The Anatomical Therapeutic Chemical (ATC) classification system developed and maintained by the World Health Organization (WHO) Collaborating Center for Drug Statistics Methodology (WHOCC), is currently the most widely recognized classification system for drugs. It divides drug substances into different groups according to the organ or system on which they act and their therapeutic, pharmacological and chemical properties.

BATMAN-TCM is the first online bioinformatics analysis tool specially designed for the research of molecular mechanism of TCM, mainly based on TCM ingredients’ target prediction and the following network pharmacology analyses of the potential targets, aiming to contribute to the understanding of the “multi-component, multi-target and multi-pathway” combinational therapeutic mechanism of TCM and to provide clues for the following experimental validation.

CAPER (Chromosome-Assembled human Proteome browsER) is designed to visualize human proteomic datasets and related annotation information based on chromosomes, to connect chromosome, genome, transcriptome, proteome and even the phenotype, benefiting the Human Proteome Project (HPP) and human physiology and pathology researches.

The Chromosome-centric Human Proteome Project (C-HPP) aims to catalog the genome-encoded proteins on the chromosome-by-chromosome basis. As the C-HPP proceeds, the increasing requirement of data-intensive analyses for the MS/MS data poses a challenge to the proteomic community, especially those small laboratories lacking computational infrastructures. To address this challenge, we update the previous CAPER browser into a higher version, CAPER 3.0 – a scalable cloud-based system for the data-intensive analysis of C-HPP datasets. CAPER 3.0 uses cloud computing technology to facilitate MS/MS-based peptide identification. In particular, it can use both public and private cloud, aiming to help analyze the C-HPP datasets.

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks. Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. SLIPPER (SeLf-Interacting Protein PrEdictoR) is designed to predict whether a protein can interact with itself, and meanwhile provide various related annotation information to facilitate its further functional research.

UbiBrowser is a resource of known and predicted human ubiquitin ligase (E3) - substrate interaction network. The E3-substrate interactions are derived from severn data sources: mannual cruration, protein ortholog, protein domain, protein motif and network topology. A computational framework is used to to combine multiple biological evidences to generate a confidence score using Naïve Bayesian Network.

Data analysis poses a significant challenge to the large-scale proteomics studies. Based on the structured and controlled vocabularies - Gene Ontology (GO), and the GO annotation from related databases, a strategy (named GOfact) is developed to identify the functional distribution and the significantly enriched functional categories of the proteomic expression profile. It would be helpful for understanding the overall functions of these identified proteins and supply the fundamental information for further bioinformatics exploration.

High-throughput screens inevitably generate a significant number of false positives, which result in erroneous data and thus misleading our conclusions. Strategies should be taken to assess the confidence of high-throughput data and minimize the high degree "false-positive". Here we introduce the Bayesian method to evaluate the potential biological relevance of the identified interactions.

Autoantibodies are antibodies that are produced by the B-cell immune system against an individual’s own antigens and play pivotal roles in the maintenance of healthy individuals’ homeostasis, as well as in tumors and autoimmune diseases. In the last two decades, tremendous efforts have been devoted to elucidate the generation, evolution and function of autoantibodies and their targets, autoantigens [1-9]. However, the previously identified autoantigens are randomly dispersed in the literature.

PNmerger (biological Pathway and protein Netwrok merge) is a java based plug-in for the widely used open-source Cytoscape molecular interaction viewer. For an interaction network, PNmerger will automatically annotate the network proteins with the KEGG pathway information, to find the known pathway elements in protein network, and also to predict the possible pathway elements. To present the pathway information for the protein network , PNmerger can illustrate the clusters of the nodes with the same biological pathway, and also present the the potential crosstalks between different pathways. This information will be helpful for the users to find the important clues for knowledge discovery and also experimental design.

sapFinder, an R software package, for detection of the variant peptides based on tandem mass spectrometry (MS/MS)-based proteomics data. This package automates the construction of variation-associated databases from public SNV repositories or sample-specific next-generation sequencing (NGS) data and the identification of SAPs through database searching, post-processing and generation of HTML-based report with visualized interface.

