<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/26539? 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/28200/machine-learning Fri, 01 Jul 2016 12:57:12 -0500 https://bioinformaticsonline.com/bookmarks/view/28200/machine-learning <![CDATA[Machine Learning !!!]]> In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

]]> Gudiya Pal https://bioinformaticsonline.com/bookmarks/view/12963/cosmos-our-workflow-management-system-for-ngs-data Wed, 23 Jul 2014 07:29:14 -0500 https://bioinformaticsonline.com/bookmarks/view/12963/cosmos-our-workflow-management-system-for-ngs-data <![CDATA[COSMOS, our workflow management system for NGS data]]> COSMOS, our Python-based management system for implementing large-scale parallel workflows focusing on, but not restricted to, large-scale short-read "NGS" sequencing data is open-access published via Advance Access in Bioinformatics (Gafni et al. 2014).  It is also available for download for non-commercial academic and research purposes at:

 http://cosmos.hms.harvard.edu/.

Address of the bookmark: https://cosmos.hms.harvard.edu/

]]> Jit https://bioinformaticsonline.com/news/view/20331/type-hinting Fri, 09 Jan 2015 22:26:13 -0600 https://bioinformaticsonline.com/news/view/20331/type-hinting <![CDATA[Type Hinting]]> Python creator Guido van Rossum’s proposal for static type-checking annotations is inching closer to reality, and the feature has taken on a new name: type hinting.

image

Back in August, van Rossum published a proposal on the Python mailing list recommending type-checking annotations as a valuable feature for the next version of Python to improve the performance of editors and IDEs, linter capabilities, standard notation, and refactoring. Van Rossum’s latest proposal, posted late last month, outlined plans to publish a Python Enhancement Proposal (PEP) in early January to put the feature now known as type hinting on track for inclusion in Python 3.5, slated for release this September.

Reference

https://quip.com/r69HA9GhGa7J

]]> Pranjali Yadav https://bioinformaticsonline.com/pages/view/2261/best-book-titles-for-learning-bionformatics Tue, 13 Aug 2013 17:31:51 -0500 https://bioinformaticsonline.com/pages/view/2261/best-book-titles-for-learning-bionformatics <![CDATA[Best book Titles for Learning Bionformatics]]> Nothing can add to our intellect more than reading a book.  In books, we can experience new things that we would not normally be able to experience. It is proved that books can change our lives and other people’s lives. Reading can make us more intelligent, updated, imaginative. Without reading we wouldn’t know anything that we know today. There are several book, online and offile to read and I can't mentioned all of them here in the list. Therefore, I mentioned some bioinformatics and its related books in subgroups. Hope you will like the list. 

