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. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium.
In "Finding Hidden Messages in DNA", we discussed how to separate some of the signal from the apparent noise of DNA sequences. But how do we know what the DNA sequence making up a genome is in the first place? After all, DNA nucleotides are far too small to view with a normal microscope, and biologists still do not possess technology that would read all the nucleotides of your genome from beginning to end.
In this course, you will learn how entire genomes are assembled from millions of short overlapping pieces of DNA. The scale of this problem (the human genome is 3 billion nucleotides long!) implies that computers must be involved. Yet the problem is even more complex than it may appear ... to solve it, we will need to travel back in time to meet three famous mathematicians, and learn about algorithms based on graph theory.
Later in the course, we will see that sequencing genomes is not the only task related to decoding biological macromolecules. Another difficult problem is sequencing antibiotics, short mini-proteins engineered by bacteria to fight each other. Even though antibiotics often contain fewer than 10 amino acids, sequencing them is a formidable challenge. Decoding the sequence of amino acids making up an antibiotic is an important biomedical problem, but the practical barriers to sequencing short antibiotics are often more substantial than barriers to assembling a genome with millions of nucleotides! To address this computational challenge, we will learn about brute force algorithms that often succeed in various bioinformatics applications.
Finally in this course, you will learn how to apply popular bioinformatics software tools to assemble a deadly Staphylococcus bacterium. You will also be introduced to the popular cloud service BaseSpace offered by Illumina, the leading DNA sequencing company, thus joining the thousands of biologists and bioinformaticians who use BaseSpace every day.
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. Please learn more about the Bioinformatics Specialization (including why we are wearing these crazy outfits) by watching our introductory video. You can purchase the Specialization's printed companion, Bioinformatics Algorithms: An Active Learning Approach, from the textbook website. This Specialization also features an "Honors Track" (called "hacker track" in previous runs of the course). The Honors Track allows you to get your hands dirty by implementing the bioinformatics algorithms that you encounter along the way in a series of dozens of code challenges. By completing the Honors Track, you will be a true bioinformatics software professional! :)
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.
In this course, we will see how evolutionary trees resolve quandaries from finding the origin of a deadly virus to locating the birthplace of modern humans. We will then use methods from computational proteomics to test whether we can reconstruct Tyrannosaurus rex proteins and prove that birds evolved from dinosaurs.
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. 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.
After sequencing genomes, we would like to compare them. We will see that dynamic programming is a powerful algorithmic tool when we compare two genes (i.e., short sequences of DNA) or two proteins. When we "zoom out" to compare entire genomes, we will employ combinatorial algorithms.
In this class, we will compare DNA from an individual against a reference human genome to find potentially disease-causing mutations. We will also 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 course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat.
A big welcome to “Bioinformatics: Introduction and Methods” from Peking University! 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.
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.
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.
Learn various methods of analysis including: unsupervised clustering, gene-set enrichment analyses, Bayesian integration, network visualization, and supervised machine learning applications to LINCS data and other relevant Big Data from high content molecular and phenotype profiling of human cells.
MOOCs – Massive Open Online Courses – enable students around the world to take university courses online. This guide, by the instructors of edX’s most successful MOOC in 2013-2014, Principles of Written English (based on both enrollments and rate of completion), advises current and future students how to get the most out of their online study, covering areas such as what types of courses are offered and who offers them, what resources students need, how to register, how to work effectively with other students, how to interact with professors and staff, and how to handle assignments. This second edition offers a new chapter on how to stay motivated. This book is suitable for both native and non-native speakers of English, and is applicable to MOOC classes on any subject (and indeed, for just about any type of online study).