A pipeline with an R package, assigned as a PGA utility, was developed that enables automated treatment to the tandem mass spectrometry (MS/MS) data acquired from different MS platforms and construction of customized protein databases based on RNA-Seq data with or without a reference genome guide. PGA can identify novel peptides (SNV, INDEL, novel splice junction and transcript-derived peptides ) and generate an HTML-based report with a visualized interface.

Protein domains are structural, functional and evolutionary units of proteins. So, it’s important to resolve the domain features to get full understanding of the structural, functional and evolutionary characteristics of the proteins. However, there’s lack such a platform to do that automatically and comprehensively. Here, we propose the ProDoctor, a one-stop platform for the protein domain feature analysis.

Large-scale efforts for parallel acquisition of multiomics profiling continue to generate extensive amounts ofmulti-dimensional biomedical data. Thus, integrated clustering of multiple types of omics data is essential for developing individual-based treatments and precision medicine. However, while rapid progress has been made, methods for integrated clustering are lacking an intuitive web interface that facilitates the biomedical researchers without sufficient programming skills. Here, we present a web tool, named Integrated Clustering of Multidimensional biomedical data (ICM), that provides an interface from which to fuse, cluster and visualize multi-dimensional biomedical data and knowledge. With ICM, users can explore the heterogeneity of a disease or a biological process by identifying subgroups of patients. The results obtained can then be interactively modified by using an intuitive user interface. Researchers can also exchange the results from ICM with collaborators via a web link containing a Project ID number that will directly pull up the analysis results being shared. ICM also support incremental clustering that allows users to add new sample data into the data of a previous study to obtain a clustering result. Currently, the ICM web server is available with no login requirement and at no cost at http://biotech.bmi.ac.cn/icm/.

We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, BioCarta, Reactome, NetPath, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, IntAct, BioGRID, MINT and DIP, to develop integrated dataset named PathPPI.

The main function of pGlyco+ is as follows: (1) automatically search intact glycopeptides by intact glycopeptide-spectrum match scoring; (2) both support identification of N-Glycopeptide and O-Glycopeptide. Its main technical features include: (1) identification of glycopeptide sugar chain by using open search method, and then use a limited search method to identify the de-glycopeptide; (2) According to the Bayesian algorithm, using the EM method to get the false rate of glycopeptide identification.

ITMSQ is a professional software to deal with pairs of peak in MSMS spectrum for same precursor. It provide two modules, including qualitative and quantitative, and the qualitative module has the function of splitting spectrum. ITMSQ can increase the number of protein more than 20 percent, while the quantitative module have multiple parameters that can be set.

PD-OmicsPilot is a simple software developed by MATLAB, for the design of primer. PD-OmicsPilot designs multiple pairs of primers based on DNA sequences from multiple species, and its amplified sequences are species-specific. PD-OmicsPilot can set parameters such as primer length, amplification sequence length, and calculate the sequence annealing temperature and molecular weight. The software read * .qscore file produced by ClustalX, a multiple sequence alignment software, and produce the results of primer pairs, then export to Excel.

The software system provides an electronic questionnaire containing the user's habits, eating habits, physiological indicators, sickness, occupational history, family history, personal physical exercise, environmental status and other information for the user to fill. After the information is submitted, the system gives the health risk assessment report of the tumor, cardiovascular and cerebrovascular diseases according to the user's personal information (gender, age) and the submitted questionnaire. After extraction of information from various diseases (tumor: lung cancer, liver cancer, colon cancer, stomach cancer, esophageal cancer, bladder cancer, cardiovascular and cerebrovascular: coronary heart disease, stroke, diabetes) in the system, disease-related risk factors were derived. The disease assessment report was derived by risk factors rating score table. The optimal improvement measures will be given according to the relevant knowledgebase.

