BOL: Related items

RA Bioinformatics at Jamia Hamdard

Sat, 28 Nov 2015 03:37:36 -0600

Research Associate, Bioinformatics Facility
Jamia Hamdard - New Delhi, Delhi
Research Associate, Bioinformatics Facility, Jamia Hamdard.
Emoluments: Rs. 22,000 + HRA 30%
Qualification: PhD or equivalent or having 3 years of research, teaching and design and development experience after M. Pharm./M.VSc./M.E./M.Tech. PhD in life sciences and related areas with experience in Bioinformatics may apply. Company Info.
Jamia Hamdard

Jamia Hamdard New Delhi - 110062 Additional Information States & U.T State & Union Territories Delhi How To Apply Apply Details
Last date of application: December 5, 2015 Web/Notification URL http://www.jamiahamdard.ac.in/

UAS Dharwad Assistant Professor

Fri, 04 Dec 2015 21:06:23 -0600

UNIVERSITY OF AGRICULTURAL SCIENCES, DHARWAD

Applications are invited in the prescribed form for filling up the following posts of Teachers and Service Personnel from the candidates who are qualified as on the last date fixed for receipt of applications in the University of Agricultural Sciences, Dharwad.

I. ASSISTANT PROFESSOR CADRE (Scale of pay Rs.15600-39100 + AGP Rs. 6000) (UGC / ICAR pay-scales)

I. Re-notified posts:

9. Assistant Professor of Bioinformatics 1 GM-1

More Info : https://sites.google.com/a/uasd.in/recruitment/

Research Fellow Bioinformatics at Central University of Rajasthan

Tue, 02 Feb 2016 00:04:50 -0600

Research Fellow Bioinformatics

Eligibility : MSc(Bio-Chemistry, Bio-Informatics, Bio-Tech)
Location : Ajmer
Last Date : 13 Feb 2016
Hiring Process : Face to Face Interview
Central University of Rajasthan

Research Fellow Job vacancies in Central University of Rajasthan

Project Title : “Development of natural product derived febrifugine ananlogues as a novel therapeutics against visceral leishmaniasis”

No. of Post : 01

Qualification : Master of Biochemistry, Biotechnology, Bioinformatics and related biological sciences with minimum 55%, Age limit as per government rule.

Desirable Experience : Candidates with experience in cell culture, Chemoinformatics, and Parasitology will be preferred.

Fellowship : Rs. 25,000/- p.m. consolidated for NET qualified (14,000/- p.m. consolidated for Non-NET)
How to apply

The candidates may apply on a plain paper, along with their curriculum vitae (including name, date of birth, academic qualification starting from 10th class, summary of research experience, email id, phone number and passport size photograph) and email to vkprajapati@curaj.ac.in or post the hard copy to the Dr. Vijay Kumar Prajapati PI, DST-SERB Project (YSS/2015/000716), Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, Bandarsindri, Dist- Ajmer, Rajasthan, 305 817 on or before 13th February 2016.

More at http://www.curaj.ac.in/Default.aspx?PageId=241

n50PlottingTools

Jit — Mon, 08 Feb 2016 15:39:04 -0600

Tools to create plots showing N-statistics for genome assemblies

More at https://github.com/dentearl/n50PlottingTools

Address of the bookmark: https://github.com/dentearl/n50PlottingTools

destruct

Jitendra Prajapati — Mon, 29 Feb 2016 17:34:59 -0600

Destruct is a tool for joint prediction of rearrangement breakpoints from single or multiple tumour samples.

More at https://bitbucket.org/dranew/destruct

Address of the bookmark: https://bitbucket.org/dranew/destruct

REAPR: a universal tool for genome assembly evaluation

Jitendra Prajapati — Wed, 06 Apr 2016 18:26:31 -0500

REAPR is a tool that evaluates the accuracy of a genome assembly using mapped paired end reads, without the use of a reference genome for comparison. It can be used in any stage of an assembly pipeline to automatically break incorrect scaffolds and flag other errors in an assembly for manual inspection. It reports mis-assemblies and other warnings, and produces a new broken assembly based on the error calls.

The software requires as input an assembly in FASTA format and paired reads mapped to the assembly in a BAM file. Mapping information such as the fragment coverage and insert size distribution is analysed to locate mis-assemblies. REAPR works best using mapped read pairs from a large insert library (at least 1000bp). Additionally, if a short insert Illumina library is also available, REAPR can combine this with the large insert library in order to score each base of the assembly.

http://www.sanger.ac.uk/science/tools/reapr

Address of the bookmark: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-5-r47

Stand-alone programs for Bioinformatician

Radha Agarkar — Sat, 21 May 2016 22:50:15 -0500

This directory contains applications for stand-alone use, built specifically for a Linux 64-bit machine.

