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.

Other tools

https://github.com/shenwei356/taxonkit

• ETE, version: 3.1.1
• BioPython, version: 1.73
• taxadb, version: 0.10.1
• TaxonKit, version: 0.5.0

Address of the bookmark: https://pypi.org/project/ete3/3.1.1/

Run pip install luigi to install the latest stable version from PyPI. Documentation for the latest release is hosted on readthedocs.

Run pip install luigi[toml] to install Luigi with TOML-based configs support.

Address of the bookmark: https://github.com/spotify/luigi

Use the Terminal or an Anaconda Prompt for the following steps.

1. To create an environment:

   conda create --name myenv
   

   NOTE: Replace myenv with the environment name.

2. When conda asks you to proceed, type y:

   proceed ([y]/n)?
   

This creates the myenv environment in /envs/. This environment uses the same version of Python that you are currently using, because you did not specify a version.

To create an environment with a specific version of Python:

conda create -n myenv python=3.4

To create an environment with a specific package:

conda create -n myenv scipy

OR:

conda create -n myenv python
conda install -n myenv scipy

To create an environment with a specific version of a package:

conda create -n myenv scipy=0.15.0

OR:

conda create -n myenv python
conda install -n myenv scipy=0.15.0

To create an environment with a specific version of Python and multiple packages:

conda create -n myenv python=3.4 scipy=0.15.0 astroid babel

TIP: Install all the programs that you want in this environment at the same time. Installing 1 program at a time can lead to dependency conflicts.

To automatically install pip or another program every time a new environment is created, add the default programs to the create_default_packages section of your .condarc configuration file. The default packages are installed every time you create a new environment. If you do not want the default packages installed in a particular environment, use the --no-default-packages flag:

conda create --no-default-packages -n myenv python

TIP: You can add much more to the conda create command. For details, run conda create --help.

➜ redundans git:(master) ✗ conda create --name py27 python=2.7
Solving environment: done

==> WARNING: A newer version of conda exists. <==
current version: 4.5.0
latest version: 4.5.2

Please update conda by running

$ conda update -n base conda

## Package Plan ##

environment location: /home/urbe/anaconda3/envs/py27

added / updated specs:
- python=2.7

The following packages will be downloaded:

package | build
---------------------------|-----------------
wheel-0.31.0 | py27_0 61 KB
python-2.7.15 | h1571d57_0 12.1 MB
certifi-2018.4.16 | py27_0 142 KB
sqlite-3.23.1 | he433501_0 1.5 MB
setuptools-39.1.0 | py27_0 582 KB
openssl-1.0.2o | h20670df_0 3.4 MB
pip-10.0.1 | py27_0 1.7 MB
ca-certificates-2018.03.07 | 0 124 KB
------------------------------------------------------------
Total: 19.6 MB

The following NEW packages will be INSTALLED:

ca-certificates: 2018.03.07-0
certifi: 2018.4.16-py27_0
libedit: 3.1-heed3624_0
libffi: 3.2.1-hd88cf55_4
libgcc-ng: 7.2.0-hdf63c60_3
libstdcxx-ng: 7.2.0-hdf63c60_3
ncurses: 6.0-h9df7e31_2
openssl: 1.0.2o-h20670df_0
pip: 10.0.1-py27_0
python: 2.7.15-h1571d57_0
readline: 7.0-ha6073c6_4
setuptools: 39.1.0-py27_0
sqlite: 3.23.1-he433501_0
tk: 8.6.7-hc745277_3
wheel: 0.31.0-py27_0
zlib: 1.2.11-ha838bed_2

Proceed ([y]/n)? y

Downloading and Extracting Packages
wheel 0.31.0: #################################################################################################################################################################################################### | 100%
python 2.7.15: ################################################################################################################################################################################################### | 100%
certifi 2018.4.16: ############################################################################################################################################################################################### | 100%
sqlite 3.23.1: ################################################################################################################################################################################################### | 100%
setuptools 39.1.0: ############################################################################################################################################################################################### | 100%
openssl 1.0.2o: ################################################################################################################################################################################################## | 100%
pip 10.0.1: ###################################################################################################################################################################################################### | 100%
ca-certificates 2018.03.07: ###################################################################################################################################################################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use:
# > source activate py27
#
# To deactivate an active environment, use:
# > source deactivate
#

➜ redundans git:(master) ✗ source activate py27

To install the stable version: pip3 install --user tinycov

To install the development version:

git clone https://github.com/cmdoret/tinycov.git
cd tinycov
pip install .

Address of the bookmark: https://github.com/cmdoret/tinycov

Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.

Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.

Address of the bookmark: https://github.com/lh3/wgsim

"In neuroscience, the project will use neuroinformatics and brain simulation to collect and integrate experimental data, identifying and filling gaps in our knowledge, and prioritising future experiments.

In medicine, the HBP will use medical informatics to identify biological signatures of brain disease, allowing diagnosis at an early stage, before the disease has done irreversible damage, and enabling personalized treatment, adapted to the needs of individual patients. Better diagnosis, combined with disease and drug simulation, will accelerate the discovery of new treatments, drastically lowering the cost of drug discovery.

In computing, new techniques of interactive supercomputing, driven by the needs of brain simulation, will impact a vast range of industries. Devices and systems, modelled after the brain, will overcome fundamental limits on the energy-efficiency, reliability and programmability of current technologies, clearing the road for systems with brain-like intelligence."

Source: http://www.forbes.com/sites/jenniferhicks/2013/10/07/the-human-brain-project-begins/ 

(https://www.facebook.com/humanbrainproj/info)

Home Page:

https://www.humanbrainproject.eu/

Jobs:

https://www.humanbrainproject.eu/participate/jobs

Address of the bookmark: https://github.com/PapenfussLab/gridss

Address of the bookmark: https://github.com/SysBioChalmers/RAVEN

Address of the bookmark: https://github.com/soedinglab/MMseqs2