We use algorithms every day. For example, a recipe for baking a cake is an algorithm. Most programs, with the exception of some artificial intelligence applications, consist of algorithms. Inventing elegant algorithms -- algorithms that are simple and require the fewest steps possible -- is one of the principal challenges in programming. An algorithm is a description of a procedure which terminates with a result. In other words an algorithm is a set of instructions, sometimes called a procedure or a function, that is used to perform a certain task. This can be a simple process, such as adding two numbers together, or a complex function, such as adding effects to an image. For example, in order to sharpen a digital photo, the algorithm would need to process each pixel in the image and determine which ones to change and how much to change them in order to make the image look sharper.

In mathematics, computer science, and related subjects, an algorithm is an effective method for solving a problem using a finite sequence of instructions. Algorithms are used for calculation, data processing, and many other fields.
Each algorithm is a list of well-defined instructions for completing a task. Starting from an initial state, the instructions describe a computation that proceeds through a well-defined series of successive states, eventually terminating in a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate randomness.

History

The origin of the term comes from the ancients. The concept becomes more precise with the use of variables in mathematics. Algorithm in the sense of what is now used by computers appeared as soon as first mechanical engines were invented.
The word algorithm comes from the name of the 9th century Persian Muslim mathematician Abu Abdullah Muhammad ibn Musa Al-Khwarizmi. The word algorism originally referred only to the rules of performing arithmetic using Hindu-Arabic numerals but evolved via European Latin translation of Al-Khwarizmi's name into algorithm by the 18th century. The use of the word evolved to include all definite procedures for solving problems or performing tasks.
The algorithm of Archimedes gives an approximation of the Pi number.
Eratosthenes has defined an algorithim for retrieving prime numbers.
Averroès (1126-1198) was using algorithmic methods for calculations.
Adelard de Bath (12 th) introduces the algorismus term, from Al-Khwarizmi.
During the 1800's up to the mid-1900's:

- George Boole (1847) has invented the binary algebra, the basis of computers. Actually he has unified logic and calculation in a common symbolism.

- Gottlob Frege (1879) formula language's, that is a lingua characterica, a language written with special symbols, "for pure thought", that is free from rhetorical embellishments... constructed from specific symbols that are manipulated according to definite rules.

- Giuseppe Peano (1888) It's The principles of arithmetic, presented by a new method was the first attempt at an axiomatization of mathematics in a symbolic language.

- Alfred North Whitehead and Bertrand Russell in their Principia Mathematica (1910-1913) has further simplified and amplified the work of Frege.

- Kurt Goëdel (1931) cites the paradox of the liar that completely reduces rules of recursion to numbers.

The concept of algorithm was formalized in 1936 through Alan Turing's Turing machines and Alonzo Church's lambda calculus, which in turn formed the foundation of computer science.
Stephen C. Kleene (1943) defined his now-famous thesis known as the "Church-Turing Thesis". In this context:

" Algorithmic theories... In setting up a complete algorithmic theory, what we do is to describe a procedure, performable for each set of values of the independent variables, which procedure necessarily terminates and in such manner that from the outcome we can read a definite answer, "yes" or "no," to the question, "is the predicate value true?"

Classification

Classification by purpose

Each algorithm has a goal, for example, the purpose of the Quick Sort algorithm is to sort data in ascending or descending order. But the number of goals is infinite, and we have to group them by kind of purposes:

Classification by implementation

An algorithm may be implemeted according to different basical principles.

• Recursive or iterative

A recursive algorithm is one that calls itself repeatedly until a certain condition matches. It is a method common to functional programming. 
Iterative algorithms use repetitive constructs like loops.
Some problems are better suited for one implementation or the other. For example, the towers of hanoi problem is well understood in recursive implementation. Every recursive version has an iterative equivalent iterative, and vice versa.

• Logical or procedural

An algorithm may be viewed as controlled logical deduction. 
A logic component expresses the axioms which may be used in the computation and a control component determines the way in which deduction is applied to the axioms. 
This is the basis of the logic programming. In pure logic programming languages the control component is fixed and algorithms are specified by supplying only the logic component.

• Serial or parallel

Algorithms are usually discussed with the assumption that computers execute one instruction of an algorithm at a time. This is a serial algorithm, as opposed to parallel algorithms, which take advantage of computer architectures to process several instructions at once. They divide the problem into sub-problems and pass them to several processors. Iterative algorithms are generally parallelizable. Sorting algorithms can be parallelized efficiently.

• Deterministic or non-deterministic

Deterministic algorithms solve the problem with a predefined process whereas non-deterministic algorithm must perform guesses of best solution at each step through the use of heuristics.

Classification by design paradigm

A design paradigm is a domain in research or class of problems that requires a dedicated kind of algorithm:

• Divide and conquer

A divide and conquer algorithm repeatedly reduces an instance of a problem to one or more smaller instances of the same problem (usually recursively), until the instances are small enough to solve easily. One such example of divide and conquer is merge sorting. Sorting can be done on each segment of data after dividing data into segments and sorting of entire data can be obtained in conquer phase by merging them.
The binary search algorithm is an example of a variant of divide and conquer called decrease and conquer algorithm, that solves an identical subproblem and uses the solution of this subproblem to solve the bigger problem.

• Dynamic programming

The shortest path in a weighted graph can be found by using the shortest path to the goal from all adjacent vertices. 
When the optimal solution to a problem can be constructed from optimal solutions to subproblems, using dynamic programming avoids recomputing solutions that have already been computed. 
- The main difference with the "divide and conquer" approach is, subproblems are independent in divide and conquer, where as the overlap of subproblems occur in dynamic programming. 
- Dynamic programming and memoization go together. The difference with straightforward recursion is in caching or memoization of recursive calls. Where subproblems are independent, this is useless. By using memoization or maintaining a table of subproblems already solved, dynamic programming reduces the exponential nature of many problems to polynomial complexity.

