Prof. Wickert (see above) has also created an advanced video course on “Statistical Signal Processing” that again has good notes (http://www.eas.uccs.edu/wickert/ece5615). 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). Stanford, Prof. Jennifer Widom (Fall 2011), “This course covers database design and the use of database management systems for applications. This is a rigorous engineering approach to the subject for hard-core pixel jockeys. 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. Prof. Mattuck's development of the subject is fairly traditional, but is supplemented by updated “wrappers” in the MIT courseware that provide helpful visualizations and simulations of the sort to which many modern treatments of the subject are trending. An Online Bioinformatics Curriculum.pdf. Core courses are those deemed central to the track, and should be taken if the material has not already been mastered elsewhere. Bioinformatics is the application of mathematical, statistical, and computational approaches to understand biological processes. UC San Diego Department of Bioengineering 9500 Gilman Drive, MC 0412 La Jolla, CA 92093-0412 We identify below a set of five possible tracks, noting two-letter abbreviations used in Tables 1–4 where the recommended distributions of courses for each track are indicated using symbols defined in the key at the bottom of each table. Yes In each case the main course offering for a given topic was adjudged superior to the alternatives based on a variety of criteria including coverage, production quality, availability of ancillary course material, and incorporation of the latest modular courseware technologies described above. Prof. Strang is a legend as an educator, charmingly diffident in his delivery yet never lacking in clarity. In several cases, courses were selected as main offerings despite being scheduled but not yet online; such judgments were made based on instructors' proven teaching backgrounds and in some instances after direct consultation with them on the syllabi. Available via license: CC BY 4.0. It is an individual effort by a math professor, in screencast format, with a wealth of ancillary web resources including training in MatLab. Is the Subject Area "Bioinformatics" applicable to this article? However, the field of bioinformatics by its nature may offer the best chance for finding ways to involve distance learners directly in ongoing scientific research, and that would seem to be a worthy goal for the burgeoning online education movement. One flavor of stochastic processes that is especially important in bioinformatics is taught in “Introduction to Markov Processes” by Prof. Christof Schutte, head of the Biocomputing Group at the Freie Universität Berlin (http://www.networkmaths.ie/videos/list_videos.php?course=mar). One undeniable truism is that independent study requires motivation and discipline in the extreme. One possible direction to go from here is into the realm of iPhones and iPads. This material should be considered core to bioinformatics of any stripe. For enterprise-wide bioinformatics programming Java is the language of choice, and the class text, “Head First Java” [34], is reputed to be one of the least painful ways to learn this (or any) language—high praise indeed. Nor does classroom learning by itself, virtual or otherwise, fully prepare one for establishing real-world error models, dealing with missing data, establishing a statistical case for some result, arguing and defending scientific positions, navigating the publication process, and sundry other practical skills. For a treatment of probability, statistics, and stochastic processes that makes reference to bioinformatics throughout, see the book “Statistical Methods in Bioinformatics” by University of Pennsylvania Prof. Warren Ewens and Gregory Grant [13]. Indian Institute of Technology (IIT) Kharagpur, EC61501, Prof. P.K. This series of 13 extended guest lecturers in course format is offered every other year by the National Human Genome Research Institute (NHGRI) of the U.S. National Institutes of Health (NIH). A friendlier user environment is provided by tools like Weka (http://www.cs.waikato.ac.nz/ml/weka), widely used in teaching, or Orange, which has add-ons for bioinformatics and text mining (http://orange.biolab.si); both are open source. Besides introducing machine learning, which should be pursued further in the next course listed, this course introduces knowledge representation, important as a foundation for biological ontologies; Bayesian nets, useful in biological network causal analysis; and natural language understanding, which is highly relevant to biomedical text mining. 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. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.”