TargetRNA2: identifying targets of small regulatory RNAs in bacteria

Many small, noncoding RNAs (sRNAs) in bacteria act as posttranscriptional regulators of messenger RNAs. TargetRNA2 is a web server that identifies mRNA targets of sRNA regulatory action in bacteria. As input, TargetRNA2 takes the sequence of an sRNA and the name of a sequenced bacterial replicon. When searching for targets of RNA regulation, TargetRNA2 uses a variety of features, including conservation of the sRNA in other bacteria, the secondary structure of the sRNA, the secondary structure of each candidate mRNA target and the hybridization energy between the sRNA and each candidate mRNA target. TargetRNA2 outputs a ranked list of likely regulatory targets for the input sRNA. When evaluated on a comprehensive set of sRNA-target interactions, TargetRNA2 was found to be both accurate and efficient in identifying targets of sRNA regulatory action. Furthermore, TargetRNA2 has the ability to integrate RNA-seq data, if available. If an sRNA is differentially expressed in two or more RNA-seq experiments, TargetRNA2 considers co-differential gene expression when searching for regulatory targets, significantly improving the accuracy of target identifications. The TargetRNA2 web server is freely available for use at http://cs.wellesley.edu/∼btjaden/TargetRNA2

Notes on Notebooks: Is Jupyter the Bringer of Jollity?

As the interactive computational notebook becomes a more prominent code development medium, we examine advantages and disadvantages of this particular source code format. We specify the structure of a coding notebook layout. We describe complexities in notebook programming; some of these are incidental whereas others may be inherent complexities. We outline how we envisage research and development might proceed to advance the cause of notebook programming

Paths Explored, Paths Omitted, Paths Obscured: Decision Points & Selective Reporting in End-to-End Data Analysis

Drawing reliable inferences from data involves many, sometimes arbitrary, decisions across phases of data collection, wrangling, and modeling. As different choices can lead to diverging conclusions, understanding how researchers make analytic decisions is important for supporting robust and replicable analysis. In this study, we pore over nine published research studies and conduct semi-structured interviews with their authors. We observe that researchers often base their decisions on methodological or theoretical concerns, but subject to constraints arising from the data, expertise, or perceived interpretability. We confirm that researchers may experiment with choices in search of desirable results, but also identify other reasons why researchers explore alternatives yet omit findings. In concert with our interviews, we also contribute visualizations for communicating decision processes throughout an analysis. Based on our results, we identify design opportunities for strengthening end-to-end analysis, for instance via tracking and meta-analysis of multiple decision paths

FileWeaver: Gestion Flexible de Fichiers avec Suivi Automatique des Dépendances

International audienceKnowledge management and sharing involves a variety of specialized but isolated software tools, tied together by the files that these tools use and produce. We interviewed 23 scientists and found that they all had difficulties using the file system to keep track of, re-find and maintain consistency among related but distributed information. We introduce FileWeaver, a system that automatically detects dependencies among files without explicit user action, tracks their history, and lets users interact directly with the graphs representing these dependencies and version history. Changes to a file can trigger recipes, either automatically or under user control, to keep the file consistent with its dependants. Users can merge variants of a file, e.g. different output formats, into a polymorphic file, or morph, and automate the management of these variants. By making dependencies among files explicit and visible, FileWeaver facilitates the automation of workflows by scientists and other users who rely on the file system to manage their data