TSNAdb: A Database for Tumor-specific Neoantigens from Immunogenomics Data Analysis

Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database (TSNAdb v1.0), based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen (HLA) allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas (TCGA) and The Cancer Immunome Atlas (TCIA). We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients, which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy. TSNAdb is freely available at http://biopharm.zju.edu.cn/tsnadb.     

Elevation of Eosinophil-Derived Neurotoxin in Plasma of the Subjects with Aspirin-Exacerbated Respiratory Disease: A Possible Peripheral Blood Protein Biomarker

Aspirin-exacerbated respiratory disease (AERD) remains widely underdiagnosed in asthmatics, primarily due to insufficient awareness of the relationship between aspirin ingestion and asthma exacerbation. The identification of aspirin hypersensitivity is therefore essential to avoid serious aspirin complications. The goal of the study was to develop plasma biomarkers to predict AERD. We identified differentially expressed genes in peripheral blood mononuclear cells (PBMC) between subjects with AERD and those with aspirin-tolerant asthma (ATA). The genes were matched with the secreted protein database (http://spd.cbi.pku.edu.cn/) to select candidate proteins in the plasma. Plasma levels of the candidate proteins were then measured in AERD (n = 40) and ATA (n = 40) subjects using an enzyme-linked immunosorbent assay (ELISA). Target genes were validated as AERD biomarkers using an ROC curve analysis. From 175 differentially expressed genes (p-value <0.0001) that were queried to the secreted protein database, 11 secreted proteins were retrieved. The gene expression patterns were predicted as elevated for 7 genes and decreased for 4 genes in AERD as compared with ATA subjects. Among these genes, significantly higher levels of plasma eosinophil-derived neurotoxin (RNASE2) were observed in AERD as compared with ATA subjects (70(14.62∼311.92) µg/ml vs. 12(2.55∼272.84) µg/ml, p-value <0.0003). Based on the ROC curve analysis, the AUC was 0.74 (p-value = 0.0001, asymptotic 95% confidence interval [lower bound: 0.62, upper bound: 0.83]) with 95% sensitivity, 60% specificity, and a cut-off value of 27.15 µg/ml. Eosinophil-derived neurotoxin represents a novel biomarker to distinguish AERD from ATA.     

HomeoDB: a database of homeobox gene diversity

The homeobox genes are a large and diverse group of genes, many of which play important roles in the embryonic development of animals. Comparative study of homeobox genes, both within and between species, requires an evolutionary-based classification. HomeoDB was designed and implemented as a manually curated database to collect and present homeobox genes in an evolutionarily structured way, allowing genes, gene families and gene classes to be compared between species more readily than was possible previously. In its first release, HomeoDB includes all homeobox genes from human, amphioxus (Branchiostoma floridae) and fruitfly (Drosophila melanogaster); additional species can be added. HomeoDB is freely accessible at (http://homeodb.cbi.pku.edu.cn).     

EDdb: A web resource for eating disorder and its application to identify an extended adipocytokine signaling pathway related to eating disorder

Eating disorder is a group of physiological and psychological disorders affecting approximately 1% of the female population worldwide. Although the genetic epidemiology of eating disorder is becoming increasingly clear with accumulated studies, the underlying molecular mechanisms are still unclear. Recently, integration of various high-throughput data expanded the range of candidate genes and started to generate hypotheses for understanding potential pathogenesis in complex diseases. This article presents EDdb (Eating Disorder database), the first evidence-based gene resource for eating disorder. Fifty-nine experimentally validated genes from the literature in relation to eating disorder were collected as the core dataset. Another four datasets with 2824 candidate genes across 601 genome regions were expanded based on the core dataset using different criteria (e.g., protein-protein interactions, shared cytobands, and related complex diseases). Based on human protein-protein interaction data, we reconstructed a potential molecular sub-network related to eating disorder. Furthermore, with an integrative pathway enrichment analysis of genes in EDdb, we identified an extended adipocytokine signaling pathway in eating disorder. Three genes in EDdb (ADIPO (adiponectin), TNF (tumor necrosis factor) and NR3C1 (nuclear receptor subfamily 3, group C, member 1)) link the KEGG (Kyoto Encyclopedia of Genes and Genomes) “adipocytokine signaling pathway” with the BioCarta “visceral fat deposits and the metabolic syndrome” pathway to form a joint pathway. In total, the joint pathway contains 43 genes, among which 39 genes are related to eating disorder. As the first comprehensive gene resource for eating disorder, EDdb (http://eddb.cbi.pku.edu.cn) enables the exploration of gene-disease relationships and cross-talk mechanisms between related disorders. Through pathway statistical studies, we revealed that abnormal body weight caused by eating disorder and obesity may both be related to dysregulation of the novel joint pathway of adipocytokine signaling. In addition, this joint pathway may be the common pathway for body weight regulation in complex human diseases related to unhealthy lifestyle.     

