MetaBase--the wiki-database of biological databases

Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition

Complex disease phenotypes often span multiple molecular processes. Functional characterization of these processes can shed light on disease mechanisms and drug effects. Thermal Proteome Profiling (TPP) is a mass-spectrometry (MS) based technique assessing changes in thermal protein stability that can serve as proxies of functional protein changes. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a multi-omics integration framework, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover consequences of Poly (ADP-ribose) polymerase (PARP) inhibition in ovarian cancer cells on cell cycle and DNA damage response as well as interferon and hippo signaling. We found that TPP offers a complementary perspective to other omics data modalities, and that its integration allowed us to obtain a more complete molecular overview of PARP inhibition. We anticipate that this strategy can be used to integrate functional proteomics with other omics to study molecular processes.

Identifying a causal link between prolactin signaling pathways and COVID-19 vaccine-induced menstrual changes

COVID-19 vaccines have been instrumental tools in the fight against SARS-CoV-2 helping to reduce disease severity and mortality. At the same time, just like any other therapeutic, COVID-19 vaccines were associated with adverse events. Women have reported menstrual cycle irregularity after receiving COVID-19 vaccines, and this led to renewed fears concerning COVID-19 vaccines and their effects on fertility. Herein we devised an informatics workflow to explore the causal drivers of menstrual cycle irregularity in response to vaccination with mRNA COVID-19 vaccine BNT162b2. Our methods relied on gene expression analysis in response to vaccination, followed by network biology analysis to derive testable hypotheses regarding the causal links between BNT162b2 and menstrual cycle irregularity. Five high-confidence transcription factors were identified as causal drivers of BNT162b2-induced menstrual irregularity, namely: IRF1, STAT1, RelA (p65 NF-kB subunit), STAT2 and IRF3. Furthermore, some biomarkers of menstrual irregularity, including TNF, IL6R, IL6ST, LIF, BIRC3, FGF2, ARHGDIB, RPS3, RHOU, MIF, were identified as topological genes and predicted as causal drivers of menstrual irregularity. Our network-based mechanism reconstruction results indicated that BNT162b2 exerted biological effects similar to those resulting from prolactin signaling. However, these effects were short-lived and didn't raise concerns about long-term infertility issues. This approach can be applied to interrogate the functional links between drugs/vaccines and other side effects.

DBHR: a collection of databases relevant to human research

Background:

The achievement of the human genome project provides a basis for the systematic study of the human genome from evolutionary history to disease-specific medicine. With the explosive growth of biological data, a growing number of biological databases are being established to support human-related research.

Objective:

The main objective of our study is to store, organize and share data in a structured and searchable manner. In short, we have planned the future development of new features in the database research area.

Materials & methods:

In total, we collected and integrated 680 human databases from scientific published work. Multiple options are presented for accessing the data, while original links and short descriptions are also presented for each database.

Results & discussion:

We have provided the latest collection of human research databases on a single platform with six categories: DNA database, RNA database, protein database, expression database, pathway database and disease database.

Conclusion:

Taken together, our database will be useful for further human research study and will be modified over time. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://habdsk.org/database.php.

We have compiled the most recent collection of human research datasets into six categories – DNA database, RNA database, protein database, expression database, pathway database and disease database – on a single platform. In all, 680 human datasets were acquired and incorporated from scientifically published studies. There are several ways to retrieve the data, as well as original links and short descriptions for each database. The primary goal of our research is to store, organize and exchange data in an organized and searchable format. In brief, we have planned the future development of additional features in the database. Our database will be beneficial for future human research studies and will be updated throughout time. We firmly believe that every researcher should have access to essential biological databases, so we have gathered a collection of human-related databases that are regularly used and currently available but have not previously been presented in such a simple and welcoming manner.

