BOL: Related items

Apache server setting !

Abhi — Fri, 29 Oct 2021 04:29:51 -0500

Apache is an open source web server that’s available for Linux servers free of charge.

In this tutorial we’ll be going through the steps of setting up an Apache server.

What you’ll learn

How to set up Apache
Some basic Apache configuration

Address of the bookmark: https://ubuntu.com/tutorials/install-and-configure-apache#3-creating-your-own-website

Seeing Theory and Learn

LEGE — Tue, 04 Jun 2024 00:31:54 -0500

Seeing Theory was created by Daniel Kunin while an undergraduate at Brown University. The goal of this website is to make statistics more accessible through interactive visualizations (designed using Mike Bostock’s JavaScript library D3.js).

Address of the bookmark: https://seeing-theory.brown.edu/

Variant Calling Resequencing-Based Genome Inference

Abhi — Wed, 31 Jul 2024 02:02:24 -0500

Variant Calling - Resequencing-Based Genome Inference

Erik Garrison
University of Tennessee Health Science Center
Workshop on Genomics - Český Krumlov
January 12, 2024

https://evomics.org/wp-content/uploads/2024/01/Variant-calling-Workshop-on-Genomics-2024-Cesky-Krumlov.pdf

Address of the bookmark: https://evomics.org/wp-content/uploads/2024/01/Variant-calling-Workshop-on-Genomics-2024-Cesky-Krumlov.pdf

Interactive Bioinformatics Resources !

Jit — Thu, 12 Aug 2021 00:09:00 -0500

Learn how to use bioinformatics tools right from your browser.
Everything runs in a sandbox, so you can experiment all you want.

More at sandbox.bio

Address of the bookmark: http://sandbox.bio

Pangolin tutorial !

Abhi — Fri, 10 Dec 2021 05:58:59 -0600

This is a tutorial for using the Pangolin Web Application. For information on using the command line tool, please visit the command line tool usage page.

https://cov-lineages.org/resources/pangolin/tutorial.html

Address of the bookmark: https://cov-lineages.org/resources/pangolin/tutorial.html

Free resources to learn statistics

BioStar — Sat, 06 Jul 2024 10:30:50 -0500

Welcome to the course notes for STAT 414: Introduction to Probability Theory. These notes are designed and developed by Penn State's Department of Statistics and offered as open educational resources. These notes are free to use under Creative Commons license CC BY-NC 4.0.

A free online version of the second edition of the book based on Stat 110, Introduction to Probability by Joe Blitzstein and Jessica Hwang, is now available at http://probabilitybook.net

Print copies are available via CRC Press, Amazon, and elsewhere.

Stat110x is also available as an edX course. Free signup at https://www.edx.org/course/introduction-to-probability-0

The edX course focuses on animations, interactive features, readings, and problem-solving, and is complementary to the Stat 110 lecture videos on YouTube, which are available at https://goo.gl/i7njSb

The Stat110x animations are available within the course and at https://goo.gl/g7pqTo

https://projects.iq.harvard.edu/stat110/home

Address of the bookmark: https://online.stat.psu.edu/stat414/

Programming language to build synthetic DNA

Jit — Mon, 30 Sep 2013 16:37:24 -0500

A team led by Georg Seelig (http://homes.cs.washington.edu/~seelig/index.html) at University of Washington has developed a programming language for chemistry that it hopes will streamline efforts to design a network that can guide the behavior of chemical-reaction mixtures in the same way that embedded electronic controllers guide cars, robots and other devices. In medicine, such networks could serve as “smart” drug deliverers or disease detectors at the cellular level.

Reference & More @

http://www.nature.com/nnano/journal/vaop/ncurrent/full/nnano.2013.189.html

http://www.washington.edu/news/2013/09/30/uw-engineers-invent-programming-language-to-build-synthetic-dna/

Image source: washington.edu

bipype

Jit — Thu, 01 Dec 2016 08:47:38 -0600

Bipype is a very useful program, which prepare a lot of types of bioinformatics analyses. There are three input options: amplicons, WGS (whole genome sequences) and metatranscriptomic data. If amplicons are input data, then bipype does reconstruction and pairs merging. After that biodiversity is searching. There are two types of searching depending on the amplicons types (ITS or 16S). If WGS are chosen, then bipype finds the SA coordinates of the input reads and generates alignments in the SAM format given single-end reads, aligns reads to reference sequence(s). All of these analyses will be shown with Krona program, which allows to show hierarchical data with pie charts.

Address of the bookmark: https://readthedocs.org/projects/bipype/

Julia Programming Language, a Python and R rival

Radha Agarkar — Sat, 25 Aug 2018 04:46:39 -0500

Big data has grown to become one of the most lucrative fields. In fact, data scientists are some of the most sought people. They are usually hired to analyze, control and parse large chunks of data. Implementing these actions using traditional techniques is not a walk in the park. This is why most data scientists prefer using programming languages such as R and Python. However, there is one more programming language that can do the job. That is Julia programming language.

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.

MOSS: A System for Detecting Software Similarity

Jit — Sat, 09 Dec 2017 08:59:07 -0600

Moss (for a Measure Of Software Similarity) is an automatic system for determining the similarity of programs. To date, the main application of Moss has been in detecting plagiarism in programming classes. Since its development in 1994, Moss has been very effective in this role. The algorithm behind moss is a significant improvement over other cheating detection algorithms (at least, over those known to us).

Moss can currently analyze code written in the following languages:

C, C++, Java, C#, Python, Visual Basic, Javascript, FORTRAN, ML, Haskell, Lisp, Scheme, Pascal, Modula2, Ada, Perl, TCL, Matlab, VHDL, Verilog, Spice, MIPS assembly, a8086 assembly, a8086 assembly, MIPS assembly, HCL2.

Address of the bookmark: https://theory.stanford.edu/~aiken/moss/