Comparative study of apoptosis-related gene loci in human, mouse and rat genomes

Many genes are involved in mammalian cell apoptosis pathway. These apoptosis genes often contain characteristic functional domains, and can be classified into at least 15 functional groups, according to previous reports. Using an integrated bioinformatics platform for motif or domain search from three public mammalian proteomes (International Protein Index database for human, mouse, and rat), we systematically cataloged all of the proteins involved in mammalian apoptosis pathway. By localizing those proteins onto the genomes, we obtained a gene locus centric apoptosis gene catalog for human, mouse and rat.Further phylogenetic analysis showed that most of the apoptosis related gene loci are conserved among these three mammals. Interestingly, about one-third of apoptosis gene loci form gene clusters on mammal chromosomes, and exist in the three species, which indicated that mammalian apoptosis gene orders are also conserved. In addition, some tandem duplicated gene loci were revealed by comparing gene loci clusters in the three species. All data produced in this work were stored in a relational database and may be viewed at http://pcas.cbi.pku.edu.cn/database/apd.php.     

GSDS: 基因结构显示系统

构建了一个用于绘制基因结构示意图的网站系统(http://gsds.cbi.pku.edu.cn/)。用户可提交核酸序列、NCBI核酸序列号或基因外显子位置信息, 得到基因结构示意图; 并可指定在基因结构图上标注某些特定区域。系统允许用户同时输入多个基因, 并指定输出次序和标注区域。结果可用位图和矢量图两种图形格式显示。点击位图格式结果, 可以查看相应序列。系统提供中英文两种用户界面。     

ABCGrid: Application for Bioinformatics Computing Grid

We have developed a package named Application for Bioinformatics Computing Grid (ABCGrid). ABCGrid was designed for biology laboratories to use heterogeneous computing resources and access bioinformatics applications from one master node. ABCGrid is very easy to install and maintain at the premise of robustness and high performance. We implement a mechanism to install and update all applications and databases in worker nodes automatically to reduce the workload of manual maintenance. We use a backup task method and self-adaptive job dispatch approach to improve performance. Currently, ABCGrid integrates NCBI_BLAST, Hmmpfam and CE, running on a number of computing platforms including UNIX/Linux, Windows and Mac OS X. AVAILABILITY: The source code, executables and documents can be downloaded from http://abcgrid.cbi.pku.edu.cn     

TSdb: A database of transporter substrates linking metabolic pathways and transporter systems on a genome scale via their shared substrates

TSdb (http://tsdb.cbi.pku.edu.cn) is the first manually curated central repository that stores formatted information on the substrates of transporters. In total, 37608 transporters with 15075 substrates from 884 organisms were curated from UniProt functional annotation. A unique feature of TSdb is that all the substrates are mapped to identifiers from the KEGG Ligand compound database. Thus, TSdb links current metabolic pathway schema with compound transporter systems via the shared compounds in the pathways. Furthermore, all the transporter substrates in TSdb are classified according to their biochemical properties, biological roles and subcellular localizations. In addition to the functional annotation of transporters, extensive compound annotation that includes inhibitor information from the KEGG Ligand and BRENDA databases has been integrated, making TSdb a useful source for the discovery of potential inhibitory mechanisms linking transporter substrates and metabolic enzymes. User-friendly web interfaces are designed for easy access, query and download of the data. Text and BLAST searches against all transporters in the database are provided. We will regularly update the substrate data with evidence from new publications.     