The structure of complex biological systems of microbial communities reflects not only their diversity or function but also the environments they live in. By using a graph-theory-based algorithm, the Reverse Ecology approach can analyze metabolic networks of all species in a microbial community, and predicting the metabolic interface between species and their environment. Here, we present RevEcoR, an R package and a shiny web application that implements Reverse Ecology algorithm, which can obtain large-scale ecological insights into species’ ecology directly from high-throughput metagenomic data. The software shows great potential in the study of microbiomes. http://yiluheihei.shinyapps.io/RevEcoR.

In this work, we adopted GPS 3.0 algorithm and built GPS-MSP (Methyl-group Specific Predictor) for the prediction of general or type-specific methylline and methylarginine residues in proteins. We also construced up to 28 organism-specific predictors. We implemented GPS-MSP into a webserver, which also provides the information of secondary structures, protein surface accessibility, and the statistics of prediction results .

The dbPAF (database of Phospho-sites in Animals and Fungi) is an online data resource specifically designed for protein phosphorylation in seven eukaryotic species, including H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, S. pombe and S. cerevisiae. From the scientific literature, we collected 294,370 non-redundant phosphorylation sites of 40,432 proteins. We also integrated known phosphorylation sites from a number of public databases, such as Phospho.ELM (Diella, et al., 2004; Diella, et al., 2008), dbPTM (Huang, et al., 2015; Lee, et al., 2006), PHOSIDA (Gnad, et al., 2011; Olsen, et al., 2006), PhosphositePlus (Hornbeck, et al., 2004; Hornbeck, et al., 2015), PhosphoPep (Bodenmiller, et al., 2008; Bodenmiller, et al., 2007), PhosphoGRID (Sadowski, et al., 2013; Stark, et al., 2010), SysPTM (Li, et al., 2009; Li, et al., 2014), HPRD (Goel, et al., 2012) and UniProt (The UniProt Consortium, 2015). In total, dbPAF 1.0 contained 483,001 known phosphorylation sites of 54,148 protein substrates, as a comprehensive data resource for human, animals and fungi.

During cell cycle, numerous proteins temporally and spatially localized in distinct sub-cellular regions including centrosome (spindle pole in budding yeast), kinetochore/centromere, cleavage furrow/midbody (related or homolog structures in plants and budding yeast called as phragmoplast and bud neck, respectively), telomere and spindle spatially and temporally. These sub-cellular regions play important roles in various biological processes. In this work, we have collected all proteins identified to be localized on kinetochore, centrosome, midbody, telomere and spindle from two fungi (S. cerevisiae and S. pombe) and five animals, including C. elegans, D. melanogaster, X. laevis, M. musculus and H. sapiens based on the rationale of "Seeing is believing" (Bloom K et al., 2005). Through ortholog searches, the proteins potentially localized at these sub-cellular regions were detected in 144 eukaryotes. Then the integrated and searchable database MiCroKiTS - Midbody, Centrosome, Kinetochore, Telomere and Spindle has been established. Currently, the MiCroKiTS 4.0 database was updated on Sep. 6, 2014, containing 87,983 unique protein entries. The database will be updated routinely as new microkits proteins are reported.

In this work, we present a novel software of HemI (Heatmap Illustrator, version 1.0) for experimentalists, to prepare publication-quality figures of heatmap.The HemI is written in JAVA 1.6 (J2SE 6.0) and packaged with Install4j 4.0.8. Thus, the HemI 1.0 can be easily installed on a computer. Then we developed several packages to support three major operating systems (OS), including Windows, Unix/Linux, and Mac. The Windows XP/7, Ubuntu, Apple Mac OS X 10.5 (Leopard) were chosen to test the stability of HemI 1.0 for Windows and Linux systems, a Java Runtime Environment 6 (JRE6) package of Oracle should be installed first or you can choose install package which include JRE.

The EKPD 1.1 was lastly updated on Sep 10th, 2013, containing 61,729 unique protein entries including 50,433 protein kinases and 11,296 protein phosphatases. The online service of EKPD was implemented in PHP + MySQL + JavaScript. The database will be updated routinely as new protein kinases or protein phosphatases are reported.

