Text mining for metabolic pathways, signaling cascades, and protein networks

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

Using uncertainty to link and rank evidence from biomedical literature for model curation

MOTIVATION

In recent years, there has been great progress in the field of automated curation of biomedical networks and models, aided by text mining methods that provide evidence from literature. Such methods must not only extract snippets of text that relate to model interactions, but also be able to contextualize the evidence and provide additional confidence scores for the interaction in question. Although various approaches calculating confidence scores have focused primarily on the quality of the extracted information, there has been little work on exploring the textual uncertainty conveyed by the author. Despite textual uncertainty being acknowledged in biomedical text mining as an attribute of text mined interactions (events), it is significantly understudied as a means of providing a confidence measure for interactions in pathways or other biomedical models. In this work, we focus on improving identification of textual uncertainty for events and explore how it can be used as an additional measure of confidence for biomedical models.

RESULTS

We present a novel method for extracting uncertainty from the literature using a hybrid approach that combines rule induction and machine learning. Variations of this hybrid approach are then discussed, alongside their advantages and disadvantages. We use subjective logic theory to combine multiple uncertainty values extracted from different sources for the same interaction. Our approach achieves F-scores of 0.76 and 0.88 based on the BioNLP-ST and Genia-MK corpora, respectively, making considerable improvements over previously published work. Moreover, we evaluate our proposed system on pathways related to two different areas, namely leukemia and melanoma cancer research.

AVAILABILITY AND IMPLEMENTATION

The leukemia pathway model used is available in Pathway Studio while the Ras model is available via PathwayCommons. Online demonstration of the uncertainty extraction system is available for research purposes at http://argo.nactem.ac.uk/test. The related code is available on https://github.com/c-zrv/uncertainty_components.git. Details on the above are available in the Supplementary Material.

CONTACT

sophia.ananiadou@manchester.ac.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein–protein interactions (ChiPPI) that uses the domain–domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer.

Information Retrieval and Text Mining Technologies for Chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

METSP: a maximum-entropy classifier based text mining tool for transporter-substrate identification with semistructured text

The substrates of a transporter are not only useful for inferring function of the transporter, but also important to discover compound-compound interaction and to reconstruct metabolic pathway. Though plenty of data has been accumulated with the developing of new technologies such as in vitro transporter assays, the search for substrates of transporters is far from complete. In this article, we introduce METSP, a maximum-entropy classifier devoted to retrieve transporter-substrate pairs (TSPs) from semistructured text. Based on the high quality annotation from UniProt, METSP achieves high precision and recall in cross-validation experiments. When METSP is applied to 182,829 human transporter annotation sentences in UniProt, it identifies 3942 sentences with transporter and compound information. Finally, 1547 confidential human TSPs are identified for further manual curation, among which 58.37% pairs with novel substrates not annotated in public transporter databases. METSP is the first efficient tool to extract TSPs from semistructured annotation text in UniProt. This tool can help to determine the precise substrates and drugs of transporters, thus facilitating drug-target prediction, metabolic network reconstruction, and literature classification.

ChiTaRS 2.1--an improved database of the chimeric transcripts and RNA-seq data with novel sense-antisense chimeric RNA transcripts

Chimeric RNAs that comprise two or more different transcripts have been identified in many cancers and among the Expressed Sequence Tags (ESTs) isolated from different organisms; they might represent functional proteins and produce different disease phenotypes. The ChiTaRS 2.1 database of chimeric transcripts and RNA-Seq data (http://chitars.bioinfo.cnio.es/) is the second version of the ChiTaRS database and includes improvements in content and functionality. Chimeras from eight organisms have been collated including novel sense–antisense (SAS) chimeras resulting from the slippage of the sense and anti-sense intragenic regions. The new database version collects more than 29 000 chimeric transcripts and indicates the expression and tissue specificity for 333 entries confirmed by RNA-seq reads mapping the chimeric junction sites. User interface allows for rapid and easy analysis of evolutionary conservation of fusions, literature references and experimental data supporting fusions in different organisms. More than 1428 cancer breakpoints have been automatically collected from public databases and manually verified to identify their correct cross-references, genomic sequences and junction sites. As a result, the ChiTaRS 2.1 collection of chimeras from eight organisms and human cancer breakpoints extends our understanding of the evolution of chimeric transcripts in eukaryotes as well as their functional role in carcinogenic processes.