• The greedy method

A greedy algorithm is similar to a dynamic programming algorithm, but the difference is that solutions to the subproblems do not have to be known at each stage. Instead a "greedy" choice can be made of what looks the best solution for the moment. 
The most popular greedy algorithm is finding the minimal spanning tree as given by Kruskal.

• Linear programming

The problem is expressed as a set of linear inequalities and then an attempt is made to maximize or minimize the inputs. This can solve many problems such as the maximum flow for directed graphs, notably by using the simplex algorithm. 
A complex variant of linear programming is called integer programming, where the solution space is restricted to all integers.

• Reduction also called transform and conquer

Solve a problem by transforming it into another problem. A simple example: finding the median in an unsorted list is first translating this problem into sorting problem and finding the middle element in sorted list. The main goal of reduction is finding the simplest transformation possible.

• Using graphs

Many problems, such as playing chess, can be modeled as problems on graphs. A graph exploration algorithms are used. 
This category also includes the search algorithms and backtracking.

The probabilistic and heuristic paradigm

• Probabilistic

Those that make some choices randomly.

• Genetic

Attempt to find solutions to problems by mimicking biological evolutionary processes, with a cycle of random mutations yielding successive generations of "solutions". Thus, they emulate reproduction and "survival of the fittest".

• Heuristic

Whose general purpose is not to find an optimal solution, but an approximate solution where the time or resources to find a perfect solution are not practical.

Classification by complexity

Some algorithms complete in linear time, and some complete in exponential amount of time, and some never complete.

Algorithms resources on net.

Graph Algorithms in Bioinformatics

Bioinformatics Algorithms Description

Bioinformatics Algorithms Course Page

Bioinformatics Algorithm Demonstrations

Introduction to Bioinformatics Algorithms Lectures 1-2 by Dr. Max Alekseyev USC, 2009

Online Lectures on Bioinformatics

Sequence Alignment Algorithms

Algorithm for sequence alignment: dynamic programming

Network Protocol Analysis using Bioinformatics Algorithms

Bioinformatics Algorithms Links

Dynamic Programming

Particularly good sites...

•http://www.cis.upenn.edu/~sahuguet/MSA/
•http://www.blc.arizona.edu/courses/bioinformatics/align.html
•http://www.cs.monash.edu.au/~lloyd/tildeStrings/Notes/DPA.html
•http://www.cs.orst.edu/~schut/cs325/dynamic.htm
•http://www.catalase.com/dprog.htm
•http://bioweb.ncsa.uiuc.edu/~bioph490/BIOPH2.html#SEQUENCE_COMP
•http://www.qucis.queensu.ca/home/cisc365/javascript/dp1/index.html
Other sites...
•http://bioweb.ncsa.uiuc.edu/~bioph490/dynamic_programming_demo.html
•http://www.qucis.queensu.ca/home/cisc365/365overheads.html
•http://www.qucis.queensu.ca/home/cisc365/dp/dp.p01.html
•http://www.dgp.toronto.edu/csc270/tut_dp.html
•http://queue.ieor.berkeley.edu/~jshu/knapsack/DP/dp.html
•http://mat.gsia.cmu.edu/classes/dynamic/dynamic.html
•http://www.cs.sandia.gov/~scistra/class_3
•http://levine.sscnet.ucla.edu/Econ101/dynamic.htm
•http://mat.gsia.cmu.edu/classes/stoch_dynamic/stoch_dynamic.html
•http://mat.gsia.cmu.edu/classes/dynamic/node8.html
•http://www.maths.mu.oz.au/~moshe/dp/bibl/bibliography.html
•http://cartan.gmd.de/PAPER/ismb95/ismb_html.html
•http://screwdriver.bu.edu/bibliography/dynamic_programming.htm
•http://www.norvig.com/design-patterns/
•http://tome.cbs.univ-montp1.fr/htmltxt/Doc/manual/node137.html
•http://poem.princeton.edu/~verdu/dynamic.html
•http://www.orca1.com/opushelpweb/opusDynamic_Programming.html
•http://screwdriver.bu.edu/cn760-lectures/l7/index.htm
•http://www.ms.unimelb.edu.au/~moshe/dp/dp.html
•http://mat.gsia.cmu.edu/ORCS/0255.html
•http://aae.wisc.edu/e703/notes/a13dynpr.htm
•http://bioweb.pasteur.fr/docs/modeller/node137.html
•http://www2.uwindsor.ca/~lama/my470/ddynamic.htm
•http://students.ceid.upatras.gr/~papagel/project/ex5_6_1.htm
•http://www.cs.sunysb.edu/~algorith/lectures-good/node12.html
•http://www.cs.sunysb.edu/~algorith/lectures-good/node12.html
•http://www.utdallas.edu/~scniu/documents/7315.htm
•http://www.ii.uib.no/~pinar/seminar/larry.html
•http://www.deakin.edu.au/~gecole/books.html
•http://www.cseg.engr.uark.edu/~wessels/algs/notes/dynamic.html
•http://www.csc.liv.ac.uk/~ped/teachadmin/algor/dyprog.html
•http://www.eli.sdsu.edu/courses/fall96/cs660/notes/dynamicProg/dynamicProg.html
•http://www.cs.indiana.edu/l/www/ftp/techreports/TR514.html
•http://www.cs.brandeis.edu/~mairson/poems/node3.html
•http://www.cis.tu-graz.ac.at/igi/oaich/animations/Dynamic2.html
•http://bioweb.ncsa.uiuc.edu/~workshop/

