BOL: Related items

ChromHMM: Chromatin state discovery and characterization

Abhimanyu Singh — Wed, 19 Apr 2017 04:06:23 -0500

ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.

Address of the bookmark: http://compbio.mit.edu/ChromHMM/

Boku Chair of Bioinformatics

Sun, 14 Jul 2013 12:37:23 -0500

The Bioinformatics group at Boku University has two main areas of interest, underpinning a common goal, the study of complex systems in living organisms. To overcome the engineered redundancies and combinatorial effects prevalent in higher eukaryotes, novel views augmenting the classical gene by gene approaches are required. We combine
Work to establish improved quantitative experimental assays (such as microarrays or differential in-gel electrophoresis) and
Development of modern computational methods (such as hierarchical probabilistic models or integration of heterogeneous data sources)

Link @ http://bioinf.boku.ac.at/

DIAMOND

Jit — Thu, 27 Apr 2017 04:21:54 -0500

DIAMOND is a sequence aligner for protein and translated DNA searches and functions as a drop-in replacement for the NCBI BLAST software tools. It is suitable for protein-protein search as well as DNA-protein search on short reads and longer sequences including contigs and assemblies, providing a speedup of BLAST ranging up to x20,000.

More at file:///home/urbe/Downloads/diamond_manual.pdf

http://www.nature.com/nmeth/journal/v12/n1/full/nmeth.3176.html

Address of the bookmark: https://github.com/bbuchfink/diamond

6 PhD Students @ TU Dresden

Sun, 14 Jul 2013 13:42:06 -0500

At TU Dresden, Faculty of Computer Science, the DFG Research Training Group GRK 1907 “Role-based Software Infrastructures for continuous-context-sensitive Systems” offers the positions of 6 PhD Students (E 13 TV-L)

for applicants interested in performing high-quality research on the connection between software engineering, database systems, and theoretical computer science as well as their applications in bioinformatics and business informatics. The research programme will start on October 1, 2013 until 30.09.2016. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz – WissZeitVG).

This research programme is a joint activity of Professors Lehner, Assmann, Baader, Baier, Schill, Schlegel, Schroeder, and Strahringer at TU Dresden. Alongside their research, an individual mentoring and qualification approach are arranged with specialized courses that prepare them optimally for their research, a research seminar where they can meet internationally renowned researchers in the field, and soft skills and language courses.

Requirements: Applicants should have an excellent academic record, and hold a MSc (or an equivalent university degree) in computer science or related disciplines (such as mathematics, bioinformatics or business informatics). Fluency in spoken and written English is required. Applicants with a good knowledge of software engineering or one of the application areas mentioned above are preferred. TU Dresden is committed to increase the proportion of women in research.

Applications from women are particularly welcome. The same applies to disabled people.

Please send enquiries to: wolfgang.lehner@tu-dresden.de

Applications consist of a CV, the names of two referees, transcipts of documents summarizing their academic performance, and a statement of interest. Application by email in pdf format is preferred, and should be submitted to wolfgang.lehner@tu-dresden.de in an electronically signed and encrypted form by July 30, 2013 (stamped arrival date of the university central mail service applies). Alternatively, applications can be sent to: TU Dresden, Fakultät Informatik, Institut für Systemarchitektur, Prof. Dr.-Ing. Wolfgang Lehner, 01062 Dresden, Germany.

Shortlisted candidates will be invited to Dresden in the middle of August to give a presentation on their Master’s thesis and discuss their research interest with the participating professors. Candidates that have not yet finished their degree when they send in their application should send preliminary transcripts of their academic records as well as a letter by the thesis adviser that comments on their progress so far and on the expected date of completion of their MSc or equivalent degree.

