The recent editorial by Dr Longo and Dr Drazen in the New England Journal of Medicine (Longo and Drazen, 2016) has stirred up quite a bit of controversy. As Executive Officers of the International Society of Computational Biology, Inc. (ISCB), we express our deep concern about the restrictive and potentially damaging opinions voiced in this editorial, and while ISCB works to write a detailed response, we felt it necessary to promptly address the editorial with this reaction. Although some of the concerns voiced by the authors of the editorial are worth considering, large parts of the statement purport an obsolete view of hegemony over data that is neither in line with today’s spirit of open access nor furthering an atmosphere where the potential of data can be fully realized. ISCB acknowledges that the additional comment on the editorial (Drazen, 2016) eases some of the polemics unfortunately without addressing some of the core issues. We still feel, however, that we need to contrast the opinion voiced in the editorial with what we consider the axioms of our scientific society, statements that lead into a fruitful future of data-driven science: Data produced with public money should be public in benefit of the science and society Restrictions on the use of public data hamper science and slow progress Open data is the best way to combat fraud and misinterpretations Current large data collections proceed from many sources, are continually accumulated, and require a variety of analytical approaches. Data generation and data analysis overlap in time and are continually updated with new data sets produced by new techniques and new analysis methodologies. Furthermore, in many cases current science functions in consortia in which scientists collaborate toward common goals while preserving their own scientific objectives. Dividing scientists into data providers and data analysts is simplistic and gives a misleading impression of the actual state of biological and biomedical science. ISCB very much supports collaboration between disciplines, including experimental and clinical as well as bioinformatics, as the best way forward to address complex biological problems. But this collaboration cannot be based on imposed restrictions to data access and cannot be contained in professional silos. (The use of expressions such as ‘research parasites’ clearly does not help.) Many bio-communities have made significant progress by endorsing open data policies and, gratefully, public funding agencies have connected to the spirit that they are distributing taxpayers’ money to science and that, therefore, the data that are generated in the course belong to the public. It is, perhaps, natural that some areas of biomedical research are slow in adopting these policies. History and the confidential nature of the relevant data are surely among the reasons. However, in our opinion data hegemony is another, a reason that has to be overcome. The sooner these barriers to progress are removed the sooner the patients will benefit from the current flourishing of biomedical research. Conflict of Interest: none declared.

This message is a response from the ISCB in light of the recent the New England Journal of Medicine (NEJM) editorial around data sharing.

The International Society for Computational Biology, ISCB, organizes the largest event in the field of computational biology and bioinformatics, namely the annual international conference on Intelligent Systems for Molecular Biology, the ISMB. This year at ISMB 2012 in Long Beach, ISCB celebrated the 20th anniversary of its flagship meeting. ISCB is a young, lean and efficient society that aspires to make a significant impact with only limited resources. Many constraints make the choice of venues for ISMB a tough challenge. Here, we describe those challenges and invite the contribution of ideas for solutions.

BACKGROUND: The process of horizontal gene transfer (HGT) is believed to be widespread in Bacteria and Archaea, but little comparative data is available addressing its occurrence in complete microbial genomes. Collection of high-quality, automated HGT prediction data based on phylogenetic evidence has previously been impractical for large numbers of genomes at once, due to prohibitive computational demands. DarkHorse, a recently described statistical method for discovering phylogenetically atypical genes on a genome-wide basis, provides a means to solve this problem through lineage probability index (LPI) ranking scores. LPI scores inversely reflect phylogenetic distance between a test amino acid sequence and its closest available database matches. Proteins with low LPI scores are good horizontal gene transfer candidates; those with high scores are not. DESCRIPTION: The DarkHorse algorithm has been applied to 955 microbial genome sequences, and the results organized into a web-searchable relational database, called the DarkHorse HGT Candidate Resource http://darkhorse.ucsd.edu. Users can select individual genomes or groups of genomes to screen by LPI score, search for protein functions by descriptive annotation or amino acid sequence similarity, or select proteins with unusual G+C composition in their underlying coding sequences. The search engine reports LPI scores for match partners as well as query sequences, providing the opportunity to explore whether potential HGT donor sequences are phylogenetically typical or atypical within their own genomes. This information can be used to predict whether or not sufficient information is available to build a well-supported phylogenetic tree using the potential donor sequence. CONCLUSION: The DarkHorse HGT Candidate database provides a powerful, flexible set of tools for identifying phylogenetically atypical proteins, allowing researchers to explore both individual HGT events in single genomes, and large-scale HGT patterns among protein families and genome groups. Although the DarkHorse algorithm cannot, by itself, provide definitive proof of horizontal gene transfer, it is a flexible, powerful tool that can be combined with slower, more rigorous methods in situations where these other methods could not otherwise be applied.

Catherine A. Ball, Gavin Sherlock, Helen Parkinson, Philippe Rocca-Sera, Catherine Brooksbank, Helen C. Causton, Duccio Cavalieri, Terry Gaasterland, Pascal Hin

This paper presents a rigorous methodology for using annotated logic programming techniques to handle user preferences and needs in answering database queries. Two alternative transformations turn a database program into a new program that returns an