Sequence Analysis and General Bioinformatics

  • BLAST, Ian Korf, Mark Yandell, Joseph Bedell, 2003, O'Reilly
  • Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases, Scott Markel, Darryl Leon, 2003, O'Reilly
  • Bioinformatics for Geneticists, Michael Barnes, Ian C Gray (Editors), 2003, John Wiley & Sons
  • Bioinformatics for Dummies, Jean-Michel Claverie, Cedric Notredame, 2003, John Wiley & Sons
  • Mathematics of Genome Analysis, Jerome K. Percus, 2002, Cambridge Univ Press
  • Bioinformatics Computing, Bryan P. Bergeron, 2002, Prentice Hall
  • Evolutionary Computation in Bioinformatics, Gary B. Fogel, David W. Corne (Editors), 2002, Morgan Kaufmann
  • Introduction to Bioinformatics, Arthur M. Lesk, 2002, Oxford University Press
  • Instant Notes in Bioinformatics, D.R. Westhead, J. H. Parish, R.M. Twyman, 2002, Bios Scientific Pub
  • Fundamental Concepts of Bioinformatics, Dan E. Krane, Michael L. Raymer, Michaeel L. Raymer, Elaine Nicpon Marieb, 2002, Benjamin/Cummings
  • Essentials of Genomics and Bioinformatics, C. W. Sensen (Editor), 2002, John Wiley & Sons
  • Current Topics in Computational Molecular Biology (Computational Molecular Biology), Tao Jiang, Ying Xu, Michael Zhang (Editors), 2002, MIT Press
  • Hidden Markov Models for Bioinformatics, Timo Koski, Timo Koskinen, 2001, Kluwer Academic Publishers
  • Bioinformatics: From Genomes to Drugs, Thomas Lengauer (Editor), 2001, John Wiley & Sons
  • Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health), Warren Ewens, Gregory Grant, 2001, Springer Verlag
  • Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition, Andreas D. Baxevanis, B. F. Francis Ouellette, 2001, Wiley-Interscience
  • Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning), Pierre Baldi, Soren Brunak, Sren Brunak, 2001, MIT Press
  • Introduction to Bioinformatics, T eresa Attwood, David Parry-Smith, 2001, Prentice Hall
  • Bioinformatics: A Primer, Charles Staben, 2001, Jones & Bartlett Pub
  • Data Analysis and Classification for Bioinformatics, Arun Jagota, 2000, AKJ Academics
  • Bioinformatics: Sequence and Genome Analysis, David W. Mount, 2001, Cold Spring Harbor Laboratory Press
  • Bioinformatics: A Biologist's Guide to Biocomputing and the Internet, Stuart M. Brown, 2000, Eaton Pub Co
  • Bioinformatics: Sequence, Structure and Databanks: A Practical Approach (The Practical Approach Series, 236), Des Higgins (Editor), Willie Taylor (Editor), 2000, Oxford Univ Press
  • Neural Networks and Genome Informatics, Cathy H. Wu, Jerry W. McLarty, 2000, Elsevier Science
  • Computational Molecular Biology: An Introduction (Wiley Series in Mathematical and Computational Biology), Peter Clote and Rolf Backofen, 2000, John Wiley & Sons
  • Computational Molecular Biology: An Algorithmic Approach, Pavel A. Pevzner, 2000, MIT Press
  • Post-Genome Informatics, Minoru Kanehisa, 2000, Oxford Univ Press
  • Mathematical and Computational Biology: Computational Morphogenesis, Hierarchical Complexity, and Digital Evolution, Chrystopher L. Nehaniv, 1999, American Mathematical Society
  • Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications, Jason T. L. Wang, Bruce A. Shapiro, Dennis Elliott Shasha (Editors), 1999, Oxford Univ Press
  • Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, David Sankoff and Joseph Kruskal (Editors), 1999, Cambridge University Press
  • Bioinformatics Basics: Applications in Biological Science and Medicine, Hooman Rashidi, 1999, CRC Press
  • Bioinformatics: Methods and Protocols (Methods in Molecular Biology, Vol 132), Stephen Misener and Stephen A. Krawetz (Editors),1999, Humana Press
  • Bioinformatics: Databases and Systems, Stanley Letovsky (Editor),1999, Kluwer Academic Publishers
  • Computational Molecular Biology, P. Green, 1998, Blackwell Science Inc.
  • Computational Methods in Molecular Biology (New Comprehensive Biochemistry, V. 32), Steven L. Salzberg, David B. Searls, Simon Kasif (Editors), 1998, Elsevier Science Ltd.
  • Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Richard Durbin, S. Eddy, A. Krogh, G. Mitchison, 1998, Cambridge University Press
  • Guide to Human Genome Computing, M. J. Bishop (Editor), 1998, Academic Press
  • Introduction to Computational Molecular Biology, Joao Meidanis, Joao C. Setabal, 1997, PWS Pub. Co.
  • Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Dan Gusfield, 1997, Cambridge University Press
  • Sequence Data Analysis Guidebook, Simon R. Swindell (Editor), 1997, Humana Press
  • High Performance Computational Methods for Biological Sequence Analysis, Tieng K. Yap, Ophir Frieder, Robert L. Martino, 1996, Kluwer Academic Pub.
  • Computer Methods for Macromolecular Sequence Analysis, Methods in Enzymology, volume 266, Russell F. Doolittle (Editor), 1996, Academic Press
  • DNA and Protein Sequence Analysis: A Practical Approach (Practical Approach Series , No 171), 1996, M. J. Bishop and C. J. Rawlings (Editors), 1996, IRL Press
  • Molecular Bioinformatics: Algorithms and Applications, Steffen Schulze-Kremer, 1995, Walter De Gruyter
  • Introduction to Computational Biology - Maps, sequences and genomes, Michael S. Waterman, 1995, Chapman & Hall
  • Computer Analysis of Sequence Data, Annette M. Griffin and Hugh G. Griffin (Editors), 1994, Humana Press
  • Artificial Intelligence and Molecular Biology, Lawrence Hunter (Editor), 1993, AAAI Press
  • Sequence Analysis Primer, Michael Gribskov and John Devereux (Editors), 1992, Oxford University Press
  • Mathematical Methods of Analysis of Biopolymer Sequences (Dimacs Series in Discrete Mathematics and Theoretical Computer Science ; Volume 8), S. G. Gindikin, 1992, American Mathematical Society
  • Mathematical Methods for DNA Sequences, Michael S. Waterman (Editor), 1989, CRC Press