. The book “Human Molecular Genetics” by Drs. Linear Algebra and Probability are helpful. It now teaches Python 3 (after many years of using Scheme, a LISP dialect and thus more purely functional) to get across the “big ideas” of programming, covering design principles, analysis of performance, confirmation of correctness, and management of complexity. Yes Qualified students have the option to test out of BMI 713, Computing Skills for Biomedical Sciences, by completing an online assessment 1 month prior to matriculation. The continuation of the first-year Berkeley program, Biology 1B, spends a third of the course covering plant biology in more detail than is necessary for bioinformatics, but also provides a solid introduction to genetics and phylogeny that may be preferred as being more molecular (http://webcast.berkeley.edu/playlist#c,d,Biology,434C6A29FA3A4580). In a somewhat different vein, the non-profit Saylor Foundation compiled a comprehensive online university curriculum comprising courses that are essentially mashups of video and text resources from many existing sources, including a number of those described above (http://www.saylor.org). Perhaps the best way to approach this is for students to make a habit of reading the key journals in their field so as to discover systematic gaps in their knowledge. Oregon State University offers a two-term course in “General Biochemistry” taught by Dr. Kevin Ahern, both of which are available, but the visuals are sometimes unclear (http://www.youtube.com/playlist?list=PL850269AA28EF394A and http://www.youtube.com/playlist?list=PL347B70A1CC0D91C6). This full-time, 36-credit-hour doctoral program integrates biology, chemistry, computer science, mathematics and statistics in a research-focused curriculum. And in May of 2012, barely six months after MIT had rolled out its new MITx platform, they and Harvard announced that the institutions were investing $30 million each in a joint online learning initiative called edX (http://www.edxonline.org). degree will be required to fulfill the Bioinformatics and Computational Biology M.S. Armando Fox and David Patterson (Spring 2012), http://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?id=496893325, “Ideas and techniques for designing, developing, and modifying large software systems. He has helped design academic curricula as part of a major training grant and taught at both an undergraduate and graduate level, though not extensively, having spent most of his career in the computer and then the pharmaceutical industries. The Ph.D. in Bioinformatics is a 90-credit-hour program that includes core courses, research rotations, the choice of a minor, qualifying examinations, and a dissertation. The “EMBO Practical Course on Analysis of High-Throughput Sequence Data” (http://www.ebi.ac.uk/training/online/course/embo-practical-course-analysis-high-throughput-seq) is highly recommended as a hands-on introduction to modern genomic analysis. 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.”. It closely coordinates video lectures with detailed analysis exercises, with tutorial handouts and code supplied, using R and Bioconductor. For more information about PLOS Subject Areas, click A still more comprehensive treatment of graph theory proper is offered by Prof. L. Sunil Chandran of IISc Bangalore through NPTEL (http://nptel.iitm.ac.in/courses/106108054). I will introduce basic concepts in network theory, discuss metrics and models, use software analysis tools to experiment with a wide variety of real-world network data, and study applications to areas such as information retrieval.”. Stanford's first course is “Programming Methodology,” which teaches Java by jumping in the deep end, paying a fair amount of attention along the way to good software engineering practice (http://see.stanford.edu/SEE/courseinfo.aspx?coll=824a47e1-135f-4508-a5aa-866adcae1111). Students should supplement the course with the seminar “Combinatorial Optimization in Bioinformatics” by Prof. Clarisse Dhaenens of the University of Lille (http://videolectures.net/prib2010_dhaenens_oaab). Profs. Online Bioinformatics Courses and Programs. 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. The current version of this course touches on not only parallelism but Cloud computing, also very relevant to bioinformatics. Yale, ECON 159, Prof. Ben Polak (Fall 2007), “This course is an introduction to game theory and strategic thinking. Programming skills in Python or Java. The curriculum offered here is slanted toward systems biology in this regard, but individuals may prefer to study topics such as evolutionary dynamics or mathematical genetics that would require additional study. Adam Arkin and John Doyle gave “A Short Course on Mathematical Modeling of Signaling Mechanisms in Biology” at NIH (http://videocast.nih.gov/launch.asp?9948). Just as the Amazon Cloud now makes large-scale computing accessible and economically feasible without the support of a large institutional data center, the decreasing cost of sequencing technology and the synthetic biology movement are both suggestive of the possibility of analogous sorts of remote biology. Perhaps most importantly, as Prof. Ullman points out, surveys of Stanford grads show that this course was one of the most useful in their subsequent careers, for the mindset it engendered in solving many real-world computational challenges. These individuals would be well advised to take on substantial projects in the biological domain that go beyond the requirements of the courses taken. Bioinformatics PhD. The graduate certificate program in bioinformatics at University of Maryland Global Campus can help prepare you to become a qualified bioinformatics professional for public or private-sector organizations. Rangarajan, http://nptel.iitm.ac.in/courses/104108056, “[Topics include] cis-acting elements and trans-acting factors … domain structure of eukaryotic transcription factors … role of chromatin … synthesis of mRNA, rRNA, and tRNA … cell surface receptors … intracellular receptors … regulation of gene expression during development … recombinant protein expression systems … gene therapy and transgenic technology …”. Scientists and engineers must know how to model the world in terms of differential equations, and how to solve those equations and interpret the solutions. Tom Strachan and Andrew Reed, now in its 4th edition, goes deeper into modern techniques [5]. The educational institutions listed below have submitted information on their bioinformatics … Topics include heuristic search, problem solving, game playing, knowledge representation, logical inference, planning, reasoning under uncertainty, expert systems, learning, perception, language understanding.”