Smith Waterman
•http://genome-www.stanford.edu/Saccharomyces/help/sw_alignment.html
•http://genome-www.stanford.edu/Saccharomyces/help/sw_details.html
•http://www.stanford.edu/~sntaylor/bioc218/final.htm
•http://www.maths.tcd.ie/~lily/pres2/sld009.htm
•http://bioweb.ncsa.uiuc.edu/~workshop/Lab_3/Smith-Waterman.htm
•http://www.tigem.it/LOCAL/SW/threshold.html
•http://sgbcd.weizmann.ac.il/genweb/help/smith-waterman.html
•http://cbrg.ethz.ch/ServerBooklet/section2_3_5.html
Needleman & Wunsch
•http://www.maths.tcd.ie/~lily/pres2/sld003.htm
•http://acer.gen.tcd.ie/~amclysag/nwswat.html
•http://www.nada.kth.se/~erikw/thesis/chapter2_3.html
•http://www.irbm.it/irbm-course95/gb/docs/amps/subsection3_6_1.html
•http://www.ibc.wustl.edu/~zuker/Bio-5495/align-html/node3.html

General (NW vs. SW vs. HMM, etc.)

•http://www.maths.tcd.ie/~lily/pres2/
•http://acer.gen.tcd.ie/~amclysag/nwswat.html
•http://laguerre.psc.edu/biomed/TUTORIALS/SEQUENCE/MULTIPLE/tutorial.html
•http://www.cse.ucsc.edu/research/compbio/

Hmms

•http://www.medmicro.mds.qmw.ac.uk/HMMER/main.html
•http://alfredo.wustl.edu/ismb96/abs/p02.html
•http://www.cse.ucsc.edu/research/compbio/html_format_papers/hughkrogh96/cabios.html
•http://wwwsyseng.anu.edu.au/~jason/hmmlinks.html
•http://www.breadfan.com/markov.html
•http://cslu.cse.ogi.edu/HLTsurvey/ch1node34.html
•http://www.ibc.wustl.edu/service/hmmalign/glocal.html
•http://www.cse.ucsc.edu/research/compbio/html_format_papers/ismb94/node5.html
•http://www.iscs.nus.edu.sg/~luakt/ic3222/lecture/nlp18new/index.htm
•http://www.cse.ucsc.edu/research/compbio/sam.html SAM Software for HMMs

Genetic Algorithms

•http://www.staff.uiuc.edu/~carroll/ga.html
•http://kal-el.ugr.es/gags.html
•http://kal-el.ugr.es/~jmerelo/GAJS.html
•http://www.genetic-programming.org/
•http://www.iitk.ac.in/kangal/deb_tut.shtml

www.cytosolve.com

Post - System Biologist

Job Description: Role of system biology is to design quantitative models of bimolecular networks and to study interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (Enzyme, metabolites and pathway).

Qualification : B.Tech or M.Sc in Bioinformatics

Required Skills:

1) Basic knowledge of cell signaling pathways, chemical/enzyme kinetics, and differential equation based modeling approach.
2) Previous laboratory experience could be an advantage
3) Good Communication skills.

santhiya.ram@mproductions.com and 044-42946555.

1. Big Data and Bioinformatics

The exponential growth in biological data, driven by advancements in sequencing technologies and high-throughput experiments, has made bioinformatics an indispensable tool. By 2030, we anticipate:

• Petabyte-Scale Data Management: Enhanced storage solutions and cloud computing platforms will allow researchers to handle the vast amounts of data generated from omics studies, including genomics, transcriptomics, and proteomics.

• AI and Machine Learning Integration: Sophisticated algorithms will uncover patterns and relationships in large datasets, enabling predictions about gene function, disease susceptibility, and therapeutic outcomes.

2. Personalized Medicine and Genomics

Bioinformatics will play a pivotal role in tailoring healthcare to individual patients. Key developments include:

• Whole-Genome Sequencing in Clinics: The decreasing cost of sequencing will make it routine in medical diagnostics, enabling personalized treatment plans based on an individual’s genetic makeup.

• Drug Repurposing and Development: Computational tools will identify potential new uses for existing drugs, accelerating the development of targeted therapies.

3. Advancing Computational Tools

The future will see the development of more user-friendly and powerful bioinformatics tools:

• Graph-Based Approaches: Enhanced algorithms for analyzing complex biological networks, such as protein-protein interaction maps.

• Visualization Tools: Intuitive software for visualizing multi-dimensional data, enabling researchers to interpret findings more effectively.

4. Synthetic Biology and Systems Biology

Bioinformatics will continue to drive progress in synthetic and systems biology by:

• Gene Circuit Design: Leveraging computational models to design and simulate synthetic biological systems.

• Understanding Cellular Pathways: Integrating multi-omics data to model cellular processes with unprecedented accuracy.

5. Bioinformatics in Agriculture and Environmental Science

Beyond healthcare, bioinformatics will revolutionize agriculture and environmental conservation:

• Crop Improvement: Genomic studies will help develop high-yield, disease-resistant, and climate-resilient crops.

• Microbial Ecology: Metagenomics will enhance our understanding of microbial communities, aiding in bioremediation and ecosystem management.

6. Democratization of Bioinformatics

Open-source software and accessible education will broaden participation in bioinformatics research:

• Community-Driven Projects: Collaborative platforms like GitHub will continue to foster innovation in tool development.