CPLM (Compendium of Protein Lysine Modifications) is an online data resource specifically designed for protein lysine modifications (PLMs). The CPLM database was extended and adapted from our CPLA 1.0 (Compendium of Protein Lysine Acetylation) database (Liu et al., 2011), and the 2.0 release contains 203,972 modification events on 189,919 modified lysines in 45,748 proteins for 12 types of PLMs, including Nε-lysine acetylation (Yang et al., 2007; Shahbazian et al., 2007; Smith et al., 2009), ubiquitination (Gao, et al., 2013), methylation (Chen, et al., 2006), sumoylation (Ren, et al., 2009; Xue, et al., 2006), glycation (Priego-Capote, et al., 2010), butyrylation (Chen, et al., 2007; Cheng, et al., 2009; Zhang, et al., 2009), crotonylation (Tan, et al., 2011), malonylation (Xie, et al., 2012), propionylation (Chen, et al., 2007; Cheng, et al., 2009; Zhang, et al., 2009), succinylation (Xie, et al., 2012; Zhang, et al., 2011), phosphoglycerylation (Moellering, R. E. and B. F. Cravatt, 2013) and prokaryotic Pupylation (Liu, et al., 2011).

As one of the PTMs with tremendous studies, Phosphorylation regulates a wide variety of biological processes, including signal transduction events (Cohen, 1982). Whereas eukaryotic proteins of phosphorylation have been extensively studied, only limited information is available for phosphorylated proteins in prokaryotic organisms. Previous studies about constructing database of prokaryotic phosphorylation sites mainly focus on tyrosine-, serine, and threonine-phosphorylated proteins (Wurgler-Murphy, King and Kennelly, 2004). However, for the purpose of elucidating the mechanisms of phosphorylation in prokaryotic organisms, other residues which can also be phosphorylated should not be neglected. For example, protein histidine or aspartate phosphorylation plays important roles in two-signal-transduction events (Galperin, Nikolskaya, Koonin, 2001; Swanson, Alex and Simon, 1994). To settle these challenges, we provide a comprehensive database of Prokaryotic Protein Phosphorylation Sites for 7 types of residues, including 7,391 phosphorylation sites in 3,750 proteins.

Histone acetylation is a hallmark of chromatin that has an open structure that can be accessed by DNA and RNA polymerases as well as transcription factors, resulting in the activation of gene transcription (Filippakopoulos and Knapp, 2014). Correspondingly, histone methylation increases the basicity and hydrophobicity of histone tails and the affinity of certain proteins, such as transcription factors, toward DNA (Teperino et al., 2010), thus affecting the gene expression. In this database, we have collected 584 non-redundant protein data of 8 organisms including H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, A. thaliana, S. pombe and S. cerevisiae from the literature. The data are further classified into 15 families for histone acetylation writers, erasers and readers and 32 families for histone methylation writers, erasers and readers, respectively. WERAM 1.0 is a comprehensive Eukaryotic Writers, Erasers and Readers protein of Histone Acetylation and Methylation system Database for 148 eukaryotic species. And here we provide two approaches for users to browse the database: (i) By species; (ii) By classifications.