. The course explores issues of security, scalability, and cross-browser support and also discusses enterprise-level deployments of websites, including third-party hosting, virtualization, colocation in data centers, firewalling, and load-balancing.”. Unfortunately a few lectures are missing, but all the slides are separately available (http://perso.uclouvain.be/paul.vandooren/DublinCourse.pdf). 20+ Experts have compiled this list of Best +Free Bioinformatics Course, Masters, Training, Class and Certification available online for 2020. By way of evidence, a suggested curriculum will be laid out that is supported by existing online resources. Asymptotic equipartition property. This track is meant to afford the capability to develop standalone tools of significant sophistication for bioinformatics analysis, visualization, presentation, and local data management. The fundamental question of the optimal content for bioinformatics training would probably elude universal consensus in any case, and perhaps the most that can be hoped for is that what follows will contribute meaningfully to the dialogue. This one is chosen somewhat arbitrarily, but in particular because it makes use of Python. Among a number of resources inspired by the recent Darwin centennial, one of the best is the Stanford course “Darwin's Legacy” (http://www.youtube.com/playlist?list=PLF2E17B4CDCCE15F5). The Degree Development Workshop proposed a set of core and elective modules that should be incorporated into a degree program in bioinformatics by coursework and dissertation. MIT offers “Genomics and Computational Biology” by Prof. George Church (http://ocw.mit.edu/courses/health-sciences-and-technology/hst-508-genomics-and-computational-biology-fall-2002), but the online version is now 10 years old, and is audio-only so that the user must coordinate the lecture with a separate, rather massive set of slides. A somewhat more detailed (but also considerably more protracted) treatment of basic research statistics is to be found in Berkeley Prof. Frederic Theunissen's “Research and Data Analysis in Psychology” (http://www.youtube.com/view_play_list?p=A07B0BAB1D82C53C). Signal and system representations are developed for both time and frequency domains. This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. MIT, 18.03SC, Prof. Arthur Mattuck (Fall 2011), http://ocw.mit.edu/courses/mathematics/18-03sc-differential-equations-fall-2011, “The laws of nature are expressed as differential equations. Bioinformatics methods depend on statistics to a much greater degree and in much greater depth than biologists typically encounter in their training for analysis of variance and experimental design. There is a more classical and in-depth database course by Profs. Students should first take Learning Systems or similar. Several topics that fall under the rubric of discrete math are covered more extensively by other courses in this curriculum, such as “Introduction to Probability” and “Analytic Combinatorics.” Additional topics in discrete math include Boolean algebra and mathematical logic, which are very well-covered in a Coursera offering by Stanford Prof. Michael Genesereth (https://www.coursera.org/course/intrologic). A particular piece of advice it offers is to pay special attention to doing programming projects in the biological domain. Despite the title of this course, it brings hardware into the picture only as it relates to designing fast and memory-efficient code. Countless aggregators also assemble collections of video courses, but generally with little value added. It should be emphasized that different institutions and individuals may have other views on bioinformatics curricula, disagreeing on appropriate electives and even on core courses. This course focuses first on GPU programming with CUDA and then on MapReduce/Hadoop programming on the Amazon Cloud. No bioinformatics professional dealing with high-dimensional data can afford to neglect an understanding of matrix math, with many bioinformatics methods currently making use of various matrix factorizations, transformations, decompositions, and eigenwhatevers. However, the mathematical depth of this course will only be necessary for serious theorists. No, Is the Subject Area "Human learning" applicable to this article? Note that in all cases iTunes has the order of courses reversed in its listing. 