A guide for building biological pathways along with two case studies: hair and breast development

Genomic information is being underlined in the format of biological pathways. Building these biological pathways is an ongoing demand and benefits from methods for extracting information from biomedical literature with the aid of text-mining tools. Here we hopefully guide you in the attempt of building a customized pathway or chart representation of a system. Our manual is based on a group of software designed to look at biointeractions in a set of abstracts retrieved from PubMed. However, they aim to support the work of someone with biological background, who does not need to be an expert on the subject and will play the role of manual curator while designing the representation of the system, the pathway. We therefore illustrate with two challenging case studies: hair and breast development. They were chosen for focusing on recent acquisitions of human evolution. We produced sub-pathways for each study, representing different phases of development. Differently from most charts present in current databases, we present detailed descriptions, which will additionally guide PESCADOR users along the process. The implementation as a web interface makes PESCADOR a unique tool for guiding the user along the biointeractions, which will constitute a novel pathway.

PathNER: a tool for systematic identification of biological pathway mentions in the literature

Background

Biological pathways are central to many biomedical studies and are frequently discussed in the literature. Several curated databases have been established to collate the knowledge of molecular processes constituting pathways. Yet, there has been little focus on enabling systematic detection of pathway mentions in the literature.

Results

We developed a tool, named PathNER (Pathway Named Entity Recognition), for the systematic identification of pathway mentions in the literature. PathNER is based on soft dictionary matching and rules, with the dictionary generated from public pathway databases. The rules utilise general pathway-specific keywords, syntactic information and gene/protein mentions. Detection results from both components are merged. On a gold-standard corpus, PathNER achieved an F1-score of 84%. To illustrate its potential, we applied PathNER on a collection of articles related to Alzheimer's disease to identify associated pathways, highlighting cases that can complement an existing manually curated knowledgebase.

Conclusions

In contrast to existing text-mining efforts that target the automatic reconstruction of pathway details from molecular interactions mentioned in the literature, PathNER focuses on identifying specific named pathway mentions. These mentions can be used to support large-scale curation and pathway-related systems biology applications, as demonstrated in the example of Alzheimer's disease. PathNER is implemented in Java and made freely available online at http://sourceforge.net/projects/pathner/.

Reverse differentiation as a gene filtering tool in genome expression profiling of adipogenesis for fat marker gene selection and their analysis

BACKGROUND

During mesenchymal stem cell (MSC) conversion into adipocytes, the adipogenic cocktail consisting of insulin, dexamethasone, indomethacin and 3-isobutyl-1-methylxanthine not only induces adipogenic-specific but also genes for non-adipogenic processes. Therefore, not all significantly expressed genes represent adipogenic-specific marker genes. So, our aim was to filter only adipogenic-specific out of all expressed genes. We hypothesize that exclusively adipogenic-specific genes change their expression during adipogenesis, and reverse during dedifferentiation. Thus, MSC were adipogenic differentiated and dedifferentiated.

RESULTS

Adipogenesis and reverse adipogenesis was verified by Oil Red O staining and expression of PPARG and FABP4. Based on GeneChips, 991 genes were differentially expressed during adipogenesis and grouped in 4 clusters. According to bioinformatic analysis the relevance of genes with adipogenic-linked biological annotations, expression sites, molecular functions, signaling pathways and transcription factor binding sites was high in cluster 1, including all prominent adipogenic genes like ADIPOQ, C/EBPA, LPL, PPARG and FABP4, moderate in clusters 2-3, and negligible in cluster 4. During reversed adipogenesis, only 782 expressed genes (clusters 1-3) were reverted, including 597 genes not reported for adipogenesis before. We identified APCDD1, CHI3L1, RARRES1 and SEMA3G as potential adipogenic-specific genes.

