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1 | Bioinformatic Contents - Courses | List of online material in the field of bioinformatics: Computer Science; Mathematics and Biology. | Last date of change: 05/01/2017 | ||
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3 | Area | Institution | Course | Description | Link |
4 | Computer Science | Johns Hopkins University | Introduction to Genomic Technologies | This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed. | https://www.coursera.org/learn/introduction-genomics |
5 | Computer Science | Johns Hopkins University | Genomic Data Science with Galaxy | Learn to use the tools that are available from the Galaxy Project. This is the second course in the Genomic Big Data Science Specialization. | https://www.coursera.org/learn/galaxy-project |
6 | Computer Science | Johns Hopkins University | Python for Genomic Data Science | This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University. | https://www.coursera.org/learn/python-genomics |
7 | Computer Science | Johns Hopkins University | Algorithms for DNA Sequencing | We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets. | https://www.coursera.org/learn/dna-sequencing |
8 | Computer Science | Johns Hopkins University | Command Line Tools for Genomic Data Science | Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University. | https://www.coursera.org/learn/genomic-tools |
9 | Computer Science | Johns Hopkins University | Bioconductor for Genomic Data Science | Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University. | https://www.coursera.org/learn/bioconductor |
10 | Computer Science | Johns Hopkins University | Statistics for Genomic Data Science | An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University. | https://www.coursera.org/learn/statistical-genomics |
11 | Computer Science | Johns Hopkins University | Genomic Data Science Capstone | In this culminating project, you will deploy the tools and techniques that you've mastered over the course of the specialization. You'll work with a real data set to perform analyses and prepare a report of your findings. | https://www.coursera.org/learn/genomic-data-science-project |
12 | Computer Science | University of California, San Diego | Finding Hidden Messages in DNA (Bioinformatics I) | In the first half of the course, we investigate DNA replication, and ask the question, where in the genome does DNA replication begin? We will see that we can answer this question for many bacteria using only some straightforward algorithms to look for hidden messages in the genome. In the second half of the course, we examine a different biological question, when we 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, which roll dice and flip coins in order to solve problems. Finally, you will get your hands dirty and apply existing software tools to find recurring biological motifs within genes that are responsible for helping Mycobacterium tuberculosis go "dormant" within a host for many years before causing an active infection. | https://www.coursera.org/learn/dna-analysis |
13 | Computer Science | University of California, San Diego | Genome Sequencing (Bioinformatics II) | You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance. | https://www.coursera.org/learn/genome-sequencing |
14 | Computer Science | University of California, San Diego | Comparing Genes, Proteins, and Genomes (Bioinformatics III) | Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST. | https://www.coursera.org/learn/comparing-genomes |
15 | Computer Science | University of California, San Diego | Molecular Evolution (Bioinformatics IV) | In the previous course in the Specialization, we learned how to compare genes, proteins, and genomes. One way we can use these methods is in order to construct a "Tree of Life" showing how a large collection of related organisms have evolved over time. In the first half of the course, we will discuss approaches for evolutionary tree construction that have been the subject of some of the most cited scientific papers of all time, and show how they can resolve quandaries from finding the origin of a deadly virus to locating the birthplace of modern humans. In the second half of the course, we will shift gears and examine the old claim that birds evolved from dinosaurs. How can we prove this? In particular, we will examine a result that claimed that peptides harvested from a T. rex fossil closely matched peptides found in chickens. In particular, we will use methods from computational proteomics to ask how we could assess whether this result is valid or due to some form of contamination. Finally, you will learn how to apply popular bioinformatics software tools to reconstruct an evolutionary tree of ebolaviruses and identify the source of the recent Ebola epidemic that caused global headlines. | https://www.coursera.org/learn/molecular-evolution |
16 | Computer Science | University of California, San Diego | Genomic Data Science and Clustering (Bioinformatics V) | How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters. In the first half of the course, we will introduce algorithms for clustering a group of objects into a collection of clusters based on their similarity, a classic problem in data science, and see how these algorithms can be applied to gene expression data. In the second half of the course, we will introduce another classic tool in data science called principal components analysis that can be used to preprocess multidimensional data before clustering in an effort to greatly reduce the number dimensions without losing much of the "signal" in the data. Finally, you will learn how to apply popular bioinformatics software tools to solve a real problem in clustering. | https://www.coursera.org/learn/genomic-data-science-project |
