Bioinformatics

 

 


 

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E.g., 2016-12-06
E.g., 2016-12-06
E.g., 2016-12-06
Dec 5th 2016

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: 10 (1 vote)
Dec 5th 2016

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)
Dec 5th 2016

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)
Dec 5th 2016

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: 9 (2 votes)
Dec 5th 2016

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)
Dec 5th 2016

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: 7 (1 vote)
Dec 5th 2016

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 (8 votes)
Dec 5th 2016

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: 4.8 (4 votes)
Nov 28th 2016

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: 7.2 (5 votes)
Nov 28th 2016

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: 2.7 (3 votes)
Nov 28th 2016

Курс «Введение в биоинформатику» адресован тем, кто хочет получить расширенное представление о том, что такое биоинформатика и как она помогает биологам и медикам в их работе. The course is aimed at those who would like to have a better idea of what bioinformatics is and how it helps biologists and medical scientists in research and clinical work.

Average: 5.3 (3 votes)
Nov 21st 2016

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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Nov 7th 2016

Learn how developments in genomics are transforming our knowledge and treatment of conditions such as diabetes. There have been huge advances in the field of genetics in the last 10 years since the sequencing of the first human genome in 2003. It is now possible to analyse all 20,000 human genes in a single experiment, rather than focussing on one gene at a time. We are in the genomics era.

Average: 9 (1 vote)
Sep 19th 2016

Learn about the role of clinical bioinformaticians in healthcare and how their work is helping to realise the genomics revolution. This free online course aims to raise awareness amongst healthcare professionals of the role of Clinical Bioinformatics and Genomics in healthcare today. We will illustrate how the discipline of Clinical Bioinformatics provides an important bridge between the cutting edge science and the delivery of genomic medicine in clinical practice.

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May 23rd 2016

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: 7.6 (5 votes)
May 9th 2016

Ce cours explique comment l’informatique contribue à l’analyse de l’information génétique. Il introduit conjointement les notions de génomique et d’algorithmique impliquées. Dans ce cours, nous verrons comment l’informatique permet d’interpréter le texte des génomes. Doté d’algorithmes adaptés, mis en œuvre sous forme de programmes efficaces, l’ordinateur produit des prédictions quant à la localisation des milliers de gènes d’un organisme vivant et les fonctions que remplissent les protéines qu’ils codent.

Average: 3 (1 vote)
Jan 25th 2016

This course will cover algorithms for solving various biological problems along with a handful of programming challenges testing your ability to implement these algorithms. It offers a gentler-paced alternative to the instructors' two other courses, Bioinformatics Algorithms (Part 1 and Part 2).

Average: 7 (4 votes)
Nov 2nd 2015

This course explains how computer science supports the interpretation of the text of genomes. It introduces genomics and algorithmics in a joint approach.

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Jun 3rd 2015

This course will provide an introduction to genomic medicine and a better understanding of the issues associated with personal genomic information.

Average: 4.5 (2 votes)
Mar 16th 2015

This is the second course in a two-part series on bioinformatics algorithms, covering the following topics: evolutionary tree reconstruction, applications of combinatorial pattern matching for read mapping, gene regulatory analysis, protein classification, computational proteomics, and computational aspects of human genetics.

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