Genome-wide association study of susceptibility to hospitalised respiratory infections

Background: Globally, respiratory infections contribute to significant morbidity and mortality. However, genetic determinants of respiratory infections are understudied and remain poorly understood. Methods: We conducted a genome-wide association study in 19,459 hospitalised respiratory infection cases and 101,438 controls from UK Biobank. We followed-up well-imputed top signals from the UK Biobank discovery analysis in 50,912 respiratory infection cases and 150,442 controls from 11 cohorts. We aggregated effect estimates across studies using inverse variance-weighted meta-analyses. Additionally, we investigated the function of the top signals in order to gain understanding of the underlying biological mechanisms. Results: In the discovery analysis, we report 56 signals at P<5×10-6, one of which was genome-wide significant (P<5×10-8). The genome-wide significant signal was in an intron of PBX3, a gene that encodes pre-B-cell leukaemia transcription factor 3, a homeodomain-containing transcription factor. Further, the genome-wide significant signal was found to colocalise with gene-specific expression quantitative trait loci (eQTLs) affecting expression of PBX3 in lung tissue, where the respiratory infection risk alleles were associated with decreased PBX3 expression in lung tissue, highlighting a possible biological mechanism. Of the 56 signals, 40 were well-imputed in UK Biobank and were investigated in the 11 follow-up cohorts. None of the 40 signals replicated, with effect estimates attenuated. Conclusions: Our discovery analysis implicated PBX3 as a candidate causal gene and suggests a possible role of transcription factor binding activity in respiratory infection susceptibility. However, the PBX3 signal, and the other well-imputed signals, did not replicate when aggregating effect estimates across 11 independent cohorts. Significant phenotypic heterogeneity and differences in study ascertainment may have contributed to this lack of statistical replication. Overall, our study highlighted putative associations and possible biological mechanisms that may provide insight into respiratory infection susceptibility.

Integrative DNA Methylation and Gene Expression Analysis in the Prefrontal Cortex of Mexicans Who Died by Suicide

BACKGROUND

Suicide represents a major health concern, especially in developing countries. While many demographic risk factors have been proposed, the underlying molecular pathology of suicide remains poorly understood. A body of evidence suggests that aberrant DNA methylation and expression is involved. In this study, we examined DNA methylation profiles and concordant gene expression changes in the prefrontal cortex of Mexicans who died by suicide.

METHODS

In collaboration with the coroner's office in Mexico City, brain samples of males who died by suicide (n = 35) and age-matched sudden death controls (n = 13) were collected. DNA and RNA were extracted from prefrontal cortex tissue and analyzed with the Infinium Methylation480k and the HumanHT-12 v4 Expression Beadchips, respectively.

RESULTS

We report evidence of altered DNA methylation profiles at 4430 genomic regions together with 622 genes characterized by differential expression in cases vs controls. Seventy genes were found to have concordant methylation and expression changes. Metacore-enriched analysis identified 10 genes with biological relevance to psychiatric phenotypes and suicide (ADCY9, CRH, NFATC4, ABCC8, HMGA1, KAT2A, EPHA2, TRRAP, CD22, and CBLN1) and highlighted the association that ADCY9 has with various pathways, including signal transduction regulated by the cAMP-responsive element modulator, neurophysiological process regulated by the corticotrophin-releasing hormone, and synaptic plasticity. We therefore went on to validate the observed hypomethylation of ADCY9 in cases vs control through targeted bisulfite sequencing.

CONCLUSION

Our study represents the first, to our knowledge, analysis of DNA methylation and gene expression associated with suicide in a Mexican population using postmortem brain, providing novel insights for convergent molecular alterations associated with suicide.