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. California Institute of Technology, CS 156, Prof. Yaser Abu-Mostafa (Spring 2012), “Introduction to the theory, algorithms, and applications of automated learning. Udacity offers a “Web Application Engineering” course taught by web entrepreneur Steve Huffman (http://www.udacity.com/overview/Course/cs253). These principles are necessary to understanding the basic mechanisms of life and anchor the biological knowledge that is required to understand many of the challenges in everyday life, from human health and disease to loss of biodiversity and environmental quality.”. 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. https://doi.org/10.1371/journal.pcbi.1002632.t001, https://doi.org/10.1371/journal.pcbi.1002632.t002, https://doi.org/10.1371/journal.pcbi.1002632.t003, https://doi.org/10.1371/journal.pcbi.1002632.t004. Students who are admitted directly after a B.S. Students may test out of these. Distributions … Descriptive statistics. 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.”. It's hard to imagine a better way for biologists to be introduced to the theory of computation. With this video course we introduce a resource developed by the Indian National Programme on Technology Enhanced Learning (NPTEL), whose ambition is “to build at least one version of each course offered in all of Science and Engineering in India, from BTech/BSc to PhD programs” (http://nptel.iitm.ac.in). The course makes use of the free statistical software package R (http://www.r-project.org), which bioinformatics practitioners should have in their toolbox not only for classical statistical tests taught here but for more advanced applications such as linear and nonlinear modeling, time-series analysis, classification, clustering, etc. For biologists possessing only end-user experience with computers, several courses are available that offer a modest introduction to actual programming, generally in the context of an overview of computer science. A textbook entitled “A Short Course in Discrete Mathematics” is now available online for free, and offers a traditional approach by U.C. Elementary principles of software engineering. Sample code is provided in each of Maple, MathCad, Mathematica, and MatLab, none of which are free, but the Octave free software package (http://www.gnu.org/software/octave) closely approaches the core functionality of MatLab, which is heavily used in this and several other listed courses for numerical computation and matrix math. Introduction to Biology. Storage management. The book “Introduction to Statistical Signal processing” by Stanford Prof. Robert Gray and University of Maryland Prof. L. D. Davisson is freely available online [31]. Apple has also put its distinctive stamp on online learning with iTunes U (http://www.apple.com/education/itunes-u), also organized by institution but with integrated search capability and, of course, deployment to iPad and iPhone apps. This well-produced introductory course was actually taught by Purdue faculty in a summer program at Trinity College Dublin, the Network Mathematics Graduate Programme, along with several other courses listed in this curriculum. Texas A&M, Math 614, Prof. Michael Pilant (2004), http://www.math.tamu.edu/~mpilant/math614, “Discrete maps; continuous flows; dynamical systems; Poincare maps; symbolic dynamics; chaos, strange attractors; fractals; computer simulation of dynamical systems.”, This should be considered an advanced elective for mathematically talented students interested in a deep understanding of dynamical systems modeling in biology. Next we introduce principles of genetics and then apply them in clinical genetics and other large-scale sequencing projects. This is a relatively short but well-constructed course that was yet another variation on Stanford Engineering's courseware initiatives. Scientists in any quantitative field probably ought to be familiar with such basic ideas as the prisoners' dilemma, Pareto optimality, and Nash equilibria, or indeed with any field that has produced eight Nobel prizes. Statistical power … t-tests, chi-square tests. Less than two years ago, the author published an online bioinformatics curriculum in this journal and made the claim (with some important caveats) that a sufficient number and variety of free video courses had made their way to the web that it was possible to obtain a reasonably comprehensive bioinformatics education on one's laptop .In that compilation of courseware, only a … here. University of Pennsylvania on Coursera, Prof. Emma Meagher (Summer 2012), “This [course] will discuss the discipline of pharmacology and its integration throughout medical science. Many additional key database topics from the design and application-building perspective are also covered: indexes, views, transactions, authorization, integrity constraints, triggers, on-line analytical processing (OLAP), and emerging ‘NoSQL’ systems.”. The charismatic Prof. N.J. Wildberger of the University of New South Wales offers a similar course (http://www.youtube.com/playlist?list=PL01A21B9E302D50C1). UC Davis has a course by Prof. Dan Gusfield, who has also published a book on computational biology algorithms [36] and includes two lectures on RNA folding in his discussion of dynamic programming (http://www.cs.ucdavis.edu/~gusfield/cs122f10/videolist.html). Dan Jurafsky and Christopher Manning (TBA), “This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering. It includes both paid and free resources to help you learn Bioinformatics and these courses are suitable for beginners, intermediate learners as well as experts. Introduction to Biology, Biochemistry, or equivalent. ... Bioinformatics and Clinical Informatics. As noted in the introduction, a well-publicized live course by Stanford's Prof. Sebastian Thrun and Google's Peter Norvig was offered in the Fall of 2011 (https://www.ai-class.com); the lectures and quizzes are now accessible on YouTube but in a rather awkward format. