To begin, input your data using one of the three input styles.

1. "File" takes a correctly formatted.csv file.
2. "List" takes up to 6 different lists that contain unique elements, similar to that used in the web applications BioVenn (Hulsen et al., 2008) and jvenn (Bardou et al., 2014)
3. "Expression" takes the input used by the venneuler R package (Wilkinson, 2015)

Address of the bookmark: https://gehlenborglab.shinyapps.io/upsetr/

• Whole-genome sequencing
• Whole-exome sequencing
• RNA-seq (speical mode available)
• ChIP-seq

Address of the bookmark: http://qualimap.bioinfo.cipf.es/

R script that makes a plotly interactive and/or static (png/pdf) dot plot.

Shiny app available for testing

Address of the bookmark: https://github.com/tpoorten/dotPlotly

More recently, we've been enthused about using k-mer based similarity measures and computing and searching k-mer-based sketch search databases for all the things.

But I haven't spent too much talking about using k-mers for taxonomy, although that has become an ahem area of interest recently, if you read into our papers a bit.

In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!

Address of the bookmark: http://ivory.idyll.org/blog/2017-something-about-kmers.html

Address of the bookmark: https://practical-stats-med-r.netlify.app/

Address of the bookmark: http://www.biotnet.org/training-materials/python-programmers

Back in August, van Rossum published a proposal on the Python mailing list recommending type-checking annotations as a valuable feature for the next version of Python to improve the performance of editors and IDEs, linter capabilities, standard notation, and refactoring. Van Rossum’s latest proposal, posted late last month, outlined plans to publish a Python Enhancement Proposal (PEP) in early January to put the feature now known as type hinting on track for inclusion in Python 3.5, slated for release this September.

Reference

https://quip.com/r69HA9GhGa7J

ICRISAT is headquartered in Patancheru near Hyderabad, India, with two regional hubs and five country offices in sub-Saharan Africa. ICRISAT, established in 1972, is a member of the CGIAR Consortium. For more details, see www.icrisat.org.

Responsibilities:Design efficient SQL queries for pulling large sequencing projects.
Serve as a technical adviser to the project leadership and provide computational perspective on product design and deliverability.
Develop and oversee a rapid and incremental software development and release schedule.
Design the software architecture, oversee the implementation and evolution of the design on appropriate hardware platforms.
Working collaboratively in a team environment to design, code, test, debug, and document programs for an integrated genomic analysis pipeline in a rapid and incremental software development and release schedule.
Supervise and review code development and ensure that software products meet project objectives in terms of functionality, scalability, robustness and user experience.
Implement and oversee the QA/QC practices to ensure the development team is adhering to quality standards.
Work closely with the application specialist to integrate feedbacks from teams in each CGIAR center into software customization and improvement.
Assist in training of breeders in the CGIAR centers to use software developed.
Personal Profile:

The applicant should have:

Understanding of genomics data and advanced knowledge of Java, and C/C++ as the programming languages and any of the scripting language like perl and/or Python, SQL
High Performance Computing, data architecture, database platforms and QA/QC practices in software engineering.
She/he should have solid experience in software development projects, preferably as a senior programmer or in the software project management role, and in projects involving big data.
Excellent communication skills are needed to work in this multi-disciplinary, multi-location and multi-cultural team.
Ability to mentor colleagues in quality software development practices is desired.
Educational Qualification : Ph. D or Masters Degree in Computational Biology / Computational Genomics or Equivalent with Research Experience in Mentioned Areas.

More at http://www.icrisat.org/careers/

The primary home page for DendroPy, with detailed tutorials and documentation, is at:

http://dendropy.org/

DendroPy is also hosted in the official Python repository:

http://packages.python.org/DendroPy/

Address of the bookmark: https://pypi.org/project/DendroPy/

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.