• Education and Training: Online courses and workshops will bridge skill gaps, enabling researchers from diverse backgrounds to contribute.

Challenges and Ethical Considerations

While the future is bright, challenges remain. Data privacy and ethical concerns surrounding genetic information require careful navigation. Furthermore, addressing the digital divide is critical to ensuring equitable access to bioinformatics resources globally.

Conclusion

The future of bioinformatics is boundless, with opportunities to revolutionize our understanding of life and improve human health. As technologies evolve and collaborations flourish, bioinformatics will undoubtedly remain at the forefront of scientific discovery, unlocking the secrets of life one dataset at a time.

A walk-in Interview for the following position tenable at the Bioinformatics Infrastructure Facility (BIF), Department of Biotechnology, Alagappa University will be held at the Department of Biotechnology, Science Campus, Alagappa University, Karaikudi 630 004 on 03.08.2013 (Saturday) at 12:30 PM. This national facility is funded by the Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi. The main objectives of the Centre involve teaching and research activities in bioinformatics/biotechnology.

1. Studentship (One Post):

Stipend : Rs. 5000 p.m. (consolidated)

Qualification: M.Sc., in Bioinformatics/Biotechnology/Biophysics/Biochemistry/
Life Sciences

Interested candidates are encouraged to send their Curriculum Vitae by email to alagappauniv.btisnet@nic.in in advance. On the day of interview, the candidates must produce original certificates in proof of their educational qualification and experience and a recommendation letter from the Head of the Department/Institution where last studied/worked. Candidates who have already passed the required Degree alone are eligible to appear for interview. No TA&DA will be given for attending the interview.

Advertisement: http://www.alagappabiotech.org/Notification.pdf

1️⃣ Data Analysis for the Life Sciences
A fantastic starting point to learn statistics, R programming, and exploratory data analysis in the context of biology. The best part? It’s available free online from HarvardX.

2️⃣ Practical Computing for Biologists
The very first book I picked up when I started learning computational biology. It’s beginner-friendly and focuses on essential computing skills every biologist needs.

3️⃣ A Primer for Computational Biology
An open-access, hands-on introduction to computational biology concepts and coding techniques. Perfect if you want to learn through real examples.

4️⃣ Computational Genomics with R
For those who already know R and want to dive deeper into genome-scale data analysis, from sequence alignment to gene expression.

5️⃣ The Biologist’s Guide to Computing
Bridges the gap between biological problems and computational thinking, making it easier for life scientists to approach programming and data analysis.

6️⃣ Bioinformatics Data Skills
A must-read to sharpen your bioinformatics toolkit — from command-line skills to reproducible research workflows. Ideal once you’ve covered the basics.

7️⃣ Bioinformatics Workbook
A practical tutorial series to help scientists design bioinformatics projects, analyze data, and understand best practices.

8️⃣ Modern Statistics for Modern Biology
An essential guide to modern statistical methods applied to biology, blending theory with hands-on examples in R.

9️⃣ Algorithms on Strings, Trees, and Sequences by Dan Gusfield
A classic reference for anyone wanting to understand the algorithms behind sequence alignment, genome assembly, and biological data structures.

People using computers were often seen
as helpers, not leaders—
useful, but not essential.

Sometimes, the criticism was direct.
Sometimes subtle.
But the message was the same—
this work doesn’t really count.

Then biology changed.
The questions became bigger,
and experiments alone
were no longer enough.

Organizing knowledge by hand worked once.
Now it needs computers—
to handle scale, speed, and complexity.

Some patterns are simply invisible
if you look at one sample.
You need many—
and the right tools to understand them.

So we started building maps—
of genomes, cells, and systems.
Not perfect,
but extremely useful.

Ideas also had to become clearer.
It’s no longer enough to say something sounds right—
you have to measure it.

The divide between “types” of biologists
never really made sense.
We are solving the same problems—
just in different ways.

Progress didn’t wait for agreement.
It moved forward with data,
with code,
and with careful analysis.

What matters now is simple:
• Biology depends on computation
• Coding is an important skill
• Statistics helps us think clearly
• And the people building these tools
are shaping the future of science

1. Accelrys Software Solution Pvt Ltd.
12th Floor, Discover, ITPL, White Field, Bangalore-65.
www.accelrys.com

2. Apticraft Systems (P) Ltd.
142, Electronics Complex, Pardeshipura, Indore – 452010 (M.P.), India
www.apticraft.com

3. Aptuit Informatics
Plot No. 100-103, Export Promotion Industrial Park, White Field, Bangalore-560066
www.aptiuit.com

4. Bigtec
J. K. Towers, 8th Block, Sangam Circle,46th Cross, Bangalore-560082.
www.bigtec.org

5. Bijam Biosciences Private Limited
Nagarjuna Hills, Hyderabad 500 082, India
www.nagarjunagroup.com

6. Bio Base Databases India Pvt Ltd.
Crescent Towers, 4th Floor, No : 32/1, Crescent Road, Bnagalore – 560 001
www.biobase-international.com

7. BioImagene India Pvt. Ltd.
4th floor, C-Wing, Godrej Eternia, Shivajinagar, Pune-411005
www.bioimagene.com

8. BioInformatics Institute Of India – Noida
C-56 A/28, Sector -62, Noida – 201 301
www.bii.in

9. CLC bio India Pvt Ltd
#Plot No. 51, H.No. 8-3-214/51, Srinivasa Nagar (West) Ameerpet Hyderabad – 500 038
www.clcbio.com/india

