Phytozome version 12.0) based on integrating of comparative- and transcriptome- based approach. Six plant genomes; Arabidopsis (Arabidopsis thaliana), Poplar (Populus trichocarpa), Castor bean (Ricinus communis), Jatropha (Jatropha curcas) and 2 cassava cultivars (W14 and KU50) were retrieved from plant genome databases for ncRNAs prediction by using comparative-based algorithm, RNAz. Seventy-four cassava RNA-seq datasets generated from 5 publications and previously published ncRNAs in RNAcentral, NCBI, GreeNC, CANTATAdb, miRbase and Rfam were used for verification. Finally, novel ncRNAs with expression supporting and passed through the filtering criteria were designated as Me-lncRNAs. Expression evidence of Me-lncRNAs indicates by number of publications supporting the expression. Of these with differential expression under cold and/or drought stress were predicted their targets based on cis- or trans- regulation.
Suksamran R, Saithong, T, Thammarongtham C, Kalapanulak S (2020) Genomic and transcriptomic analysis identified novel putative cassava lncRNAs involved in cold and drought stress, Genes, 11(4): 366.
MeTRN: A Cassava Whole-genome Transcriptional Regulatory Network
The cassava whole-genome transcriptional regulatory network (MeTRN) was built using three approaches: (1) template-based, (2) reverse engineering-based, and (3) cis-regulatory element-based methods. This database contains 4,812,519 transcriptional interactions between 33,006 protein-coding genes (covered 99.9% of all genes in the cassava genome) and 2,116 transcription factor genes. This database provide user to search on transcription regulators of interested target genes, target genes of transcription regulators, and MeTRN sub-network of interest pathways.
Sriwichai N, Saithong T, Kalapanulak S (2022) MeTRN 1.0: An integrative database for reconstructing transcriptional regulatory network in cassava (Manihot esculenta crantz). Proceedings of The 13th International Conference on Application of Information Technology in Agriculture Asian-Pacific region (APFITA2022), 24-26 November 2022, Hanoi, Vietnam.
Plant-DTI: Plant DBD-TFBS Interaction Prediction
The Plant-DTI was implemented as a user-friendly application tool to predict the interactions between DBD and TFBS in plants. It will facilitate all interested users to fill the knowledge gap of TF-TFBS interactions or TF-Target gene interactions and improve understanding of transcriptional regulation in plant species. This tool was constructed based on machine learning approach using experimental data of DNA binding domain (DBD) and transcription factor binding site (TFBS) interactions from CIS-BP.
Ruengsrichaiya B, Nukoolkit C, Kalapanulak S and Saithong T (2022) Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach. Front. Plant Sci. 13:970018.
DTScreen: Genome-scale screening tool for drug targets identification
DTscreen v1 is a user-friendly and free bioinformatics tool, was developed for facilitating any researchers in biomedical field to perform a genome-scale screening for identifying all attractive drug targets against pathogenic diseases. The proposed drug targets were reported based on 1) non-homolog with human proteins in term of protein signatures and 2) gene essentiality reported from a large-scale wet experiment
The tool was constructed in Visual Basic language and implemented as an application platform. DTscreen provides a fast and automated performance for identifying drug targets in the three model pathogenic bacteria, including Salmonella typhi CT18, Mycobacterium tuberculosis H37Rv and Escherichia coli K12-MG1655. Fortunately, users can apply DTscreen v1 to other interested pathogens by inserting required biological information of each pathogen.
Kalapanulak S, Juntrapirom A, Saithong T (2012) DTscreen v1: A Novel Drug Targets Identification Tool for Pathogenic Diseases Through Protein Signature-based Approach. Proceedings of The 23rd Annual Meeting of the Thai Society for Biotechnology “Systems Biotechnology: Quality & Success” (TSB2011), Bangkok, Thailand.
N-assimilation pathway of cassava
The N-assimilation pathway was reconstructed by using comparative genomics approach based upon 11 plant templates: (i.e. Arabidopsis, rice, maize, castor bean, potato, turnip, chickpea, physic nut, barrel medic, common bean, and soy bean). It was visualized by SmartDraw. The reconstructed pathway maps can be download as below.
Siriwat W, Muhardina V, Thammarongtham C, Kalapanulak S, Saithong T (2019) Nitrogen assimilation in cassava: implications for carbon metabolism and biomass synthesis, Journal of Physics: Conference Series, 1232:012002.
Metabolic pathways of starch biosynthesis in cassava
The metabolic pathway of starch biosynthesis in cassava, including carbon dioxide fixation (Calvin cycle), sucrose synthesis and storage starch synthesis, were reconstructed based on the comparative genomics approach. The cassava genes comprised of the starch biosynthesis pathway were identified from their orthologues presented in the five template plants (i.e. Arabidopsis, rice, maize, castor bean, and potato). There constructed pathways were presented in both SmartDraw (an informative format of the pathway map) and VANTED (an interactive format of the pathway map for omics data integration) platforms. The reconstructed pathway maps and the other supplemental data of this work are ready to download.
Saithong T, Rongsirikul O, Kalapanulak S, Chiewchankaset P, Siriwat W, Netrphan S, Suksangpanomrung M, Meechai A, Cheevadhanarak S (2013) Starch Biosynthesis in Cassava: a Genome-based Pathway Reconstruction and Its Exploitation in Data Integration, BMC Systems Biology 7(1): 75-92.
Cassava metabolic pathway
The reconstructed pathway can be used as a template to facilitate the omics data integration into the metabolic pathways. This cassava carbon assimilation pathway includes cell wall biosynthesis, starch biosynthesis, and respiration pathways.
This work was presented in the Conference on Computational Systems-Biology and Bioinformatics (CSBio 2012), 3-5 October 2012, Centara Grand at Central Plaza Ladprao, Bangkok, Thailand in title of “Transcriptomic data integration inferring the dominance of starch biosynthesis in carbon utilization of developing cassava roots”, and was published in Elsevier Procedia Computer Science open access. Regarding the research work, the three key carbon metabolism pathways—cell wall biosynthesis, starch biosynthesis, and respiration pathway—were reconstructed following the protocol of Rongsirikul et al., 2010. In brief, the cassava metabolic pathways were reconstructed through comparative genomics approach (reciprocal BLASTp) using multiple plant templates: Arabidopsis, rice, maize, castor bean, and potato. The supplemental data infers the carbon partitioning during cassava root development at the transcriptional level—fibrous root (FR), developing storage root (DR), and mature storage root (MR) from Yang et al., 2011—was provided as the Appendix A.
Siriwat W, Kalapanulak S, Suksangpanomrung M, Netrphan S, Meechai A, Saithong T (2012) Transcriptomic Data Integration Inferring the Dominance of Starch Biosynthesis in Carbon Utilization of Developing Cassava Roots, Proceedings of the 3rd International Conference on Computational Systems-Biology and Bioinformatics (CSBio2012), Bangkok, Thailand.