Development of universal genetic markers based on single-copy orthologous (COSII) genes in Poaceae

Key message

We develop a set of universal genetic markers based on single-copy orthologous (COSII) genes in Poaceae.

Abstract

Being evolutionary conserved, single-copy orthologous (COSII) genes are particularly useful in comparative mapping and phylogenetic investigation among species. In this study, we identified 2,684 COSII genes based on five sequenced Poaceae genomes including rice, maize, sorghum, foxtail millet, and brachypodium, and then developed 1,072 COSII markers whose transferability and polymorphism among five bamboo species were further evaluated with 46 pairs of randomly selected primers. 91.3 % of the 46 primers obtained clear amplification in at least one bamboo species, and 65.2 % of them produced polymorphism in more than one species. We also used 42 of them to construct the phylogeny for the five bamboo species, and it might reflect more precise evolutionary relationship than the one based on the vegetative morphology. The results indicated a promising prospect of applying these markers to the investigation of genetic diversity and the classification of Poaceae. To ease and facilitate access of the information of common interest to readers, a web-based database of the COSII markers is provided (http://www.sicau.edu.cn/web/yms/PCOSWeb/PCOS.html).     

MicroPhenoDB Associates Metagenomic Datawith Pathogenic Microbes,Microbial Core Genes,and Human Disease Phenotypes

Microbes play important roles in human health and disease. The interaction between microbes and hosts is a reciprocal relationship, which remains largely under-explored. Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes, microbial core genes, and disease phenotypes. We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data. MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites. MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes. Disease phenotypes are classified and described using the Experimental Factor Ontology (EFO). A refined score model was developed to prioritize the associations based on evidential metrics. The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly. MicroPhenoDB offers data browsing, searching, and visualization through user-friendly web interfaces and web service application programming interfaces. MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes. It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases. MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.     

miREnvironment database: providing a bridge for microRNAs, environmental factors and phenotypes

The interaction between genetic factors and environmental factors has critical roles in determining the phenotype of an organism. In recent years, a number of studies have reported that the dysfunctions on microRNA (miRNAs), environmental factors and their interactions have strong effects on phenotypes and even may result in abnormal phenotypes and diseases, whereas there has been no a database linking miRNAs, environmental factors and phenotypes. Such a resource platform is believed to be of great value in the understanding of miRNAs, environmental factors, especially drugs and diseases. In this study, we constructed the miREnvironment database, which contains a comprehensive collection and curation of experimentally supported interactions among miRNAs, environmental factors and phenotypes. The names of miRNAs, phenotypes, environmental factors, conditions of environmental factors, samples, species, evidence and references were further annotated. miREnvironment represents a biomedical resource for researches on miRNAs, environmental factors and diseases. AVAILABILITY: http://cmbi.bjmu.edu.cn/miren. CONTACT: cuiqinghua@hsc.pku.edu.cn.     

Genes and (common) pathways underlying drug addiction

Drug addiction is a serious worldwide problem with strong genetic and environmental influences. Different technologies have revealed a variety of genes and pathways underlying addiction; however, each individual technology can be biased and incomplete. We integrated 2,343 items of evidence from peer-reviewed publications between 1976 and 2006 linking genes and chromosome regions to addiction by single-gene strategies, microrray, proteomics, or genetic studies. We identified 1,500 human addiction-related genes and developed KARG (http://karg.cbi.pku.edu.cn), the first molecular database for addiction-related genes with extensive annotations and a friendly Web interface. We then performed a meta-analysis of 396 genes that were supported by two or more independent items of evidence to identify 18 molecular pathways that were statistically significantly enriched, covering both upstream signaling events and downstream effects. Five molecular pathways significantly enriched for all four different types of addictive drugs were identified as common pathways which may underlie shared rewarding and addictive actions, including two new ones, GnRH signaling pathway and gap junction. We connected the common pathways into a hypothetical common molecular network for addiction. We observed that fast and slow positive feedback loops were interlinked through CAMKII, which may provide clues to explain some of the irreversible features of addiction.