More at http://bioinf.spbau.ru/quast

http://bioinformatics.oxfordjournals.org/content/early/2013/03/09/bioinformatics.btt086.long

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2013/03/09/bioinformatics.btt086.long

1. National Science Foundation: For the love of science! Head here when searching for ways to pay for that gargantuan geology or bigtime biology project. And other disciplines, of course.
2. Alexander von Humboldt Foundation: Humbolt fellows embody the spirit of science and leadership alike, and the organization sponsors thinkers in Germany and abroad alike.
3. National Parks Foundation: Americans who want to preserve their country’s gorgeous parks and trails pitch projects to this governing body, concerned largely with ecology and accessibility issues.
4. Alfred P. Sloan Foundation: Money is available here throughout the year, covering science and engineering and their overlaps with civics, education, and economics.
5. The Whitehall Foundation: The Whitehall Foundation, through its program of grants and grants-in-aid, assists scholarly research in the life sciences. It is the Foundation’s policy to assist those dynamic areas of basic biological research that are not heavily supported by Federal Agencies or other foundations with specialized missions.
6. Human Frontier Science Program: Research grants from the Human Frontier Science Program are provided for teams of scientists from different countries who wish to combine their expertise in innovative approaches to questions that could not be answered by individual laboratories.
7. The U.S. Small Business Administration: The U.S. Small Business Administration offers research grants to small businesses that are engaged in scientific research and development projects that meet federal R&D objectives and have a high potential for commercialization.
8. The Welch Foundation: The Welch Foundation provides grants for a minimum of $60,000 in funding to support research in chemistry by a full-time tenured or tenure-track faculty member who serves as principal investigator. Applications are restricted to universities, colleges, or other educational institutions located within the state of Texas.
9. The Leakey Foundation: The Leakey Foundation offers research grants of up to $25,000 to doctoral and post-doctoral students as well as senior scientists, for research related specifically to human origins.
10. American College of Sports Medicine: The American College of Sports Medicine offers several possible grants to research students in the areas of general and applied science.
11. BD Biosciences: BD Biosciences Research Grants* aim to reward and enable important research by providing vital funding to scientists pursuing innovative experiments that advance the scientific understanding of disease. This ongoing program includes grants for immunology and stem cell research, totaling $240,000 annually in BD Biosciences research reagents.
12. Sigma Xi: The Sigma Xi program awards grants for research in areas of science, engineering, astronomy and vision.
13. The United Engineering Foundation: The United Engineering Foundation advances the engineering arts and sciences for the welfare of humanity. It supports engineering and education by, among other means, making grants.
14. National Institutes of Health: Foreign and American medical professionals hoping to advance their research might want to consider one of these prestigious (and generous) endowments.
15. Whitaker International Program: Biomedical engineering’s global reach serves as this organization’s focus, so applications here need to open themselves up to international institutions and applications.
16. U.S. National Library of Medicine: From tech to small businesses, the USNLM’s funding programs cover a diverse range of fields that feed into medicine.
17. American Heart Association: Most of the AHA’s research involves cardiovascular disease and stroke, with funding in these areas available in the winter and the summer.
18. Society for Women’s Health Research: Female engineers and scientists benefit from these grants meant to support anything that improves women’s health and education on a global scale.
19. Damon Runyon Cancer Research Foundation: Every cent donated to the DRCRF directly feeds into fellowships and awards bringing humanity closer to cancer cures and improved prevention regimens.
20. Burroughs Wellcome Fund: Emerging scientists working in largely underrecognized and underfunded biomedical fields are the main recipients of this private foundation’s money.
21. The Foundation for Alcohol Research: As one can probably assume from the name, The Foundation for Alcohol Research contributes to projects studying how alcohol impacts human physical and mental health.
22. Alex’s Lemonade Stand: These grants go towards doctors, nurses, and medical researchers concerned with curing childhood cancer.
23. National Cancer Institute: Thanks to a little help from their friends in Congress, the National Cancer Institute have $4.9 billion to share with medical science.
24. Bush Foundation Fellowship Program: Leadership’s many forms are the main focus of the BFFP, who give money to folks dedicated to improving their communities.
25. The David & Lucile Packard Foundation: Nonprofit organizations dedicated to growing education, charities, health, and other social justice causes should consider seeing what money they can land through this foundation.
26. American Federation for Aging Research: AFAR provides up to $100,000 for a one- to two-year award to junior faculty (M.D.s and Ph.D.s) to conduct research that will serve as the basis for longer term research efforts in the areas of Biomedical and clinical research.
27. The Muscular Dystrophy Association: The MDA is pursuing the full spectrum of research approaches that are geared toward combating neuromuscular diseases. MDA also helps spread this scientific knowledge and train the next generation of scientific leaders by funding national and international research conferences and career development grants.
28. The Cystic Fibrosis Foundation: The CF Foundation offers competitive awards for research related to cystic fibrosis. Studies may be carried out at the subcellular, cellular, animal, or patient levels. Two of these funding mechanisms include Pilot and Feasibility Awards and Research Grants.
29. The National Ataxia Foundation: The National Ataxia Foundation (NAF) is committed to funding the best science relevant to hereditary and sporadic types of ataxia in both basic and translational research. NAF invites research applications from U.S.A. and International non-profit and for-profit institutions.