Transcriptomic response of breast cancer cells to anacardic acid

Anacardic acid (AnAc), a potential dietary agent for preventing and treating breast cancer, inhibited the proliferation of estrogen receptor α (ERα) positive MCF-7 and MDA-MB-231 triple negative breast cancer cells. To characterize potential regulators of AnAc action, MCF-7 and MDA-MB-231 cells were treated for 6 h with purified AnAc 24:1n5 congener followed by next generation transcriptomic sequencing (RNA-seq) and network analysis. We reported that AnAc-differentially regulated miRNA transcriptomes in each cell line and now identify AnAc-regulated changes in mRNA and lncRNA transcript expression. In MCF-7 cells, 80 AnAc-responsive genes were identified, including lncRNA MIR22HG. More AnAc-responsive genes (886) were identified in MDA-MB-231 cells. Only six genes were commonly altered by AnAc in both cell lines: SCD, INSIG1, and TGM2 were decreased and PDK4, GPR176, and ZBT20 were increased. Modeling of AnAc-induced gene changes suggests that AnAc inhibits monounsaturated fatty acid biosynthesis in both cell lines and increases endoplasmic reticulum stress in MDA-MB-231 cells. Since modeling of downregulated genes implicated NFκB in MCF-7, we confirmed that AnAc inhibited TNFα-induced NFκB reporter activity in MCF-7 cells. These data identify new targets and pathways that may account for AnAc’s anti-proliferative and pro-apoptotic activity.

Profiling microRNAs in individuals at risk of progression to rheumatoid arthritis

Background

Individuals at risk of rheumatoid arthritis (RA) demonstrate systemic autoimmunity in the form of anti-citrullinated peptide antibodies (ACPA). MicroRNAs (miRNAs) are implicated in established RA. This study aimed to (1) compare miRNA expression between healthy individuals and those at risk of and those that develop RA, (2) evaluate the change in expression of miRNA from “at-risk” to early RA and (3) explore whether these miRNAs could inform a signature predictive of progression from “at-risk” to RA.

Methods

We performed global profiling of 754 miRNAs per patient on a matched serum sample cohort of 12 anti-cyclic citrullinated peptide (CCP) + “at-risk” individuals that progressed to RA. Each individual had a serum sample from baseline and at time of detection of synovitis, forming the matched element. Healthy controls were also studied. miRNAs with a fold difference/fold change of four in expression level met our primary criterion for selection as candidate miRNAs. Validation of the miRNAs of interest was conducted using custom miRNA array cards on matched samples (baseline and follow up) in 24 CCP+ individuals; 12 RA progressors and 12 RA non-progressors.

Results

We report on the first study to use matched serum samples and a comprehensive miRNA array approach to identify in particular, three miRNAs (miR-22, miR-486-3p, and miR-382) associated with progression from systemic autoimmunity to RA inflammation. MiR-22 demonstrated significant fold difference between progressors and non-progressors indicating a potential biomarker role for at-risk individuals.

Conclusions

This first study using a cohort with matched serum samples provides important mechanistic insights in the transition from systemic autoimmunity to inflammatory disease for future investigation, and with further evaluation, might also serve as a predictive biomarker.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-017-1492-9) contains supplementary material, which is available to authorized users.

LiverWiki: a wiki-based database for human liver

BACKGROUND

Recent advances in omics technology have produced a large amount of liver-related data. A comprehensive and up-to-date source of liver-related data is needed to allow biologists to access the latest data. However, current liver-related data sources each cover only a specific part of the liver. It is difficult for them to keep pace with the rapid increase of liver-related data available at those data resources. Integrating diverse liver-related data is a critical yet formidable challenge, as it requires sustained human effort.

RESULTS

We present LiverWiki, a first wiki-based database that integrates liver-related genes, homolog genes, gene expressions in microarray datasets and RNA-Seq datasets, proteins, protein interactions, post-translational modifications, associated pathways, diseases, metabolites identified in the metabolomics datasets, and literatures into an easily accessible and searchable resource for community-driven sharing. LiverWiki houses information in a total of 141,897 content pages, including 19,787 liver-related gene pages, 17,077 homolog gene pages, 50,251 liver-related protein pages, 36,122 gene expression pages, 2067 metabolites identified in the metabolomics datasets, 16,366 disease-related molecules, and 227 liver disease pages. Other than assisting users in searching, browsing, reviewing, refining the contents on LiverWiki, the most important contribution of LiverWiki is to allow the community to create and update biological data of liver in visible and editable tables. This integrates newly produced data with existing knowledge. Implemented in mediawiki, LiverWiki provides powerful extensions to support community contributions.

