A recent proliferation of Massive Open Online Courses (MOOCs) and other web-based educational resources has greatly increased the potential for effective self-study in many fields. This article introduces a catalog of several hundred free video courses of potential interest to those wishing to expand their knowledge of bioinformatics and computational biology. The courses are organized into eleven subject areas modeled on university departments and are accompanied by commentary and career advice.

The success of online courseware such as that offered by the Massachusetts Institute of Technology (MIT) (http:// ocw.mit.edu) and now by many other institutions, together with a plethora of recent announcements of major new initiatives in this arena such as Coursera (https://www.coursera

Online learning initiatives over the past decade have become increasingly comprehensive in their selection of courses and sophisticated in their presentation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university education on the internet, free of charge, a real possibility.At this pivotal moment it is appropriate to explore the potential for obtaining comprehensive bioinformatics training with currently existing free video resources.This article presents such a bioinformatics curriculum in the form of a virtual course catalog, together with editorial commentary, and an assessment of strengths, weaknesses, and likely future directions for open online learning in this field. Online Learning Comes of AgeOnline academic ''courseware'' at the university level has now been available to the public for a decade, the earliest concerted effort having originated in 2002 with the Massachusetts Institute of Technology (MIT) and their OpenCour-seWare initiative (http://ocw.mit.edu).This project offered up the syllabi, lecture notes, quizzes, exams, and/or other study materials for a very large number of courses, at the discretion of professors but with strong support and encouragement from the MIT administration.Only in a minority of cases were videos of lectures posted.Even before this, The University of California, Berkeley, had started webcasting lectures, and eventually began posting both audio and video for public consumption at their Berkeley Webcast site (http:// webcast.berkeley.edu),though without the ancillary materials of MIT's OpenCourse-Ware.A number of other universities followed suit, though seldom so extensively; among these was Stanford with its ClassX streaming service (http://classx.stanford.edu/ClassX)and an earlier effort called Stanford Engineering Everywhere (http://see.stanford.edu/see/courses.aspx).In many cases, individual faculty members

Every new scientific discipline or methodology reaches a point in its maturation where it is fruitful for it to turn its gaze inward, as well as backward. Such introspection helps to clarify the essential structure of a field of study, facilitating communication, pedagogy, standardization, and the like, while retrospection aids this process by accounting for its beginnings and underpinnings. In this spirit, PLoS Computational Biology is launching a new series of themed articles tracing the roots of bioinformatics. Essays from prominent workers in the field will relate how selected scientific, technological, economic, and even cultural threads came to influence the development of the field we know today. These are not intended to be review articles, nor personal reminiscences, but rather narratives from individual perspectives about the origins and foundations of bioinformatics, and are expected to provide both historical and technical insights. Ideally, these articles will offer an archival record of the field's development, as well as a human face on an important segment of science, for the benefit of current and future workers. Upcoming articles, already commissioned, will cover the roots of bioinformatics in structural biology, in evolutionary biology, and in artificial intelligence, with more in the works. These topics are obviously very broad, and so are likely to be subdivided or otherwise revisited in future installments by authors with varying perspectives. Topics and authors will be chosen at the discretion of the editors along lines broadly corresponding to the usual content of this journal. The author, having been asked to serve as Series Editor by the Editor-in-Chief, will endeavor to maintain a uniform flow of articles solicited from luminaries in the field. As a starting point to the series, I offer below a few vignettes and reflections on some longer-term influences that have shaped the discipline. I first consider the unique status of bioinformatics vis-a-vis science and technology, and then explore historical trends in biology and related fields that anticipated and prepared the way for bioinformatics. Examining the context of key moments when computers were first taken up by early adopters reveals how deep the roots of bioinformatics go.

One of the most significant decisions we face as scientists comes at the end of our formal education. Choosing between industry and academia is easy for some, incredibly fraught for others. The author has made two complete cycles between these career destinations, including on the one hand 16 years in academia, as grad student (twice, in biology and in computer science), post-doc, and faculty, and on the other hand 19 years in two different industries (computer and pharmaceutical). The following rules reflect that experience, and my own opinions.

The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a 'lightweight' approach we have implemented within an industrial context.