CONCLUSION

The model system of adipogenesis linked to reverse adipogenesis allowed the filtration of 782 adipogenic-specific genes out of total 991 significantly expressed genes. Database analysis of adipogenic-specific biological annotations, transcription factors and signaling pathways further validated and valued our concept, because most of the filtered 782 genes showed affiliation to adipogenesis. Based on this approach, the selected and filtered genes would be potentially important for characterization of adipogenesis and monitoring of clinical translation for soft-tissue regeneration. Moreover, we report 4 new marker genes.

Biological network extraction from scientific literature: state of the art and challenges

Networks of molecular interactions explain complex biological processes, and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities. It is necessary to be able to combine data from all available resources to judge the functionality, complexity and completeness of any given network overall, but especially the full integration of relevant information from the scientific literature is still an ongoing and complex task. Currently, the text-mining research community is steadily moving towards processing the full body of the scientific literature by making use of rich linguistic features such as full text parsing, to extract biological interactions. The next step will be to combine these with information from scientific databases to support hypothesis generation for the discovery of new knowledge and the extension of biological networks. The generation of comprehensive networks requires technologies such as entity grounding, coordination resolution and co-reference resolution, which are not fully solved and are required to further improve the quality of results. Here, we analyse the state of the art for the extraction of network information from the scientific literature and the evaluation of extraction methods against reference corpora, discuss challenges involved and identify directions for future research.

A text-mining system for extracting metabolic reactions from full-text articles

BACKGROUND

Increasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway - metabolic pathways - has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein-protein interactions.

RESULTS

When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task.

CONCLUSIONS

We conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein-protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.

CIDeR: multifactorial interaction networks in human diseases

The pathobiology of common diseases is influenced by heterogeneous factors interacting in complex networks. CIDeR http://mips.helmholtz-muenchen.de/cider/ is a publicly available, manually curated, integrative database of metabolic and neurological disorders. The resource provides structured information on 18,813 experimentally validated interactions between molecules, bioprocesses and environmental factors extracted from the scientific literature. Systematic annotation and interactive graphical representation of disease networks make CIDeR a versatile knowledge base for biologists, analysis of large-scale data and systems biology approaches.

Uncovering the molecular machinery of the human spindle--an integration of wet and dry systems biology

The mitotic spindle is an essential molecular machine involved in cell division, whose composition has been studied extensively by detailed cellular biology, high-throughput proteomics, and RNA interference experiments. However, because of its dynamic organization and complex regulation it is difficult to obtain a complete description of its molecular composition. We have implemented an integrated computational approach to characterize novel human spindle components and have analysed in detail the individual candidates predicted to be spindle proteins, as well as the network of predicted relations connecting known and putative spindle proteins. The subsequent experimental validation of a number of predicted novel proteins confirmed not only their association with the spindle apparatus but also their role in mitosis. We found that 75% of our tested proteins are localizing to the spindle apparatus compared to a success rate of 35% when expert knowledge alone was used. We compare our results to the previously published MitoCheck study and see that our approach does validate some findings by this consortium. Further, we predict so-called "hidden spindle hub", proteins whose network of interactions is still poorly characterised by experimental means and which are thought to influence the functionality of the mitotic spindle on a large scale. Our analyses suggest that we are still far from knowing the complete repertoire of functionally important components of the human spindle network. Combining integrated bio-computational approaches and single gene experimental follow-ups could be key to exploring the still hidden regions of the human spindle system.

Connecting the dots between PubMed abstracts

Background

There are now a multitude of articles published in a diversity of journals providing information about genes, proteins, pathways, and diseases. Each article investigates subsets of a biological process, but to gain insight into the functioning of a system as a whole, we must integrate information from multiple publications. Particularly, unraveling relationships between extra-cellular inputs and downstream molecular response mechanisms requires integrating conclusions from diverse publications.

Methodology

We present an automated approach to biological knowledge discovery from PubMed abstracts, suitable for “connecting the dots” across the literature. We describe a storytelling algorithm that, given a start and end publication, typically with little or no overlap in content, identifies a chain of intermediate publications from one to the other, such that neighboring publications have significant content similarity. The quality of discovered stories is measured using local criteria such as the size of supporting neighborhoods for each link and the strength of individual links connecting publications, as well as global metrics of dispersion. To ensure that the story stays coherent as it meanders from one publication to another, we demonstrate the design of novel coherence and overlap filters for use as post-processing steps.

