The Reactome BioMart

The m6A methylation regulates gonadal sex differentiation in chicken embryo

Background

As a ubiquitous reversible epigenetic RNA modification, N6-methyladenosine (m6A) plays crucial regulatory roles in multiple biological pathways. However, its functional mechanisms in sex determination and differentiation during gonadal development of chicken embryos are not clear. Therefore, we established a transcriptome-wide m6A map in the female and male chicken left gonads of embryonic day 7 (E7) by methylated RNA immunoprecipitation sequencing (MeRIP-seq) to offer insight into the landscape of m6A methylation and investigate the post-transcriptional modification underlying gonadal differentiation.

Results

The chicken embryonic gonadal transcriptome was extensively methylated. We found 15,191 and 16,111 m6A peaks in the female and male left gonads, respectively, which were mainly enriched in the coding sequence (CDS) and stop codon. Among these m6A peaks, we identified that 1013 and 751 were hypermethylated in females and males, respectively. These differential peaks covered 281 and 327 genes, such as BMP2, SMAD2, SOX9 and CYP19A1, which were primarily associated with development, morphogenesis and sex differentiation by functional enrichment. Further analysis revealed that the m6A methylation level was positively correlated with gene expression abundance. Furthermore, we found that YTHDC2 could regulate the expression of sex-related genes, especially HEMGN and SOX9, in male mesonephros/gonad mingle cells, which was verified by in vitro experiments, suggesting a regulatory role of m6A methylation in chicken gonad differentiation.

Conclusions

This work provided a comprehensive m6A methylation profile of chicken embryonic gonads and revealed YTHDC2 as a key regulator responsible for sex differentiation. Our results contribute to a better understanding of epigenetic factors involved in chicken sex determination and differentiation and to promoting the future development of sex manipulation in poultry industry.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-022-00710-6.

Polygenic networks in peripheral leukocytes indicate patterns associated with HIV infection and context-dependent effects of cannabis use

In spite of suppressive antiretroviral therapies (ART), Human Immunodeficiency Virus (HIV)-infected subjects still experience the consequences of viral persistence and chronic inflammation. In the brain, where most HIV-1 targets are of innate immune origin, neurological and cognitive impairments are detectable and enhanced by highly prevalent substance use disorders. Cannabis is one of the most prevalent substances among HIV+ ​subjects, compared to non-infected populations, either prescribed for improving various symptoms or used recreationally, as well as a component of polysubstance use. The mechanisms by which addictive substances and HIV interact are multifactorial and poorly understood. Importantly, the HIV brain target cells, macrophages and microglia, express receptors to neurotransmitters elevated by such drugs, and express receptors to cannabinoids, particularly CB2R. We have tested a panel of 784 transcripts associated with neurological disorders, digitally multiplexed and detectable in peripheral blood cells from a small cohort (n ​= ​102) of HIV-positive (HIV+) and HIV-negative (HIV-) specimens, stratified based on criteria of lifetime (LT) dependence of cannabis (CAN+) or not (CAN-). Demographic homogeneity and low incidence of co-morbidities helped increase power and allowed the identification of key differences consistent with HIV infection, cannabis exposure, or their interactions. A small percentage of these subjects used cannabis as well as other drugs. The data was analyzed using robust systems and visualization strategies to detect orchestrated patterns in gene networks connected based on molecular interfaces with higher power than in single genes. We found that the effects of cannabis differed drastically between HIV- and HIV+ ​groups, particularly in gene networks playing a role in inflammation, neurodegeneration, apoptosis and leukocyte adhesion and transmigration. At the level of individual genes, we identified detrimental effects that were associated with polysubstance use as a covariate, particularly methamphetamine. Transcription factor usage predictions suggest that the effects of cannabis are associated with transcriptional co-regulation at the gene promoters by multiple factors that vary by context. Overall, we have found that the effects of cannabis may be context-dependent, with potential benefits in the context of HIV reflected by improvements in cognition, but in the absence of the polysubstance use component.

