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

Address of the bookmark: https://chunlab.wordpress.com/tetra-nucleotide-analysis/

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.

Our Research Team prides itself on driving cutting edge, industrial-scale
science to make an impact that belies the team’s size, in an environment
and culture that fosters creativity, innovation, collaboration, and fun.

More than 80% of our customers consent to participate in research, and as
a result of their participation, we have one of the largest recontactable,
genotyped, and phenotyped research cohorts in the world. The scope and
breadth of our vision means that most of the methods and tools necessary
to unlock the potential of this unique resource for discovery have yet
to be developed.

Our science has garnered the respect of many members of the
broader scientific community. For a list of our publications, see
www.23andme.com/publications/for-scientists/.

Join us! Visit our Careers page (www.23andMe.com/careers) to learn more
about these open positions:

• Scientist, Research Communications
• Bioinformaticist
• Computational Biologist, Ancestry R&D
• Scientist/Senior Scientist, Statistical Genetics
• Scientist/Senior Scientist, Survey Methodology
• Scientist/Senior Scientist, Health R&D
• Senior Computational Biologist
• Biostatistician

pfontanillas@23andme.com

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

ABACAS, that is able to contiguate contigs from a de novo assembly against a closely related reference.

IMAGE, an iterative approach for closing gaps in assembled genomes using mate pair information. It is able to close gaps left open by the assembler in a draft genome, even when using the same data sets as used by the original assembler.

iCORN, that enables errors in the consensus sequence to be corrected by iteratively mapping reads to the current assembly. An improved version, especially correction Pacfic Bioscience assemblies (PacBio) can be found here.

RATT, a tool to transfer the annotation from a reference genome, or an earlier assembly, onto the latest assembly.

PAGIT bundles these software and makes them more accessible for users.

Address of the bookmark: http://www.sanger.ac.uk/science/tools/pagit

This course is organized with support from the IAP “Belgian Medical Genomics Initiative”, SymBioSys and the Genomics Core.

For inquiries, please email Ms Narcisse Opdekamp ( narcisse.opdekamp@uzleuven.be ).

More at >> http://gc.uzleuven.be/

For example, you can run a search in Assembly and use check boxes (see left side of screenshot below) to refine the set of genome assemblies of interest. Then, just open the “Download assemblies” menu, choose the source database (GenBank or RefSeq), choose the file type, and start the download. An archive file will be saved to your computer that can be expanded into a folder containing your selected genome data files.

More at https://ncbiinsights.ncbi.nlm.nih.gov/2017/05/08/genome-data-download-made-easy/

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 :).

We describe a new method for predicting the ancestral order and orientation of those intervals from their observed adjacencies in modern species. We combine the results from this method with data from chromosome painting experiments to produce a map of an early mammalian genome that accounts for 96.8% of the available human genome sequence data. The precision is further increased by mapping inversions as small as 31 bp. Analysis of the predicted evolutionary breakpoints in the human lineage confirms certain published observations but disagrees with others. Although only a few mammalian genomes are currently sequenced to high precision, our theoretical analyses and computer simulations indicate that our results are reasonably accurate and that they will become highly accurate in the foreseeable future. Our methods were developed as part of a project to reconstruct the genome sequence of the last ancestor of human, dogs, and most other placental mammals;

Address of the bookmark: http://www.bx.psu.edu/miller_lab/car/