C. elegans predictions

About this dataset: These are the most reliable 200,000 PPI predictions for C. elegans. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>数据集概况：本数据集包含针对秀丽隐杆线虫（Caenorhabditis elegans）的20万条最高置信度蛋白质-蛋白质相互作用（Protein-protein interactions, PPI）预测结果，格式为CSV文件。<br>研究背景：蛋白质-蛋白质相互作用（PPI）在诸多细胞过程中发挥核心作用。当前多数PPI注释混杂了实验数据与计算预测结果，这种混合方式虽提升了覆盖范围，却模糊了注释的原始来源。尽管部分资源专注于可靠的实验数据领域，但本数据集仅基于序列预测方法，聚焦于针对多种模式生物的全新互作蛋白对。<br>研究结果：我们从公共数据库中提取了8种不同模式生物的可靠二元蛋白质相互作用实验数据，涵盖大肠杆菌（Escherichia coli）、粟酒裂殖酵母（Schizosaccharomyces pombe）、恶性疟原虫（Plasmodium falciparum）、黑腹果蝇（Drosophila melanogaster）、秀丽隐杆线虫（Caenorhabditis elegans）、小家鼠（Mus musculus）、褐家鼠（Rattus norvegicus）、拟南芥（Arabidopsis thaliana），以及此前已被使用的智人（Homo sapiens）和酿酒酵母（Saccharomyces cerevisiae）。以此数据为基础，我们为每种模式生物开发了基于谱核支持向量机（profile-kernel Support Vector Machine, SVM）的PPI预测方法，该方法利用了进化信息。利用构建完成的8个模型，我们预测了每种生物内所有可能的蛋白质对，并通过Web应用程序公开了排名靠前的预测结果。本次公开的绝大多数PPI预测均来自此前未被观测到存在相互作用的蛋白对，针对研究程度较低的生物尤为明显。因此，本工作可作为现有资源的有效补充，且由于其独特性，对实验设计具有重要指导价值。实验注释与计算预测均会受到“部分蛋白拥有大量互作伙伴，而部分蛋白仅拥有少量伙伴”这一特性的显著影响。为优化机器学习模型效果，我们的新方法明确忽略了此类蛋白质互作网络的结构特性，这或许也是本方法的另一优势。展示本研究结果的数据库接口可通过https://rostlab.org/services/ppipair/ 访问。<br>使用本数据集时请引用如下文献：<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>