Regulation of Long Non-coding RNA KCNQ1OT1 Network in Colorectal Cancer Immunity

Over the past few decades, researchers have become aware of the importance of non-coding RNA, which makes up the vast majority of the transcriptome. Long non-coding RNAs (lncRNAs) in turn constitute the largest fraction of non-coding transcripts. Increasing evidence has been found for the crucial roles of lncRNAs in both tissue homeostasis and development, and for their functional contributions to and regulation of the development and progression of various human diseases such as cancers. However, so far, only few findings with regards to functional lncRNAs in cancers have been translated into clinical applications. Based on multiple factors such as binding affinity of miRNAs to their lncRNA sponges, we analyzed the competitive endogenous RNA (ceRNA) network for the colorectal cancer RNA-seq datasets from The Cancer Genome Atlas (TCGA). After performing the ceRNA network construction and survival analysis, the lncRNA KCNQ1OT1 was found to be significantly upregulated in colorectal cancer tissues and associated with the survival of patients. A KCNQ1OT1-related lncRNA-miRNA-mRNA ceRNA network was constructed. A gene set variation analysis (GSVA) indicated that the expression of the KCNQ1OT1 ceRNA network in colorectal cancer tissues and normal tissues were significantly different, not only in the TCGA-COAD dataset but also in three other GEO datasets used as validation. By predicting comprehensive immune cell subsets from gene expression data, in samples grouped by differential expression levels of the KCNQ1OT1 ceRNA network in a cohort of patients, we found that CD4⁺, CD8⁺, and cytotoxic T cells and 14 other immune cell subsets were at different levels in the high- and low-KCNQ1OT1 ceRNA network score groups. These results indicated that the KCNQ1OT1 ceRNA network could be involved in the regulation of the tumor microenvironment, which would provide the rationale to further exploit KCNQ1OT1 as a possible functional contributor to and therapeutic target for colorectal cancer.

Pathway Analysis Integrating Genome-Wide and Functional Data Identifies PLCG2 as a Candidate Gene for Age-Related Macular Degeneration

Purpose

Age-related macular degeneration (AMD) is the worldwide leading cause of blindness among the elderly. Although genome-wide association studies (GWAS) have identified AMD risk variants, their roles in disease etiology are not well-characterized, and they only explain a portion of AMD heritability.

Methods

We performed pathway analyses using summary statistics from the International AMD Genomics Consortium's 2016 GWAS and multiple pathway databases to identify biological pathways wherein genetic association signals for AMD may be aggregating. We determined which genes contributed most to significant pathway signals across the databases. We characterized these genes by constructing protein-protein interaction networks and performing motif analysis.

Results

We determined that eight genes (C2, C3, LIPC, MICA, NOTCH4, PLCG2, PPARA, and RAD51B) "drive" the statistical signals observed across pathways curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Gene Ontology (GO) databases. We further refined our definition of statistical driver gene to identify PLCG2 as a candidate gene for AMD due to its significant gene-level signals (P < 0.0001) across KEGG, Reactome, GO, and NetPath pathways.

Conclusions

We performed pathway analyses on the largest available collection of advanced AMD cases and controls in the world. Eight genes strongly contributed to significant pathways from the three larger databases, and one gene (PLCG2) was central to significant pathways from all four databases. This is, to our knowledge, the first study to identify PLCG2 as a candidate gene for AMD based solely on genetic burden. Our findings reinforce the utility of integrating in silico genetic and biological pathway data to investigate the genetic architecture of AMD.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Common developmental genome deprogramming in schizophrenia - Role of Integrative Nuclear FGFR1 Signaling (INFS)

