Harvard Extension School, Computer Science E-75, Prof. David Malan (Fall 2010), “This course teaches students how to build dynamic websites with Ajax and with Linux, Apache, MySQL, and PHP (LAMP), one of today's most popular frameworks. MIT, RES.6-007, Prof. Alan Oppenheim (1987), http://ocw.mit.edu/resources/res-6-007-signals-and-systems-spring-2011, “The course presents and integrates the basic concepts for both continuous-time and discrete-time signals and systems. 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. This course exposes students to techniques of abstraction at several levels: (a) within a programming language, using higher-order functions, manifest types, data-directed programming, and message-passing; (b) between programming languages, using functional and rule-based languages as examples.”. Yes For bioinformatics, Hidden Markov Models (HMMs) are terrifically important (not just for sequence profiles, but also Copy Number Variation discovery, Single Nucleotide Polymorphism genotyping, gene prediction, etc.). The course examines debates about justice prominent in moral and political philosophy, and invites students to subject their own views on these controversies to critical examination.”. Biswas, http://nptel.iitm.ac.in/video.php?subjectId=117105079, “Digital image fundamentals … Image enhancement in spatial domain … Edge detection … Image filtering in frequency domain … Image restoration … Color image processing … Morphological Image Processing … Image segmentation … Texture Analysis …”. MIT, 7.012, Profs. (See individual course descriptions above for explanations of source abbreviations and further elaboration of requirements.) The UC Davis program described in the previous entry also offers an “Image Processing and Analysis” course (http://itunes.apple.com/us/itunes-u/image-processing-analysis/id458753849). Short courses available on Videolectures.net (see Computational Seminars below) include, among others, “Learning in Computer Vision” by Prof. Simon Lucey of Carnegie Mellon University (http://videolectures.net/mlss08au_lucey_linv) and “Markov Random Fields for Vision and Graphics” by Prof. Richard Hartley of the Australian National University (http://videolectures.net/ssll09_hartley_covi). Dr. Heather Tienson of the University of California, Los Angeles, teaches the introductory course in their biochemistry series entitled “Biochemistry: Introduction to Structure, Enzymes, and Metabolism” (http://www.oid.ucla.edu/webcasts/courses/2011-2012/2012winter/chem153a-1). Stanford is planning a Coursera offering on this topic taught by an economist and computer scientist tandem, which may make it a better choice in this context (https://www.coursera.org/course/gametheory). As in academia, any gaps can be addressed, or special interests accommodated, by independent study. 2) Linear programming: a) general form, b) Simplex method, c) applications in network flow. This course goes straight to 3D graphics, using Open GL and Qt for a considerable amount of high-level coding. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. In this course, you will learn the numerical methods for the following mathematical procedures and topics - Differentiation, Nonlinear Equations, Simultaneous Linear Equations, Interpolation, Regression, Integration, and Ordinary Differential Equations. Project team organization and management.”. The theory has emerged over recent decades as essential both for the scientific analysis of algorithms in computer science and for the study of scientific models in many other disciplines, including probability theory, statistical physics, computational biology and information theory. Bioinformatics is a blend of biology, computer science, and mathematics. Indian Institute of Technology (IIT), Kharagpur, BT20001, Prof. Swagata Dasgupta, http://nptel.iitm.ac.in/video.php?subjectId=102105034, “Chemistry and metabolism of biopolymers (carbohydrates, lipids, proteins, nucleic acids, and nucleoproteins), vitamins, and hormones. Stony Brook University, Prof. Steven Skiena, CSE 547 (1999), http://www.cs.sunysb.edu/%7Ealgorith/math-video, “The mathematical analysis of algorithms uses a variety of topics from discrete mathematics—combinatorial analysis, number theory, and graph theory. As noted at the outset, students will come to online learning from different backgrounds and with different goals in mind, and moreover will have different amounts of time to devote to the process. Hal Abelson and Gerald Jay Sussman, young versions of whom can be seen delivering the full set of lectures online in 25-year-old videos (http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-001-structure-and-interpretation-of-computer-programs-spring-2005). Many students who learn bioinformatics will be exposed to the very latest advances in both biotechnology and computing, probably the two fields that result in the greatest rate of business startups, especially from academic spinoffs. How much information is needed to learn a task, how much computation is involved, and how it can be accomplished. Harvard, ER22, Prof. Michael Sandel (Fall 2008), “A critical analysis of classical and contemporary theories of justice, including discussion of present-day applications. Information theory and Kelly gambling. Prof. Jim Hefferon of Saint Michael's College has a nice introductory online textbook (http://joshua.smcvt.edu/linearalgebra). The type of independent study