CONCLUSIONS

The main goal of LiverWiki is to provide the research community with comprehensive liver-related data, as well as to allow the research community to share their liver-related data flexibly and efficiently. It also enables rapid sharing new discoveries by allowing the discoveries to be integrated and shared immediately, rather than relying on expert curators. The database is available online at http://liverwiki.hupo.org.cn /.

CHEMGENIE: integration of chemogenomics data for applications in chemical biology

Increasing amounts of biological data are accumulating in the pharmaceutical industry and academic institutions. However, data does not equal actionable information, and guidelines for appropriate data capture, harmonization, integration, mining, and visualization need to be established to fully harness its potential. Here, we describe ongoing efforts at Merck & Co. to structure data in the area of chemogenomics. We are integrating complementary data from both internal and external data sources into one chemogenomics database (Chemical Genetic Interaction Enterprise; CHEMGENIE). Here, we demonstrate how this well-curated database facilitates compound set design, tool compound selection, target deconvolution in phenotypic screening, and predictive model building.

Genome-wide miRNA response to anacardic acid in breast cancer cells

MicroRNAs are biomarkers and potential therapeutic targets for breast cancer. Anacardic acid (AnAc) is a dietary phenolic lipid that inhibits both MCF-7 estrogen receptor α (ERα) positive and MDA-MB-231 triple negative breast cancer (TNBC) cell proliferation with IC50s of 13.5 and 35 μM, respectively. To identify potential mediators of AnAc action in breast cancer, we profiled the genome-wide microRNA transcriptome (microRNAome) in these two cell lines altered by the AnAc 24:1n5 congener. Whole genome expression profiling (RNA-seq) and subsequent network analysis in MetaCore Gene Ontology (GO) algorithm was used to characterize the biological pathways altered by AnAc. In MCF-7 cells, 69 AnAc-responsive miRNAs were identified, e.g., increased let-7a and reduced miR-584. Fewer, i.e., 37 AnAc-responsive miRNAs were identified in MDA-MB-231 cells, e.g., decreased miR-23b and increased miR-1257. Only two miRNAs were increased by AnAc in both cell lines: miR-612 and miR-20b; however, opposite miRNA arm preference was noted: miR-20b-3p and miR-20b-5p were upregulated in MCF-7 and MDA-MB-231, respectively. miR-20b-5p target EFNB2 transcript levels were reduced by AnAc in MDA-MB-231 cells. AnAc reduced miR-378g that targets VIM (vimentin) and VIM mRNA transcript expression was increased in AnAc-treated MCF-7 cells, suggesting a reciprocal relationship. The top three enriched GO terms for AnAc-treated MCF-7 cells were B cell receptor signaling pathway and ribosomal large subunit biogenesis and S-adenosylmethionine metabolic process for AnAc-treated MDA-MB-231 cells. The pathways modulated by these AnAc-regulated miRNAs suggest that key nodal molecules, e.g., Cyclin D1, MYC, c-FOS, PPARγ, and SIN3, are targets of AnAc activity.

RDFIO: extending Semantic MediaWiki for interoperable biomedical data management

BACKGROUND

Biological sciences are characterised not only by an increasing amount but also the extreme complexity of its data. This stresses the need for efficient ways of integrating these data in a coherent description of biological systems. In many cases, biological data needs organization before integration. This is not seldom a collaborative effort, and it is thus important that tools for data integration support a collaborative way of working. Wiki systems with support for structured semantic data authoring, such as Semantic MediaWiki, provide a powerful solution for collaborative editing of data combined with machine-readability, so that data can be handled in an automated fashion in any downstream analyses. Semantic MediaWiki lacks a built-in data import function though, which hinders efficient round-tripping of data between interoperable Semantic Web formats such as RDF and the internal wiki format.

RESULTS

To solve this deficiency, the RDFIO suite of tools is presented, which supports importing of RDF data into Semantic MediaWiki, with metadata needed to export it again in the same RDF format, or ontology. Additionally, the new functionality enables mash-ups of automated data imports combined with manually created data presentations. The application of the suite of tools is demonstrated by importing drug discovery related data about rare diseases from Orphanet and acid dissociation constants from Wikidata. The RDFIO suite of tools is freely available for download via pharmb.io/project/rdfio .