Conclusions

We demonstrate the application of our storytelling algorithm to three case studies: i) a many-one study exploring relationships between multiple cellular inputs and a molecule responsible for cell-fate decisions, ii) a many-many study exploring the relationships between multiple cytokines and multiple downstream transcription factors, and iii) a one-to-one study to showcase the ability to recover a cancer related association, viz. the Warburg effect, from past literature. The storytelling pipeline helps narrow down a scientist's focus from several hundreds of thousands of relevant documents to only around a hundred stories. We argue that our approach can serve as a valuable discovery aid for hypothesis generation and connection exploration in large unstructured biological knowledge bases.

Which species is it? Species-driven gene name disambiguation using random walks over a mixture of adjacency matrices

MOTIVATION

The scientific literature contains a wealth of information about biological systems. Manual curation lacks the scalability to extract this information due to the ever-increasing numbers of papers being published. The development and application of text mining technologies has been proposed as a way of dealing with this problem. However, the inter-species ambiguity of the genomic nomenclature makes mapping of gene mentions identified in text to their corresponding Entrez gene identifiers an extremely difficult task. We propose a novel method, which transforms a MEDLINE record into a mixture of adjacency matrices; by performing a random walkover the resulting graph, we can perform multi-class supervised classification allowing the assignment of taxonomy identifiers to individual gene mentions. The ability to achieve good performance at this task has a direct impact on the performance of normalizing gene mentions to Entrez gene identifiers. Such graph mixtures add flexibility and allow us to generate probabilistic classification schemes that naturally reflect the uncertainties inherent, even in literature-derived data.

RESULTS

Our method performs well in terms of both micro- and macro-averaged performance, achieving micro-F(1) of 0.76 and macro-F(1) of 0.36 on the publicly available DECA corpus. Re-curation of the DECA corpus was performed, with our method achieving 0.88 micro-F(1) and 0.51 macro-F(1). Our method improves over standard classification techniques [such as support vector machines (SVMs)] in a number of ways: flexibility, interpretability and its resistance to the effects of class bias in the training data. Good performance is achieved without the need for computationally expensive parse tree generation or 'bag of words classification'.

Claudin 28b and F-actin are involved in rainbow trout gill pavement cell tight junction remodeling under osmotic stress

Permeability of rainbow trout gill pavement cells cultured on permeable supports (single seeded inserts) changes upon exposure to freshwater or treatment with cortisol. The molecular components of this change are largely unknown, but tight junctions that regulate the paracellular pathway are prime candidates in this adaptational process. Using differential display polymerase chain reaction we found a set of 17 differentially regulated genes in trout pavement cells that had been exposed to freshwater apically for 24 h. Five genes were related to the cell-cell contact. One of these genes was isolated and identified as encoding claudin 28b, an integral component of the tight junction. Immunohistochemical reactivity to claudin 28b protein was concentrated in a circumferential ring colocalized to the cortical F-actin ring. To study the contribution of this isoform to changes in transepithelial resistance and Phenol Red diffusion under apical hypo-or hyperosmotic exposure we quantified the fluorescence signal of this claudin isoform in immunohistochemical stainings together with the fluorescence of phalloidin-probed F-actin. Upon hypo-osmotic stress claudin 28b fluorescence and epithelial tightness remained stable. Under hyperosmotic stress, the presence of claudin 28b at the junction significantly decreased, and epithelial tightness was severely reduced. Cortical F-actin fluorescence increased upon hypo-osmotic stress, whereas hyperosmotic stress led to a separation of cortical F-actin rings and the number of apical crypt-like pores increased. Addition of cortisol to the basolateral medium attenuated cortical F-actin separation and pore formation during hyperosmotic stress and reduced claudin 28b in junctions except after recovery of cells from exposure to freshwater. Our results showed that short-term salinity stress response in cultured trout gill cells was dependent on a dynamic remodeling of tight junctions, which involves claudin 28b and the supporting F-actin ring.