The watershed-hypothesis of schizophrenia asserts that over 200 different mutations dysregulate distinct pathways that converge on an unspecified common mechanism(s) that controls disease ontogeny. Consistent with this hypothesis, our RNA-sequencing of neuron committed cells (NCCs) differentiated from established iPSCs of 4 schizophrenia patients and 4 control subjects uncovered a dysregulated transcriptome of 1349 mRNAs common to all patients. Data reveals a global dysregulation of developmental genome, deconstruction of coordinated mRNA networks, and the formation of aberrant, new coordinated mRNA networks indicating a concerted action of the responsible factor(s). Sequencing of miRNA transcriptomes demonstrated an overexpression of 16 miRNAs and deconstruction of interactive miRNA-mRNA networks in schizophrenia NCCs. ChiPseq revealed that the nuclear (n) form of FGFR1, a pan-ontogenic regulator, is overexpressed in schizophrenia NCCs and overtargets dysregulated mRNA and miRNA genes. The nFGFR1 targeted 54% of all human gene promoters and 84.4% of schizophrenia dysregulated genes. The upregulated genes reside within major developmental pathways that control neurogenesis and neuron formation, whereas downregulated genes are involved in oligodendrogenesis. Our results indicate (i) an early (preneuronal) genomic etiology of schizophrenia, (ii) dysregulated genes and new coordinated gene networks are common to unrelated cases of schizophrenia, (iii) gene dysregulations are accompanied by increased nFGFR1-genome interactions, and (iv) modeling of increased nFGFR1 by an overexpression of a nFGFR1 lead to up or downregulation of selected genes as observed in schizophrenia NCCs. Together our results designate nFGFR1 signaling as a potential common dysregulated mechanism in investigated patients and potential therapeutic target in schizophrenia.

In silico analysis of the transportome in human pancreatic ductal adenocarcinoma

The altered expression and/or activity of ion channels and transporters (transportome) have been associated with malignant behavior of cancer cells and were proposed to be a hallmark of cancer. However, the impact of altered transportome in epithelial cancers, such as pancreatic ductal adenocarcinoma (PDAC), as well as its pathophysiological consequences, still remains unclear. Here, we report the in silico analysis of 840 transportome genes in PDAC patients' tissues. Our study was focused on the transportome changes and their correlation with functional and behavioral responses in PDAC tumor and stromal compartments. The dysregulated gene expression datasets were filtered using a cut-off of fold-change values ≤-2 or ≥2 (adjusted p value ≤0.05). The dysregulated transportome genes were clearly associated with impaired physiological secretory mechanisms and/or pH regulation, control of cell volume, and cell polarity. Additionally, some down-regulated transportome genes were found to be closely linked to epithelial cell differentiation. Furthermore, the observed decrease in genes coding for calcium and chloride transport might be a mechanism for evasion of apoptosis. In conclusion, the current work provides a comprehensive overview of the altered transportome expression and its association with predicted PDAC malignancy with special focus on the epithelial compartment.

BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology

BindingDB, www.bindingdb.org, is a publicly accessible database of experimental protein-small molecule interaction data. Its collection of over a million data entries derives primarily from scientific articles and, increasingly, US patents. BindingDB provides many ways to browse and search for data of interest, including an advanced search tool, which can cross searches of multiple query types, including text, chemical structure, protein sequence and numerical affinities. The PDB and PubMed provide links to data in BindingDB, and vice versa; and BindingDB provides links to pathway information, the ZINC catalog of available compounds, and other resources. The BindingDB website offers specialized tools that take advantage of its large data collection, including ones to generate hypotheses for the protein targets bound by a bioactive compound, and for the compounds bound by a new protein of known sequence; and virtual compound screening by maximal chemical similarity, binary kernel discrimination, and support vector machine methods. Specialized data sets are also available, such as binding data for hundreds of congeneric series of ligands, drawn from BindingDB and organized for use in validating drug design methods. BindingDB offers several forms of programmatic access, and comes with extensive background material and documentation. Here, we provide the first update of BindingDB since 2007, focusing on new and unique features and highlighting directions of importance to the field as a whole.

