BOL: Related items

Project Assistant II Jobs opportunity in National Chemical Laboratory (NCL) on temporary basis

Fri, 06 Nov 2015 01:36:16 -0600

No. Bio/NCIM/3

Project Assistant II Jobs opportunity in National Chemical Laboratory (NCL) on temporary basis

Project Code No. : GAP312626

Title of the Project : Microbial ecology and distribution of geochemical cycling genes in an hot spring ecosystem

No. of Post : 01

Qualifications : M.Sc./B.Tech/M.Tech in Computational biology/ Bioinformatics from recognized university with minimum 60 % marks (aggregate)

Desirable : Good knowledge of computational skills, Linux (command line and GUI) and Unix; Perl / Python / R /C-programming. Practical knowledge of analysis of Next generation sequence datasets (amplicon sequencing, whole metagenome, and complete genome sequencing) with reference to microbes. Analysis and statistical validation of NGS data generated from different chemistry platforms. Some wet-lab experience in microbial system would be an added advantage as project involves some travel.

Emoluments : Rs. 16,000/-

Age Limit : 28 years
How to apply

The application with the above information duly signed together with photo-copies of relevant certificates/testimonials should be addressed to : The Head, NCIM Resource Centre (Attn Dr. M.S. DHARNE), National Chemical Laboratory, Pune 411 008, so as to reach on or before 16th November 2015.

More at http://www.ncl-india.org/files/JoinUs/JobVacancies/TemporaryJobs.aspx?menuid=ql6&childmenustripid=divSubQL6

SRF/JRF Bioinformatics at National Bureau of Animal Genetic Resources

Thu, 26 Nov 2015 01:54:07 -0600

National Bureau of Animal Genetic Resources

SRF Job vacancies in National Bureau of Animal Genetic Resources (NBAGR) on contractual basis

No. of Posts : 03

Qualifications : Master degree in any one of discipline/ Subjects Biotechnology/Bioinformatics/Life Sciences/ Computer Applications with 4 years/5 years of Bachelor’s degree. Candidates having post graduate degree in above mentioned subjects with 3 years Bachelor’s degree should be NET qualified.

Note : i. The Ph.D. Degree holders are eligible for the posts. ii. The Master’s degree holders with the integrated degrees like B.Tech. & M. Tech. (5 years) are eligible. iii. Graduates with three years Bachelor’s degree, who have acquired professional Master’s degree (3 years) like M.Sc. (Ag.), Masters in Computer Applications, M. Tech., etc., are eligible.

Desirable : The candidates should have zeal to work in R environment and NGS data analysis

Emolument : Rs. 28,000/-

Age Limit : 35 year for Men and 40 years for Women in case of SRF on the date of Interview.
How to apply

Walk-in-Interview is proposed to be held at National Bureau of Animal Genetic Resources, Karnal (Haryana)-132 001 at 10:30 AM on 02.12.2015. Interested candidates may attend Walk-in-Interview at 10:30 AM on 02.12.2015 alongwith original certificates and typed bio-data with one set of attested copies of each their certificates with passport size photograph.

CIAB Recruits Scientist, Engineer, Informatics Officer

Sat, 28 Nov 2015 03:40:28 -0600

Center of Innovative and Applied Bioprocessing (CIAB) - Punjab

Center of Innovative and Applied Bioprocessing (CIAB) invites applications to recruit on vacant posts of Scientist, Associate Plant Manager/ Plant Engineer, Informatics Officer. Applications against these Government Jobs can be submitted on or before 7th January 2016.
CIAB Vacancy 2015 Details

9. Research & Development – Informatics Officer
Qualification: M. Tech or M.E. in computer science or information technology or bioinformatics with 4 years of R&D experience (after Master’s Degree) in an R&D organization with work profile of large data assembly, analysis.
Age Limit: 45 years
How to Apply : Duly filled-in applications in prescribed application format along with copies of required documents should be reach to: Chief Exec