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. Game theory has long been used in the study of evolutionary dynamics, an increasingly important field, and backward induction is a generalization of the same sort of dynamic programming used in biological sequence analysis, applied to such problems as choosing optimal strategies in sports. For the former, a home computer with a high-end Nvidia GPU should be sufficient (the pyCUDA Python binding is used), though online students will of course not have access to the GPU cluster used in the course. Model organisms and forward and reverse genetics screens are then discussed, along with quantitative trait locus (QTL) and eQTL analysis. While it would be a shame to miss the chance to learn this material at the feet of the esteemed Prof. Cover, this newer version will provide the distinct benefits of a structured, modular format. However, Prof. Thrun now has a similar AI course on Udacity, which uses Python and is keyed to programming a robotic car (http://www.udacity.com/overview/Course/cs373). We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.”. 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. One, by Prof. Sean Luke of George Mason University, offers general coverage [25], while another by Profs. Without a doubt there are gaps, and quality is not uniform. The courses above offer taster menus of various aspects of computer science and only basic programming skills, and as such are appropriate for bioinformatics professionals who need exposure to programming but will not be doing it for a living. Information for Current Students Learn and complete your Professional MS Bioinformatics degree! Ideally this would include exposure to laboratory science, which of course is unlikely in the case of online learners. No, Is the Subject Area "Human genomics" applicable to this article? Despite the name, this course also extends to formal language theory and introduces tractability. Nevertheless applications can be found and are emerging in systems biology, modeling, experimental design, metabolic engineering, and now synthetic biology. It currently offers some 110 full video courses, skewed toward engineering, but with plans for up to 400 total. Bioinformatics is a blend of multiple areas of study including biology, data science, mathematics and computer science. 2) Linear programming: a) general form, b) Simplex method, c) applications in network flow. There are a number of viable alternatives. Thus, a useful adjunct to online learning in bioinformatics might be a portfolio of suggested projects based on real-world datasets that would help exercise the skills of trainees, perhaps in the context of an online community of peers. Linear Algebra, Data Structures, strong programming skills. Shortly afterwards, MIT set up a similar approach on a new platform called MITx, offering a course in electronic circuits that attracted comparable numbers of students (https://6002x.mitx.mit.edu). The student will learn the C language, mainly because it is close to the machine, and this is still very important to bioinformatics developers who need to tune the performance of compute-intensive applications. 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. As such, this is an appropriate forum to assess the current potential for a freely accessible online bioinformatics education. UC Irvine also offers a beginning course by Prof. James Nowick, more tightly focused on straight organic chemistry (http://ocw.uci.edu/courses/Chemistry-51A-Organic-Chemistry.aspx). For an exploration of the interface of systems biology with pharmacology, the two-day NIH workshop on “Quantitative and Systems Pharmacology” held in 2008 is still very relevant (http://videocast.nih.gov/launch.asp?14673 and http://videocast.nih.gov/launch.asp?14674). UCLA offers “Probability for Life Science” (Math 3C), a somewhat gentler approach to the topic, taught by the late Prof. Herbert Enderton (best known for his work in mathematical logic) (http://www.youtube.com/playlist?list=PL5BE09709EECF36AA&feature=plcp). Game theory also bears on modeling and network theory. Eric Lander, Robert Weinberg, Tyler Jacks, Hazel Sive, Graham Walker, Sallie Chisholm, and Dr. Michelle Mischke (Fall 2011), http://ocw.mit.edu/courses/biology/7-01sc-fundamentals-of-biology-fall-2011, “Fundamentals of Biology focuses on the basic principles of biochemistry, molecular biology, genetics, and recombinant DNA. 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. Exposure to aspects of discrete math, especially proof techniques and basic probability theory, that would be well satisfied by the Automata and Introduction to Probability courses above. Introduction to the Java programming language.”. Kolmogorov complexity. Basic elements of the theory are important in machine learning approaches to data mining and appear frequently in bioinformatics tools and algorithms, including sequence motif analysis and many other applications. 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