Programming Books for Bioinformatics

  • Mastering Perl for Bioinformatics, James D. Tisdall, 2003, O'Reilly
  • Genomic Perl: From Bioinformatics Basics to Working Code, Rex A. Dwyer, 2002, Cambridge University Press
  • Beginning Perl for Bioinformatics, James Tisdall, 2001, O'Reilly
  • Developing Bioinformatics Computer Skills, Cynthia Gibas, Per Jambeck, 2001, O'Reilly

General Genomics

  • Functional Microbial Genomics (Volume 33), Brendan Wren, Nick Dorrell, 2003, Academic Press
  • Discovering Genomics, Proteomics, and Bioinformatics, A. Malcolm Campbell, Laurie J. Heyer, 2002, Benjamin/Cummings
  • Genomes, Terence A. Brown, 2002, John Wiley & Sons
  • Essentials of Medical Genomics, Stuart M. Brown , 2002, John Wiley & Sons
  • A Primer of Genome Science, Greg Gibson, Spencer V. Muse, 2002, Sinauer Associates
  • Pathogen Genomics: Impact on Human Health, Karen Joy, Phd Shaw (Editors), 2002, Humana Press
  • Genomics, John E. Antonopoulos, 2000, Xlibris Corporation
  • Genomics and Proteomics: Functional and Computational Aspects, Sandor Suhai (Editor), 2000, Plenum Pub Corp
  • Functional Genomics: A Practical Approach (The Practical Approach Series, 235), S. Hunt and F. Livesey (Editors), 2000, Oxford Univ Press
  • Human Molecular Genetics, Andrew P. Read, Tom Strachan 1999, BIOS Scientific Publishers Ltd.
  • Genomics: The Science and Technology Behind the Human Genome Project, Charles R. Cantor and Cassandra L. Smith, 1999, John Wiley & Sons
  • Cells: A Laboratory Manual, 3 volumes, David L. Spector, Robert D. Goldman, Leslie A. Leinwand, 1998, Cold Spring Harbor Laboratory Press
  • Genome Analysis: A Laboratory Manual, 4 volumes, Bruce Birren, et al. (Editors), 1997, Cold Spring Harbor Laboratory Press
  • The Human Genome Project, N. G. Cooper (Editor), 1994, University Science Books

Comparative Genomics

  • Handbook of Comparative Genomics: Principles and Methodology, Cecilia Saccone, Graziano Pesole, 2003, Wiley-Liss
  • Sequence - Evolution - Function: Computational Approaches in Comparative Genomics, Eugene V. Koonin, Michael Y. Galperin, 2002, Kluwer Academic Publishers
  • Comparative Genomics - Empirical and Analytical Approaches to Gene Order Dynamics, Map Alignment and the Evolution of Gene Families, David Sankoff and Joseph H. Nadeau, 2000, Kluwer Academic Pub
  • Comparative Genomics, Melody Clark (Editor), 2000, Kluwer Academic Pub

