BOL: Related items

Bioinformatics Made Easy Search: Bioinformatics tools and run genomic analysis in the cloud

Rahul Nayak — Thu, 20 Aug 2015 02:21:20 -0500

InsideDNA makes hundreds of bioinformatics tools immediately available to run via an easy-to-use web interface and allows an accurate search across all functions, tools and pipelines.

With InsideDNA, you can upload and store your own genomic/genetic datasets in a limitless cloud space, and instantly analyze it with a powerful compute instance, without any tool installation or set up hassle.

More at https://insidedna.me/

Address of the bookmark: https://insidedna.me/

Finding Patterns in Biological Sequences

Jit — Thu, 22 Dec 2016 10:30:49 -0600

In this report we provide an overview of known techniques for discovery of patterns of biological sequences (DNA and proteins). We also provide biological motivation, and methods of biological verification of such patterns. Finally we list publicly available tools and databases for pattern discovery. On-line supplement is available through http://genetics.uwaterloo.ca/∼tvinar/cs798g/motif.

Address of the bookmark: http://engr.case.edu/li_jing/papers/00798gpattern.pdf

Learning Python Programming - a bioinformatician perspective !

Rahul Nayak — Mon, 14 May 2018 16:33:03 -0500

Python Programming is a general purpose programming language that is open source, flexible, powerful and easy to use. One of the most important features of python is its rich set of utilities and libraries for data processing and analytics tasks. In the current era of big biological data, python and biopython is getting more popularity due to its easy-to-use features which supports big data processing.

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

It's widely used in the scientific community
It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
It lend itself well to being integrated with other, existing tools
It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
Python is dynamically typed, so the variables types are defined automatically.
Python is strongly typed. So the developers need to cast the type manually.
Less code and more use makes it more acceptable.
Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.