17 | Computer Science | University of California, San Diego | Finding Mutations in DNA and Proteins (Bioinformatics VI) | In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory. In the second half of the course, we will learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable. This is the case, for example, in HIV studies, since the virus often mutates so quickly that researchers can struggle to study it. The approach we will use is based on a powerful machine learning tool called a hidden Markov model. Finally, you will learn how to apply popular bioinformatics software tools applying hidden Markov models to compare a protein against a related family of proteins. | https://www.coursera.org/learn/dna-mutations |
18 | Computer Science | University of California, San Diego | Bioinformatics Capstone: Big Data in Biology | In this course, you will learn how to use the BaseSpace cloud platform developed by Illumina (our industry partner) to apply several standard bioinformatics software approaches to real biological data. In particular, in a series of Application Challenges will see how genome assembly can be used to track the source of a food poisoning outbreak, how RNA-Sequencing can help us analyze gene expression data on the tissue level, and compare the pros and cons of whole genome vs. whole exome sequencing for finding potentially harmful mutations in a human sample. Plus, hacker track students will have the option to build their own genome assembler and apply it to real data! | https://www.coursera.org/learn/bioinformatics-project |
19 | Computer Science | University of Manchester | Bioinformatics to Transcriptomics | The place of microarray data analysis in current research in the life sciences in outlined in the talk 'Microarrays: their design and use'. (Tomlinson, C.R. (2009). As in the introductory talk, this course focuses on microarray analysis in the context of modelling for a systems approach to biology. Course participants will work through practical examples using maxdview, a visualisation environment for microarray data from The University of Manchester, and/or Bioconductor. The course is designed to be taken either as a stand-alone course for those already working with microarrays, or as a final module for those working towards a formal qualification in bioinformatics or systems biology. In the context of a full programme, the module links to the earlier module Bioinformatics for Systems Biology. | http://www.cs.manchester.ac.uk/study/professional-development/study-options/distance-learning/digital-biology/course-modules/biol61010/ |
20 | Computational Biology | Icahn School of Medicine at Mount Sinai | Introduction to Systems Biology | This course will introduce the student to contemporary Systems Biology focused on mammalian cells, their constituents and their functions. Biology is moving from molecular to modular. As our knowledge of our genome and gene expression deepens and we develop lists of molecules (proteins, lipids, ions) involved in cellular processes, we need to understand how these molecules interact with each other to form modules that act as discrete functional systems. These systems underlie core subcellular processes such as signal transduction, transcription, motility and electrical excitability. In turn these processes come together to exhibit cellular behaviors such as secretion, proliferation and action potentials. What are the properties of such subcellular and cellular systems? What are the mechanisms by which emergent behaviors of systems arise? What types of experiments inform systems-level thinking? Why do we need computation and simulations to understand these systems? The course will develop multiple lines of reasoning to answer the questions listed above. Two major reasoning threads are: the design, execution and interpretation of multivariable experiments that produce large data sets; quantitative reasoning, models and simulations. Examples will be discussed to demonstrate “how” cell- level functions arise and “why” mechanistic knowledge allows us to predict cellular behaviors leading to disease states and drug responses. | https://www.coursera.org/learn/systems-biology |
21 | Computational Biology | Icahn School of Medicine at Mount Sinai | Experimental Methods in Systems Biology | Learn about the technologies underlying experimentation used in systems biology, with particular focus on RNA sequencing, mass spec-based proteomics, flow/mass cytometry and live-cell imaging. A key driver of the systems biology field is the technology allowing us to delve deeper and wider into how cells respond to experimental perturbations. This in turns allows us to build more detailed quantitative models of cellular function, which can give important insight into applications ranging from biotechnology to human disease. This course gives a broad overview of a variety of current experimental techniques used in modern systems biology, with focus on obtaining the quantitative data needed for computational modeling purposes in downstream analyses. We dive deeply into four technologies in particular, mRNA sequencing, mass spectrometry-based proteomics, flow/mass cytometry, and live-cell imaging. These techniques are often used in systems biology and range from genome-wide coverage to single molecule coverage, millions of cells to single cells, and single time points to frequently sampled time courses. We present not only the theoretical background upon which these technologies work, but also enter real wet lab environments to provide instruction on how these techniques are performed in practice, and how resultant data are analyzed for quality and content. | https://www.coursera.org/learn/experimental-methods |