Proteomics

  • Proteins and Proteomics: A Laboratory Manual, Richard J. Simpson (Editor), Cold Spring Harbor Laboratory
  • Proteomics in Practice: A Laboratory Manual of Proteome Analysis , Reiner Westermeier, Tom Naven, 2002, John Wiley & Sons
  • Posttranslational Modifications of Proteins: Tools for Functional Proteomics (Methods in Molecular Biology, Vol 194) , Christoph Kannicht (Editor), 2002, Humana Press
  • Peptide Arrays on Membrane Supports: Synthesis and Applications (Springer Lab Manual), Joachim Koch, Michael Mahler (Editors), 2002, Springer Verlag
  • Proteomics , Timothy Palzkill, 2002, Kluwer Academic Publishers
  • Introduction to Proteomics: Tools for the New Biology , Daniel C. Liebler (Editor), 2001, Humana Press
  • Proteome Research: Mass Spectrometry (Principles and Practice) , P. James (Editor), 2001, Springer Verlag
  • Interpreting Protein Mass Spectra: A Comprehensive Resource , A. Peter Snyder, 2000, American Chemical Society
  • Protein Sequencing and Identification Using Tandem Mass Spectrometry , Michael Kinter, Nicholas E. Sherman, 2000, Wiley-Interscience
  • From Genome to Proteome: Advances in the Practice and Application of Proteomics, Michael J. Dunn (Editor), 2000, Vch Verlagsgesellschaft Mbh
  • Proteomics: From Protein Sequence to Function, S. Pennington (Editor), M. Dunn (Editor), 2000, Springer Verlag
  • Proteome Research: Two-Dimensional Gel Electrophoresis and Detection Methods (Principles and Practice), T. Rabilloud (Editor), 2000, Springer Verlag
  • Proteome and Protein Analysis, R. M. Kamp, D. Kyriakidis, th Choli-Papadopoulou (Editor), 1999, Springer Verlag
  • Proteome Research: New Frontiers in Functional Genomics, M. R. Wilkins, et al. (Editors), 1997, Springer Verlag

Protein Structure

  • Structural Bioinformatics, Philip E. Bourne, Helge Weissig (Editors), 2003, John Wiley & Sons
  • Protein Structure Prediction: Bioinfomatic Approach, I.F. Tsigelny, 2002, International University Line
  • Introduction to Protein Architecture: The Structural Biology of Proteins, Arthur M. Lesk, 2001, Oxford University Press
  • Protein Structure Prediction: Methods and Protocols, David M. Webster (Editor), 2000, Humana Press
  • Introduction to Protein Structure, Carl-Ivar Branden, John Tooze, 1999, Garland Publishing
  • Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding, Alan Fersht, 1999, Freeman

Pharmacogenomics

  • Pharmacogenomics: Social, Ethical, and Clinical Dimensions, Mark A. Rothstein (Editor), 2003, Wiley-Liss
  • Pharmacogenomics: The Search for Individualized Therapies, Julio Licinio, Ma-Li Wong (Editors), 2002, John Wiley & Sons
  • Pharmacogenomics, Werner Kalow, Urs A. Meyer, Rachel Tyndale (Editors), 2001, Marcel Dekker
  • Pharmacogenetics and Pharmcogenomics: Recent Conceptual and Technical Advances (Pharmacology, Volume 61, Number 3, 2000), Elliot S. Vesell (Editor), 2000, S. Karger Publishing
  • Pharmacogenetics, Wendell Weber, 1997, Oxford University Press

DNA Microarrays

  • Statistical Analysis of Gene Expression Microarray Data, T. P. Speed (Editor), 2003, CRC Press
  • Microarray Gene Expression Data Analysis: A Beginner's Guide, Helen C. Causton, John Quackenbush, Alvis Brazma, 2003, Blackwell Publishers
  • The Analysis of Gene Expression Data (Statistics for Biology and Health), G. Parmigiani, E. S. Garrett, R. A. Irizarry, S. Zeger , Graeme Clark (Editors), 2003, Springer Verlag
  • A Practical Approach to Microarray Data Analysis, Daniel P. Berrar, Werner Dubitzky, Martin Granzow (Editors), 2002, Kluwer Academic Publishers
  • DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling, Pierre Baldi, G. Wesley Hatfield, 2002, Cambridge University Press
  • DNA Microarrays: A Molecular Cloning Manual, David Bowtell, Joseph Sambrook (Editors), 2002, Cold Spring Harbor Laboratory
  • DNA Array Image Analysis: Nuts & Bolts, Gerda Kamberova, Shishir Shah, 2002, DNA Press
  • Microarray Analysis, Mark Schena, 2002, John Wiley & Sons
  • A Biologist's Guide to Analysis of DNA Microarray Data, Steen Knudsen, 2002, John Wiley & Sons
  • Microarrays for an Integrative Genomics (Computational Molecular Biology), Isaac S. Kohane, Alvin Kho, Atul J. Butte, 2002, MIT Press
  • Microarrays for the Neurosciences: An Essential Guide (Cellular and Molecular Neuroscience), Daniel H. Geschwind, Jeffrey P. Gregg (Editors), 2002, MIT Press
  • DNA Microarrays: Gene Expression Applications, Bertrand Jordan (Editor), 2001, Springer Verlag
  • DNA Arrays: Methods and Protocols (Methods in Molecular Biology, Volume 170), Jang B. Rampal (Editor), 2001, Humana Press
  • DNA Arrays: Technologies and Experimental Strategies, Elena V. Grigorenko (Editor), 2001, CRC Press
  • Microarray Biochip Technology, Mark Schena (Editor), 2000, Eaton Pub
  • Expression Genetics: Accelerated and High-Throughput Methods (Biotechniques Update Series), Michael McClelland (Editor), Arthur B. Pardee (Editor), 1999, Eaton Pub
  • DNA Microarrays: A Practical Approach (Practical Approach Series 205), Mark Schena (Editor), 1999, Oxford Univ Press
  • cDNA Preparation and Characterization (Methods in Enzymology Volume 303), S.M. Weissman (Editor), 1999, Academic Press