Tetra-Nucleotide Analysis

Jit — Thu, 04 May 2017 05:07:41 -0500

A tetra-nucleotide is a fragment of DNA sequence with 4 bases (e.g. AGTC or TTGG). Pride et al. (2003) showed that the frequency of tetra-nucleotides in bacterial genomes contain useful, albeit weak, phylogenetic signals. Even though tetra-nucleotide analysis (TNA) utilizes the information of whole genome, it is evident that it cannot replace other alignment-based phylogenetic methods such as OrthoANI or 16S rRNA phylogeny. However, TNA can be useful for phylogenetic characterization when whole genome or 16S rRNA gene information is not available. For example, a partial genomic fragment obtained from a metagenome can be identified by TNA (Teeling et al., 2004). TNA is also fast enough that it can be used as a search engine against a large genome database.

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

The Laboratoire de genomique fonctionelle

Sun, 14 Jul 2013 13:03:18 -0500

One persistent challenge of post genome biology remains the determination of the functions of all potential genes. In mammals this task is formidable given that a single gene can produce numerous protein isoforms through alternative pre-mRNA splicing. Protein isoforms from a single gene can have diverse, and in some cases antagonistic, functions. AS plays a pivotal biological role in protein diversity and developmental regulation. It is now believed that AS occurs in up to 74% of human genes, making it more of a rule than an exception.

Link @ http://lgfus.ca/public/

Ten International Scholarships for Indian Biotechnology and Bioinformatics Students

Priya Singh — Wed, 10 May 2017 04:51:02 -0500

Wherever you go around the world, Indian students are in demand. With countries such as Canada and Australia providing huge incentives to Indian students to lure them to their shores, there are many institutions around the world that offer scholarships exclusively to Indian students. Historically these scholarships tend to be aimed towards Masters and PhD programmes however on the rise are scholarships for undergraduate students. Student World Online takes a look at ten international scholarships for Indian undergraduate students abroad.

1. TATA SCHOLARSHIP - Cornell University, New York State, USA
Tata, the Indian multinational conglomerate company, have a foundation known as the Tata Education & Development Trust which has endowed a multi million dollar sum to Cornell University to provide undergraduate scholarships to 20 Indian students every year. In another example of supporting American universities, the Tata group also pledged US$50 million to Harvard University in recent years, whose executive management programme Ratan Tata attended in the 1970s. Read more...

2. BRADMAN FOUNDATION SCHOLARSHIP - University of Wollongong, Australia.
Named after Australia's cricket legend Donald Bradman, the UOW Bradman Foundation Scholarship was launched in 2012, with the help of Adam Gilchrist no less, to offer one successful Indian student each year a 50% reduction in tuition fees. Read more...

3. HUAWEI MAITREE SCHOLARSHIPS - Various Universities, China
Along with Tata, Huawei are the other huge corporation to be featured. China's massive telecoms equipment vendor are involved in these scholarships offered to Indian students studying in China. In 2013 there are 10 generous scholarships available which provide full tuition fees and living expenses. The courses on which the scholarships are offered include Science and Technology courses, Social Sciences and Culture and Development courses. Read more...

4. DR. MANMOHAN SINGH SCHOLARSHIPS - Cambridge University, England, UK
These scholarships have been designed to help budding Indian minds follow in the footsteps of Indian prime minister Manmohan Singh by studying at the prestigious Cambridge University. The scholarships can be applied to any undergarduate course (with the two exceptions of medicine and veterinary science) and cover everything, i.e. tuition and college fees, living expenses and an additional grant to go towards travel expenses. Read more...

5. OXFORD AND CAMBRIDGE SOCIETY OF INDIA - Oxford & Cambridge Universities, England, UK
As the name might suggest, these are scholarships available for students wishing to study at Oxford or Cambridge (cleverly known together as Oxbridge). It is only available for applicants who are completing or have completed a degree at an Indian university, however these scholarships are for both undergraduate and graduate students. Read more...

6. EDINBURGH NAPIER UNIVERSITY - Scotland, UK
This one applies to all countries in the Indian subcontinent and is for both undergraduate and graduate courses. Edinburgh Napier University offers a merit based discount of £2,000 Pounds. Read more...