Mismatch repair deficiency endows tumors with a unique mutation signature and sensitivity to DNA double-strand breaks

DNA replication errors that persist as mismatch mutations make up the molecular fingerprint of mismatch repair (MMR)-deficient tumors and convey them with resistance to standard therapy. Using whole-genome and whole-exome sequencing, we here confirm an MMR-deficient mutation signature that is distinct from other tumor genomes, but surprisingly similar to germ-line DNA, indicating that a substantial fraction of human genetic variation arises through mutations escaping MMR. Moreover, we identify a large set of recurrent indels that may serve to detect microsatellite instability (MSI). Indeed, using endometrial tumors with immunohistochemically proven MMR deficiency, we optimize a novel marker set capable of detecting MSI and show it to have greater specificity and selectivity than standard MSI tests. Additionally, we show that recurrent indels are enriched for the ‘DNA double-strand break repair by homologous recombination’ pathway. Consequently, DSB repair is reduced in MMR-deficient tumors, triggering a dose-dependent sensitivity of MMR-deficient tumor cultures to DSB inducers.

DOI:http://dx.doi.org/10.7554/eLife.02725.001

Before a cell divides, it must first copy all of its genetic material. Any mistakes that are made during this process are called mutations. Mutations can give rise to new traits but are mostly harmful to the cells, or cause cancer; therefore, cells have evolved tools that can efficiently spot these mistakes and repair them. One of the main tools is called mismatch repair (MMR).

Defects in the cell's mismatch repair tools can wreak havoc as this allows many mutations to accumulate. Zhao et al. looked at the genomes of tumors where mismatch repair was not working properly to see what makes these ‘MMR-deficient tumors’ different from other tumors. This revealed that MMR-deficient tumors have similar patterns of mutations to those seen in egg and sperm cells. This was unexpected and suggests that mutations that are not corrected by mismatch repair are an important source of the genetic differences found between different humans, and between humans and their ancestors.

Identifying cancerous tumors that are MMR-deficient is vital, as these tumors tend not to respond to commonly used cancer treatments. However, current clinical methods to identify MMR-deficient tumors often fail or produce results that are difficult to interpret. MMR-deficient tumors commonly contain mutations called indels, where short fragments of DNA are inserted or deleted into longer DNA sequences. Zhao et al. have found 59 indels that can be used to detect MMR-deficient tumors, where each indel had been identified in several tumors taken from different tissues. This new approach allowed MMR-deficiency to be identified in several types of tumor, including colon and ovarian cancers, with greater sensitivity and accuracy than the existing methods.

Zhao et al. also found that the indels in MMR-deficient tumors reduce the ability of the tumors to repair a type of DNA damage called double-strand breaks. In these, both strands of DNA that make up the double helix are broken and the DNA chain is severed. As this kind of damage is very harmful to a cell, making more double-strand breaks could therefore form part of a more effective treatment against MMR-deficient tumors; further research is needed to investigate this possibility.

DOI:http://dx.doi.org/10.7554/eLife.02725.002

Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development

BACKGROUND

The ever-growing wealth of biological information available through multiple comprehensive database repositories can be leveraged for advanced analysis of data. We have now extensively revised and updated the multi-purpose software tool Biofilter that allows researchers to annotate and/or filter data as well as generate gene-gene interaction models based on existing biological knowledge. Biofilter now has the Library of Knowledge Integration (LOKI), for accessing and integrating existing comprehensive database information, including more flexibility for how ambiguity of gene identifiers are handled. We have also updated the way importance scores for interaction models are generated. In addition, Biofilter 2.0 now works with a range of types and formats of data, including single nucleotide polymorphism (SNP) identifiers, rare variant identifiers, base pair positions, gene symbols, genetic regions, and copy number variant (CNV) location information.