In 1995, when I left a faculty position at the University of Pennsylvania to join the fledgling bioinformatics unit at what was then SmithKline Beecham Pharmaceuticals, the friendly jibes by academic colleagues about “going over to the Dark Side” were, I suspect, only half in jest. There weren't all that many of us practicing bioinformatics at that time, and there was genuine concern that a brain drain to industry might curtail the training of a new generation. Dire talk of “eating our seed corn” made its way into print [1–3]. Other misgivings grew out of the first wave of human genome-wide data then arriving in profusion: the short single-pass cDNA sequences called expressed sequence tags (ESTs) that bubbled up new gene identifications seemingly on a daily basis and that appeared to be suddenly short-circuiting the stately, hierarchical progression of the genome effort through ever-finer stages of mapping while breakthroughs in sequencing technology were labored after [4–6]. The data-management challenges arising from this heady sampling of the genome were making a strong impression, in both the public and private sectors, and the as-yet-unresolved (and highly charged) question of the patentability of genes led to a land rush on intellectual property [7–9]. At the same time a surge of startups with liberal venture-capital grubstakes only increased the demand for skilled gene prospectors [10,11]. All these developments fed concerns that a reckless commercialization of the human genome would be somehow unfairly turbocharged by lavish spending and pure computational horsepower in industry, with legions of apostate academics mining the data.

Biologists have long made use of linguistic metaphors in describing and naming cellular processes involving nucleic acid and protein sequences. Indeed, it is very natural to view the genetic 'text' and its sequential transliterations in these terms. However, a metaphor is not a tool, and it is necessary to ask whether the techniques used in analyzing other kinds of languages, such as human and computer languages, can in fact be of any use in tackling problems in molecular biology. This paper reviews the work of the author and others in applying the methods of computational linguistics to biological sequences.

The National Center for Biotechnology Information (NCBI) has created a database collection that includes several protein and nucleic acid sequence databases, a biosequence-specific subset of MEDLINE, as well as value-added information such as links between similar sequences. Information in the NCBI database is modeled in Abstract Syntax Notation 1 (ASN.1) an Open Systems Interconnection protocol designed for the purpose of exchanging structured data between software applications rather than as a data model for database systems. While the NCBI database is distributed with an easy-to-use information retrieval system, ENTREZ, the ASN.1 data model currently lacks an ad hoc query language for general-purpose data access. For that reason, we have developed a software package, SORTEZ, that transforms the ASN.1 database (or other databases with nested data structures) to a relational data model and subsequently to a relational database management system (Sybase) where information can be accessed through the relational query language, SQL. Because the need to transform data from one data model and schema to another arises naturally in several important contexts, including efficient execution of specific applications, access to multiple databases and adaptation to database evolution this work also serves as a practical study of the issues involved in the various stages of database transformation. We show that transformation from the ASN.1 data model to a relational data model can be largely automated, but that schema transformation and data conversion require considerable domain expertise and would greatly benefit from additional support tools.

The software package RSVP (Rapid Sequence Visualization in PostScript) has a suite of visually oriented sequence analysis routines implemented entirely in the page description language PostScript, a widely used standard that is built into many printers. RSVP is thus a relatively platform-independent tool for providing a 'quick look' at sequence data, using form and color to help point out patterns, in advance of more sophisticated sequence analyses.

The detection of similarities between DNA sequences can be accomplished using the signal-processing technique of cross-correlation. An early method used the fast Fourier transform (FFT) to perform correlations on DNA sequences in O(n log n) time for any length sequence. However, this method requires many FFTs (nine), runs no faster if one sequence is much shorter than the other, and measures only global similarity, so that significant short local matches may be missed. We report that, through the use of alternative encodings of the DNA sequence in the complex plane, the number of FFTs performed can be traded off against (i) signal-to-noise ratio, and (ii) a certain degree of filtering for local similarity via k-tuple correlation. Also, when comparing probe sequences against much longer targets, the algorithm can be sped up by decomposing the target and performing multiple small FFTs in an overlap-save arrangement. Finally, by decomposing the probe sequence as well, the detection of local similarities can be further enhanced. With current advances in extremely fast hardware implementations of signal-processing operations, this approach may prove more practical than heretofore.