CONCLUSIONS

Through a set of biomedical demonstrators, it is demonstrated how the new functionality enables a number of usage scenarios where the interoperability of SMW and the wider Semantic Web is leveraged for biomedical data sets, to create an easy to use and flexible platform for exploring and working with biomedical data.

The miR-29 transcriptome in endocrine-sensitive and resistant breast cancer cells

Aberrant microRNA expression contributes to breast cancer progression and endocrine resistance. We reported that although tamoxifen stimulated miR-29b-1/a transcription in tamoxifen (TAM)-resistant breast cancer cells, ectopic expression of miR-29b-1/a did not drive TAM-resistance in MCF-7 breast cancer cells. However, miR-29b-1/a overexpression significantly repressed TAM-resistant LCC9 cell proliferation, suggesting that miR-29b-1/a is not mediating TAM resistance but acts as a tumor suppressor in TAM-resistant cells. The target genes mediating this tumor suppressor activity were unknown. Here, we identify miR-29b-1 and miR-29a target transcripts in both MCF-7 and LCC9 cells. We find that miR-29b-1 and miR-29a regulate common and unique transcripts in each cell line. The cell-specific and common downregulated genes were characterized using the MetaCore Gene Ontology (GO) enrichment analysis algorithm. LCC9-sepecific miR-29b-1/a-regulated GO processes include oxidative phosphorylation, ATP metabolism, and apoptosis. Extracellular flux analysis of cells transfected with anti- or pre- miR-29a confirmed that miR-29a inhibits mitochondrial bioenergetics in LCC9 cells. qPCR,luciferase reporter assays, and western blot also verified the ATP synthase subunit genes ATP5G1 and ATPIF1 as bone fide miR29b-1/a targets. Our results suggest that miR-29 repression of TAM-resistant breast cancer cell proliferation is mediated in part through repression of genes important in mitochondrial bioenergetics.

Regulation of miR-29b-1/a transcription and identification of target mRNAs in CHO-K1 cells

miR-29b and miR-29a transcript levels were reported to increase in exponentially growing CHO-K1 cells. Here, we examine the regulation of miR-29b-1/a in CHO-K1 cells. We observed that 4-hydroxytamoxifen (4-OHT) increased pri-miR-29b-1 and pri-miR-29a transcription in CHO-K1 cells by activating endogenous estrogen receptor α (ERα). DICER, an established, bona fide target of miR-29b-1/a, was shown to be regulated by 4-OHT in CHO-K1 cells. We showed that miR-29b-1 and miR-29a serve a repressive role in cell proliferation, migration, invasion, and colony formation in CHO-K1 cells. To identify other targets of miR-29b-1 and miR-29a, RNA sequencing was performed by transfecting cells with anti-miR-29a, which inhibits both miR-29a and miR-29b-1, pre-miR-29b-1, and/or pre-miR-29a. In silico network analysis in MetaCore™ identified common and unique putative gene targets of miR-29b-1 and miR-29a. Pathway analysis of identified putative miR-29 targets were related to cell adhesion, cytoskeletal remodeling, and development. Further inquiry revealed regulation of pathways mediating responses to growth factor stimulus and cell cycle regulation.

Use of "big data" in drug discovery and clinical trials

Oncology is undergoing a data-driven metamorphosis. Armed with new and ever more efficient molecular and information technologies, we have entered an era where data is helping us spearhead the fight against cancer. This technology driven data explosion, often referred to as "big data", is not only expediting biomedical discovery, but it is also rapidly transforming the practice of oncology into an information science. This evolution is critical, as results to-date have revealed the immense complexity and genetic heterogeneity of patients and their tumors, a sobering reminder of the challenge facing every patient and their oncologist. This can only be addressed through development of clinico-molecular data analytics that provide a deeper understanding of the mechanisms controlling the biological and clinical response to available therapeutic options. Beyond the exciting implications for improved patient care, such advancements in predictive and evidence-based analytics stand to profoundly affect the processes of cancer drug discovery and associated clinical trials.