Next generation functional proteomics in non-model plants: A survey on techniques and applications for the analysis of protein complexes and post-translational modifications

The congruent development of computational technology, bioinformatics and analytical instrumentation makes proteomics ready for the next leap. Present-day state of the art proteomics grew from a descriptive method towards a full stake holder in systems biology. High throughput and genome wide studies are now made at the functional level. These include quantitative aspects, functional aspects with respect to protein interactions as well as post translational modifications and advanced computational methods that aid in predicting protein function and mapping these functionalities across the species border. In this review an overview is given of the current status of these aspects in plant studies with special attention to non-genomic model plants.

Integrated bio-entity network: a system for biological knowledge discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs.

Integrative computational biology for cancer research

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards.

Insights into global mechanisms and disease by gene expression profiling

Transcriptomics has played an essential role as proof of concept in the development of experimental and bioinformatics approaches for the generation and analysis of Omics data. We are giving an introduction on how large-scale technologies for gene expression profiling, especially microarrays, have changed the view from studying single molecular events to a systems level view of global mechanisms in a cell, the biological processes, and their pathological mutations. The main platforms available for gene expression profiling (from microarrays to RNA-seq) are presented and the general concepts that need to be taken into account for proper data analysis in order to extract objective and general conclusions from transcriptomics experiments are introduced. We also describe the available main bioinformatics resources used for this purpose.

Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

BACKGROUND

It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes.

RESULTS

We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%.

CONCLUSIONS

We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the possibility of predicting non-hubs, party hubs and date hubs based on their biological features with acceptable accuracy. If such a hypothesis is correct for other species as well, similar methods can be applied to predict the roles of proteins in those species.

The BioPAX community standard for pathway data sharing

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein-protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods.

Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text

Background

Pharmacogenomics studies the relationship between genetic variation and the variation in drug response phenotypes. The field is rapidly gaining importance: it promises drugs targeted to particular subpopulations based on genetic background. The pharmacogenomics literature has expanded rapidly, but is dispersed in many journals. It is challenging, therefore, to identify important associations between drugs and molecular entities – particularly genes and gene variants, and thus these critical connections are often lost. Text mining techniques can allow us to convert the free-style text to a computable, searchable format in which pharmacogenomic concepts (such as genes, drugs, polymorphisms, and diseases) are identified, and important links between these concepts are recorded. Availability of full text articles as input into text mining engines is key, as literature abstracts often do not contain sufficient information to identify these pharmacogenomic associations.

Results

Thus, building on a tool called Textpresso, we have created the Pharmspresso tool to assist in identifying important pharmacogenomic facts in full text articles. Pharmspresso parses text to find references to human genes, polymorphisms, drugs and diseases and their relationships. It presents these as a series of marked-up text fragments, in which key concepts are visually highlighted. To evaluate Pharmspresso, we used a gold standard of 45 human-curated articles. Pharmspresso identified 78%, 61%, and 74% of target gene, polymorphism, and drug concepts, respectively.

Conclusion

Pharmspresso is a text analysis tool that extracts pharmacogenomic concepts from the literature automatically and thus captures our current understanding of gene-drug interactions in a computable form. We have made Pharmspresso available at .

Defrosting the digital library: bibliographic tools for the next generation web

Many scientists now manage the bulk of their bibliographic information electronically, thereby organizing their publications and citation material from digital libraries. However, a library has been described as "thought in cold storage," and unfortunately many digital libraries can be cold, impersonal, isolated, and inaccessible places. In this Review, we discuss the current chilly state of digital libraries for the computational biologist, including PubMed, IEEE Xplore, the ACM digital library, ISI Web of Knowledge, Scopus, Citeseer, arXiv, DBLP, and Google Scholar. We illustrate the current process of using these libraries with a typical workflow, and highlight problems with managing data and metadata using URIs. We then examine a range of new applications such as Zotero, Mendeley, Mekentosj Papers, MyNCBI, CiteULike, Connotea, and HubMed that exploit the Web to make these digital libraries more personal, sociable, integrated, and accessible places. We conclude with how these applications may begin to help achieve a digital defrost, and discuss some of the issues that will help or hinder this in terms of making libraries on the Web warmer places in the future, becoming resources that are considerably more useful to both humans and machines.