10. CytoGenomics India (P) Ltd.
#3004, 12A Main HAL 2nd Stage, Bangalore 560008
www.silicocyte.com

11. Genotypic Technology
211, 6th Cross, 80ft Road, RMV II Stage, Bangalore 560094
www.genotypic.co.in

12. Genvea Biosciences
Dr. D. T. Singh, CSO, 53, Craig Rd. #04-01, Singapore-089691
www.genvea.com

13. Helix Info Systems
132 A, II Floor, Sterling Towers, IV Cross Street, Sterling Road, Nungambakkam, Chennai.
www.helixinfosystems.com

14. Jalaja Technologies Pvt. Ltd.,
21/1,Victoria Layout, Victoria Road, Bangalore-47
www.jalaja.com

15. Jubilant Biosys Ltd
#96, Industrial Subrub, 2nd Stage, Yeshwanthpur, Bangalore- 560022
Jubilant Organosys Ltd.
1A, Sector 16A, Noida – 201 301 (India)
www.jubl.com

16. Kshema Technologies
#1, Global Village, Mylasandra, Mysore Road, Bangalore-560 059.
www.mphasis.com

17. LabNetworx
B-704, Gitanjali Apartments, Vikas Marg Extension, New Delhi – 110 092
www.labnetworx.com

18. LabVantage Solutions Pvt. Ltd.
Bengal Intelligent Park, Building C, 2nd Floor, Sector V, Salt Lake Electronics Complex, Kolkata – 700 091
www.labvantage.com

19. LeadInvent, 
2nd Floor, Biotech Centre, University of Delhi, South Campus, Benito Juarez Road, New Delhi 110021, India
Contact no: +91 11 24119241
Email: contact@leadinvent.com
www.leadinvent.com

20. Mascon Life Sciences
B – 8/ 10, Vasant Vihar, New Delhi 110057, India
www.masconlifesciences.com

21. Molecular Connections P Ltd
Kandala Mansion, 2/2 Kariappa Road, Near Krishna Rao Park, Basavangudi, Bangalore – 4
www.molecularconnections.com

22.Novo Informatics Pvt. Ltd.
TBIU, 2nd Floor, Synergy Building, Indian Institute of Technology, Hauz Khas, New Delhi-16.
Contact: 91-11-26581524, 91-11-26581766(Extension: 28)
Email: info@novoinformatics.com
www.novoinformatics.com

23. Ocimum Biosolutions (India) Ltd
6th Floor, Reliance Classic, Road No.1 Banjara Hills, Hyderabad 500 034, India.
www.ocimumbio.com

24. Scube Scientific Software Solutions
613, Hemkunt Chambers, 89, Nehru Place, New Delhi -110 019
www.scribeindia.com

25. Siri Technologies Pvt Ltd.
38/C -23, South End Road, Basavanagudi, Bangalore-56004.
www.siritech.com

26. Strand Life Sciences Pvt. Ltd.
#237, Sir C. V. Raman Avenue, Raj Mahal Vilas, Bangalore 560 080 INDIA
www.strandls.com

27. SooryaKiran Bioinformatics (P) Ltd
TBIC-13, Tejaswini Building, Technopark, Thriruvananthapuram- 695 584, Keralam, India

Ph: +91 471 4060979,+91 9895404104
Email: reachus@sooryakiran.com
http://www.sooryakiran.com

28. Systat Software Asia Pacific
4th Floor, Block 1, Shankar Narayan Building, No.25, MG Road, Bangalore – 560001
www.systat.com

29. ABC Genomics (India) Pvt. Ltd.
Biotech Park, Sector G, Jankipuram, Kursi Road, Lucknow-226021, U.P., INDIA
Tel +91-522-4068579, Email: director@abcgenomics.com
www.abcgenomics.com

30. en-GENE-ier's Core Technology Services,
1/340, Virat Khand, Gomtinagar, 
(Near Maharaja Agrasen Public School)
lucknow-226010, U.P., India.
http://www.bio.egicore.com/

Best of luck for your job hunts :).