For help on the bigBed and bigWig applications see:
http://genome.ucsc.edu/goldenPath/help/bigBed.html
http://genome.ucsc.edu/goldenPath/help/bigWig.html

View the file 'FOOTER' to see the usage statement for each of the applications.

Address of the bookmark: http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/

Cheatsheet for Linux !!

Jit — Wed, 22 Jun 2016 07:55:06 -0500

Linux Commands Cheat Sheet

    File System

    ls — list items in current directory

    ls -l — list items in current directory and show in long format to see perimissions, size, an modification date

    ls -a — list all items in current directory, including hidden files

    ls -F — list all items in current directory and show directories with a slash and executables with a star

    ls dir — list all items in directory dir

    cd dir — change directory to dir

    cd .. — go up one directory

    cd / — go to the root directory

    cd ~ — go to to your home directory

    cd - — go to the last directory you were just in

    pwd — show present working directory

    mkdir dir — make directory dir

    rm file — remove file

    rm -r dir — remove directory dir recursively

    cp file1 file2 — copy file1 to file2

    cp -r dir1 dir2 — copy directory dir1 to dir2 recursively

    mv file1 file2 — move (rename) file1 to file2

    ln -s file link — create symbolic link to file

    touch file — create or update file

    cat file — output the contents of file

    less file — view file with page navigation

    head file — output the first 10 lines of file

    tail file — output the last 10 lines of file

    tail -f file — output the contents of file as it grows, starting with the last 10 lines

    vim file — edit file

    alias name 'command' — create an alias for a command
    System

    shutdown — shut down machine

    reboot — restart machine

    date — show the current date and time

    whoami — who you are logged in as

    finger user — display information about user

    man command — show the manual for command

    df — show disk usage

    du — show directory space usage

    free — show memory and swap usage

    whereis app — show possible locations of app

    which app — show which app will be run by default
    Process Management

    ps — display your currently active processes

    top — display all running processes

    kill pid — kill process id pid

    kill -9 pid — force kill process id pid
    Permissions

    ls -l — list items in current directory and show permissions

    chmod ugo file — change permissions of file to ugo - u is the user's permissions, g is the group's permissions, and o is everyone else's permissions. The values of u, g, and o can be any number between 0 and 7.

    7 — full permissions

    6 — read and write only

    5 — read and execute only

    4 — read only

    3 — write and execute only

    2 — write only

    1 — execute only

    0 — no permissions

    chmod 600 file — you can read and write - good for files

    chmod 700 file — you can read, write, and execute - good for scripts

    chmod 644 file — you can read and write, and everyone else can only read - good for web pages

    chmod 755 file — you can read, write, and execute, and everyone else can read and execute - good for programs that you want to share
    Networking

    wget file — download a file

    curl file — download a file

    scp user@host:file dir — secure copy a file from remote server to the dir directory on your machine

    scp file user@host:dir — secure copy a file from your machine to the dir directory on a remote server

    scp -r user@host:dir dir — secure copy the directory dir from remote server to the directory dir on your machine

    ssh user@host — connect to host as user

    ssh -p port user@host — connect to host on port as user

    ssh-copy-id user@host — add your key to host for user to enable a keyed or passwordless login

    ping host — ping host and output results

    whois domain — get information for domain

    dig domain — get DNS information for domain

    dig -x host — reverse lookup host

    lsof -i tcp:1337 — list all processes running on port 1337
    Searching

    grep pattern files — search for pattern in files

    grep -r pattern dir — search recursively for pattern in dir

    grep -rn pattern dir — search recursively for pattern in dir and show the line number found

    grep -r pattern dir --include='*.ext — search recursively for pattern in dir and only search in files with .ext extension

    command | grep pattern — search for pattern in the output of command

    find file — find all instances of file in real system

    locate file — find all instances of file using indexed database built from the updatedb command. Much faster than find

    sed -i 's/day/night/g' file — find all occurrences of day in a file and replace them with night - s means substitude and g means global - sed also supports regular expressions
    Compression

    tar cf file.tar files — create a tar named file.tar containing files

    tar xf file.tar — extract the files from file.tar

    tar czf file.tar.gz files — create a tar with Gzip compression

    tar xzf file.tar.gz — extract a tar using Gzip

    gzip file — compresses file and renames it to file.gz

    gzip -d file.gz — decompresses file.gz back to file
    Shortcuts

    ctrl+a — move cursor to beginning of line

    ctrl+f — move cursor to end of line

    alt+f — move cursor forward 1 word

    alt+b — move cursor backward 1 word

Linux Cheat Sheet

Jitendra Narayan — Tue, 09 Jul 2013 17:30:04 -0500

In an attempt to find a good Linux reference for bioinformatician and BOL readers, I was unsuccessful at finding a decent one on the Internet. So, we decided to make a cheat sheet for biological programmers.

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