Circadian clock exists endogenously in almost every organism and drives oscillatory changes in a myriad of behavioral and physiological processes. Depending on the organism and cell type, the circadian clock promotes rhythmic expression of 1% to over 60% of the genome, serving as the molecular basis for rhythmic control at the system’s level (Li et al., 2015). The CGDB (Circadian Gene DataBase) is an online resource containing over 72,800 genes in more than 148 organisms that exhibit (or may exhibit) daily oscillation at the transcript level. These include 1,382 genes with oscillatory transcripts that have been experimentally validated by techniques including RT-PCR, Northern blot, and in situ hybridization. Because the same gene often exhibits different pattern of temporal expression in different tissues/cells of an organism, and within an organism each tissue/cell type has a unique set of genes that show oscillation at the transcript level (Zhang et al., 2014), we have indicated the phase (time of peak and trough expression) of the oscillation for each gene and the tissue/cell type in which it was identified to be cycling. We have also calculated the amplitude of the oscillation by dividing the peak value with the trough value. Ortholog search for these genes identified another 44,836 which are included in the database as potentially oscillating genes. In addition, we have incorporated 26,582 cycling genes identified in transcriptome profiling studies using microarray or RNA-sequencing. Since post-translational modifications (PTMs) play a key role in regulating circadian timing (Diernfelner et al., 2011; Hardin et al., 2011; Kusakina et al., 2012; Lowrey et al., 2011), we have integrated known PTM sites from published databases including dbPAF (Ullah et al., 2016), dbPPT (Cheng et al., 2014), and CPLM (Liu et al., 2014). All in all, CGDB can serve as a tool to search for genes with oscillatory expression and identify new cycling genes.

PTMs of proteins play essential roles in almost all cellular processes, and are closely related to physiological activity and disease development of living organisms. The collection of these data will provide invaluable source to understand the cellular processes and the signaling pathways regulated by PTMs. While many databases have been established to collect information of protein PTM sites, there is no such resource providing a searchable platform for quick and in-depth analysis of user PTM datasets by comparing to the known PTMs. SysPTM was thus designed to incorporates the existing features of numerous previous databases and modification datasets from MS/MS experiments reported in the literature, presented in a user-friendly browser with a suit of analysis tools to support high-throughput annotation of proteomics PTMs. The database is publicly available at http://lifecenter.sgst.cn/SysPTM/.

Three types of data were integrated in PhoSigNet: phosphorylation site information, signal transduction information, protein node annotation information. In protein node annotation, other than general function information, cancer-related information was integrated

dbPHCC is a database(db) for the prognosis(P) of hepatocellular carcinoma (HCC). It is a system for all the experimentally identified molecules which are related to HCC prognosis. It was curated from 1479 records in 595 literature articles which were published in the past 13 (2002-2014) years. Eventually the dbPHCC collected 323 proteins, 154 genes, 90 miRNAs, some items were recorded multiple times

Elucidation of human disease similarities has provided new insights into etiology, disease classification and drug repositioning. Since dysfunctional regulation would be manifested as the decoupling of expression correlation, disease similarity (DS) in terms of dysfunctional regulation mechanism (DRM) could be estimated by using a differential coexpression based approach, which is described in a companion paper. Due to the lack of tools for estimating DS from the viewpoint of DRM in public domain, we implemented an R package ‘DSviaDRM’ to identify significant DS via DRM based on transcriptomic data. DSviaDRM contains five easy-to-use functions, DCEA, DCpathway, DS, comDCGL and comDCGLplot, for identifying disease relationships and showing common differential regulation information shared by similar diseases.

GPS-Lipid is a tool for the prediction of four classes of lipid modifications by integrating the Particle Swarm Optimization with an aging leader and challengers (ALC-PSO) algorithm. Proven to be evidently superior to other similar tools, GPS-Lipid is expected to serve as useful resource for the research on lipid modifications, especially on their coregulation.

GPS-SUMO is a new tool for the prediction of both sumoylation sites and SUMO-interaction motifs (SIMs) in proteins. To obtain an accurate performance, a new generation group-based prediction system (GPS) algorithm integrated with Particle Swarm Optimization approach was applied. By critical evaluation and comparison, GPS-SUMO was demonstrated to be substantially superior against other existing tools and methods. With the help of GPS-SUMO, it is now possible to further investigate the relationship between sumoylation and SUMO interaction processes.

PLOSP is a personal library of scientific paper in biomedical sciences. It integrates and synchronizes biomedical literature resources such as PubMed, PMC and Web of Science. PLOSP provides online space for users to search, collect, update, and browse their favourite journals, paper, and researchers on the platform, and enable users to publish their own interpretations and comments on the paper, even in foreign language. Centered on biomedical paper, PLOSP provides a friendly and comfortable environment for researchers and students to communicate with each other.