JRF Bioinformatics at CUK

Thu, 03 Dec 2015 23:40:38 -0600

JRF Bioinformatics

Eligibility : MSc(Bio-Informatics), BE/B.Tech

Location : Kasaragod

Last Date : 20 Dec 2015

Hiring Process : Face to Face Interview
Central University of Kerala

JRF job opportunity in Central University of Kerala (CUK) on temporary basis

Project Title : "Targeting TAL effector mediated susceptibility for durable and broad-spectrum resistance to bacterial blight in Rice"

No. of Post : 01

Qualification : MSc in any subject under Life Science or Bioinformatics/ B.Tech in Bioinformatics + 1 yr experience

Stipend : Rs. 14,000/-
How to apply

Interested candidates are requested to send their applications explaining their interest in the position with an updated CV to Dr. Ginny Antony, Assistant Professor, Department of Plant Science, School of Biological Sciences, Central University of Kerala, Padannakkad, Kasaragod, Kerala - 671 314 email: ginnycuk2013@gmail.com on or before 20th December, 2015.

JRF Bioinformatics job position in University of Hyderabad

Tue, 29 Dec 2015 01:00:18 -0600

JRF Bioinformatics

Eligibility : ME/M.Tech(Bio-Informatics/Bio-Chemistry Engg), MSc(Bio-Informatics)

Location : Hyderabad

Last Date : 30 Dec 2015

Hiring Process : Written-test
University of Hyderabad

JRF Bioinformatics job position in University of Hyderabad

Project Entitled : Programming protective humoral responses with novel vaccine formulations against Dengue Virus

Essential Qualifications : M. Sc. in Life Sciences/Bioinformatics/ or M. Tech in Bioinformatics with a minimum of 60% marks and with CSIR-UGC JRF/NET or valid GATE score are preferred. Desirable : Candidates should have six months to one year hands on experience in molecular biology techniques like gene cloning, protein expression and purification, hands on various nano-formulations for drug/vaccine delivery, immunological assays including animal handling, immunizations, T cells and B cell assays or Candidate should have strong background in Bioinformatics and computational Biology, good programming skill particularly proficiency in R/PERL/PYTHON. Candidates interested in above position should send a one page statement clearly explaining how their skills are relevant to the above said position. The candidates should also enclose detailed CV and the name/Email IDs of three references

Fellowship : Rs. 16,000 p.m. + 30% H.R.A. for the first two years (Revised DBT fellowship guidelines maybe applicable)

Duration : The appointment will be on temporary basis for a period of one year. Based on performance, the appointment could be extended till the end of project

How to apply

Submit your application (hardcopy) in a closed envelope to Dr.Nooruddin
Khan, Room S-66B,Department of Biotechnology and Bioinfromatics, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad 500 046 or E mail: nklabsls@gmail.com. Last date for receipt of applications is on or before 30.12.2015

More at http://uohyd.ac.in/index.php/component/content/article/87-administration/recruitments/129

Best book Titles for Learning Bionformatics

Rahul Agarwal — Tue, 13 Aug 2013 17:31:51 -0500

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.

Following the scientific literature: A personal practical guide for young computational biologists

Rahul Agarwal — Fri, 23 Aug 2013 07:18:51 -0500

The goal of this guide is to describe why, when, where and how can you follow the most up-to-date science of interest and what papers/journals you should follow. The guide is biased towards the fields of genomics/systems biology.(from article)

Source: Igor Ulitsky & Ron Shamir

Address of the bookmark: http://acgt.cs.tau.ac.il/guides/LiteratureGuide.htm

Python learning resources for bioinformatics and computational biologist !