Systems Biology, Genetic and Biochemical Network

  • Handbook of Graphs and Networks : From the Genome to the Internet, Stefan Bornholdt, Heinz Georg Schuster (Editors), 2003, Vch Verlagsgesellschaft Mbh
  • Computational Cell Biology, Christopher Fall, Eric Marland, John Wagner, John Tyson (Editors), 2002, Springer Verlag
  • Gene Regulation and Metabolism: Post-Genomic Computational Approaches (Computational Molecular Biology), Julio Collado-Vides, Ralf Hofestadt (Editors), 2002, MIT Press
  • Foundations of Systems Biology, Hiroaki Kitano (Editor), 2001, MIT Press
  • Genomic Regulatory Systems: Development and Evolution, Eric H. Davidson , 2001, Academic Press
  • Genes & Signals, Mark Ptashne, Alexander Gann, 2001, Cold Spring Harbor Laboratory
  • Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology), James M. Bower and Hamid Bolouri (Editors), 2001, MIT Press
  • Protein-Protein Interactions: A Molecular Cloning Manual, Erica Golemis (Editor), 2001, Cold Spring Harbor Laboratory
  • Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists, Eberhard O. Voit, 2000, Cambridge University Press
  • Mathematical Physiology, James P. Keener, James Sneyd, 1998, Springer Verlag

 

DNA Sequencing

  • DNA Sequencing: From Experimental Methods to Bioinformatics (Introduction to Biotechniques Series), Luke Alphey, 1997, Springer Verlag
  • Automated DNA sequencing and analysis, Adams M.D., Fields C., Venter J.C. (Editors), 1994, Academic Press

 

Apart from above mentioned books, you can also find some useful books links at following mentioned URLs:

 

http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713

http://www.amazon.com/Bioinformatics-Genes-Proteins-Computers-Advanced/dp/1859960545

http://www.amazon.com/Introduction-Bioinformatics-Algorithms-Computational-Molecular/dp/0262101068

http://books.google.no/books?id=pxSM7R1sdeQC&dq=Pierre+baldi+%2B+bioinformatics&printsec=frontcover&source=bn&hl=en&ei=IoGRS6uCIJT-NYLA8Z0N&sa=X&oi=book_result&ct=result&redir_esc=y#v=onepage&q&f=false

http://www.amazon.com/Statistical-Methods-Bioinformatics-Introduction-Statistics/dp/0387400826

 

If you think your favourite books are not listed then please write it down in comment section for the benefits of other users. Feel free to add many more books in comment section. 

]]> Rahul Agarwal https://bioinformaticsonline.com/pages/view/36603/learning-python-programming-a-bioinformatician-perspective Mon, 14 May 2018 16:33:03 -0500 https://bioinformaticsonline.com/pages/view/36603/learning-python-programming-a-bioinformatician-perspective <![CDATA[Learning Python Programming - a bioinformatician perspective !]]> Python Programming is a general purpose programming language that is open source, flexible, powerful and easy to use. One of the most important features of python is its rich set of utilities and libraries for data processing and analytics tasks. In the current era of big biological data, python and biopython is getting more popularity due to its easy-to-use features which supports big data processing.