Sequence Analysis and General Bioinformatics

• BLAST, Ian Korf, Mark Yandell, Joseph Bedell, 2003, O'Reilly
• Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases, Scott Markel, Darryl Leon, 2003, O'Reilly
• Bioinformatics for Geneticists, Michael Barnes, Ian C Gray (Editors), 2003, John Wiley & Sons
• Bioinformatics for Dummies, Jean-Michel Claverie, Cedric Notredame, 2003, John Wiley & Sons
• Mathematics of Genome Analysis, Jerome K. Percus, 2002, Cambridge Univ Press
• Bioinformatics Computing, Bryan P. Bergeron, 2002, Prentice Hall
• Evolutionary Computation in Bioinformatics, Gary B. Fogel, David W. Corne (Editors), 2002, Morgan Kaufmann
• Introduction to Bioinformatics, Arthur M. Lesk, 2002, Oxford University Press
• Instant Notes in Bioinformatics, D.R. Westhead, J. H. Parish, R.M. Twyman, 2002, Bios Scientific Pub
• Fundamental Concepts of Bioinformatics, Dan E. Krane, Michael L. Raymer, Michaeel L. Raymer, Elaine Nicpon Marieb, 2002, Benjamin/Cummings
• Essentials of Genomics and Bioinformatics, C. W. Sensen (Editor), 2002, John Wiley & Sons
• Current Topics in Computational Molecular Biology (Computational Molecular Biology), Tao Jiang, Ying Xu, Michael Zhang (Editors), 2002, MIT Press
• Hidden Markov Models for Bioinformatics, Timo Koski, Timo Koskinen, 2001, Kluwer Academic Publishers
• Bioinformatics: From Genomes to Drugs, Thomas Lengauer (Editor), 2001, John Wiley & Sons
• Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health), Warren Ewens, Gregory Grant, 2001, Springer Verlag
• Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition, Andreas D. Baxevanis, B. F. Francis Ouellette, 2001, Wiley-Interscience
• Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning), Pierre Baldi, Soren Brunak, Sren Brunak, 2001, MIT Press
• Introduction to Bioinformatics, T eresa Attwood, David Parry-Smith, 2001, Prentice Hall
• Bioinformatics: A Primer, Charles Staben, 2001, Jones & Bartlett Pub
• Data Analysis and Classification for Bioinformatics, Arun Jagota, 2000, AKJ Academics
• Bioinformatics: Sequence and Genome Analysis, David W. Mount, 2001, Cold Spring Harbor Laboratory Press
• Bioinformatics: A Biologist's Guide to Biocomputing and the Internet, Stuart M. Brown, 2000, Eaton Pub Co
• Bioinformatics: Sequence, Structure and Databanks: A Practical Approach (The Practical Approach Series, 236), Des Higgins (Editor), Willie Taylor (Editor), 2000, Oxford Univ Press
• Neural Networks and Genome Informatics, Cathy H. Wu, Jerry W. McLarty, 2000, Elsevier Science
• Computational Molecular Biology: An Introduction (Wiley Series in Mathematical and Computational Biology), Peter Clote and Rolf Backofen, 2000, John Wiley & Sons
• Computational Molecular Biology: An Algorithmic Approach, Pavel A. Pevzner, 2000, MIT Press
• Post-Genome Informatics, Minoru Kanehisa, 2000, Oxford Univ Press
• Mathematical and Computational Biology: Computational Morphogenesis, Hierarchical Complexity, and Digital Evolution, Chrystopher L. Nehaniv, 1999, American Mathematical Society
• Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications, Jason T. L. Wang, Bruce A. Shapiro, Dennis Elliott Shasha (Editors), 1999, Oxford Univ Press
• Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, David Sankoff and Joseph Kruskal (Editors), 1999, Cambridge University Press
• Bioinformatics Basics: Applications in Biological Science and Medicine, Hooman Rashidi, 1999, CRC Press
• Bioinformatics: Methods and Protocols (Methods in Molecular Biology, Vol 132), Stephen Misener and Stephen A. Krawetz (Editors),1999, Humana Press
• Bioinformatics: Databases and Systems, Stanley Letovsky (Editor),1999, Kluwer Academic Publishers
• Computational Molecular Biology, P. Green, 1998, Blackwell Science Inc.
• Computational Methods in Molecular Biology (New Comprehensive Biochemistry, V. 32), Steven L. Salzberg, David B. Searls, Simon Kasif (Editors), 1998, Elsevier Science Ltd.
• Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Richard Durbin, S. Eddy, A. Krogh, G. Mitchison, 1998, Cambridge University Press
• Guide to Human Genome Computing, M. J. Bishop (Editor), 1998, Academic Press
• Introduction to Computational Molecular Biology, Joao Meidanis, Joao C. Setabal, 1997, PWS Pub. Co.
• Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Dan Gusfield, 1997, Cambridge University Press
• Sequence Data Analysis Guidebook, Simon R. Swindell (Editor), 1997, Humana Press
• High Performance Computational Methods for Biological Sequence Analysis, Tieng K. Yap, Ophir Frieder, Robert L. Martino, 1996, Kluwer Academic Pub.
• Computer Methods for Macromolecular Sequence Analysis, Methods in Enzymology, volume 266, Russell F. Doolittle (Editor), 1996, Academic Press
• DNA and Protein Sequence Analysis: A Practical Approach (Practical Approach Series , No 171), 1996, M. J. Bishop and C. J. Rawlings (Editors), 1996, IRL Press
• Molecular Bioinformatics: Algorithms and Applications, Steffen Schulze-Kremer, 1995, Walter De Gruyter
• Introduction to Computational Biology - Maps, sequences and genomes, Michael S. Waterman, 1995, Chapman & Hall
• Computer Analysis of Sequence Data, Annette M. Griffin and Hugh G. Griffin (Editors), 1994, Humana Press
• Artificial Intelligence and Molecular Biology, Lawrence Hunter (Editor), 1993, AAAI Press
• Sequence Analysis Primer, Michael Gribskov and John Devereux (Editors), 1992, Oxford University Press
• Mathematical Methods of Analysis of Biopolymer Sequences (Dimacs Series in Discrete Mathematics and Theoretical Computer Science ; Volume 8), S. G. Gindikin, 1992, American Mathematical Society
• Mathematical Methods for DNA Sequences, Michael S. Waterman (Editor), 1989, CRC Press

Programming Books for Bioinformatics

• Mastering Perl for Bioinformatics, James D. Tisdall, 2003, O'Reilly
• Genomic Perl: From Bioinformatics Basics to Working Code, Rex A. Dwyer, 2002, Cambridge University Press
• Beginning Perl for Bioinformatics, James Tisdall, 2001, O'Reilly
• Developing Bioinformatics Computer Skills, Cynthia Gibas, Per Jambeck, 2001, O'Reilly