Illustrator of biological sequences (IBS) is a software package that can be used for representing the organization of either protein or nucleotide sequences in a convenient, efficient and precise manner. Multiple options are provided in IBS, and biological sequences can be manipulated, recolored or rescaled in a user-defined mode. Also, the final representational artwork can be directly exported into a publication-quality figure.

PAPER-CMD is a command line toolkit for PAPER (A Pathway-Assembled Profile browsER), which is an open-source, open-access browser to visualize and analyze profile datasets at the level of biological pathway, to navigate and design the pathway diagrams for specific scientific interest, and to share and discuss the unpublished primary result within your own scientific community. It contains two executable file that can be used to generate pathway graph from a specified Reactome database.

dbDEPC (database of Differentially Expressed Proteins in Human Cancer) is designed to store and display differentially expressed proteins (DEPs) in human cancers detected by mass spectrometry (MS) approach. The current version contains 4029 DEPs in 20 cancers, curated from 331 MS experiments in 219 peer-reviewed publications. 56% analysis focused on cancer vs. normal comparison, while others (9%, 11% and 24%) focused on cancer vs. cancer, metastasis study, treatment comparison, respectively. Proteins can be queried through the web-interface using protein name, accession number or sequence similarity, and also browsed by interested cancers or MS experiments. We provided multiple conditions to help you filter the various experiments down to your interested results. The retrieved protein pages contain basic protein description, DEP profiles across multiple caners in each analysis type, related MS experiments, low throughput validation assays and diverse annotations including protein function, sequence, gene ontology and involved pathway. For a set of proteins and multiple cancers, differential expression profile can be visualized in a heatmap to explore the difference and similarity between cancers. In addition, a protein association network is introduced to facilitate the discovery of DEPs associated with your query proteins. Furthermore, the change of protein expression of DEPs is probably relevant to important structural variation in the human genome, such as single amino acid alterations. So these variation sites were highlighted as especially related to development of human cancers. We try to make dbDEPC a continuingly growing resource to facilitate cancer proteomic research and contribute to biomarker discovery.

The Chromosome-centric Human Proteome Project (C-HPP) aims to map and annotate the entire human proteome by the "chromosome-by-chromosome" strategy. As the C-HPP proceeds, the increasing volume of proteomic data sets presents a challenge for customized and reproducible bioinformatics data analyses for mining biological knowledge. To address this challenge, we updated the previous static proteome browser CAPER into a higher version, CAPER 2.0 - an interactive, configurable and extensible workflow-based platform for C-HPP data analyses. In addition to the previous visualization functions of track-view and heatmap-view, CAPER 2.0 presents a powerful toolbox for C-HPP data analyses and also integrates a configurable workflow system that supports the view, construction, edit, run, and share of workflows. These features allow users to easily conduct their own C-HPP proteomic data analyses and visualization by CAPER 2.0. We illustrate the usage of CAPER 2.0 with four specific workflows for finding missing proteins, mapping peptides to chromosomes for genome annotation, integrating peptides with transcription factor binding sites from ENCODE data sets, and functionally annotating proteins.

KOBAS (KEGG Orthology Based Annotation System) is a web server for gene/protein functional annotation (Annotation module) and functional set enrichment (Enrichment module). Given a set of genes or protein, it can determine whether a pathway, disease, and Gene Ontology(GO) term shows statistically significant. The last version of KOBAS, KOBAS 2.0, has abundant annotation information of gene sets from multiple databases covering pathways (KEGG PATHWAY, Reactome, Biocyc, Panther), diseases (KEGG DISEASE, OMIM, NHGRI GWAS Catalog), and GO terms, and more than 4,000 species are supported. Since KOBAS 2.0 is widely used by worldwide researchers, we updated it to KOBAS 3.0, which supports more data formats as input and more accurate functional enrichment algorithms.