22 | Computational Biology | Icahn School of Medicine at Mount Sinai | Network Analysis in Systems Biology | An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory (http://icahn.mssm.edu/research/labs/maayan-laboratory) from the Icahn School of Medicine at Mount Sinai, but also other freely available data analysis and visualization tools. The ultimate aim of the course is to enable participants to utilize the methods presented in this course for analyzing their own data for their own projects. For those participants that do not work in the field, the course introduces the current research challenges faced in the field of computational systems biology. | https://www.coursera.org/learn/network-biology |
23 | Computational Biology | Icahn School of Medicine at Mount Sinai | Dynamical Modeling Methods for Systems Biology | An introduction to dynamical modeling techniques used in contemporary Systems Biology research. We take a case-based approach to teach contemporary mathematical modeling techniques. The course is appropriate for advanced undergraduates and beginning graduate students. Lectures provide biological background and describe the development of both classical mathematical models and more recent representations of biological processes. The course will be useful for students who plan to use experimental techniques as their approach in the laboratory and employ computational modeling as a tool to draw deeper understanding of experiments. The course should also be valuable as an introductory overview for students planning to conduct original research in modeling biological systems. This course focuses on dynamical modeling techniques used in Systems Biology research. These techniques are based on biological mechanisms, and simulations with these models generate predictions that can subsequently be tested experimentally. These testable predictions frequently provide novel insight into biological processes. The approaches taught here can be grouped into the following categories: 1) ordinary differential equation-based models, 2) partial differential equation-based models, and 3) stochastic models. | https://www.coursera.org/learn/dynamical-modeling |
24 | Computational Biology | Icahn School of Medicine at Mount Sinai | Integrated Analysis in Systems Biology | This course will focus on developing integrative skills through directed reading and analysis of the current primary literature to enable the student to develop the capstone project as the overall final exam for the specialization in systems biology. | https://www.coursera.org/learn/integrated-analysis |
25 | Computational Biology | Icahn School of Medicine at Mount Sinai | Systems Biology and Biotechnology Capstone | NOTE: In order to take this course you should have taken and complete the following courses in the Signature Track: Introduction to Systems Biology, Network Analysis in Systems Biology, Dynamical Modeling Methods for Systems Biology, Experimental Methods in SB and Integrated Analysis In Systems Biology | https://www.coursera.org/learn/systems-biology-capstone |
26 | Computer Science | University of California, San Diego | Biology Meets Programming: Bioinformatics for Beginners | 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 gently-paced introduction to our Bioinformatics Specialization (https://www.coursera.org/specializations/bioinformatics), preparing learners to take the first course in the Specialization, "Finding Hidden Messages in DNA" (https://www.coursera.org/learn/dna-analysis). 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, but these videos are optional. | https://www.coursera.org/learn/bioinformatics-project |
27 | Biology | Duke University | Introduction to Genetics and Evolution | Introduction to Genetics and Evolution is a college-level class being offered simultaneously to new students at Duke University. The course gives interested people a very basic overview of some principles behind these very fundamental areas of biology. We often hear about new "genome sequences," commercial kits that can tell you about your ancestry (including pre-human) from your DNA or disease predispositions, debates about the truth of evolution, why animals behave the way they do, and how people found "genetic evidence for natural selection." This course provides the basic biology you need to understand all of these issues better, tries to clarify some misconceptions, and tries to prepare students for future, more advanced coursework in Biology (and especially evolutionary genetics). No prior coursework is assumed. | https://www.coursera.org/learn/genetics-evolution |
28 | Bioinformatics | University of Toronto | Bioinformatic Methods I | Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on. | https://www.coursera.org/learn/bioinformatics-methods-1 |
