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© 2017 Wiley Periodicals, Inc. The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.

Key Points Drug discovery has traditionally responded to the 'pull' of unmet medical need and commercial potential. Here, we evaluate the scientific areas that are providing 'push' in terms of scientific innovation, as measured by publications and patents.The amount of funding from the US National Institutes of Health, the number of doctoral degrees awarded, the number of publications and the number of approvals for new molecular entities issued by the US Food and Drug Administration show a general upward trend from the 1950s to the present. Although explanations can be suggested for some of the short-term ups and downs, these trends are also affected by numerous hidden variables, and well as time lags, feedback loops and other complications.The numbers of publications in various disease areas correlate well with global disease burden.The correlation is greater in the developed world, where the impact of infectious and parasitic diseases, respiratory infections and infant mortality, is considerably lower than in the developing world.A few therapeutic areas stand out when analysing trends in publications, citations, publications in high-impact journals and patents. Oncology is the clearest outlier on almost every metric. Viruses, nutrition and metabolism, and the immune system also show increases in the majority of metrics.In terms of individual diseases, insulin resistance, orthomyxoviridae infections, depression, autism, macular degeneration, inflammation, obesity, cognitive disorders and ventricular dysfunction have strong publication growth in both 2- and 5-year periods. Publications related to the genes encoding forkhead box P3, leucine-rich repeat kinase 2, janus kinase 2, transcription factor 7-like 2, interleukin-17A, toll-like receptor 2 (TLR2), TLR4, FK506 binding protein 12-rapamycin associated protein 1and ADIPOQ (adiponectin, C1Q and collagen domain-containing) show the strongest publication growth in the same periods.Assessing scientific innovation is a complex endeavour; however, the 'bibliome' seems to offer many approaches that could enhance decision-making in drug discovery. Supplementary information The online version of this article (doi:10.1038/nrd2973) contains supplementary material, which is available to authorized users., Here, the authors use bibliometrics and related data-mining methods to analyse PubMed abstracts, literature citation data and patent filings. The analyses are used to identify trends in disease-related scientific activity that are likely to give new therapeutic opportunities. Supplementary information The online version of this article (doi:10.1038/nrd2973) contains supplementary material, which is available to authorized users., Drug discovery must be guided not only by medical need and commercial potential, but also by the areas in which new science is creating therapeutic opportunities, such as target identification and the understanding of disease mechanisms. To systematically identify such areas of high scientific activity, we use bibliometrics and related data-mining methods to analyse over half a terabyte of data, including PubMed abstracts, literature citation data and patent filings. These analyses reveal trends in scientific activity related to disease studied at varying levels, down to individual genes and pathways, and provide methods to monitor areas in which scientific advances are likely to create new therapeutic opportunities. Supplementary information The online version of this article (doi:10.1038/nrd2973) contains supplementary material, which is available to authorized users.

The effective integration of data and knowledge from many disparate sources will be crucial to future drug discovery. Data integration is a key element of conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realizing economies of scale in large enterprises. However, viewing data integration as simply an 'IT problem' underestimates the novel and serious scientific and management challenges it embodies - challenges that could require significant methodological and even cultural changes in our approach to data.

Genomic and proteomic platform data constitute a hugely important resource to current efforts in disease understanding, systems biology and drug discovery. We review prerequisites for the adequate management of 'omic' data, the means by which such data are analyzed and converted to knowledge relevant to drug discovery and issues crucial to the integration of such data, particularly with chemical, genetic and clinical data.

Bioinformatics has, out of necessity, become a key aspect of drug discovery in the genomic revolution, contributing to both target discovery and target validation. The author describes the role that bioinformatics has played and will continue to play in response to the waves of genome-wide data sources that have become available to the industry, including expressed sequence tags, microbial genome sequences, model organism sequences, polymorphisms, gene expression data and proteomics. However, these knowledge sources must be intelligently integrated.

bioTk is a collection of graphical “widgets” and utilities that support application programming in the domain of bioinformatics. It is intended to establish a framework that encourages the development of communicating window-based applications and flexible, non-modal user interaction. The current release to bioTk has domain-specific widgets for chromosome ideogram displays, genome maps, and scrolling sequence windows.

