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The educational institutions listed below have submitted information on their bioinformatics related online courses. To post an online course offered by your institution please use this form.

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Focus Course Title University/Institution KeyInfo Goals
Bioinformatics Algorithms for DNA Sequencing Johns Hopkins University This course is delivered online on the Coursera MOOC platform. It consists of: (a) about 1 hour of lectures per week by Prof. Langmead, (b) about 1 hour of practical lectures per week by Prof. Langmead and Jacob Pritt, (c) one multiple choice homework assignment per module, (d) one programming-based homework assignment per module and (e) some optional lectures covering a broader selection of research ideas. DNA sequencing is now a ubiquitous tool in life science. You can observe this trend just by reading the news. This course examines the computational problems that come with this onslaught of DNA sequencing data. How do we take a huge collection of DNA "puzzle pieces" and assemble them into a genome? How do we make it quick and easy to find a DNA "needle" in an enormous genomic "haystack"? We will spend the bulk of the course understanding the algorithms and data structures that underlie software tools for analyzing sequencing data. The course is also an opporunity to practice programming skills and gain exposure to basic algorithms and data structures.
Bioinformatics Bioinformatics Specialization on Coursera University of California San Diego How do we sequence and compare genomes? How do we identify the genetic basis for disease? When you complete this Specialization, you will learn how to answer many questions such as these in modern biology. In the process, you wlll learn about the algorithms and software tools that thousands of biologists apply at work every day in one of the fastest growing fields in science. The Bioinformatics Specialization's printed companion, Bioinformatics Algorithms: An Active Learning Approach, is available from the textbook website (http://bioinformaticsalgorithms.com), which contains additional educational materials, including lecture videos and slides.
Bioinformatics Bioinformatics for transcriptomics The University of Manchester The course is delivered using a virtual learning environment called Moodle. This allows you to navigate and search through course notes, protocols, practicals and references to useful texts and URLs. The course notes provide background information, as web pages. Teaching and learning are then focussed around tutor-supported individual and group exercises. In this course, participants discuss a tutorial problem for each section of the course, and then submit solutions for feedback from the course tutor. The new methods for transcriptomics are bringing new challenges in bioinformatics. This course covers microarray data analysis in depth, and also introduces the areas where new work is needed for next generation sequence (RNA-seq) analysis. The sections are : * Microarrays and experimental design * Data capture and preliminary checks * Microarray data analysis * Other methods for transcriptome data capture * Gene Class Tests The course could be taken as an individual short course, for professional development, or the credits could count towards one of our Masters programmes.
Computational Biology Bioinformatics for Systems Biology University of Manchester The course is delivered using a virtual learning environment called Moodle. This allows you to navigate and search through course notes, protocols, practicals and references to useful texts and URLs. The course notes provide background information, as web pages. Teaching and learning are then focussed around tutor-supported individual and group exercises. In this course, participants discuss a tutorial problem for each section of the course, and then submit solutions for feedback from the course tutor This course is designed as an introduction to modelling for Systems Biology. It covers the range of different types of data now available for model building. The sections are : * the use of models in biology; * public pathway and interaction databases; * reconstruction of biological networks from experimental data; * network statistics; * the analysis and interpretation of experimental data in the context of biological networks; * advanced topics (optional). The course could be taken as an individual short course, for professional development, or the credits could count towards one of our Masters programmes.
Math/Statistics Mathematics for metabolic modelling University of Manchester The course is delivered using a virtual learning environment called Moodle. This allows you to navigate and search through course notes, protocols, practicals and references to useful texts and URLs. The course notes provide background information, as web pages. Teaching and learning are then focussed around tutor-supported individual and group exercises. This course aims to ensure that the successful student has an understanding of the core mathematical concepts and techniques used in mathematical modelling of biological systems; is able to express in mathematical terms simple representations of a biological system, manipulate and develop simplifying approximations of those representations in order to gain insight into the behaviour of the mathematical model and hence the real biological system; has a basic understanding of how parameters within a mathematical model are inferred from or fitted to experimental data, and the basic issues and pitfalls of model fitting. It is designed to prepare participants for our core modelling course in ’Computational simulation and analysis of biochemical networks’.
Bioinformatics Introduction to Genomic Data Science University of California, San Diego Course allowing students to understand basics of genomic data science, applying introductory algorithms to find surprisingly deep results in DNA analysis. The crux of the course is implementing these algorithms, but the text powering the course is an interactive tutoring system, featuring beginner modules that allow students to learn to program while they apply what they are learning to real applications in modern biology. In the first half of this course, we'll investigate DNA replication, and ask the question, where in the genome does DNA replication begin? You will learn how to answer this question for many bacteria using straightforward algorithms to look for hidden messages in the genome. In the second half of the course, we'll examine a different biological question, and ask which DNA patterns play the role of molecular clocks. The cells in your body manage to maintain a circadian rhythm, but how is this achieved on the level of DNA? Once again, we will see that by knowing which hidden messages to look for, we can start to understand the amazingly complex language of DNA. Perhaps surprisingly, we will apply randomized algorithms to solve problems.