29 | Biology | Saylor Academy | Introduction to Molecular and Celular Biology | This course is intended for the student interested in understanding and appreciating common biological topics in the study of the smallest units within biology: molecules and cells. Molecular and cellular biology is a dynamic field. There are thousands of opportunities within the medical, pharmaceutical, agricultural, and industrial fields (just to name a few) for a person with a concentrated knowledge of molecular and cellular processes. This course will give you a general introduction of these topics. In addition to preparing for a diversity of career paths, an understanding of molecular and cell biology will help you make sound decisions in your everyday life that can positively impact your diet and health. | https://learn.saylor.org/course/bio101 |
30 | Bioinformatics | Peking University | Bioinformatics: Introduction and Methods | In this MOOC you will become familiar with the concepts and computational methods in the exciting interdisciplinary field of bioinformatics and their applications in biology, the knowledge and skills in bioinformatics you acquired will help you in your future study and research. | https://www.coursera.org/learn/bioinformatics-pku |
31 | Bioinformatics | The State University of New York | Big Data, Genes, and Medicine | This course distills for you expert knowledge and skills mastered by professionals in Health Big Data Science and Bioinformatics. You will learn exciting facts about the human body biology and chemistry, genetics, and medicine that will be intertwined with the science of Big Data and skills to harness the avalanche of data openly available at your fingertips and which we are just starting to make sense of. We’ll investigate the different steps required to master Big Data analytics on real datasets, including Next Generation Sequencing data, in a healthcare and biological context, from preparing data for analysis to completing the analysis, interpreting the results, visualizing them, and sharing the results. Needless to say, when you master these high-demand skills, you will be well positioned to apply for or move to positions in biomedical data analytics and bioinformatics. No matter what your skill levels are in biomedical or technical areas, you will gain highly valuable new or sharpened skills that will make you stand-out as a professional and want to dive even deeper in biomedical Big Data. It is my hope that this course will spark your interest in the vast possibilities offered by publicly available Big Data to better understand, prevent, and treat diseases. | https://www.coursera.org/learn/data-genes-medicine |
32 | Bioinformatics | University of Toronto | Bioinformatic Methods II | Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on. | https://www.coursera.org/learn/bioinformatics-methods-2 |
33 | Computer Science | University of California, San Diego | Algorithmic Toolbox | The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second). | https://www.coursera.org/learn/algorithmic-toolbox |
34 | Computer Science | University of California, San Diego | Data Structures | A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space! | https://www.coursera.org/learn/data-structures |
35 | Computer Science | University of California, San Diego | Algorithms on Graphs | If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs. In this course, you will first learn what a graph is and what are some of the most important properties. Then you'll learn several ways to traverse graphs and how you can do useful things while traversing the graph in some order. We will then talk about shortest paths algorithms — from the basic ones to those which open door for 1000000 times faster algorithms used in Google Maps and other navigational services. You will use these algorithms if you choose to work on our Fast Shortest Routes industrial capstone project. We will finish with minimum spanning trees which are used to plan road, telephone and computer networks and also find applications in clustering and approximate algorithms. | https://www.coursera.org/learn/algorithms-on-graphs |
36 | Computer Science | University of California, San Diego | Algorithms on Strings | World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome. | https://www.coursera.org/learn/algorithms-on-strings |
37 | Computer Science | University of California, San Diego | Advanced Algorithms and Complexity | You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset. | https://www.coursera.org/learn/advanced-algorithms-and-complexity |
38 | Computer Science | University of California, San Diego | Genome Assembly Programming Challenge | In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this Genome Assembly Programming Challenge, you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome. | https://www.coursera.org/learn/assembling-genomes |
39 | Computer Science | Stanford | Divide and Conquer, Sorting and Searching, and Randomized Algorithms | The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts). | https://www.coursera.org/learn/algorithms-divide-conquer |
40 | Computer Science | Stanford | Graph Search, Shortest Paths, and Data Structures | The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis). | https://www.coursera.org/learn/algorithms-graphs-data-structures |
