<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/35800? https://bioinformaticsonline.com/blog/view/36483/popular-bioinformatics-educational-resources Fri, 04 May 2018 19:43:21 -0500 https://bioinformaticsonline.com/blog/view/36483/popular-bioinformatics-educational-resources <![CDATA[Popular bioinformatics educational resources !]]> Followings are the list of popular bioinformatics educational resources

Bii.a-star.edu.sg

Bio research and development. Has course information and research information.

Isb-sib.ch

SIB operates the ExPASy proteomics server and the Swiss node of EMBnet. Teaching activities include a series of post-graduate courses given at the Universities of Geneva and Lausanne, as well as at the EPFL, and a Masters Degree in bioinformatics. Major research areas include the development of integrated databases and software resources in the field of proteomics.

Bioinformatics.ca

Provides information about bioinformatics in Canada. Workshops, certification and resources.

Chickscope.beckman.uiuc.edu

Students raise chicken embryos in the classroom and obtain magnetic resonance images through the Internet.

Bcb.iastate.edu

Graduate program at Iowa State University offering Undergraduate Major (BCBio) and the PhD program (BCB).

Bu.edu/bioinformatics/

Interdisciplinary PhD and Masters Programs that include an internship in the local industry companies. In conjunction with the NE masters program.

Bioinformatics.ubc.ca

A computational biology research centre covering many areas of genomics, proteomics, computer science and statistics. Research, training, news and events, resources and support, director's message, faculty and personnel.

Openhelix.com

Provides onsite training on specific bioinformatics databases and tools. Also offers bioinformatic software testing and research consulting services.

Igb.uci.edu

Specializing in making publicly available software and database services for computational biology.

Bioinformatics.pe.kr

Maintained by Dr. Seyeon Weon, Korea providing information on courses, a database archive, software archive and online resources.

Groups.yahoo.com/group/bimatics/

Bioinformatics group for students interested and/or working in the bioinformatics/computationalbiology fields. Offers opportunities to exchanging information and sharing ideas.

Ncbi.nlm.nih.gov/books/NBK22183/

Information about several medically important genes and related diseases. Illustrates the use of bioinformatics in their study.

Bioinfo.mbb.yale.edu/mbb452a/2003/

Bioinformatics course at Yale University. All course slides are available online.

Cs.iastate.edu/~honavar/comp-bio-courses.html

Listing of computational molecular biology course pages that have extensive online course materials.

Bioinf.manchester.ac.uk/dbbrowser/bioactivity/prefacefrm.html

A web-based tutorial associated with "Introduction to bioinformatics" published by Addison Wesley Longman.

Northeastern.edu/bioinformatics/

From the Biology department and in cooperation with Boston University. Emphasis on the ability to integrate knowledge from biological, computational, and mathematical disciplines.

Biocomp.unibo.it/lsbioinfo/

A two year, international master's programme in bioinformatics at the Universita di Bologna, Italy.

Cs.helsinki.fi/bioinformatiikka/mbi/programme.html

A two year Masters Degree Programme in Bioinformatics (MBI) offered by the University of Helsinki and Helsinki University of Technology, Finland.

Ornl.gov/sci/techresources/Human_Genome/education/education.shtml

A resource for introductory information on the Human Genome Project.

His.se/bioinformatics

A one-year, international master's programme in bioinformatics at the University of Skovde, Sweden.

Members.tripod.com/C.elegans/

Resources in biochemical, molecular, cellular, system, and organism biology, including over 25,000 indexed links, accumulated since 2000, from topic menus or from search interface.

Bioinformatics.org/faq/#contents

Summary of basics of bioinformatics for the intelligent newcomer.

Jiscmail.ac.uk/archives/bioinformatics.html

Forum featuring various aspects, events and developments in the bioinformatics field.

Biinoida.blogspot.com

Blog focusing on bioinformatics, biotechnology, pharma regulatory affairs, IPR and clinical trials.

Colorbasepair.com/bioinformatics_courses_tutorials.html

A list of on-line course materials and tutorials for bioinformatics and computational biology.

Geospiza.com/education/

Instructional materials for teaching bioinformatics. These include animated tutorials on topicssuch as BLAST, finding mutations in a protein, and graphing with MS-Excel.

Bioinformatics.fi

An international, two-year Master's programme jointly managed by the University of Tampere and the University of Turku, Finland.