Detailed physical maps of the human genome are important resources for the identification and isolation of disease genes and for studying the structure and function of the genome. We used data from STS content mapping of YACs and natural and induced chromosomal breakpoints to anchor contigs of overlapping yeast artificial chromosome (YAC) clones spanning extensive regions of human chromosome 22. The STSs were assigned to specific regions (bins) on the chromosome using cell lines from a somatic hybrid mapping panel defining a maximum of 25 intervals. YAC libraries were screened by PCR amplification of hierarchical pools of yeast DNA with 238 markers, and a total of 587 YAC clones were identified. These YACs were assembled into contigs based upon their shared STS content using a simulated annealing algorithm. Fifteen contigs, containing between 2 and 74 STSs were assembled, and ordered along the chromosome based upon the cytogenetic breakpoint, meiotic and PFG maps. Additional singleton YACs were assigned to unique chromosomal bins. These ordered YAC contigs will be useful for identifying disease genes and chromosomal breakpoints by positional cloning and will provide the foundation for higher resolution physical maps for large scale sequencing of the chromosome.

Polymeric macromolecules, when viewed abstractly as strings of symbols, can be treated in terms of formal language theory, providing a mathematical foundation for characterizing such strings both as collections and in terms of their individual structures. In addition this approach offers a framework for analysis of macromolecules by tools and conventions widely used in computational linguistics. This article introduces the ways that linguistics can be and has been applied to molecular biology, covering the relevant formal language theory at a relatively nontechnical level. Analogies between macromolecules and human natural language are used to provide intuitive insights into the relevance of grammars, parsing, and analysis of language complexity to biology. © 2012 Wiley Periodicals, Inc. Biopolymers 99: 203–217, 2013.

International audience; The IUPHAR database (IUPHAR-DB) integrates peer-reviewed pharmacological, chemical, genetic, functional and anatomical information on the 354 non-sensory G protein-coupled receptors (GPCRs), 71 ligand-gated ion channel subunits and 141 voltage-gated-like ion channel subunits encoded by the human, rat and mouse genomes. These genes represent the targets of approximately one-third of currently approved drugs and are a major focus of drug discovery and development programs in the pharmaceutical industry. IUPHAR-DB provides a comprehensive description of the genes and their functions, with information on protein structure and interactions, ligands, expression patterns, signaling mechanisms, functional assays and biologically important receptor variants (e.g. single nucleotide polymorphisms and splice variants). In addition, the phenotypes resulting from altered gene expression (e.g. in genetically altered animals or in human genetic disorders) are described. The content of the database is peer reviewed by members of the International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification (NC-IUPHAR); the data are provided through manual curation of the primary literature by a network of over 60 subcommittees of NC-IUPHAR. Links to other bioinformatics resources, such as NCBI, Uniprot, HGNC and the rat and mouse genome databases are provided. IUPHAR-DB is freely available at http://www.iuphar-db.org.

The beacon logic-based configurator is designed to ensure the correct and complete configuration of large computer systems at the time of order entry. BEACON is based on a semantic network, KNET , implemented in PROLOG. Unlike other configuration expert systems, BEACON uses a declarative, logic-based approach, as opposed to a data-driven production system or hybrid design. Among other virtues, this allows for a completely interactive ordering session which is guaranteed correct with respect to the underlying model, rather than a batch-mode order-correcting system. KNET allows for configuration by individuation of an abstract, generic representation in a manner that is analogous to the instantiation of logical variables in a PROLOG-style proof.

Since the first application of context-free grammars to RNA secondary structures in 1988, many researchers have used both ad hoc and formal methods from computational linguistics to model RNA and protein structure. We show how nearly all of these methods are based on the same core principles and can be converted into equivalent approaches in the framework of tree-adjoining grammars and related formalisms. We also propose some new approaches that extend these core principles in novel ways.