From data repositories to submission portals: rethinking the role of domain-specific databases in CollecTF

Domain-specific databases are essential resources for the biomedical community, leveraging expert knowledge to curate published literature and provide access to referenced data and knowledge. The limited scope of these databases, however, poses important challenges on their infrastructure, visibility, funding and usefulness to the broader scientific community. CollecTF is a community-oriented database documenting experimentally validated transcription factor (TF)-binding sites in the Bacteria domain. In its quest to become a community resource for the annotation of transcriptional regulatory elements in bacterial genomes, CollecTF aims to move away from the conventional data-repository paradigm of domain-specific databases. Through the adoption of well-established ontologies, identifiers and collaborations, CollecTF has progressively become also a portal for the annotation and submission of information on transcriptional regulatory elements to major biological sequence resources (RefSeq, UniProtKB and the Gene Ontology Consortium). This fundamental change in database conception capitalizes on the domain-specific knowledge of contributing communities to provide high-quality annotations, while leveraging the availability of stable information hubs to promote long-term access and provide high-visibility to the data. As a submission portal, CollecTF generates TF-binding site information through direct annotation of RefSeq genome records, definition of TF-based regulatory networks in UniProtKB entries and submission of functional annotations to the Gene Ontology. As a database, CollecTF provides enhanced search and browsing, targeted data exports, binding motif analysis tools and integration with motif discovery and search platforms. This innovative approach will allow CollecTF to focus its limited resources on the generation of high-quality information and the provision of specialized access to the data.Database URL: http://www.collectf.org/.

BioImg.org: A Catalog of Virtual Machine Images for the Life Sciences

Virtualization is becoming increasingly important in bioscience, enabling assembly and provisioning of complete computer setups, including operating system, data, software, and services packaged as virtual machine images (VMIs). We present an open catalog of VMIs for the life sciences, where scientists can share information about images and optionally upload them to a server equipped with a large file system and fast Internet connection. Other scientists can then search for and download images that can be run on the local computer or in a cloud computing environment, providing easy access to bioinformatics environments. We also describe applications where VMIs aid life science research, including distributing tools and data, supporting reproducible analysis, and facilitating education. BioImg.org is freely available at: https://bioimg.org.

Integrated Bio-Search: challenges and trends for the integration, search and comprehensive processing of biological information

Many efforts exist to design and implement approaches and tools for data capture, integration and analysis in the life sciences. Challenges are not only the heterogeneity, size and distribution of information sources, but also the danger of producing too many solutions for the same problem. Methodological, technological, infrastructural and social aspects appear to be essential for the development of a new generation of best practices and tools. In this paper, we analyse and discuss these aspects from different perspectives, by extending some of the ideas that arose during the NETTAB 2012 Workshop, making reference especially to the European context. First, relevance of using data and software models for the management and analysis of biological data is stressed. Second, some of the most relevant community achievements of the recent years, which should be taken as a starting point for future efforts in this research domain, are presented. Third, some of the main outstanding issues, challenges and trends are analysed. The challenges related to the tendency to fund and create large scale international research infrastructures and public-private partnerships in order to address the complex challenges of data intensive science are especially discussed. The needs and opportunities of Genomic Computing (the integration, search and display of genomic information at a very specific level, e.g. at the level of a single DNA region) are then considered. In the current data and network-driven era, social aspects can become crucial bottlenecks. How these may best be tackled to unleash the technical abilities for effective data integration and validation efforts is then discussed. Especially the apparent lack of incentives for already overwhelmed researchers appears to be a limitation for sharing information and knowledge with other scientists. We point out as well how the bioinformatics market is growing at an unprecedented speed due to the impact that new powerful in silico analysis promises to have on better diagnosis, prognosis, drug discovery and treatment, towards personalized medicine. An open business model for bioinformatics, which appears to be able to reduce undue duplication of efforts and support the increased reuse of valuable data sets, tools and platforms, is finally discussed.

