Address of the bookmark: https://github.com/hugheslab/snpgenie

Perl's second wave of adoption came from the growth of the world wide web. Dynamic web pages—the precursor to modern web applications—were easy to create with Perl and CGI. Thanks to Perl's ubiquity as a language for system administrators and its power to manipulate text, it was the default choice for web programming. Its presence everywhere made it popular and, in some ways, the duct tape of the Internet.

Web Application Development

The old days of CGI programs and the simple development style that represented seem clunky. Web pages have become web applications. Development has moved from generating static HTML to both client and server side programming, with rich client interfaces and powerful backends.

Perl is still well suited for developing modern web apps. The language grows more powerful and easier to use every year, the available libraries are wonderful and keep getting better, and the inventions and discoveries available in modern Perl are unsurpassed.

In particular, a modern Perl developer can do amazing things with modern Perl tools. If you still think of Perl web development as a cgi-bin directory full of messy scripts that spew warnings to STDERR, you're a decade out of date. Better yet, you can replace that mess piecemeal, thanks to the new tools and techniques of modern Perl. See, for example, the ever-growing list of technologies Built in Perl.

Modern Perl Web Frameworks

While the old wave of web development may have made the CGI.pm module central, modern Perl web programming follows a stricter separation of business logic, URL and request routing, and output. The days of slinging a string here, an array there, a Perl hash yonder, declaring every variable at the top of the program, and maybe making a subroutine are gone. The Perl world has seen the value of abstraction and ways to mechanize away boilerplate. Perl has dozens of frameworks and toolkits designed to make web development and deployment simpler.

Any of a dozen of these frameworks will help you do great things, but three in particular stand out. You can build web sites and web applications of tremendous value with all three. These are neither the only good possibilities (think of POE or Jifty or Continuity or...) nor the only mechanisms for web programming with Perl (see Mechanize or LWP or Mojo::UserAgent for more). Yet if you want three good options to choose between, start here.

Catalyst

The Catalyst framework is a flexible and powerful system for building small to large web apps. It uses the Moose object system to provide great APIs for extension and further development. It's the most mature of the modern top Perl web frameworks, yet it retains its flexibility and vibrancy. In particular, its plugin and extension ecosystem allows it to evolve to provide new and essential features.

Catalyst has embraced the Plack/PSGI standard for Perl web deployment and recent versions are exploring high-scalability, event-based request handling models.

Dancer

The Dancer framework is deliberately minimal in syntax and scope, but it also has a vibrant plugin ecosystem. Dancer particularly excels for smaller sites and applications, though good programmers can build larger things with it.

The first version of Dancer was easy to use. Dancer 2 continues that ease while improving the internals and robustness of applications.

Mojolicious

The Mojolicious (Mojo) framework has a real-time design based on high performance event handling. Its focus is solving new and interesting problems in simple and effective ways, and the project has produced a lot of new code that does old things in better ways.

In particular, Mojolicious goes to great lengths to support new web standards, such as CSS 3, web sockets, and HTTP 2.

Where Catalyst embraces the CPAN fully, Mojolicious by design provides most of what an average app might need in a single download. It's still fully compatible with the CPAN, but the intention is to provide good working defaults in a package that's easy to start with. Mojo's fans are quick to praise it as fun to develop.

A modern Perl web developer should be familiar with at least one of these frameworks.

Modern Perl Storage Mechanisms

Perl's venerable DBI module has been the focal point of database access since its invention. Its design allows it to provide the same interface to huge relational databases and flat files alike through its DBD extension mechanism. Yet the DBI by itself isn't the be-all, end-all of data storage and access in Perl.

DBIx::Class

DBIx::Class sits on top of DBI to provide an API to your database based on the concept of queries and results. This is often sufficient to remove all but the most complicated of SQL from your code, leaving you to manipulate your business models instead of the small details of how a relational database works. The power and maintainability you receive is well the small cost of the learning curve.

Even better, DBIC can manage (and even generate) your database schema for you.

Recent versions of DBIC have demonstrated that a well-written ORM can perform much better than even clever hand-written code. Because it builds on the Perl DBI, it scales everywhere from SQLite to PostgreSQL, MySQL, Oracle, and more.

Rose::DB

The lesser-known but no less powerful Rose::DB::Object builds on Rose::DB to provide an object-relational mapper for Perl. While its high level features most directly compare to those of DBIx::Class, it's often measurably faster.

NoSQL on the CPAN

Of course the CPAN has modules for almost any NoSQL database or job queue or persistence mechanism you could name, and several you have never heard of. Everything you need is a quick CPAN or cpanm away!