RESULTS

Biofilter provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of LOKI. Information within LOKI includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories.Via Biofilter 2.0 researchers can:• Annotate genomic location or region based data, such as results from association studies, or CNV analyses, with relevant biological knowledge for deeper interpretation• Filter genomic location or region based data on biological criteria, such as filtering a series SNPs to retain only SNPs present in specific genes within specific pathways of interest• Generate Predictive Models for gene-gene, SNP-SNP, or CNV-CNV interactions based on biological information, with priority for models to be tested based on biological relevance, thus narrowing the search space and reducing multiple hypothesis-testing.

CONCLUSIONS

Biofilter is a software tool that provides a flexible way to use the ever-expanding expert biological knowledge that exists to direct filtering, annotation, and complex predictive model development for elucidating the etiology of complex phenotypic outcomes.

Integrative variable selection via Bayesian model uncertainty

We are interested in developing integrative approaches for variable selection problems that incorporate external knowledge on a set of predictors of interest. In particular, we have developed an integrative Bayesian model uncertainty (iBMU) method, which formally incorporates multiple sources of data via a second-stage probit model on the probability that any predictor is associated with the outcome of interest. Using simulations, we demonstrate that iBMU leads to an increase in power to detect true marginal associations over more commonly used variable selection techniques, such as least absolute shrinkage and selection operator and elastic net. In addition, iBMU leads to a more efficient model search algorithm over the basic BMU method even when the predictor-level covariates are only modestly informative. The increase in power and efficiency of our method becomes more substantial as the predictor-level covariates become more informative. Finally, we demonstrate the power and flexibility of iBMU for integrating both gene structure and functional biomarker information into a candidate gene study investigating over 50 genes in the brain reward system and their role with smoking cessation from the Pharmacogenetics of Nicotine Addiction and Treatment Consortium.

Genome-wide analysis of LXRα activation reveals new transcriptional networks in human atherosclerotic foam cells

Increased physiological levels of oxysterols are major risk factors for developing atherosclerosis and cardiovascular disease. Lipid-loaded macrophages, termed foam cells, are important during the early development of atherosclerotic plaques. To pursue the hypothesis that ligand-based modulation of the nuclear receptor LXRα is crucial for cell homeostasis during atherosclerotic processes, we analysed genome-wide the action of LXRα in foam cells and macrophages. By integrating chromatin immunoprecipitation-sequencing (ChIP-seq) and gene expression profile analyses, we generated a highly stringent set of 186 LXRα target genes. Treatment with the nanomolar-binding ligand T0901317 and subsequent auto-regulatory LXRα activation resulted in sequence-dependent sharpening of the genome-binding patterns of LXRα. LXRα-binding loci that correlated with differential gene expression revealed 32 novel target genes with potential beneficial effects, which in part explained the implications of disease-associated genetic variation data. These observations identified highly integrated LXRα ligand-dependent transcriptional networks, including the APOE/C1/C4/C2-gene cluster, which contribute to the reversal of cholesterol efflux and the dampening of inflammation processes in foam cells to prevent atherogenesis.

Building models using Reactome pathways as templates

The first steps of building a new model can be very time-consuming, involving consulting many research papers and then assembling a plausible network of reactions. In this chapter, tools for speeding up this process will be discussed. Reactome is a database containing extensive coverage of pathways in Homo sapiens and numerous reference species. It offers researchers wishing to create new models from scratch various tools for extracting the relevant reactions, complete with layout information. In this chapter, two use cases will be described, in which a modeller provides certain essential pieces of information and Reactome automatically constructs the basic models and then dumps them in SBML-ML format.

Reconstruction of Danio rerio metabolic model accounting for subcellular compartmentalisation

Plant and microbial metabolic engineering is commonly used in the production of functional foods and quality trait improvement. Computational model-based approaches have been used in this important endeavour. However, to date, fish metabolic models have only been scarcely and partially developed, in marked contrast to their prominent success in metabolic engineering. In this study we present the reconstruction of fully compartmentalised models of the Danio rerio (zebrafish) on a global scale. This reconstruction involves extraction of known biochemical reactions in D. rerio for both primary and secondary metabolism and the implementation of methods for determining subcellular localisation and assignment of enzymes. The reconstructed model (ZebraGEM) is amenable for constraint-based modelling analysis, and accounts for 4,988 genes coding for 2,406 gene-associated reactions and only 418 non-gene-associated reactions. A set of computational validations (i.e., simulations of known metabolic functionalities and experimental data) strongly testifies to the predictive ability of the model. Overall, the reconstructed model is expected to lay down the foundations for computational-based rational design of fish metabolic engineering in aquaculture.