The Omics Discovery Index (OmicsDI) provides dataset discovery across a heterogeneous, distributed group of Transcriptomics, Genomics, Proteomics and Metabolomics data resources spanning eight repositories in three continents and six organisations, including both open and controlled access data resources. The resource provides a short description of every dataset: accession, description, sample/data protocols biological evidences, publication, etc. Based on these metadata, OmicsDI provides extensive search capabilities, as well as identification of related datasets by metadata and data content where possible. In particular, OmicsDI identifies groups of related, multi-omics datasets across repositories by shared identifiers.

The data produced by the next generation proteomics requests a powerful, integrated data processing and analyzing online platform, which can host millions of datasets and analyze hundreds and thousands of experiments at a time. However, such a platform is under developed. Here, we launched FIRMIANA (V1.0) (www.firmiana.org), a cloud Proteomics platform that allows data repository, proteome identification, quantification, bioinformatics analysis, result visualization and knowledge mining in an automated fashion. Up to now, FIRMIANA 1.0 hosted and processed over 3,000 experimental proteome datasets (>600M MS spectra) from 28 laboratories, allowing proteome comparison and bioinformatics analysis for hundreds of datasets. As a big data platform, FIRMIANA 1.0 is connecting to other databases to achieve multi-omics integration of life sciences, bridging data to knowledge. We envision that FIRMIANA 1.0 and its subsequent high-performance-computing (HPC) version will play an important role in facilitating proteomics in biology and medicine.

iProX is an integrated proteome resources center in China, which is built to accelerate the worldwide data sharing in proteomics. iProX is composed of a data submission system and a proteome database. The submission system is established under the guidance of the data-sharing policy made by ProteomeXchange consortium. Registered users can submit their proteomic datasets to iProX in public or private modes. Public proteome data is freely available, while more private data resources can be acquired by joining user groups.

UsIng MedPortal to access and share ontologies. You can create ontology-based annotations for your own text , link your own project that uses ontologies to the description of those ontologies , find and create relations between terms in different ontologies, review and comment on ontologies and their components as you browse them. Sign in to MedPortal to submit a new ontology or ontology-based project, provide comments on ontologies or add ontology mappings.

Protein Structure Prediction Tools (PSPT) was built for protein 3D structure prediction. It integrated JiangLab developed four prediction modules , that is, FR-t5-M, CIS-RR, MEFTop, PackingCluster. (1) FR-t5-M is a template based modeling (TBM) method for protein structure prediction. By incorporating MEFTop with our developed threading program FR-t5, the FR-t5-M achieved significant improvement of performance for low-homology protein targets. (2) CISRR couples a novel clash-detection guided iterative search algorithm (CIS) with rotamer relaxation (RR), which removes atomic clashes effectively and achieves high accuracy (about 86% and 76% for Chi1 and Chi1&2, respectively, within 40 degree cutoff). (3) MEFTop is an effective general model evaluation method. Furthermore, the MEFTop could be integrated with traditional threading program to improve the quality of models obviously. (4) PackingCluster present a picture of protein domain organization through an integrated consideration of the arrangements of secondary structure elements (SSE) and residue contact networks.

Biological Mirror Database is a localized mirror of 27 international important bioinformatic databases（GO、EMBL、FSSP、IntAct、Refseq、Reactome、dbSNP、Uniprot、Ensembl、InterPro、DSSP、GENE3D、PDB、CATH、HSSP、Pfam、REBASE、ClinVar… ）It is built and maintained by Bioinformatics Center of Peking University. It is built to localize major international bioinformatics resources and to provide network-based high-speed data batch download services for users all over the country.

Downstream Annotation Analysis Platform is a website platform for gene/protein annotation analysis. We integrated representative bioinformatic tools and algorithms and made them ease of use, mainly focusing on five important basic modules, that is, "Differential genes/proteins screening", "Proteins/Genes functional enrichment analysis and pathway enrichment analysis", "Annotation based on international common databases", "Proteins/Genes clustering analysis", and "Protein-Protein interaction network".