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• About PICKLE»
• Contributing Databases»
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About PICKLE

PICKLE is a meta-database for the direct protein interaction network in human developed and maintained by:

• The Department of General Biology, School of Medicine, University of Patras, Patras, Greece
• The Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece

The need for source PPI database integration and related issues

At the present time, a vast insight into the currently known human interactome can only be achieved by means of integration of source PPI databases collecting experimental PPI data from the literature. This is due to the fact that source PPI databases display limited overlap in their datasets due to different objectives, curation rules and subsets of the literature that they process. However, the integration process is cumbersome due to PPI annotation discrepancies between the source databases, stemming from distinct curation rules, the use of incompatible interactor descriptors and the selection of different primary protein identifier types (i.e. gene, nucleotide sequence (mRNA), or protein (UniProt) IDs) with which PPIs are recorded. Typically, these heterogeneous primary PPI datasets are integrated via normalization; interactions are first converted to a certain target level of genetic reference and then merged. This top-down process, however, carries two drawbacks. Firstly, the node set of the integrated network is not standardized, but depends each time on the number and type of the combined sources. Thus, different meta-databases are not directly comparable, limiting our capability to evaluate the way in which the reconstructed human protein interactome expands over time. This lack of standardization may also lead to artifacts in the integrated PPI network originating from the currently unresolved part of the human proteome. The second drawback is the irreversible nature of this integration process. The a priori normalization approach suspends the connection between the primary and the integrated PPI networks, thus hindering the identification of normalization artifacts introduced to the integrated network due to the inherent nonlinearity of the genetic information flow.

The PICKLE approach to source PPI database integration

In its launch, PICKLE (Protein InteraCtion KnowLedgebasE) employed a bottom-up approach to the node set standardization problem and adopted the use of the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as a standardized reference node set. Moreover, focusing primarily on direct physical PPIs, PICKLE introduced a PPI filtering protocol, according to which, only PPIs with at least one supporting experiment capable of suggesting direct interactions are selected from the source PPI databases. In its second version, PICKLE provided a major advance in the field of primary PPI dataset integration introducing the concept of ontological integration as an alternative to the traditional integration via normalization. PICKLE 2.0 relies on the reconstruction of the RHCP genetic information ontology network, which facilitates the integration of primary PPI datasets into an heterogeneous network without the need of any a priori transformations. The ontological integration allows the integrated network to be reversibly normalized to any level of genetic reference without loss of source information for any PPI, establishing thus a direct correspondence between the integrated network instances at different levels of genetic reference and enabling primary PPI dataset cross-checking. In this way, PICKLE 2.0 establishes an enhanced confidence scoring protocol for the PPIs being direct based on the "cross-checked" quality of the supporting experimental evidence and provides equivalent integrated human PPI networks at both the protein (UniProt) or the gene level, at three PPI filtering modes: unfiltered, standard and cross-checked (default).

The advantages of the PICKLE structural scheme

The use of the UniProtKB-defined RHCP as the basis of the instantiation of the PICKLE ontological network offers a standardized point of reference between the PPI networks of successive versions and any future releases of PICKLE. In this way, by comparing the default network of PICKLE at the UniProt level between the two current PICKLE versions and in subsequent releases, we can directly and reliably evaluate the manner of expansion of the RHCP PPI network (and its contributing primary datasets). Moreover, the reversible normalization process achieved by PICKLE guarantees a direct correspondence between the various normalized instances of the integrated PPI network. Hence, one can compare a PPI in the integrated network with all the different ways in which it was originally represented in the primary PPI datasets. This feature enables the identification of any potential normalization artifacts through the comparison of the human protein interactome at the UniProt and genel levels and a PPI reliability assessment through cross-checking of the available experimental evidence sets contributed by the various primary datasets. In addition, PICKLE 2.0 enables the storage of interactions between protein and gene or nucleotide sequence (mRNA) entities, e.g. those derived from protein-RNA or chromatin immunoprecipitation arrays/assays, as reported by certain sources, which can be used for purposes of cross-evaluating the supporting evidences provided in other primary datasets. In the same context, it is possible for assorted types of data (e.g. disease-related genes, genomic, transcriptomic or proteomic data) to be consistently integrated, viewed and interpreted in the context of the protein interaction network.