DNA sequence comparison is becomin an increasingly important activity as the volume of DNfA that has been sequenced increases. The most widely used algorithms for the comparison of two sequences of DNA are O(m.n) on the lengths, m and n, of the sequences being compared, though significant speedu s are available both through improved algorithms and parisle1 hardware implementations. This paper describes a s stem based on commercial1 available integrated circyits whicZ cpmpares DNA sequences Q correlation in time that is O(n). Usmg a hardware-based correlator connected to the expansion bus of an IBM PC compatible com uter we were able to achieve a speed up of 4 orders of magnitule over a softwarebased implementation of the correlation techruque.

Phylogenomics, which advocates an evolutionary view of genomic data, has been useful in the prediction of protein function, of significant sequence and structural elements, and of protein interactions and other relationships. Although such information is important in characterizing individual pharmacological targets, evolutionary analyses also indicate new ways to view the overall space of gene products in terms of their suitability for therapeutic intervention. This view places increased emphasis on the comprehensive analysis of the evolutionary history of targets, in particular their orthology and paralogy relationships, the rate and nature of evolutionary change they have undergone, and their involvement in evolving pathways and networks.

Linguistic metaphors have been woven into the fabric of molecular biology since its inception. The determination of the human genome sequence has brought these metaphors to the forefront of the popular imagination, with the natural extension of the notion of DNA as language to that of the genome as the 'book of life'. But do these analogies go deeper and, if so, can the methods developed for analysing languages be applied to molecular biology? In fact, many techniques used in bioinformatics, even if developed independently, may be seen to be grounded in linguistics. Further interweaving of these fields will be instrumental in extending our understanding of the language of life.

The advent of whole-genome data resources—not only sequence but also other genome-scale data collections such as gene expression, protein interaction, and genetic variation—is having two marked, complementary effects on the relatively new discipline of bioinformatics. First, the veritable flood of data is creating a need and demand for new tools for dealing adequately with the deluge, and, second, the unprecedented extent, diversity, and impending completeness of the data sets are creating opportunities for new approaches to discovery based on computational methods.

This chapter discusses the grand challenges in computational biology. One useful classification of the challenges facing computational biology is that which distinguishes the individual technical challenges, which define the field, and are termed “infrastructural challenges faced by the field.” These meta-challenges are an inevitable result of the sudden ascendancy of bioinformatics. Unlike most other scientific fields, it has become an economic force before it has been thoroughly established as an academic discipline, with the exception of the protein structure aspects of the field. Another grand challenge facing computational biology is the detection of increasingly distant homologues, or proteins related by evolution from a common ancestor. The computational elucidation of such relationships stands as the fundamental operation and most pragmatic success of the field, because finding homologues is the shortest and surest path to determining the function of a newly discovered gene.

A number of visually oriented computational technologies show promise of aiding in the exploration of genome sequences. These encompass the fields of graphical user interfaces, scientific visualization, and visual programming. This review describes examples of each, as they relate to genomics, and argues that a synthesis of such techniques may be necessary to make understanding entire genomes a tractable human enterprise.

The dating of ancient languages by a technique called glottochronology is undergoing a revival, stimulated by the computational and statistical methods used to tease out evolutionary relationships in biology.

The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and to assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current connectionist and combinatorial algorithms in predicting gene structures for sequence database entries. Parameters of the grammar rules are optimized for several different species, and mixing experiments are performed to determine the degree of species specificity and the relative importance of compositional, signal-based, and syntactic components in gene prediction. 24 refs., 5 figs., 3 tabs.

Consider the natural history of the communication of biological data. In the pre-genomic era, the scientific literature was essentially the only means by which experimental results were conveyed to the community at large. Data laboriously acquired by what we now view as ‘low-throughput’ methods were filtered through the experimentalist’s expository and interpretive medium, ie, the standard journal article. Not only was research hypothesis-driven, but the presentation of its results was as well, in the sense that data were generally offered in the context of some hypothesis, with considerable ‘value-added’ explanation, interpretation, and speculation.

Logic grammars such as Definite Clause Grammars — linguistic formalisms closely associated with the Prolog language — are powerful and highly versatile systems for specifying and detecting patterns not only in natural language, but in any suitably organized string, signal, or image. This paper reviews several such prototypes we have developed, which demonstrate the characteristics that make logic grammars a good vehicle for many Syntactic Pattern Recognition applications.