The use of Gene Ontology terms for predicting highly-connected 'hub' nodes in protein-protein interaction networks

BACKGROUND

Protein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) 12. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets.Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions.

RESULTS

A hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species.

CONCLUSION

The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.

Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival

BACKGROUND

We have engaged in an international program designated the Bank On A Cure, which has established DNA banks from multiple cooperative and institutional clinical trials, and a platform for examining the association of genetic variations with disease risk and outcomes in multiple myeloma. We describe the development and content of a novel custom SNP panel that contains 3404 SNPs in 983 genes, representing cellular functions and pathways that may influence disease severity at diagnosis, toxicity, progression or other treatment outcomes. A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions. To explore SNP associations with PFS we compared SNP profiles of short term (less than 1 year, n = 70) versus long term progression-free survivors (greater than 3 years, n = 73) in two phase III clinical trials.

RESULTS

Quality controls were established, demonstrating an accurate and robust screening panel for genetic variations, and some initial racial comparisons of allelic variation were done. A variety of analytical approaches, including machine learning tools for data mining and recursive partitioning analyses, demonstrated predictive value of the SNP panel in survival. While the entire SNP panel showed genotype predictive association with PFS, some SNP subsets were identified within drug response, cellular signaling and cell cycle genes.

CONCLUSION

A targeted gene approach was undertaken to develop an SNP panel that can test for associations with clinical outcomes in myeloma. The initial analysis provided some predictive power, demonstrating that genetic variations in the myeloma patient population may influence PFS.

Network integration and graph analysis in mammalian molecular systems biology

Abstraction of intracellular biomolecular interactions into networks is useful for data integration and graph analysis. Network analysis tools facilitate predictions of novel functions for proteins, prediction of functional interactions and identification of intracellular modules. These efforts are linked with drug and phenotype data to accelerate drug-target and biomarker discovery. This review highlights the currently available varieties of mammalian biomolecular networks, and surveys methods and tools to construct, compare, integrate, visualise and analyse such networks.

Novel metrics for evaluating the functional coherence of protein groups via protein semantic network

Metrics are presented for assessing overall functional coherence of a group of proteins based on the associated biomedical literature.

We present the metrics for assessing overall functional coherence of a group of proteins based on associated biomedical literature. A probabilistic topic model is applied to extract biologic concepts from a corpus of protein-related biomedical literature. Bipartite protein semantic networks are constructed, so that the functional coherence of a protein group can be evaluated with metrics that measure the closeness and strength of connectivity of the proteins in the network.

Challenges in plant cellular pathway reconstruction based on gene expression profiling

Microarrays are used to profile transcriptional activity, providing global cell biology insight. Particularly for plants, interpretation of transcriptional profiles is challenging because many genes have unknown functions. Furthermore, many plant gene sequences do not have clear homologs in other model organisms. Fortunately, over the past five years, various tools that assist plant scientists have been developed. Here, we evaluate the currently available in silico tools for reconstruction of cellular (metabolic, biochemical and signal transduction) pathways based on plant gene expression datasets. Furthermore, we show how expression-profile comparison at the level of these various cellular pathways contributes to the postulation of novel hypotheses which, after experimental verification, can provide further insight into decisive elements that have roles in cellular processes.

BioPP: a tool for web-publication of biological networks

Background

Cellular processes depend on the function of intracellular molecular networks. The curation of the literature relevant to specific biological pathways is important for many theoretical and experimental research teams and communities. No current tool supports web publication or hosting of user-developed large scale annotated pathway diagrams. Sharing via web publication is needed to allow real-time access to the current literature pathway knowledgebase, both privately within a research team or publicly among the outside research community. Web publication also facilitates team and/or community input into the curation process while allowing centralized control of the curation and validation process. We have developed new tool to address these needs. Biological Pathway Publisher (BioPP) is a software suite for converting CellDesigner Systems Biology Markup Language (CD-SBML) formatted pathways into a web viewable format. The BioPP suite is available for private use and for depositing knowledgebases into a newly created public repository.