30. The March of Dimes: In keeping with its mission the March of Dimes research portfolio funds many different areas of research on topics related to preventing birth defects, premature birth and infant mortality.
31. The American Tinnitus Association: The American Tinnitus Association Research Grant Program financially supports scientific studies investigating tinnitus. Studies must be directly concerned with tinnitus and contribute to ATA’s goal of finding a cure.
32. American Brain Tumor Association: The American Brain Tumor Association provides multiple grants for scientists doing research in or around the field of brain tumor research.
33. American Cancer Society: The American Cancer Society also offers grants that support the clinical and/or research training of health professionals. These Health Professional Training Grants promote excellence in cancer prevention and control by providing incentive and support for highly qualified individuals in outstanding training programs or responsible for training.
34. Thrasher Research Fund: The Thrasher Research Fund provides grants for pediatric medical research. The Fund seeks to foster an environment of creativity and discovery aimed at finding solutions to children’s health problems. The Fund awards grants for research that offers substantial promise for meaningful advances in prevention and treatment of children’s diseases, particularly research that offers broad-based​ applications.
35. Foundation for Physical Therapy: The Foundation supports research projects in any patient care specialty.
36. International OCD Foundation: The IOCDF awards grants to investigators whose research focuses on the nature, causes and treatment of OCD and related disorders.
37. Susan G. Komen: Susan G. Komen sustains a strong commitment to supporting research that will identify and deliver cures for breast cancer.
38. American Association for Cancer Research: The AACR promotes and supports the highest quality cancer research. The AACR has been designated as an organization with an approved NCI* peer review and funding system.
39. American Thyroid Foundation: The ATA is committed to supporting research into better ways to diagnose and treat thyroid disease.
40. The Foundation for Anesthesia Education and Research: The FAER provides research grant funding for anesthesiologists and anesthesiology trainees to gain additional training in basic science, clinical and translational, health services and education research.
41. The Alzheimer’s Association: The Alzheimer’s Association funds a wide variety of investigations by scientists at every stage of their careers. Each grant is designed to meet the needs of the field and to introduce fresh ideas in Alzheimer’s research.
42. The Arthritis National Research Foundation: The Arthritis National Research Foundation seeks to move arthritis research forward to find new treatments and to cure arthritis.
43. Hereditary Disease Foundation: The focus of the Hereditary Disease Foundation is on Huntington’s disease. Support will be for research projects that will contribute to identifying and understanding the basic defect in Huntington’s disease. Areas of interest include trinucleotide expansions, animal models, gene therapy, neurobiology and development of the basal ganglia, cell survival and death, and intercellular signaling in striatal neurons.
44. The Childrens Leukemia Research Association: The objective of the CLRA is to direct the funds of the Association into the most promising leukemia research projects, and where funding would not duplicate other funding sources.
45. The American Parkinson Disease Association: The APDA offers grants of up to $50,000 for Parkinson disease research to scientists affiliated with U.S. research institutions.
46. The Mary Kay Foundation: The Mary Kay Foundation offers grants to select doctors and medical scientists for research focusing on curing cancers that affect women.
47. The Crohn’s & Colitis Foundation of America: The CCFA is a leading funder of basic and clinical research in Inflammatory Bowel Diseases. CCFA supports research that increases understanding of the etiology, pathogenesis, therapy, and prevention of Crohn’s disease and ulcerative colitis.
48. The Avon Foundation for Women: Grants from the Avon Foundation go to develop new strategies to prevent breast cancer and to researching the science behind breast cancer to increase understanding.
49. The International Research Grants Program: The IRGP seeks to promote research that will have a major impact in developing knowledge of Parkinson’s disease. Effort is made to promote projects that have little hope of securing traditional funding.
50. American Gastroenterological Association: The AGA offers multiple grants for research advancing the science and practice of Gastroenterology.
51. Wilson Ornithological Society Research Grants: The Wilson Ornithological Society Research Grants offers up to four grants of $1500 dollars for work in any area of ornithology.
52. The Obesity Society: The Obesity Society offers grants of up to $25,000 dollars to members doing research in areas related to obesity.
53. The Sjögren’s Syndrome Foundation: The SSF Research Grants Program places a high priority on both clinical and basic scientific research into the cause, prevention, detection, treatment, and cure of Sjögren’s.
54. The Melanoma Research Foundation: The MRF’s Research Grant Program emphasizes both basic and clinical research projects that explore innovative approaches to understanding melanoma and its treatment.
55. GRC - Run by the American Association of State Colleges and Universities (AASCU), the Grant Resource Center includes a database "customized to smaller institutions, and staff assistance," according to one user. A paid institutional membership is required for access.
56. IRIS - The Illinois Research Information Service is free for the University of Illinois (UI) community. Outside the UI system, a paid institutional subscription is needed for access.
57. SPIN - Run by InfoEd International, SPIN (the Sponsored Programs Information Network) claims to be the most widely used funding opportunity database in the world. An institutional subscription is required for access.
58. COS - Funding Opportunities. Community of Science claims the "largest, most comprehensive database of available funding," with 700 member institutions. Individuals can register free, but this won't get you access to the funding database.
59. ResearchResearch - Based in London, ResearchResearch provides an international option for people seeking research-funding programs. A paid subscription is required for access.