Perlsource.net

Provides online courses in Perl programming for bioinformatic tools.

]]> Rahul Nayak https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist Fri, 02 Mar 2018 06:54:15 -0600 https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist <![CDATA[Python learning resources for bioinformatics and computational biologist !]]> Python is a general-purpose language, which means it can be used to build just about anything, which will be made easy with the right tools/libraries.

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

]]> Jit https://bioinformaticsonline.com/bookmarks/view/30018/bipype Thu, 01 Dec 2016 08:47:38 -0600 https://bioinformaticsonline.com/bookmarks/view/30018/bipype <![CDATA[bipype]]> Bipype is a very useful program, which prepare a lot of types of bioinformatics analyses. There are three input options: amplicons, WGS (whole genome sequences) and metatranscriptomic data. If amplicons are input data, then bipype does reconstruction and pairs merging. After that biodiversity is searching. There are two types of searching depending on the amplicons types (ITS or 16S). If WGS are chosen, then bipype finds the SA coordinates of the input reads and generates alignments in the SAM format given single-end reads, aligns reads to reference sequence(s). All of these analyses will be shown with Krona program, which allows to show hierarchical data with pie charts.

Address of the bookmark: https://readthedocs.org/projects/bipype/

]]> Jit https://bioinformaticsonline.com/pages/view/920/bioinformatics-algorithms Tue, 16 Jul 2013 03:35:15 -0500 https://bioinformaticsonline.com/pages/view/920/bioinformatics-algorithms <![CDATA[Bioinformatics Algorithms]]> 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

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

]]> Jitendra Narayan https://bioinformaticsonline.com/opportunity/view/4314/postdocs-positions-in-computer-science-in-helsinki-finland Fri, 06 Sep 2013 10:11:19 -0500 <![CDATA[PostDocs positions in computer science in HELSINKI, FINLAND]]> Several university departments in the Helsinki region, Finland, are looking for postdoctoral researchers in the field of computer science and information technology. Jobs are available at:
· Helsinki Institute for Information Technology HIIT, Aalto University and University of Helsinki, http://www.hiit.fi
· Department of Computer Science, University of Helsinki, http://www.cs.helsinki.fi
· Department of Information and Computer Science, Aalto University, http://ics.aalto.fi
· Department of Computer Science and Engineering, Aalto University, http://cse.aalto.fi
· Department of Mathematics and Statistics, University of Helsinki, http://mathstat.helsinki.fi/english/

Why Helsinki?
The collaborating Aalto University and University of Helsinki form a leading hub of computer science and modelling, including Machine learning, Data mining, Algorithms, Computational Logic, Cloud computing, Distributed computing, Human-centric ubiquitous ICT, Bioinformatics, etc.
Helsinki region is a safe, pleasant and attractive place to live in, with well-functioning services such as public transport etc. Finland has a comprehensive social security and health care system, including exceptionally good parental leaves, and children's day care services.

Positions are offered in:
Algorithm engineering (String Algorithms group)
Algorithmic bioinformatics (Genome-Scale Algorithmics group)
Automated reasoning and search, especially propositional logic (Computational Logic group)
Computational astrophysics and/or data analysis (Computational Methods and Data Analysis for Astrophysics group)
Computational biology and statistical methods in bioinformatics (Computational Systems Biology group)
Computational creativity and data mining (Discovery group)
Dynamic and large-scale networked systems (Data Communications Software group)
Intelligent multimodal information access (Content-Based Image and Information Retrieval Group)
Machine learning and neuroscience (Statistical Machine Learning group)
Machine learning for structured data (Kernel Machines, Pattern Analysis and Computational Biology group)
Machine learning methods for infectious disease epidemiology (Bayesian Statistics Group)
Probabilistic modeling and machine learning (Complex Systems Computation group)
Statistical machine learning (Statistical Machine Learning group)
Analysing ubiquitous sensor data (HIIT-Wide Focus Area)
Interactive visualization (HIIT-Wide Focus Area)
Affective computing and BCI (HIIT-Wide Focus Area)
Intelligent user interfaces and/or recommender systems (HIIT-Wide Focus Area)
Information retrieval and HCI (HIIT-Wide Focus Area)
Machine learning and data analysis, especially information retrieval, HCI, text and context data (HIIT-Wide Focus Area)
Probabilistic modeling and data analysis for bioinformatics (HIIT-Wide Focus Area)

