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E.g., 2017-09-22
E.g., 2017-09-22
E.g., 2017-09-22
Self Paced

Join us on the frontier of bioinformatics and learn how to look for hidden messages in DNA without ever needing to put on a lab coat. 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.

Average: 10 (1 vote)
Sep 25th 2017

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.

Average: 8.1 (11 votes)
Sep 25th 2017

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.

Average: 8.5 (4 votes)

Sep 25th 2017

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.

Average: 5.4 (7 votes)
Sep 25th 2017

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.

Average: 6.8 (12 votes)
Sep 25th 2017

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.

Average: 6.8 (5 votes)

Sep 25th 2017

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.

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Sep 25th 2017

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.

Average: 8.2 (6 votes)
Sep 25th 2017

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.

Average: 5.7 (6 votes)
Sep 18th 2017

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.

Average: 7.8 (13 votes)
Sep 11th 2017

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.

Average: 8.3 (3 votes)

Sep 4th 2017

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.

Average: 5.8 (8 votes)