# install.packages('VennDiagram')
library(VennDiagram)

Second step: Load data

Add filepath if “catdoge.csv” is not in working-directory.

d <- read.csv("catdoge.csv")

Address of the bookmark: http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html

• January 20th, 2016 - New! Bpipe 0.9.9 released!
• Download latest, all
• Documentation
• Mailing List (Google Group)

Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

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

Genomics in India
www.ganitlabs.in
www.sandor.co.in
www.igib.res.in
www.genotypic.co.in
www.ocimumbio.com
www.abcgenomics.com
www.xcelrisgenomics.com
www.ayugen.com
www.geneombiotech.com

The genomics platform uses cancer genome sequencing and other high-throughput techniques to identify genes critical to the development of cancer and anomalies in the genomic profile of the tumours.

For more info visit : http://oicr.on.ca/

Analysis of regulatory sequences (RSAT project)
Classification and analysis of mobile genetic elements (ACLAME project).
Analysis of molecular interaction networks (NeAT project)
Inference of metabolic pathways from genomic and post-genomic data
(metabolic pathfinding, see also metabolic pathfinding in NeAT)
Critical assesment of protein interactions (CAPRI)

Lab Page http://www.bigre.ulb.ac.be/

Blog : http://blogs.plos.org/biologue/2012/12/28/translational-bioinformatics-plos-computational-biology-presents-an-educational-resource-for-an-emerging-field/

Educational Material : http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11

Address of the bookmark: http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11