More at http://www.hiit.fi/postdoc-call-2013

]]> https://bioinformaticsonline.com/researchlabs/view/5220/paolo-ruggerone-lab Tue, 01 Oct 2013 14:15:53 -0500 <![CDATA[Paolo Ruggerone Lab]]> Efflux pumps (RND family)

Functioning of efflux systems in Gram-negative bacteria
Determinants of the compound-efflux system interactions
Action of inhibitors on efflux systems
Structural and dynamical features of the efflux systems

TatA
Assembly of the TatA system
Study of the dynamical features of the charge zipper

Methods
Setup of a kinetic Monte Carlo (KMC) scheme to study the flux of antibiotics through porins and efflux systems
Setup of protocol to integrate MD results in a ligand-based approach

Viral inhibitors
Interactions of selected compounds with RNA-dependent RNA polymerases (RdRps) of HCV and BVDV
Assessment of the role of mutations in RdRps
Antimicrobial peptides

Interactions of antimicrobial peptides with membranes: structure and dynamics
Interactions between antimicrobial peptides in the presence of different membranes
Protein-protein interactions
Effects of mutations

Lab Page
http://www.dsf.unica.it/~paolo/Site/Home.html

]]> https://bioinformaticsonline.com/bookmarks/view/19631/rosalind-bioinformatics-problems Thu, 18 Dec 2014 10:32:48 -0600 https://bioinformaticsonline.com/bookmarks/view/19631/rosalind-bioinformatics-problems <![CDATA[Rosalind Bioinformatics problems !!!]]> Rosalind is a platform for learning bioinformatics and programming through problem solving. Take a tour to get the hang of how Rosalind works.

http://rosalind.info/problems/list-view/

Address of the bookmark: http://rosalind.info/problems/list-view/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/23498/algorithms-for-dna-sequencing-course-offered-each-month Sun, 26 Jul 2015 01:57:02 -0500 https://bioinformaticsonline.com/bookmarks/view/23498/algorithms-for-dna-sequencing-course-offered-each-month <![CDATA[Algorithms for DNA Sequencing (course offered each month)]]> "We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets."

Source : https://www.coursera.org/course/ads1

 

Address of the bookmark: https://www.coursera.org/course/ads1

]]> Rahul Agarwal https://bioinformaticsonline.com/opportunity/view/41041/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-mfd Sat, 15 Feb 2020 06:13:35 -0600 <![CDATA[Post Doc Computational Biology, Bioinformatics - Network Biology & Data Science, NGS (m/f/d)]]> https://www.jobvector.de/jobs-stellenangebote/biologie-life-sciences/forschung-entwicklung/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-129867.html?suid=e522e9793b41817e52ac58d6963b94e2519920df

Requirements
Doctoral degree in Bioinformatics, Computational Biology, (Bio)physics/-mathematics, Biochemistry/Biology or similar with strong quantitative and numeric focus
Ability to numerically process complex and large data sets
Good programming skills (R/Bioconductor and/or Python preferred, Linux is a plus)
Experience in analyzing next-generation sequencing data sets using network biology
Scientific publication record in applied bioinformatics
Familiarity with single cell NGS analyses and other –omics techniques is a plus, but not essential

]]> https://bioinformaticsonline.com/opportunity/view/43272/bioinformatics-head-bioinformatics-manager-iii-cancer-genomics-research-laboratory-at-frederick-national-laboratory Wed, 18 Aug 2021 00:19:48 -0500 <![CDATA[Bioinformatics Head (Bioinformatics Manager III), Cancer Genomics Research Laboratory at Frederick National Laboratory]]> Frederick National Laboratory seeking an enthusiastic, creative, and seasoned bioinformatics professional to join our leadership team and direct the exceptional Bioinformatics Group at the Cancer Genomics Research Laboratory (CGR). CGR has a diverse team of bioinformatics and computational scientists that support all areas of bioinformatics and data analysis (infrastructure, data QC, pipeline development and maintenance, data curation and sharing, methodology development, statistical analyses, machine learning approaches, and scientific interpretation).

More at https://leidosbiomed.csod.com/ats/careersite/jobdetails.aspx?site=4&c=leidosbiomed&id=2040

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