Results

BioPP suite is a web-based application that allows pathway knowledgebases stored in CD-SBML to be web published with an easily navigated user interface. The BioPP suite consists of four interrelated elements: a pathway publisher, an upload web-interface, a pathway repository for user-deposited knowledgebases and a pathway navigator. Users have the option to convert their CD-SBML files to HTML for restricted use or to allow their knowledgebase to be web-accessible to the scientific community. All entities in all knowledgebases in the repository are linked to public database entries as well as to a newly created public wiki which provides a discussion forum.

Conclusion

BioPP tools and the public repository facilitate sharing of pathway knowledgebases and interactive curation for research teams and scientific communities. BioPP suite is accessible at

iHOP web services

iHOP provides fast, accurate, comprehensive, and up-to-date summary information on more than 80 000 biological molecules by automatically extracting key sentences from millions of PubMed documents. Its intuitive user interface and navigation scheme have made iHOP extremely successful among biologists, counting more than 500 000 visits per month (iHOP access statistics: http://www.ihop-net.org/UniPub/iHOP/info/logs/). Here we describe a public programmatic API that enables the integration of main iHOP functionalities in bioinformatic programs and workflows.

In silico pathway reconstruction: Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae

Background

Current advances in genomics, proteomics and other areas of molecular biology make the identification and reconstruction of novel pathways an emerging area of great interest. One such class of pathways is involved in the biogenesis of Iron-Sulfur Clusters (ISC).

Results

Our goal is the development of a new approach based on the use and combination of mathematical, theoretical and computational methods to identify the topology of a target network. In this approach, mathematical models play a central role for the evaluation of the alternative network structures that arise from literature data-mining, phylogenetic profiling, structural methods, and human curation. As a test case, we reconstruct the topology of the reaction and regulatory network for the mitochondrial ISC biogenesis pathway in S. cerevisiae. Predictions regarding how proteins act in ISC biogenesis are validated by comparison with published experimental results. For example, the predicted role of Arh1 and Yah1 and some of the interactions we predict for Grx5 both matches experimental evidence. A putative role for frataxin in directly regulating mitochondrial iron import is discarded from our analysis, which agrees with also published experimental results. Additionally, we propose a number of experiments for testing other predictions and further improve the identification of the network structure.

Conclusion

We propose and apply an iterative in silico procedure for predictive reconstruction of the network topology of metabolic pathways. The procedure combines structural bioinformatics tools and mathematical modeling techniques that allow the reconstruction of biochemical networks. Using the Iron Sulfur cluster biogenesis in S. cerevisiae as a test case we indicate how this procedure can be used to analyze and validate the network model against experimental results. Critical evaluation of the obtained results through this procedure allows devising new wet lab experiments to confirm its predictions or provide alternative explanations for further improving the models.

iHOPerator: user-scripting a personalized bioinformatics Web, starting with the iHOP website

Background

User-scripts are programs stored in Web browsers that can manipulate the content of websites prior to display in the browser. They provide a novel mechanism by which users can conveniently gain increased control over the content and the display of the information presented to them on the Web. As the Web is the primary medium by which scientists retrieve biological information, any improvements in the mechanisms that govern the utility or accessibility of this information may have profound effects. GreaseMonkey is a Mozilla Firefox extension that facilitates the development and deployment of user-scripts for the Firefox web-browser. We utilize this to enhance the content and the presentation of the iHOP (information Hyperlinked Over Proteins) website.

Results

The iHOPerator is a GreaseMonkey user-script that augments the gene-centred pages on iHOP by providing a compact, configurable visualization of the defining information for each gene and by enabling additional data, such as biochemical pathway diagrams, to be collected automatically from third party resources and displayed in the same browsing context.

Conclusion

This open-source script provides an extension to the iHOP website, demonstrating how user-scripts can personalize and enhance the Web browsing experience in a relevant biological setting. The novel, user-driven controls over the content and the display of Web resources made possible by user-scripts, such as the iHOPerator, herald the beginning of a transition from a resource-centric to a user-centric Web experience. We believe that this transition is a necessary step in the development of Web technology that will eventually result in profound improvements in the way life scientists interact with information.