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

  • It's widely used in the scientific community
  • It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
  • It lend itself well to being integrated with other, existing tools
  • It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

  • Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
  • Python is dynamically typed, so the variables types are defined automatically.
  • Python is strongly typed. So the developers need to cast the type manually.
  • Less code and more use makes it more acceptable.
  • Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/22961/bioscripts Sun, 28 Jun 2015 07:46:14 -0500 https://bioinformaticsonline.com/bookmarks/view/22961/bioscripts <![CDATA[BioScripts]]> You are requested to please bookmark collection of bioinformatics tools, scripts, codes that can be pieced together in a very easy and flexible manner to perform both simple and complex bioinformatics tasks.

The next-generation sequencing included whole genome sequencing(WGS), transcriptome sequencing (whole cDNA sequencing, RNA-seq), digital gene expression sequencing (Tag-Seq), ChIP-Seq, and so on. And there are many sequencing platform to generate sequece, as well know Sanger/ABi(the frist generation), Solexa/illumina, SOLiD/ABi, 454/Roche. But thier sequence format is different, also they have different error type. High quality data is very important for further analysis or data mining. There are many pipeline for raw sequence quality analysis and control with few of process for reporting reads quality statistical details, trimming, filtering, and error correction. Please bookmarks them for the benefits of bioinformatics community.

https://code.google.com/p/biowiki/

https://code.google.com/p/ngs-pipeline/source/browse/#svn%2Ftrunk

NGSand Perl scripts https://code.google.com/hosting/search?q=NGS+perl&projectsearch=Search+projects

NGS and Python scripts https://code.google.com/hosting/search?q=NGS+Python&projectsearch=Search+projects

Address of the bookmark: https://code.google.com/hosting/search?q=bioinformatics&sa=Search

]]> Rahul Nayak https://bioinformaticsonline.com/researchlabs/view/4552/imtech-lab Sun, 15 Sep 2013 09:41:04 -0500 <![CDATA[IMTECH Lab]]> Computer Aided Protein Structure Prediction; Identification of Vaccine
Candidates (T-Epitope prediction); Analysis of Nucleotide/Protein Sequences; Development of Web Server/

Software; Creation of Public Domain Resources in Biology
Present Status::

Developing prediction methods for gene, beta-turn, secondary structure and MHC-binding sites.
Area of Interest ::

Comparison of force field simulations. Analysis of DNA-protein interactions using molecular mechanics methods.Drug Target Identification using in silico biology.

More @ http://www.imtech.res.in/bic/index.php?option=com_content&view=article&id=65

PIs: http://www.imtech.res.in/bic/index.php?option=com_content&view=article&id=69

]]> https://bioinformaticsonline.com/bookmarks/view/10925/a-brief-bioinformatics-tutorial Wed, 21 May 2014 12:50:09 -0500 https://bioinformaticsonline.com/bookmarks/view/10925/a-brief-bioinformatics-tutorial <![CDATA[A Brief Bioinformatics Tutorial]]> This is about how to use a computer to find what is known about a gene of interest and also how to get new insights about it.

The tutorial is divided in three main parts:

  • In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.
  • The Structure part is about molecular visualization, homology modeling and structural domain prediction.
  • In the Function part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...

During all the three parts, we will use the S. cerevisiae VPS36 protein as an example.

Address of the bookmark: http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html

]]> Jit https://bioinformaticsonline.com/pages/view/36605/hello-python-world Mon, 14 May 2018 16:41:01 -0500 https://bioinformaticsonline.com/pages/view/36605/hello-python-world <![CDATA[Hello Python World !]]> As I mentioned earlier, I will keep on posting one Python script per day to introduce you to Python programming. Whether you are an experienced programmer or not, this tutorial is intended for everyone who wishes to learn the Python programming language.

Python is a very simple language, and has a very straightforward syntax. The simplest directive in Python is the "print" directive - it simply prints out a line (and also includes a newline).

Create a file Hello.py

print("Hello, Python World !.")

Run

python3 Hello.py

]]> Rahul Nayak