41 | Computer Science | Stanford | Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming | The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees). | https://www.coursera.org/learn/algorithms-greedy |
42 | Computer Science | Stanford | Shortest Paths Revisited, NP-Complete Problems and What To Do About Them | The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search). | https://www.coursera.org/learn/algorithms-npcomplete |
43 | Computer Science | Princeton University | Algorithms, Part I | This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. | https://www.coursera.org/learn/introduction-to-algorithms |
44 | Computer Science | Princeton University | Algorithms, Part 2 | This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. | https://www.coursera.org/learn/java-data-structures-algorithms-2 |
45 | Computer Science | Princeton University | Analysis of Algorithms | This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings. | https://www.coursera.org/learn/analysis-of-algorithms |
46 | Biology | Massachusetts Institute of Technology | Introduction to Biology - The Secret of Life | How to describe the building blocks of life and how their interactions dictate structure and function in biology How to predict genotypes and phenotypes given genetics data How to explain the central dogma of molecular biology and convert DNA sequence to RNA sequence to protein sequence How to use molecular tools to study biology How to describe the principles of early sequencing as well as modern sequencing and the effects of these technologies on the filed of genomics How to apply the principles of modern biology to issues in today's society | https://www.edx.org/course/introduction-biology-secret-life-mitx-7-00x-4 |
47 | Bioinformatics | Hasso Plattner Institut | In-Memory Data Management | The online course focuses on the management of enterprise data in column-oriented in-memory databases. Latest hardware and software trends led to the development of a new revolutionary technology that enables flexible and lightning-fast analysis of massive amounts of enterprise data. The basic concepts and design principles of this technology are explained in detail. Beyond that, the implications of the underlying design principles for future enterprise applications and their development are discussed. Unbelievable things are possible and you will understand why this is true using an in-memory column-oriented database instead of a traditional row-oriented disk-based one. | https://open.hpi.de/courses/imdb2012 |
48 | Bioinformatics | Hasso Plattner Institut | Code of Life - When Computer Science Meets Genetics | Welcome to the class: we are very excited that you are interested in learning more about the foundations of life. In this openHPI course, we will give an introduction about components of human cells and their functions. We dive into the cell core to explore the Deoxyribonucleic Acid (DNA), its structure, and how it stores the code of life. Furthermore, we will explore how to discover genetic variants and mutations and how to assess their impact on the cell functions and the whole human body. Ultimately, we will outline how individual genetic variants can be connected to complex diseases, such as cancer. Just two decades ago, all these tasks would have been impossible due to missing knowledge about the DNA and a lack of computational power. As a result, you will learn basic concepts about how to incorporate latest computer science aspects to explore the code of life interactively. | https://open.hpi.de/courses/ehealth2016 |
50 | Computer Science | Stanford | Databases | "Databases" was one of Stanford's three inaugural massive open online courses in the fall of 2011; it was offered again in MOOC format in 2013 and 2014. The course is now being offered as a set of smaller self-paced "mini-courses", which can be assembled in a variety of ways to learn about different aspects of databases. All of the mini-courses are based around video lectures and/or video demos. Many of them include in-video quizzes to check understanding, in-depth standalone quizzes, and/or a variety of automatically-checked interactive programming exercises. Each mini-course also includes a discussion forum and pointers to readings and resources. Individual mini-courses can be accessed by selecting the title from the dropdown list above. The mini-courses are described briefly below, along with suggested pathways through them. Taught by Professor Jennifer Widom, the overall curriculum draws from Stanford's popular Databases course. | https://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about |
51 | Computer Science | Stepik.org | Data Structures | This interactive textbook was written with the intention of teaching Computer Science students about various data structures as well as the applications in which each data structure would be appropriate to use. This textbook utilizes the Active Learning approach to instruction, meaning it has various activities embedded throughout to help stimulate your learning and improve your understanding of the materials we will cover. You will encounter STOP and Think questions that will help you reflect on the material, Exercise Breaks that will test your knowledge and understanding of the concepts discussed, and Code Challenges that will allow you to actually implement some of the algorithms we will cover. | https://stepik.org/course/Data-Structures-579 |
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