Parameter estimate of signal transduction pathways

BACKGROUND

The "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway.

RESULTS

Using the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions.

CONCLUSION

Despite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data.

Text mining and its potential applications in systems biology

With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.

BBP: Brucella genome annotation with literature mining and curation

BACKGROUND

Brucella species are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and animals. Sequences of four Brucella genomes have been published, and various Brucella gene and genome data and analysis resources exist. A web gateway to integrate these resources will greatly facilitate Brucella research. Brucella genome data in current databases is largely derived from computational analysis without experimental validation typically found in peer-reviewed publications. It is partially due to the lack of a literature mining and curation system able to efficiently incorporate the large amount of literature data into genome annotation. It is further hypothesized that literature-based Brucella gene annotation would increase understanding of complicated Brucella pathogenesis mechanisms.

RESULTS

The Brucella Bioinformatics Portal (BBP) is developed to integrate existing Brucella genome data and analysis tools with literature mining and curation. The BBP InterBru database and Brucella Genome Browser allow users to search and analyze genes of 4 currently available Brucella genomes and link to more than 20 existing databases and analysis programs. Brucella literature publications in PubMed are extracted and can be searched by a TextPresso-powered natural language processing method, a MeSH browser, a keywords search, and an automatic literature update service. To efficiently annotate Brucella genes using the large amount of literature publications, a literature mining and curation system coined Limix is developed to integrate computational literature mining methods with a PubSearch-powered manual curation and management system. The Limix system is used to quickly find and confirm 107 Brucella gene mutations including 75 genes shown to be essential for Brucella virulence. The 75 genes are further clustered using COG. In addition, 62 Brucella genetic interactions are extracted from literature publications. These results make possible more comprehensive investigation of Brucella pathogenesis. Other BBP features include publication email alert service, Brucella researchers' contact database, and discussion forum.

CONCLUSION

BBP is a gateway for Brucella researchers to search, analyze, and curate Brucella genome data originated from public databases and literature. Brucella gene mutations and genetic interactions are annotated using Limix leading to better understanding of Brucella pathogenesis.

Neuroinformatics in clinical practice: are computers going to help neurological patients and their physicians?

Neuroinformatics is the field that merges the power of computational analysis with neuroscience. It is a discipline that has evolved from the original use of computers for data organization to the current development and application of sophisticated computational tools for large-scale data and image management, analysis and modeling of brain function in health and disease. Neuroinformatics has the potential to be a powerful instrument in the discovery of biological markers of neurological diseases, as well as in the development of new and more effective therapies. Owing to the exponential growth in size and complexity of the information available in the neurosciences, neuroinformatic methods are becoming indispensable in modern neurological research. We predict that, in the near future, they will also be essential at the bedside.

Synapse proteomics of multiprotein complexes: en route from genes to nervous system diseases

Proteomic experiments have produced a draft profile of the overall molecular composition of the mammalian neuronal synapse. It appears that synapses have over 1000 protein components and the mapping of their interactions, organization and functions will lead to a global view of the role of synapses in physiology and disease. A major functional subcomponent of the synaptic machinery is a multiprotein complex of glutamate receptors and adhesion proteins with associated adaptor and signalling enzymes totally 185 proteins known as the N-methyl-d-aspartate receptor complex/MAGUK associated signalling complex (NRC/MASC). Here, we review the proteomic studies and functions of NRC/MASC and specifically report on the role of its component genes in human diseases. Using a systematic literature search protocol, we identified reports of mutations or polymorphisms in 47 genes associated with 183 disorders, of which 54 were nervous system disorders. A similar number of genes are important in mouse synaptic plasticity and behaviour, where the NRC/MASC acts as a signalling complex with multiple functions provided by its individual protein components and their interactions. The individual gene mutations suggest not only an important role for the NRC/MASC in human diseases but that these diseases may be functionally connected by their common link to the NRC/MASC. The NRC/MASC is a rich source of genetic variation and provides a platform for understanding relationships of disease phenotype amenable to systematic studies such as the Genes to Cognition research consortium (www.genes2cognition.org) that links human and mouse genetics with proteomic studies.