 

Contributing Databases

PPI Databases			Biological Databases
	IntAct			UniProt
	BioGRID			GenBank
	HPRD			Ensembl
	MINT (from IntAct)			The European Nucleotide Archive
	DIP

 

The PICKLE team

Nicholas K. Moschonas, Professor and Head Department of General Biology School of Medicine University of Patras Rio, Patras & Collaborating Faculty Member Institute of Chemical Engineering Sciences (ICE-HT) Foundation for Research & Technology - Hellas (FORTH) Rio, Patras GREECE Phone: +30-2610-997602 e-mail: n_moschonas [a.t.] med [DOT] upatras [DOT] gr	Maria I. Klapa, Principal Researcher (Rank B) and Head Metabolic Engineering and Systems Biology Laboratory (MESBL) Institute of Chemical Engineering Sciences (ICE-HT) Foundation for Research & Technology - Hellas (FORTH) Rio, Patras GREECE Phone: +30-2610-965249 e-mail: mklapa [a.t.] iceht [DOT] forth [DOT] gr
Aris G. Gioutlakis, PhD Candidate Department of General Biology, School of Medicine University of Patras & Metabolic Engineering and Systems Biology Laboratory (MESBL) Institute of Chemical Engineering Sciences (ICE-HT) Foundation for Research & Technology - Hellas (FORTH) Rio, Patras GREECE Phone: +30-2610-997603 e-mail: gioutlakis [a.t.] upatras [DOT] gr	Georgios N. Dimitrakopoulos, PhD Department of General Biology, School of Medicine University of Patras & Metabolic Engineering and Systems Biology Laboratory (MESBL) Institute of Chemical Engineering Sciences (ICE-HT) Foundation for Research & Technology - Hellas (FORTH) Rio, Patras GREECE Phone: +30-2610-997603 e-mail: geodimitrak [a.t.] upatras [DOT] gr

 

PICKLE publications

Citing PICKLE

If you are using PICKLE, please cite:

• Gioutlakis A, Klapa MI, Moschonas NK. (2017). PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology. PLoS ONE 12(10): e0186039.
• Klapa MI, Tsafou K, Theodoridis E, Tsakalidis A, Moschonas NK. (2013). Reconstruction of the experimentally supported human protein interactome: what can we learn? BMC Syst. Biol. 7:96

 

