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.
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.
In this class, we will consider the following two central biological questions (the computational approaches needed to solve them are shown in parentheses):
How Do We Locate Disease-Causing Mutations? (Combinatorial Pattern Matching)
Why Have Biologists Still Not Developed an HIV Vaccine? (Hidden Markov Models)
Graded: How Do We Find Disease-Causing Mutations? (Week 1)
Graded: Open in order to Sync Your Progress: Stepik Interactive Text for Week 1
The Burrows-Wheeler Transform
This week, we will introduce a paradigm called the Burrows-Wheeler transform; after seeing how it can be used in string compression, we will demonstrate that it is also the foundation of modern read-mapping algorithms.
Graded: Open in order to Sync Your Progress: Stepik Interactive Text for Week 2
Speeding Up Burrows-Wheeler Read Mapping
Last week, we saw how the Burrows-Wheeler transform could be applied to multiple pattern matching. This week, we will speed up our algorithm and generalize it to the case that patterns have errors, which models the biological problem of mapping reads with errors to a reference genome.
Graded: How Do We Find Disease-Causing Mutations? (Weeks 2-3)
Graded: Open in order to Sync Your Progress: Stepik Interactive Text for Week 3
Introduction to Hidden Markov Models
This week, we will start examining the case of aligning sequences with many mutations -- such as related genes from different HIV strains -- and see that our problem formulation for sequence alignment is not adequate for highly diverged sequences.
To improve our algorithms, we will introduce a machine-learning paradigm called a hidden Markov model and see how dynamic programming helps us answer questions about these models.
Graded: Stepik Code Challenges for Week 4
Profile HMMs for Sequence Alignment
Last week, we introduced hidden Markov models. This week, we will see how hidden Markov models can be applied to sequence alignment with a profile HMM. We will then consider some advanced topics in this area, which are related to advanced methods that we considered in a previous course for clustering.
Graded: Why Have Biologists Still Not Developed an HIV Vaccine? (Weeks 4-5)
Graded: Stepik Code Challenges for Week 5
Week 6: Bioinformatics Application Challenge
This week brings our Application Challenge, in which we apply the HMM sequence alignment algorithms that we have developed.
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.
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.
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.
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.
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.
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.
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.
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.
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).