needed will depend on the background of the student and on the track they are following. Students learn how to set up domain names with DNS, how to structure pages with XHTML and CSS, how to program in JavaScript and PHP, how to configure Apache and MySQL, how to design and query databases with SQL, how to use Ajax with both XML and JSON, and how to build mashups. Introduction to Biology and Biochemistry or equivalent. Sooner or later, anyone doing bioinformatics is likely to have to create web pages that provide data and/or services to others. Dan Garcia and Michael Franklin (Fall 2011), http://webcast.berkeley.edu/playlist#c,d,Computer_Science,B96D778365083506, “The internal organization and operation of digital computers. Berkeley offers the course taught by Profs. Learning from course notes only, or even disembodied audio, simply doesn't have the immediacy of the visual experience of a lecture hall or even a tablet-based screencast. Berkeley's Biology 1A covers similar material plus somewhat more physiology and is available in several versions taught by a range of instructors, most recently one offered in Spring 2012 (http://webcast.berkeley.edu/playlist#c,d,Biology,CF8E59B3C769FB01). The primary attraction of this Coursera offering is its illustrious instructor, who literally wrote the book on automata (and on databases, on algorithms, etc.). Edward Bender and Gil Williamson entitled “Foundations of Combinatorics with Applications” [21]. Model organisms and forward and reverse genetics screens are then discussed, along with quantitative trait locus (QTL) and eQTL analysis. A particular piece of advice it offers is to pay special attention to doing programming projects in the biological domain. The Online Graduate Certificate in Applied Bioinformatics (ABNF-CERT) is offered as a graduate level program ideally suited for working professionals who wish to gain knowledge and practical experience in bioinformatics. The main subject areas are biological mechanisms, cell biology and medicine, developmental biology and evolution, chemical biology and biophysics, and global health and energy.”. This is a rigorous engineering approach to the subject for hard-core pixel jockeys. An Online Bioinformatics Curriculum.pdf. Introduction to Biology, Biochemistry, or equivalent. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Stanford Technology Ventures Program Entrepreneurship Corner, “The Stanford Technology Ventures Program (STVP) Entrepreneurship Corner is a free online archive of entrepreneurship resources for teaching and learning. A plus to the right of a course symbol (whether prerequisite or core) indicates that advanced work in the topic area of that course is recommended for students in that track. The Automata course would be excellent preparation. Core courses are those deemed central to the track, and should be taken if the material has not already been mastered elsewhere. Perhaps the last great barrier to self-learning is the absence of an advisor, with all that implies, and of membership in a working lab. Competing interests: The author has declared that no competing interests exist. Profs. The purpose of this course is to provide fluency with summations, congruences, generating functions, graph theory, and other tools of the trade. IIT Kharagpur through NPTEL offers a course in “Chaos, Fractals, and Dynamic Systems” by Prof. Soumitro Banerjee that is similarly exhaustive but approaches the subject from an engineering perspective (http://nptel.iitm.ac.in/video.php?subjectId=108105054). This anticipated Coursera entry promises to touch on all the “hot topics” in genomics, chip technologies, and next-generation sequencing, making it central to this curriculum. Coursera will also have the first part of the Stanford algorithms sequence, by Prof. Tim Roughgarden (https://www.coursera.org/course/algo). This is a relatively short but well-constructed course that was yet another variation on Stanford Engineering's courseware initiatives. Christos Papadimitriou (who has bioinformatics papers as well as several textbooks among his publications) and Satish Rao (http://itunes.apple.com/itunes-u/computer-science-170-001-spring/id496893325). Moreover, cognitive psychology offers both a theoretical basis and empirical evidence for the benefits of multimedia learning [3]. This course provides a biologist's approach to computational biology, and is thus listed separately from a corresponding course in the Computer Science Department. The curriculum for UMGC’s biotechnology master’s degree with a specialization in bioinformatics was designed with input from scholars, industry experts, and employers. Measurement … Correlation. See also a seminar by Dr. Sangeeta Bhatia on “Tissue Engineering” (http://www.ibioseminars.org/lectures/cell-bio-a-med/sangeeta-bhatia.html). The educational institutions listed below have submitted information on their bioinformatics … This track is meant to afford the capability to develop standalone tools of significant sophistication for bioinformatics analysis, visualization, presentation, and local data management. David Easley and Jon Kleinberg is excellent, and a prepublication draft is available online for free [23]. A more exhaustive introduction to the algorithms (but with no coding) is provided by IIT Madras Prof. Sukhendu Das in “Computer Graphics” via NPTEL (http://nptel.iitm.ac.in/video.php?subjectId=106106090). For the basics, there