7. SHEFFIELD UNIVERSITY - Sheffield, UK
Provides merit-based scholarships for undergraduate and graduate programmes across all subjects. Read more...

8. INDIA 4EU II - Several Universities across Europe
Pioneered by the European Union and involving partner universities in France, Finland, Germany, Italy, Portugal, Spain and Sweden, the India 4EU II initiative is aimed at encouraging Indian students to study, work and live in Europe. The initiative is well funded and allows the successful students tuition fees, expenses for living and travel costs as well as insurance during their time at one of the partner universities. Read more...

9. TRINITY COLLEGE DUBLIN - Ireland
Valid for undergraduate courses in the faculties of Arts, Humanities, Social Sciences, Science, Computer Science or Engineering, the Trinity College Dublin offers Indian students scholarships to the tune of €9,000 per annum over a year degree course. Read more...

10. UNIVERSITY COLLEGE DUBLIN - Ireland
Another of Ireland and Dublin's finest, the UCD awards one Global Excellence Undergraduate Scholarship which provides the worthy student a substantial 50% towards their tuition fees and is valid for all courses save medicine, radiography and veterinary medicine. UCD also offers a Global Undergraduate Scholarship scheme for undergrads accepted on science, social sciences, arts and business courses. This is all thanks to a €250,000 fund that will allow for 57 Indian students to benefit from scholarships at UCD. Read more...

Jayaram Lab

Sun, 14 Jul 2013 14:04:37 -0500

Responsible (a) for developing Chemgenome, Bhageerath & Sanjeevini methods & softwares for genome annotation, protein tertiary structure prediction & computer aided drug design respectively, (b) for setting up a multi-teraflop supercomputing facility for Bioinformatics & Computational Biology at IIT Delhi, and (c) for making the hardware and software freely accessible at (www.scfbio-iitd.res.in) to the global scientific user community.

Faculty facilitator/Founder Director for two start-up companies (Leadinvent incubated at IIT, Delhi from 2006-2009 & Novoinformatics, under incubation at IIT Delhi since 2011).

Research Interest
Genome Analysis, Protein Structure Prediction and Drug Design.

Link @ http://www.scfbio-iitd.res.in/

CABOG: Celera Assembler with Best Overlap Graph

Abhimanyu Singh — Mon, 15 May 2017 05:04:39 -0500

CABOG (Celera Assembler with Best Overlap Graph) is scientific software for DNA research. CABOG has been a critical component of many genome sequencing projects. CABOG operates on small genomes such as bacterial as well as large genomes such as mammalian. CABOG is an extension of the Celera Assembler software that was originally developed at Celera for the 2001 publication of the first draft human genome sequence. The software was released to the public domain in 2004. Its open source repository on Source Forge is an internet resource for scientists around the world.

CABOG is one of many software programs called genome assemblers. These programs exist to overcome the fundamental limitation of all sequencing machines, namely, that they read out very few DNA letters at a time. These programs reconstruct genomes that are billions of letters long from the hundreds of letters per read that modern sequencers provide. What these programs do is often described as a scaled up version of a family solving a jigsaw puzzle.

The CABOG software was the first to accomplish many scientific goals. It was the first to assemble the genome of a multicellular organism (Drosophila melanogaster, 2000). It was the first to assemble both parental haplotypes of one human genome (J. Craig Venter, 2007). It was the first to assemble environmental sequence from the oceans (Sargasso Sea in 2004 and Global Ocean Sampling in 2007). It was first to combine reads from first-generation Sanger sequencing machines and second-generation pyrosequencing machines (Marine microbes, 2006). Today, CABOG is one of the leading assembly programs for data sets that include paired end data from the Roche 454 line of sequencing machines.

Address of the bookmark: http://www.jcvi.org/cms/research/projects/cabog/overview/

Bioinformatics Algorithms

Jitendra Narayan — Tue, 16 Jul 2013 03:35:15 -0500

An algorithm is a computable set of steps to achieve a desired result.

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