RiceWiki: a wiki-based database for community curation of rice genes

Rice is the most important staple food for a large part of the world’s human population and also a key model organism for biological studies of crops as well as other related plants. Here we present RiceWiki (http://ricewiki.big.ac.cn), a wiki-based, publicly editable and open-content platform for community curation of rice genes. Most existing related biological databases are based on expert curation; with the exponentially exploding volume of rice knowledge and other relevant data, however, expert curation becomes increasingly laborious and time-consuming to keep knowledge up-to-date, accurate and comprehensive, struggling with the flood of data and requiring a large number of people getting involved in rice knowledge curation. Unlike extant relevant databases, RiceWiki features harnessing collective intelligence in community curation of rice genes, quantifying users' contributions in each curated gene and providing explicit authorship for each contributor in any given gene, with the aim to exploit the full potential of the scientific community for rice knowledge curation. Based on community curation, RiceWiki bears the potential to make it possible to build a rice encyclopedia by and for the scientific community that harnesses community intelligence for collaborative knowledge curation, covers all aspects of biological knowledge and keeps evolving with novel knowledge.

Community intelligence in knowledge curation: an application to managing scientific nomenclature

Harnessing community intelligence in knowledge curation bears significant promise in dealing with communication and education in the flood of scientific knowledge. As knowledge is accumulated at ever-faster rates, scientific nomenclature, a particular kind of knowledge, is concurrently generated in all kinds of fields. Since nomenclature is a system of terms used to name things in a particular discipline, accurate translation of scientific nomenclature in different languages is of critical importance, not only for communications and collaborations with English-speaking people, but also for knowledge dissemination among people in the non-English-speaking world, particularly young students and researchers. However, it lacks of accuracy and standardization when translating scientific nomenclature from English to other languages, especially for those languages that do not belong to the same language family as English. To address this issue, here we propose for the first time the application of community intelligence in scientific nomenclature management, namely, harnessing collective intelligence for translation of scientific nomenclature from English to other languages. As community intelligence applied to knowledge curation is primarily aided by wiki and Chinese is the native language for about one-fifth of the world’s population, we put the proposed application into practice, by developing a wiki-based English-to-Chinese Scientific Nomenclature Dictionary (ESND; http://esnd.big.ac.cn). ESND is a wiki-based, publicly editable and open-content platform, exploiting the whole power of the scientific community in collectively and collaboratively managing scientific nomenclature. Based on community curation, ESND is capable of achieving accurate, standard, and comprehensive scientific nomenclature, demonstrating a valuable application of community intelligence in knowledge curation.

Linking genome-scale metabolic modeling and genome annotation

Genome-scale metabolic network reconstructions, assembled from annotated genomes, serve as a platform for integrating data from heterogeneous sources and generating hypotheses for further experimental validation. Implementing constraint-based modeling techniques such as flux balance analysis (FBA) on network reconstructions allows for interrogating metabolism at a systems level, which aids in identifying and rectifying gaps in knowledge. With genome sequences for various organisms from prokaryotes to eukaryotes becoming increasingly available, a significant bottleneck lies in the structural and functional annotation of these sequences. Using topologically based and biologically inspired metabolic network refinement, we can better characterize enzymatic functions present in an organism and link annotation of these functions to candidate transcripts; both steps can be experimentally validated.

Taking Open Innovation to the Molecular Level - Strengths and Limitations

The ever-growing availability of large-scale open data and its maturation is having a significant impact on industrial drug-discovery, as well as on academic and non-profit research. As industry is changing to an 'open innovation' business concept, precompetitive initiatives and strong public-private partnerships including academic research cooperation partners are gaining more and more importance. Now, the bioinformatics and cheminformatics communities are seeking for web tools which allow the integration of this large volume of life science datasets available in the public domain. Such a data exploitation tool would ideally be able to answer complex biological questions by formulating only one search query. In this short review/perspective, we outline the use of semantic web approaches for data and knowledge integration. Further, we discuss strengths and current limitations of public available data retrieval tools and integrated platforms.

Web tools for predictive toxicology model building

INTRODUCTION

The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges.

AREAS COVERED

The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details.

EXPERT OPINION

The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.