are countless economics courses online, but the Annenberg Center has a particularly nicely produced overview (http://www.learner.org/resources/series79.html). San Diego's Prof. Ronald Graham, Stanford's Prof. Donald Knuth and Oren Patashnik [18]. Berkeley, CS 188, Prof. Pieter Abbeel (Spring 2012), http://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?id=496298636, “Basic ideas and techniques underlying the design of intelligent computer systems. This track begins with the analyst skill set but goes further to enable more sophisticated analyses of datasets that are especially complex, for example, by virtue of being very large scale, noisy, high-dimensional, semantically rich, poorly organized or integrated, etc. Specification and documentation. His self-published book [41] and web site are actually entitled “Learning from Data,” which gives a better sense of the relevance of the course to bioinformatics than does the Provider Description above. Stanford offers a course more narrowly focused on GPUs (http://itunes.apple.com/itunes-u/programming-massively-parallel/id384233322?mt=2) as well as shorter practical courses in GPUs (http://classx.stanford.edu/ClassX/system/users/web/pg/view_subject.php?subject=NVIDIA_ICME_SPRING_2010_2011), Hadoop (http://classx.stanford.edu/ClassX/system/users/web/pg/view_subject.php?subject=HADOOP_WINTER_2010_2011), and the Amazon Cloud (http://classx.stanford.edu/ClassX/system/users/web/pg/view_subject.php?subject=AEC2_WINTER_2010_2011). Harvard, Statistics 110, Prof. Joseph Blitzstein (Fall 2011), http://itunes.apple.com/us/course/statistics-110-probability/id502492375, “A comprehensive introduction to probability. The current version of this course touches on not only parallelism but Cloud computing, also very relevant to bioinformatics. It will be closely based on the long-established core course in the Penn Graduate Group in Genomics and Computational Biology, and in fact the instructors plan to use the material with their own students. Students will have some exposure to graph theory in both this course and the Algorithms course, but can find a much fuller treatment in the short course “Graph Theory and Network Analysis” taught by Prof. Paul van Dooren of the Université Catholique de Louvain (http://www.networkmaths.ie/videos/list_videos.php?course=gra), which covers not only the math (at a fairly intuitive level) but also its application to practical problems such as graph similarity, ranking, clustering, etc. The University of Illinois at Urbana-Champaign conducted a Summer School on “Computational Approaches for Simulation of Biological Systems” in 2003 that posted a number of videos relating to biophysical modeling and bioinformatics analyses of macromolecular structures, a topic otherwise underrepresented here (http://www.ks.uiuc.edu/Training/SumSchool/lectures.html). Despite the title of this course, it brings hardware into the picture only as it relates to designing fast and memory-efficient code. No, Is the Subject Area "Lectures" applicable to this article? No, Is the Subject Area "Systems biology" applicable to this article? Introduction to Algorithms is recommended, though Prof. Skiena encourages the participation of biologists. Prof. Ullman recommends portions of his free online textbook “Foundations of Computer Science” as preparation [15]. Not only is natural language processing (NLP) technology important in biological text mining applications, but grammars and parsing are relevant to several aspects of sequence analysis. On Coursera from the University of Pennsylvania, Prof. Ezekiel Emanuel has a timely course on “Health Policy and the Affordable Care Act” (https://www.coursera.org/course/healthpolicy), while his colleague Prof. Jonathan Moreno will be covering the interaction of neurosciences with ethics for “Neuroethics” (https://www.coursera.org/course/neuroethics). Students should obtain and become proficient in machine learning tools, which can be done from R or Octave (as a free alternative to MatLab) environments (see above). Princeton on Coursera, Prof. Andrew Conway (Fall 2012), “Statistics One is designed to be a friendly introduction to very simple, very basic, fundamental concepts in statistics … Random sampling and assignment. 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. Information for Current Students Learn and complete your Professional MS Bioinformatics degree! There is an accompanying set of very polished course notes (http://cs229.stanford.edu/materials.html). Markov chains: transition probabilities, stationary distributions, convergence.”. A more pertinent question is whether an online education is an adequate substitute for what is termed a resident education, in general and in the particular case of bioinformatics. Yale, EEB122, Prof. Stephen Stearns (Spring 2009), http://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122, “This course presents the principles of evolution, ecology, and behavior for students beginning their study of biology and of the environment … Recent advances have energized these fields with results that have implications well beyond their boundaries: ideas, mechanisms, and processes that should form part of the toolkit of all biologists and educated citizens.”. While these may be overkill for bioinformatics, it might just inspire some to seek deeper insights into structures in large datasets. 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