A census of human soluble protein complexes

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.

Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments

Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., co-occurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability. The scoring methods and platforms described include: CompPASS, SAINT, Decontaminator, MINT, IntAct, STRING, and FunCoup. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing interdisciplinary field of investigation.

Development of an undergraduate bioinformatics degree program at a liberal arts college

The highly interdisciplinary field of bioinformatics has emerged as a powerful modern science. There has been a great demand for undergraduate- and graduate-level trained bioinformaticists in the industry as well in the academia. In order to address the needs for trained bioinformaticists, its curriculum must be offered at the undergraduate level, especially at four-year colleges, where a majority of the United States gets its education. There are many challenges in developing an undergraduate-level bioinformatics program that needs to be carefully designed as a well-integrated and cohesive interdisciplinary curriculum that prepares the students for a wide variety of career options. This article describes the challenges of establishing a highly interdisciplinary undergraduate major, the development of an undergraduate bioinformatics degree program at Ramapo College of New Jersey, and lessons learned in the last 10 years during its management.

Profiling microRNAs in lung tissue from pigs infected with Actinobacillus pleuropneumoniae

Background

MicroRNAs (miRNAs) are a class of non-protein-coding genes that play a crucial regulatory role in mammalian development and disease. Whereas a large number of miRNAs have been annotated at the structural level during the latest years, functional annotation is sparse. Actinobacillus pleuropneumoniae (APP) causes serious lung infections in pigs. Severe damage to the lungs, in many cases deadly, is caused by toxins released by the bacterium and to some degree by host mediated tissue damage. However, understanding of the role of microRNAs in the course of this infectious disease in porcine is still very limited.

Results

In this study, the RNA extracted from visually unaffected and necrotic tissue from pigs infected with Actinobacillus pleuropneumoniae was subjected to small RNA deep sequencing. We identified 169 conserved and 11 candidate novel microRNAs in the pig. Of these, 17 were significantly up-regulated in the necrotic sample and 12 were down-regulated. The expression analysis of a number of candidates revealed microRNAs of potential importance in the innate immune response. MiR-155, a known key player in inflammation, was found expressed in both samples. Moreover, miR-664-5p, miR-451 and miR-15a appear as very promising candidates for microRNAs involved in response to pathogen infection.

Conclusions

This is the first study revealing significant differences in composition and expression profiles of miRNAs in lungs infected with a bacterial pathogen. Our results extend annotation of microRNA in pig and provide insight into the role of a number of microRNAs in regulation of bacteria induced immune and inflammatory response in porcine lung.

Critical amino acid residues in proteins: a BioMart integration of Reactome protein annotations with PRIDE mass spectrometry data and COSMIC somatic mutations

The reversible phosphorylation of serine, threonine and tyrosine hydroxyl groups is an especially prominent form of post-translational modification (PTM) of proteins. It plays critical roles in the regulation of diverse processes, and mutations that directly or indirectly affect these phosphorylation events have been associated with many cancers and other pathologies. Here, we describe the development of a new BioMart tool that gathers data from three different biological resources to provide the user with an integrated view of phosphorylation events associated with a human protein of interest, the complexes of which the protein (modified or not) is a part, the reactions in which the protein and its complexes participate and the somatic mutations that might be expected to perturb those functions. The three resources used are the Reactome, PRIDE and COSMIC databases. The Reactome knowledgebase contains annotations of phosphorylated human proteins linked to the reactions in which they are phosphorylated and dephosphorylated, to the complexes of which they are parts and to the reactions in which the phosphorylated proteins participate as substrates, catalysts and regulators. The PRIDE database holds extensive mass spectrometry data from which protein phosphorylation patterns can be inferred, and the COSMIC database holds records of somatic mutations found in human cancer cells. This tool supports both flexible, user-specified queries and standard (‘canned’) queries to retrieve frequently used combinations of data for user-specified proteins and reactions. We demonstrate using the Wnt signaling pathway and the human c-SRC protein how the tool can be used to place somatic mutation data into a functional perspective by changing critical residues involved in pathway modulation, and where available, check for mass spectrometry evidence in PRIDE supporting identification of the critical residue.

