Computing, Algorithms & Genomics
Computing, Algorithms & Genomics resources
Discover the growing importance of genomics in healthcare research, diagnosis and treatment, with these free online courses. Learn with researchers and clinicians from leading universities and medical schools.
Population and Quantitative Genetics
A book developed by Graham Coop based on the Population Biology graduate group core class and Undergraduate Population and Quantitative Genetics class at UC Davis. Population genetics is the study of genetic composition of natural populations and its evolutionary causes and consequences. Quantitative genetics is the study of the genetic basis of phenotypic variation and how phenotypic changes evolve over time. Both fields are closely conceptually aligned. They seek to describe how the genetic and phenotypic composition of populations can be changed over time by the forces of mutation, recombination, selection, migration, and genetic drift.
Special reports bundle from Front Line Genomics
Clinical Genomics 101 is designed to introduce you to how genomics is being integrated into the clinic, what goes into using a patient’s DNA to reach a diagnosis, and how this information can be communicated to primary care physicians.
Genomic Data 101 gives you an introcuction to computational genomics and genomic data handling.
Analysis of single cell RNA-seq data
This course discusses some of the questions that can be addressed using scRNA-seq as well as the computational and statistical methods avialable.
Analyzing fluorescence microscopy images with ImageJ
This online book may help you to quickly get to grips with the main principles of image analysis.
Introduction to Computational Biology and Bioinformatics
The course covers basic technology platforms, data analysis problems and algorithms in computational biology. Topics include sequence alignment and search, high throughput experiments for gene expression, transcription factor binding and epigenetic profiling, motif finding, RNA/protein structure prediction, proteomics and genome-wide association studies. Computational algorithms covered include hidden Markov model, Gibbs sampler, clustering and classification methods.
Website: https://canvas.harvard.edu/courses/49497 (general); https://canvas.harvard.edu/courses/49497/pages/course-schedule (course schedule)
Clinical Bioinformatics: Unlocking Genomics in healthcare
Free online course covering the role of Clinical Bioinformatics and Genomics in healthcare today.
Introduction to Genomic Data Science
Learn how to look for hidden messages in DNA without ever needing to put on a lab coat.
Teach Yourself Computer Science
Teach yourself computer science is a pretty good initial guide for people who want to learn computer science on their own. It also contains links to multiple useful resources.
Rosalind is a platform for learning bioinformatics and programming through problem solving.
Quantitative Biology Workshop
A workshop-style introduction to tools used in biological research. Discover how to analyse data using computational methods.
Introduction to Computing for Data Analysis
A hands-on introduction to basic programming principles and practice relevant to modern data analysis, data mining, and machine learning.
Foundations of Data Science: Computational Thinking with Python
Learn the basics of computational thinking, an essential skill in today’s data-driven world, using the popular programming language, Python.
Introduction to Python for Data Science
The ability to analyse data with Python is critical in data science. Learn the basics, and move on to create stunning visualizations.
Machine learning is the science of getting computers to act without being explicitly programmed. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course also draws from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs, where they have produced results comparable to and in some cases superior to human experts.
Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains, which make them incompatible with neuroscience evidences.
Andrew Ng Deep Learning courses provide good overview of this field of research.
The difficulty of extracting information from high dimensional data is the main motivation for renewed interest in the problems of dimensionality reduction, feature extraction and clustering. High dimensional data take many different forms: from Digital image libraries to gene expression microarrays and financial time series. Researchers in fields as diverse as finance, physics, medicine, and bioinformatics have to deal with such large data sets. By formulating the problem of dimensionality reduction and clustering in a general setting, however, many different types of data can be analyzed in the same underlying mathematical framework. This course explores this general framework and introduces several methods for dimensionality reduction, feature selection, and clustering.
Useful courses on Python programming and applications
Python is a powerful modern programing language with huge potential for genomics, data science, machine learning and similar fields.
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Data Science: R Basics
Build a foundation in R and learn how to wrangle, analyse, and visualize data. This course covers common programming commands, how to operate on vectors, and when to use advanced functions such as sorting.
Introduction to Data Science
A Course for Visualization in R
A course on data visualisation in R taking you from beginner to advanced.
Stegle Group latent variable model methodology
The single-cell latent variable model (scLVM) is an approach to reconstruct and account for hidden sources of variation in single-cell RNA_Seq studies.
Differential analysis of count data in genomics
A lecture on computational genomics by Lior Pachter.
Informatics for RNA-seq
A tutorial on RNA-seq analysis.
RNA-Seq Analysis Training/Courses/Papers
Useful resources from PhD student who “inherited” a RNA Sequencing project.
Bad Grammar, Good Syntax
Interesting resource with significant number of posts relevant to genomics.
European Bioinformatics Institute Courses
EMBL-EBI provides a range of stationary and online courses with particular relevance to quantitative biomedicine.