Computational Biology SysMIC University College London, University of Edinburgh, Open Univers SysMIC was founded to hone the mathematical and computational skills of UK bioscientists and foster cutting-edge interdisciplinary research. The course consists of a comprehensive set of on-line training materials to provide the mathematical and computational techniques needed to analyse and interpret models of biological data. SysMIC contains three progressive modules; each takes about 6 months with an approximate workload of 5 hours per week. The modules build on each other but participants choose the level of training appropriate to them. Certificates are awarded for completion of each module. Module 1 embeds basic mathematics and programming for biological research. Module 2 contains Systems Approaches to data integration and computational modelling. Module 3 offers structured computational project work in an area of the trainee’s interest. The entry to SysMIC requires no mathematics or programming qualification beyond GCSE or equivalent. All course materials, example data, and example codes are provided online. Programming is mainly done in Matlab and R. Further information on the syllabus and example material can be seen at sysmic.ac.uk The course was officially launched across the UK to BBSRC-funded PhD students in January 2013. We are now widening participation to to include PDRAs, PIs and professionals from all areas of the biosciences and biomedicine. Module 1 registrations are held twice yearly in April and November. We welcome applications from any investigator at any stage in their career but priority will be given to those supported by the BBSRC. To apply for a place or for further information please contact us at: sysmic-team@sysmic.ac.uk Sysmic.ac.uk provides a comprehensive online course in the interdisciplinary skills which are increasingly important to cutting edge biological research.
Bioinformatics Advanced sequence analysis The University of Manchester This advanced bioinformatics course is suitable for those with a first degree in either a biological science or in computer science. It covers the most recent methods for biological sequence analysis. It could be taken as an individual short course, for professional development, and could be combined with one of more units from our theme in Computational Systems Biology :- Bioinformatics for Systems Biology Mathematics for metabolic modelling Computational Systems Biology Bioinformatics for transcriptomics. A student successfully completing four units can graduate with a Postgraduate Certificate. Those people who wish to complete the Masters degree will be required to successfully complete six modules and a research project. The course provides an introduction to the data and methods for projects requiring Next Generation Sequence data analysis. It will cover : genes, genomes and genome sequencing; technologies for high throughput sequencing; understanding the data; mapping to a genome; RNA-seq : quantification and differential expression; ChIP-seq. For practical work students will have accounts on our Galaxy server.
Math/Statistics Data Analysis for the Life Sciences Series HarvardX An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals. We will provide examples by programming in R in a way that will help make the connection between concepts and implementation. Problems sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternative when data do not fit assumptions required by the standard approaches. We will also introduce the basics of using R scripts to conduct reproducible research. This class was supported in part by NIH grant R25GM114818. Topics: Distributions Exploratory Data Analysis Inference Non-parametric statistics These courses make up 2 XSeries and are self-paced: PH525.1x: Statistics and R for the Life Sciences PH525.2x: Introduction to Linear Models and Matrix Algebra PH525.3x: Statistical Inference and Modeling for High-throughput Experiments PH525.4x: High-Dimensional Data Analysis PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays PH525.6x: High-performance computing for reproducible genomics PH525.7x: Case studies in functional genomics
Bioinformatics Biology Meets Programming: Bioinformatics for Beginners University of California, San Diego Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gentler-paced alternative to the first course in our Bioinformatics Specialization (https://www.coursera.org/specializations/bioinformatics). Each of the four weeks in the course will consist of two required components. First, an interactive textbook provides Python programming challenges that arise from real biological problems. If you haven't programmed in Python before, not to worry! We provide "Just-in-Time" exercises from the Codecademy Python track (https://www.codecademy.com/learn/python). And each page in our interactive textbook has its own discussion forum, where you can interact with other learners. Second, each week will culminate in a summary quiz. Lecture videos are also provided that accompany the material.
Math/Statistics Networks and Systems East Tennessee State University The course is intended for those with degrees in math, biology, computer science, statistics and other related scientific fields who are interested in modeling biological complexity. It is part of a 5 course certificate that is offered completely online. Topics include complex networks, centrality and global measures, random models and applications in Systems Biology. The first half of the course will cover the mathematical formulation of networks while the second half is dedicated to applications including the study of molecular biology networks.
Bioinformatics DNA Sequences: Alignments and Analysis University System of Maryland This course is delivered on-line via the edX platform. You will learn about the theory and algorithms behind DNA alignments, practice doing alignments manually, and then perform more complicated alignments using web and software based approaches. Synthesize and analyze biological big data. Theory behind alignment algorithms and how they operate Examine the roles mutations play on cellular processes
Bioinformatics Proteins: Alignment, Analysis and Structure University System of Maryland This course is delivered online via the edX platform. In this course, part of the Bioinformatics MicroMasters program, you will learn about protein structure and its impact on function, practice aligning protein sequences to discover differences, and generate model structures of proteins using web and software-based approaches. Analyze biological big data How to align protein sequences to discover differences and determine structure Generate model structures of unknown proteins
Bioinformatics Statistical Analysis in Bioinformatics University System of Maryland This course is taught online via the edX platform. In this course, part of the Bioinformatics MicroMasters program, you will learn about the R language and environment and how to use it to perform statistical analyses on biological big datasets. Basic R Programming. Applying packages in the R environment to determine changes in gene expression. Applying packages in the R environment to locate genes in a full genomic sequence.
Bioinformatics ArrayGen Technologies Online/offline Courses ArrayGen Technologies ArrayGen offers the following genomics and bioinformatics training courses with a focus on improving participants' practical applications, by using the appropriate theoretical knowledge: Bioinformatics ( Understanding Genomics ) Microarray Data analysis Next Generation Sequencing (NGS) De novo genome and transcriptome assembly Chip-Seq Data Analysis RNA-Seq Data Analysis miRNA Data Analysis Metagenomic Data Analysis MethylSeq(DNA Methylation) Data Analysis Genome Variant Data Analysis Objectives To create an awareness with respect to the basic tools and techniques used in bioinformatics at industrial level To provide complete hands-on-training in the basic tools and techniques To inspire and motivate all life sciences to apply these techniques in their research programmes
Bioinformatics Python Skills for Handling Biological Data Uganda Virus Research Institute Introduction to concepts of Python programming<br /> Biological data analysis code challenges Equipping scientists, students and interns in Bioinformatics and Computational Biology disciplines with Python programming skills for handling biological data

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