Learning python and analyze the data with python
Python is a useful and viable development language for the computer programming needs of the bioinformatics community.Because scientists have long relied on the open availability of each other's research results, it was only natural that they would turn to Open Source software when it came time to apply computer processes to the study of biological processes. One of the first Open Source languages to gain popularity among biologists was Perl. Perl gained a foothold in bioinformatics based on its strong text processing facilities, which were ideally suited to analyzing early sequence data. To its credit, Perl has a history of successful use in bioinformatics and is still a very useful tool for biological research.
In comparison to Perl, Python is a relative newcomer to bioinformatics, but is steadily gaining in popularity. A few of the reasons for this popularity are the:
- Readability of Python code
- Ability to development applications quickly
- Powerful standard library of functionality
- Scalability from very small to very large programs
The Python language was designed to be as simple and accessible as possible, without giving up any of the power needed to develop sophisticated applications. Python's clean, consistent syntax leaves it free from the subtleties and nuances that can make other languages difficult to learn and programs written in those languages difficult to comprehend.
Perl for beginners
BioPerl, the Perl interface to Bioinformatics (biological data analysis using computers), is a collection of object-oriented modules that enable life science data analysis. Tasks such as sequence manipulation, software generated reports processing and parsing can be accomplished using many of the different BioPerl modules.
These modules are strong that they minimize the need to write lengthy code to get the job done, also they are flexible, extendible and generalized to be reusable across many domains. Here, we are shedding light on some of the Bioinformatics aspects where Perl can be used in addition to some of the relevant resources that can be of benefit to Monks. We also address Monks from Biology/Bioinformatics backgrounds - who are new to the Monastery - need to communicate effective Perl questions to enhance the level of interactivity between the diversified backgrounds of other Perl Monks members.
Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.t presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages.
Database Management system (DBMS)
DBMS is a software that provides services for accessing a database, while maintaining all the required features of the data.It Provides degree of abstraction – facilitates maximizing the efficiency of managing data with techniquesDBMS also provide a shield against data loss – should support quick recovery from hardware or software failures.It is the use of metadata or information about data contained in the database.
Meta data – collection of information about naming, classification, structure and use of data that reduces inconsistency & ambiguity. The use of metadata as an organizational theme that makes the centralized data-management approach easier to maintain and control.
DBMS packages in bioinformatics include Microsoft, Oracle, Sybase, Mysql AB and inter systems. DBMS can be explained in 3 levels of abstraction – physical database, conceptual database and the views.
Physical database – is the low level data and frame work i.e. defined in terms of media, bits and bytes. Low abstraction is most useful to deal directly with data and files.
Conceptual database – some what higher level of abstraction, is conserved with the most appropriate way to represents the data. The most common methods of representing the conceptual database are the entity-relationship model and data model.