Database URL:http://www.reactome.org/cgi-bin/mart

International Cancer Genome Consortium Data Portal--a one-stop shop for cancer genomics data

The International Cancer Genome Consortium (ICGC) is a collaborative effort to characterize genomic abnormalities in 50 different cancer types. To make this data available, the ICGC has created the ICGC Data Portal. Powered by the BioMart software, the Data Portal allows each ICGC member institution to manage and maintain its own databases locally, while seamlessly presenting all the data in a single access point for users. The Data Portal currently contains data from 24 cancer projects, including ICGC, The Cancer Genome Atlas (TCGA), Johns Hopkins University, and the Tumor Sequencing Project. It consists of 3478 genomes and 13 cancer types and subtypes. Available open access data types include simple somatic mutations, copy number alterations, structural rearrangements, gene expression, microRNAs, DNA methylation and exon junctions. Additionally, simple germline variations are available as controlled access data. The Data Portal uses a web-based graphical user interface (GUI) to offer researchers multiple ways to quickly and easily search and analyze the available data. The web interface can assist in constructing complicated queries across multiple data sets. Several application programming interfaces are also available for programmatic access. Here we describe the organization, functionality, and capabilities of the ICGC Data Portal.

BioMart: a data federation framework for large collaborative projects

BioMart is a freely available, open source, federated database system that provides a unified access to disparate, geographically distributed data sources. It is designed to be data agnostic and platform independent, such that existing databases can easily be incorporated into the BioMart framework. BioMart allows databases hosted on different servers to be presented seamlessly to users, facilitating collaborative projects between different research groups. BioMart contains several levels of query optimization to efficiently manage large data sets and offers a diverse selection of graphical user interfaces and application programming interfaces to ensure that queries can be performed in whatever manner is most convenient for the user. The software has now been adopted by a large number of different biological databases spanning a wide range of data types and providing a rich source of annotation available to bioinformaticians and biologists alike.

BioMart Central Portal: an open database network for the biological community

BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common interface to quickly utilize different sources without learning a new system for each. The system also simplifies cross-database searches that might otherwise require several complicated steps. Several integrated tools streamline common tasks, such as converting between ID formats and retrieving sequences. The combination of a wide variety of databases, an easy-to-use interface, robust programmatic access and the array of tools make Central Portal a one-stop shop for biological data querying. Here, we describe the structure of Central Portal and show example queries to demonstrate its capabilities.

The InterPro BioMart: federated query and web service access to the InterPro Resource

The InterPro BioMart provides users with query-optimized access to predictions of family classification, protein domains and functional sites, based on a broad spectrum of integrated computational models ('signatures') that are generated by the InterPro member databases: Gene3D, HAMAP, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. These predictions are provided for all protein sequences from both the UniProt Knowledge Base and the UniParc protein sequence archive. The InterPro BioMart is supplementary to the primary InterPro web interface (http://www.ebi.ac.uk/interpro), providing a web service and the ability to build complex, custom queries that can efficiently return thousands of rows of data in a variety of formats. This article describes the information available from the InterPro BioMart and illustrates its utility with examples of how to build queries that return useful biological information. Database URL: http://www.ebi.ac.uk/interpro/biomart/martview.