General Genomics

• Functional Microbial Genomics (Volume 33), Brendan Wren, Nick Dorrell, 2003, Academic Press
• Discovering Genomics, Proteomics, and Bioinformatics, A. Malcolm Campbell, Laurie J. Heyer, 2002, Benjamin/Cummings
• Genomes, Terence A. Brown, 2002, John Wiley & Sons
• Essentials of Medical Genomics, Stuart M. Brown , 2002, John Wiley & Sons
• A Primer of Genome Science, Greg Gibson, Spencer V. Muse, 2002, Sinauer Associates
• Pathogen Genomics: Impact on Human Health, Karen Joy, Phd Shaw (Editors), 2002, Humana Press
• Genomics, John E. Antonopoulos, 2000, Xlibris Corporation
• Genomics and Proteomics: Functional and Computational Aspects, Sandor Suhai (Editor), 2000, Plenum Pub Corp
• Functional Genomics: A Practical Approach (The Practical Approach Series, 235), S. Hunt and F. Livesey (Editors), 2000, Oxford Univ Press
• Human Molecular Genetics, Andrew P. Read, Tom Strachan 1999, BIOS Scientific Publishers Ltd.
• Genomics: The Science and Technology Behind the Human Genome Project, Charles R. Cantor and Cassandra L. Smith, 1999, John Wiley & Sons
• Cells: A Laboratory Manual, 3 volumes, David L. Spector, Robert D. Goldman, Leslie A. Leinwand, 1998, Cold Spring Harbor Laboratory Press
• Genome Analysis: A Laboratory Manual, 4 volumes, Bruce Birren, et al. (Editors), 1997, Cold Spring Harbor Laboratory Press
• The Human Genome Project, N. G. Cooper (Editor), 1994, University Science Books

Comparative Genomics

• Handbook of Comparative Genomics: Principles and Methodology, Cecilia Saccone, Graziano Pesole, 2003, Wiley-Liss
• Sequence - Evolution - Function: Computational Approaches in Comparative Genomics, Eugene V. Koonin, Michael Y. Galperin, 2002, Kluwer Academic Publishers
• Comparative Genomics - Empirical and Analytical Approaches to Gene Order Dynamics, Map Alignment and the Evolution of Gene Families, David Sankoff and Joseph H. Nadeau, 2000, Kluwer Academic Pub
• Comparative Genomics, Melody Clark (Editor), 2000, Kluwer Academic Pub

Proteomics

• Proteins and Proteomics: A Laboratory Manual, Richard J. Simpson (Editor), Cold Spring Harbor Laboratory
• Proteomics in Practice: A Laboratory Manual of Proteome Analysis , Reiner Westermeier, Tom Naven, 2002, John Wiley & Sons
• Posttranslational Modifications of Proteins: Tools for Functional Proteomics (Methods in Molecular Biology, Vol 194) , Christoph Kannicht (Editor), 2002, Humana Press
• Peptide Arrays on Membrane Supports: Synthesis and Applications (Springer Lab Manual), Joachim Koch, Michael Mahler (Editors), 2002, Springer Verlag
• Proteomics , Timothy Palzkill, 2002, Kluwer Academic Publishers
• Introduction to Proteomics: Tools for the New Biology , Daniel C. Liebler (Editor), 2001, Humana Press
• Proteome Research: Mass Spectrometry (Principles and Practice) , P. James (Editor), 2001, Springer Verlag
• Interpreting Protein Mass Spectra: A Comprehensive Resource , A. Peter Snyder, 2000, American Chemical Society
• Protein Sequencing and Identification Using Tandem Mass Spectrometry , Michael Kinter, Nicholas E. Sherman, 2000, Wiley-Interscience
• From Genome to Proteome: Advances in the Practice and Application of Proteomics, Michael J. Dunn (Editor), 2000, Vch Verlagsgesellschaft Mbh
• Proteomics: From Protein Sequence to Function, S. Pennington (Editor), M. Dunn (Editor), 2000, Springer Verlag
• Proteome Research: Two-Dimensional Gel Electrophoresis and Detection Methods (Principles and Practice), T. Rabilloud (Editor), 2000, Springer Verlag
• Proteome and Protein Analysis, R. M. Kamp, D. Kyriakidis, th Choli-Papadopoulou (Editor), 1999, Springer Verlag
• Proteome Research: New Frontiers in Functional Genomics, M. R. Wilkins, et al. (Editors), 1997, Springer Verlag

Protein Structure

• Structural Bioinformatics, Philip E. Bourne, Helge Weissig (Editors), 2003, John Wiley & Sons
• Protein Structure Prediction: Bioinfomatic Approach, I.F. Tsigelny, 2002, International University Line
• Introduction to Protein Architecture: The Structural Biology of Proteins, Arthur M. Lesk, 2001, Oxford University Press
• Protein Structure Prediction: Methods and Protocols, David M. Webster (Editor), 2000, Humana Press
• Introduction to Protein Structure, Carl-Ivar Branden, John Tooze, 1999, Garland Publishing
• Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding, Alan Fersht, 1999, Freeman

Pharmacogenomics

• Pharmacogenomics: Social, Ethical, and Clinical Dimensions, Mark A. Rothstein (Editor), 2003, Wiley-Liss
• Pharmacogenomics: The Search for Individualized Therapies, Julio Licinio, Ma-Li Wong (Editors), 2002, John Wiley & Sons
• Pharmacogenomics, Werner Kalow, Urs A. Meyer, Rachel Tyndale (Editors), 2001, Marcel Dekker
• Pharmacogenetics and Pharmcogenomics: Recent Conceptual and Technical Advances (Pharmacology, Volume 61, Number 3, 2000), Elliot S. Vesell (Editor), 2000, S. Karger Publishing
• Pharmacogenetics, Wendell Weber, 1997, Oxford University Press

