https://flowingdata.com/

Address of the bookmark: https://flowingdata.com/

Address of the bookmark: http://christophergandrud.github.io/d3Network/

What Is Julia Language?

Julia is a programming language that came into the limelight in 2012. It is a general-purpose programming language that was designed for solving scientific computations. Julia was meant to be an alternative to Python, R and other programming languages that were mainly used for manipulating data. This is because it has numerous features that can minimize the complexities of numerical computations. 

Julia optimizes on the best features of Python and R while at the same time overlooks their weaknesses. This explains why it is viewed as an alternative to these programming languages. For instance, it utilizes the readability and simplicity of Python then performs faster.

Julia is the most preferred programming language for data scientists and mathematicians. This is because its core features are similar to the ones that are used on most data software. Also, the language is ideal for these two subjects because its syntax is similar to the standard mathematical formulas.

Key Features Of Julia Language
Uses JIT Compilation
Parallelism
Dynamic Typing
Simple Syntax
Allows Metaprogramming
Accessible to Libraries
-1-Array Indexing

Julia Vs Python And R Programming Languages
1. Speed
Julia is faster than both Python and R. This is a very critical aspect that is given special attention in the big data programming. The high speed of Julia is because of JIT compilers. You will need to install external libraries on Python to achieve similar speed.

2. Syntax
Julia has a math-friendly syntax. The syntax of this programming language is similar to the mathematical formulas hence can be used to perform mathematical and scientific computations. This syntax makes it easier to learn than Python.

3. Parallelism
Although both Python and R use parallelism, Julia uses a top-level parallelism. Julia allows the processor to perform to the optimum level than what Python and R can achieve.

4. Versatility
Julia programming language is more versatile than Python and R. It allows a programmer to move from different codes and functions with ease.

The only area that Python and R are superior to Julia is in terms of community. Given that Julia is a new programming language, it has a small community as compared to others which have been around for years.

In overall Julia programming language is a better alternative that you can use to handle Big data projects. Despite having a small community, it is one of those programming languages that you can easily learn.

Address of the bookmark: http://www2.decipher.codes/

Address of the bookmark: https://cran.r-project.org/web/packages/alluvial/vignettes/alluvial.html

https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8

Address of the bookmark: http://efs.heiderlab.de/

• Single-SNP GWAS with RGWAS.normal function
• SNP-set (or gene set) GWAS with RGWAS.multisnp function (which tests multiple SNPs at the same time)
• Check epistatic (SNP-set x SNP-set interaction) effects with RGWAS.epistasis (very slow and less reliable)

https://github.com/KosukeHamazaki/RAINBOWR

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007663

https://cran.r-project.org/web/packages/RAINBOWR/index.html

Address of the bookmark: https://github.com/KosukeHamazaki/RAINBOWR

Address of the bookmark: http://www.phytools.org/Cordoba2017/ex/2/Intro-to-phylogenies.html

Live online courses on statistics with R based on this book, led by the author, are offered regularly; see this page for more information and dates.

The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you:

• Data wrangling - importing, formatting, reshaping, merging, and filtering data in R.
• Exploratory data analysis - using visualisations and multivariate techniques to explore datasets.
• Statistical inference - modern methods for testing hypotheses and computing confidence intervals.
• Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting.
• Simulation - using simulation techniques for sample size computations and evaluations of statistical methods.
• Ethics in statistics - ethical issues and good statistical practice.
• R programming - writing code that is fast, readable, and (hopefully!) free from bugs.

The book includes plenty of examples and more than 200 exercises with worked solutions. The datasets used for the examples and the exercises can be downloaded here.

Address of the bookmark: https://www.modernstatisticswithr.com/

Structural variants presentation by

Jan Korbel

European Molecular Biology Laboratory (EMBL) Heidelberg Genome Biology Research Unit

Reference: 

https://www.genome.gov/pages/research/der/1000genomesprojecttutorials/structuralvariants-jankorbel.pdf