Modern Perl Deployment Strategies

In the early days of the web, deploying a Perl web application meant putting one or more .cgi or .pl files in a special directory and hoping that your system administrator had everything configured correctly. The execution model was often slow and cumbersome, and accessing shared resources such as databases was often tricky.

Modern Perl has better choices. While deployment strategies are the source of many arguments, the return on your investment from learning the modern way is impressive.

Plack/PSGI

The PSGI specification (as exemplified by Plack) describes a strategy for building Perl web apps independent of server and with the possibility to share custom processing behaviors.

In other words, it's a standard for writing Perl apps to take advantage of the huge ecosystem of Perl development available on the CPAN without tying yourself to a server like Apache, Apache 2, nginx, or anything else.

Any good modern Perl web framework (including those listed here) supports PSGI. Several deployment mechanisms exist to meet various business needs which also support PSGI. In particular, you can deploy the same application with a local testing server on your own machine as you can to your production server or servers without changing your application at all.

mod_perl

The older but still viable mod_perl Apache httpd module embeds Perl into the web server. This was the first widespread persistence mechanism for Perl web applications themselves and it's still popular to this day, though PSGI compliance is often the choice for new development. (PSGI handlers to use mod_perl as the backend are available.)

Modern Perl developers should familiarize themselves with PSGI and the wealth of available Plack middleware.

Perl Web Development

Of course no discussion of Perl web development would be complete without mentioning the strength of the CPAN. Almost any project will benefit from the wealth of freely available libraries built to solve real problems. These distributions run the gamut from full-blown web frameworks and content management systems to APIs for web services, development tools, testing systems, and interfaces to document formats and external resources.

For example, if you need to write a web service which accepts JSON data and produces Excel spreadsheets, you can glue together a few CPAN distributions and get the job done early. If you need to consume XML from a remote service and emit a PDF, you're in luck.

Perl's prowess as a general purpose programming language as well as its flexibility and power in managing text and gluing systems together make it a wonderful fit for web development. The community's adoption of modern Perl standards such as PSGI and Plack only enhance your power.

Web application development in Perl is still viable, and modern Perl tools and techniques and libraries make it more powerful and pleasant than ever.

You can add more tutorial links in comments if found new pages.

Address of the bookmark: http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual

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.

Read more: https://edu.t-bio.info/amity-university-summer-bioinformatics-program-registrations-are-open/

More at http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Many of the Perl scripts require that the VCF files are compressed by bgzip and indexed by tabix (both tools are part of the tabix package, available for download here). The VCF files can be compressed and indexed using the following commands

bgzip my_file.vcf
tabix -p vcf my_file.vcf.gz

http://vcftools.sourceforge.net/perl_module.html

Address of the bookmark: http://vcftools.sourceforge.net/perl_module.html

http://www.broadinstitute.org/igv/

Address of the bookmark: https://wikis.utexas.edu/display/bioiteam/Integrative+Genomics+Viewer+%28IGV%29+tutorial

This is a short post giving steps on how to actually install R packages. Let’s suppose you want to install the ggplot2 package. Well nothing could be easier. We just fire up an R shell and type:

> install.packages("ggplot2")

In theory the package should just install, however:

• if you are using Linux and don’t have root access, this command won’t work.
• you will be asked to select your local mirror, i.e. which server should you use to download the package.

Installing packages without root access

First, you need to designate a directory where you will store the downloaded packages. On my machine, I use the directory /data/Rpackages/ After creating a package directory, to install a package we use the command:

> install.packages("ggplot2", lib="/data/Rpackages/")
> library(ggplot2, lib.loc="/data/Rpackages/")


It’s a bit of a pain having to type /data/Rpackages/ all the time. To avoid this burden,  we create a file .Renviron in our home area, and add the line R_LIBS=/data/Rpackages/ to it. This means that whenever you start R, the directory /data/Rpackages/ is added to the list of places to look for R packages and so:

> install.packages("ggplot2")
> library(ggplot2)

just works!

Setting the repository

Every time you install a R package, you are asked which repository R should use. To set the repository and avoid having to specify this at every package install, simply:

• create a file .Rprofile in your home area.
• Add the following piece of code to it:


cat(".Rprofile: Setting UK repositoryn")
r = getOption("repos") # hard code the UK repo for CRAN
r["CRAN"] = "http://cran.uk.r-project.org"
options(repos = r)
rm(r)


I found this tip in a stackoverflow answer .