DNA Microarrays

• Statistical Analysis of Gene Expression Microarray Data, T. P. Speed (Editor), 2003, CRC Press
• Microarray Gene Expression Data Analysis: A Beginner's Guide, Helen C. Causton, John Quackenbush, Alvis Brazma, 2003, Blackwell Publishers
• The Analysis of Gene Expression Data (Statistics for Biology and Health), G. Parmigiani, E. S. Garrett, R. A. Irizarry, S. Zeger , Graeme Clark (Editors), 2003, Springer Verlag
• A Practical Approach to Microarray Data Analysis, Daniel P. Berrar, Werner Dubitzky, Martin Granzow (Editors), 2002, Kluwer Academic Publishers
• DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling, Pierre Baldi, G. Wesley Hatfield, 2002, Cambridge University Press
• DNA Microarrays: A Molecular Cloning Manual, David Bowtell, Joseph Sambrook (Editors), 2002, Cold Spring Harbor Laboratory
• DNA Array Image Analysis: Nuts & Bolts, Gerda Kamberova, Shishir Shah, 2002, DNA Press
• Microarray Analysis, Mark Schena, 2002, John Wiley & Sons
• A Biologist's Guide to Analysis of DNA Microarray Data, Steen Knudsen, 2002, John Wiley & Sons
• Microarrays for an Integrative Genomics (Computational Molecular Biology), Isaac S. Kohane, Alvin Kho, Atul J. Butte, 2002, MIT Press
• Microarrays for the Neurosciences: An Essential Guide (Cellular and Molecular Neuroscience), Daniel H. Geschwind, Jeffrey P. Gregg (Editors), 2002, MIT Press
• DNA Microarrays: Gene Expression Applications, Bertrand Jordan (Editor), 2001, Springer Verlag
• DNA Arrays: Methods and Protocols (Methods in Molecular Biology, Volume 170), Jang B. Rampal (Editor), 2001, Humana Press
• DNA Arrays: Technologies and Experimental Strategies, Elena V. Grigorenko (Editor), 2001, CRC Press
• Microarray Biochip Technology, Mark Schena (Editor), 2000, Eaton Pub
• Expression Genetics: Accelerated and High-Throughput Methods (Biotechniques Update Series), Michael McClelland (Editor), Arthur B. Pardee (Editor), 1999, Eaton Pub
• DNA Microarrays: A Practical Approach (Practical Approach Series 205), Mark Schena (Editor), 1999, Oxford Univ Press
• cDNA Preparation and Characterization (Methods in Enzymology Volume 303), S.M. Weissman (Editor), 1999, Academic Press

Systems Biology, Genetic and Biochemical Network

• Handbook of Graphs and Networks : From the Genome to the Internet, Stefan Bornholdt, Heinz Georg Schuster (Editors), 2003, Vch Verlagsgesellschaft Mbh
• Computational Cell Biology, Christopher Fall, Eric Marland, John Wagner, John Tyson (Editors), 2002, Springer Verlag
• Gene Regulation and Metabolism: Post-Genomic Computational Approaches (Computational Molecular Biology), Julio Collado-Vides, Ralf Hofestadt (Editors), 2002, MIT Press
• Foundations of Systems Biology, Hiroaki Kitano (Editor), 2001, MIT Press
• Genomic Regulatory Systems: Development and Evolution, Eric H. Davidson , 2001, Academic Press
• Genes & Signals, Mark Ptashne, Alexander Gann, 2001, Cold Spring Harbor Laboratory
• Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology), James M. Bower and Hamid Bolouri (Editors), 2001, MIT Press
• Protein-Protein Interactions: A Molecular Cloning Manual, Erica Golemis (Editor), 2001, Cold Spring Harbor Laboratory
• Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists, Eberhard O. Voit, 2000, Cambridge University Press
• Mathematical Physiology, James P. Keener, James Sneyd, 1998, Springer Verlag

DNA Sequencing

• DNA Sequencing: From Experimental Methods to Bioinformatics (Introduction to Biotechniques Series), Luke Alphey, 1997, Springer Verlag
• Automated DNA sequencing and analysis, Adams M.D., Fields C., Venter J.C. (Editors), 1994, Academic Press

Apart from above mentioned books, you can also find some useful books links at following mentioned URLs:

http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713

http://www.amazon.com/Bioinformatics-Genes-Proteins-Computers-Advanced/dp/1859960545

http://www.amazon.com/Introduction-Bioinformatics-Algorithms-Computational-Molecular/dp/0262101068

http://books.google.no/books?id=pxSM7R1sdeQC&dq=Pierre+baldi+%2B+bioinformatics&printsec=frontcover&source=bn&hl=en&ei=IoGRS6uCIJT-NYLA8Z0N&sa=X&oi=book_result&ct=result&redir_esc=y#v=onepage&q&f=false

http://www.amazon.com/Statistical-Methods-Bioinformatics-Introduction-Statistics/dp/0387400826

If you think your favourite books are not listed then please write it down in comment section for the benefits of other users. Feel free to add many more books in comment section. 

The project involves identification and characterization of transcription factors (TFs) from the Arabidopsis shoot apical meristem stem cell niche using genomic approaches and construction of a gene regulatory network for the identified TFs.

Positions: 1

Duration: 1 year but extendable up to three years based on performance and availability of funds.

Emoluments: As per DST rules.

Essential Qualifications: M.Sc. in any branch of life sciences with excellent academic record with CSIR-UGC NET or DBT-JRF. Candidate having previous work experience in the area of bioinformatics, molecular biology and genetics is preferred, but not required.

How to Apply: Applicants are requested to send a cover letter outlining previous research experiences and reasons for joining this position. Please send your complete bio-data including the cover letter as PDF attachment by email to Dr. Ram Yadav at ryadav@iisermohali.ac.in

Last date of submission is 17.00 IST, August 10, 2013.

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