PICKLE citations

Publications citing PICKLE

• Singh, R. & Som, A. (2020). Role of Network Biology in Cancer Research. In: Recent Trends in ‘Computational Omics' ISBN: 978-1-53617-941-5. Editor: Pramod Katara. Nova Science Publishers, Inc.
• Doostparast Torshizi, A., Duan, J., & Wang, K. (2020). Cell Type-Specific Proteogenomic Signal Diffusion for Integrating Multi-Omics Data Predicts Novel Schizophrenia Risk Genes. PATTERNS-D-20-00074. http://dx.doi.org/10.2139/ssrn.3600562
• Tao, Y. T., Ding, X. B., Jin, J., Zhang, H. B., Yang, Q. L., Chen, P. C., ... & Chen, X. (2020). HIR V2: a human interactome resource for the biological interpretation of differentially expressed genes via gene set linkage analysis. 17 June 2020, PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-26127/v1
• Qi, Yang, Yang Guo, Huixin Jiao, and Xuequn Shang (2020). A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data. BMC Bioinformatics 21, no. 1 (2020): 1-21. https://doi.org/10.1186/s12859-020-03547-w
• Theodosiou, T., Papanikolaou, N., Savvaki, M., Bonetto, G., Maxouri, S., Fakoureli, E., ... & Aivaliotis, M. (2020). UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network. NAR Genomics and Bioinformatics, 2(1), lqaa005. https://doi.org/10.1093/nargab/lqaa005
• Koutrouli, M., Karatzas, E., Paez-Espino, D., & Pavlopoulos, G. A. (2020). A Guide to Conquer the Biological Network Era Using Graph Theory. Frontiers in Bioengineering and Biotechnology, 8, 34. https://doi.org/10.3389/fbioe.2020.00034
• Barreto, C. A., Baptista, S. J., Preto, A. J., Matos-Filipe, P., Mourão, J., Melo, R., & Moreira, I. (2020). Prediction and targeting of GPCR oligomer interfaces. In Progress in Molecular Biology and Translational Science (Vol. 169, pp. 105-149). Academic Press. https://doi.org/10.1016/bs.pmbts.2019.11.007
• Telonis, A. G., Loher, P., Magee, R., Pliatsika, V., Londin, E., Kirino, Y., & Rigoutsos, I. (2019). tRNA Fragments Show Intertwining with mRNAs of Specific Repeat Content and Have Links to Disparities. Cancer research, 79(12), 3034-3049. https://doi.org/10.1158/0008-5472.CAN-19-0789
• Lin, C. Y., Lee, C. H., Chuang, Y. H., Lee, J. Y., Chiu, Y. Y., Lee, Y. H. W., ... & Wu, C. H. (2019). Membrane protein-regulated networks across human cancers. Nature Communications, 10(1), 3131. https://doi.org/10.1038/s41467-019-10920-8
• Wang, Y., You, Z. H., Yang, S., Li, X., Jiang, T. H., & Zhou, X. (2019). A High Efficient Biological Language Model for Predicting Protein–Protein Interactions. Cells, 8(2), 122. https://doi.org/10.3390/cells8020122
• Pei, J., Kinch, L., & Grishin, N. V. (2019). Mutation severity spectrum of rare alleles in the human genome is predictive of disease type. PLoS Computational Biology, 16(5), e1007775. https://doi.org/10.1371/journal.pcbi.1007775
• Dimitrakopoulos, G. N., Gioutlakis, A., Klapa, M. I., & Moschonas, N. K. (2019). Evaluating the expansion of the experimentally determined human protein interactome using the PICKLE meta-database. Abstracts from the 52nd European Society of Human Genetics (ESHG) Conference: Posters. European Journal of Human Genetics, 27, 1705-1705. https://doi.org/10.1038/s41431-019-0494-2
• Sarkar, S., Gulati, K., Kairamkonda, M., Mishra, A., & Poluri, K. M. (2018). Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases. Current topics in medicinal chemistry, 18(20), 1719-1736. https://doi.org/10.2174/1568026618666181025114903
• Vastrad, C., & Vastrad, B. (2018). Bioinformatics analysis of gene expression profiles to diagnose crucial and novel genes in glioblastoma multiform. Pathology-Research and Practice, 214(9), 1395-1461. https://doi.org/10.1016/j.prp.2018.07.015
• Wang, X., Zhai, Y., Lin, Y., & Wang, F. (2018). Mining layered technological information in scientific papers: A semi-supervised method. Journal of Information Science, 0165551518816941. https://doi.org/10.1177%2F0165551518816941
• Gioutlakis A., Klapa M.I., Moschonas N.K. (2018). PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology. ELIXIR 2018 All Hands Meeting Berlin, June 4-7 2018.
• Steinrücken, M., Spence, J. P., Kamm, J. A., Wieczorek, E., & Song, Y. S. (2018). Model‐based detection and analysis of introgressed Neanderthal ancestry in modern humans. Molecular ecology, 27(19), 3873-3888. https://doi.org/10.1111/mec.14565
• Telonis, A. G., & Rigoutsos, I. (2018). Race disparities in the contribution of miRNA isoforms and tRNA-derived fragments to triple-negative breast cancer. Cancer research, 78(5), 1140-1154. https://doi.org/10.1158/0008-5472.CAN-17-1947
• Macalino, S., Basith, S., Clavio, N., Chang, H., Kang, S., & Choi, S. (2018). Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules, 23(8), 1963. https://doi.org/10.3390/molecules23081963
• Bojadzic, D., & Buchwald, P. (2018). Toward small-molecule inhibition of protein–protein interactions: General aspects and recent progress in targeting costimulatory and coinhibitory (immune checkpoint) interactions. Current topics in medicinal chemistry, 18(8), 674-699. https://doi.org/10.2174/1568026618666180531092503
• Rujirapipat, S., McGarry, K., & Nelson, D. (2017). Bioinformatic Analysis Using Complex Networks and Clustering Proteins Linked with Alzheimer’s Disease. In Advances in Computational Intelligence Systems (pp. 219-230). Springer, Cham. https://doi.org/10.1007/978-3-319-46562-3_14
• Papanikolaou, N., Pavlopoulos, G. A., Theodosiou, T., & Iliopoulos, I. (2015). Protein–protein interaction predictions using text mining methods. Methods, 74, 47-53. https://doi.org/10.1016/j.ymeth.2014.10.026
• Song, Y., & Buchwald, P. (2015). TNF superfamily protein-protein interactions: feasibility of small-molecule modulation. Current drug targets, 16(4), 393-408. https://www.ingentaconnect.com/content/ben/cdt/2015/00000016/00000004/art00013
• Makris, C., & Theodoridis, E. (2015). Computational Methods for Modeling Biological Interaction Networks. Pattern Recognition in Computational Molecular Biology: Techniques and Approaches, 505-524. https://doi.org/10.1002/9781119078845.ch26
• Song, Y. (2015). Small-Molecule Probes for the Modulation of Ligand-Receptor Interactions within the TNF Superfamily. OpenAccess Dissertations. 1398. https://scholarlyrepository.miami.edu/oa_dissertations/1398
• Nastou, K. C., Tsaousis, G. N., Kremizas, K. E., Litou, Z. I., & Hamodrakas, S. J. (2014). The human plasma membrane peripherome: visualization and analysis of interactions. BioMed research international, 2014. http://dx.doi.org/10.1155/2014/397145
• Wu, P. J., Wu, W. H., Chen, T. C., Lin, K. T., Lai, J. M., Huang, C. Y. F., & Wang, F. S. (2014). Reconstruction and analysis of a signal transduction network using HeLa cell protein–protein interaction data. Journal of the Taiwan Institute of Chemical Engineers, 45(6), 2835-2842. https://doi.org/10.1016/j.jtice.2014.07.006

 

PICKLE usage

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