Jit — Fri, 02 Mar 2018 06:54:15 -0600

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:

20 Python Libraries You Aren't Using (But Should) (Just fill the fields with any values)
A Beginner's Python Tutorial
A Byte of Python (3.x) (HTML, PDF, EPUB, Mobi)
A Guide to Python's Magic Methods - Rafe Kettler
A Whirlwind Tour of Python - Jake VanderPlas (PDF) (EPUB, MOBI)
Automate the Boring Stuff - Al Sweigart
Biopython (PDF)
Build applications in Python the antitextbook (3.x) (HTML, PDF, EPUB, Mobi)
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho, Packt. (Just fill the fields with any values)
Building Skills in Object-Oriented Design (Python) (PDF) (2.1.1)
Building Skills in Python (PDF) (2.6)
Code Like a Pythonista: Idiomatic Python
CodeCademy Python
Composing Programs (3.x)
Data Structures and Algorithms in Python - B. R. Preiss (PDF)
Dive into Python 3 - Mark Pilgrim (3.0)
- Dive into Python - Mark Pilgrim (2.3)
From Python to NumPy
Full Stack Python
Functional Programming in Python (Just fill the fields with any values)
Fundamentals of Python Programming - Richard L. Halterman (PDF) (3.2)
Google's Python Class (2.4 - 2.x)
Google's Python Style Guide
Hacking Secret Cyphers with Python - Al Sweigart (3.3)
Hadoop with Python (Just fill the fields with any values)
High Performance Python (PDF)
Hitchhiker's Guide to Python! (2.6)
How to Make Mistakes in Python - Mike Pirnat (PDF) (1st edition)
How to Think Like a Computer Scientist: Learning with Python, Interactive Edition (3.2)
- How to Think Like a Computer Scientist: Learning with Python - Allen B. Downey, Jeff Elkner and Chris Meyers (2.4)
- Think Python - Allen B. Downey (2.x & 3.0)
Intermediate Python - Muhammad Yasoob Ullah Khalid (1st edition)
Introduction to Programming with Python (3.3)
- Introduction to Programming Using Python - Cody Jackson (1st edition) (2.3)
Introduction to Python - Kracekumar (2.7.3)
Invent Your Own Computer Games With Python - Al Sweigart (3.1)
Learn Python, Break Python
Learn Python in Y minutes
Learn Python The Hard Way (2.5 - 2.6)
Learn to Program Using Python - Cody Jackson (PDF)
Learning Python - Fabrizio Romano, Packt. (Just fill the fields with any values)
Learning to Program
Lectures on scientific computing with python - J.R. Johansson (2.7)
Making Games with Python & Pygame - Al Sweigart (2.7)
Modeling Creativity: Case Studies in Python - Tom D. De Smedt (PDF)
Natural Language Processing with Python (3.x)
Non-Programmer's Tutorial for Python 3 (3.3)
- Non-Programmer's Tutorial for Python 2.6 (2.6)
Picking a Python Version: A Manifesto (Just fill the fields with any values)
Porting to Python 3: An In-Depth Guide (2.6 - 2.x & 3.1 - 3.x)
Practical Programming in Python - Jeffrey Elkner (PDF)
Problem Solving with Algorithms and Data Structures using Python - Bradley N. Miller and David L. Ranum
Program Arcade Games With Python And Pygame (3.3)
Programming Computer Vision with Python (PDF)
Python 2 Official Documentation (PDF, HTML, TEXT) (2.x)
Python 2.7 quick reference - New Mexico Tech (2.7)
Python 3 Official Documentation (PDF, EPUB, HTML, TEXT) (3.x)
Python Cookbook - David Beazley
Python Data Science Handbook - Jake VanderPlas (HTML, Jupyter Notebooks)
Python for Econometrics - Kevin Sheppard (PDF) (2.7.5)
Python for Everybody Exploring Data Using Python 3 - Charles Severance (PDF, EPUB, HTML)
- Python for Informatics: Exploring Information (2.7.5)
Python for you and me (2.7.3)
Python for you and me (3.x)
Python Idioms (PDF)
Python in Education (Just fill the fields with any values)
Python in Hydrology - Sat Kumar Tomer
Python Koans (2.7 or 3.x)
Python Module of the Week (3.x)
- Python Module of the Week (2.x)
Python Notes for Professionals - Compiled from StackOverflow documentation (3.x)
Python Practice Book (2.7.1)
Python Practice Projects
Python Programming (PDF) (2.6)
Scipy Lecture Notes
SICP in Python (3.2)
Snake Wrangling For Kids (3.x)
Suporting Python 3: An In-Depth Guide (2.6 - 2.x & 3.1 - 3.x)
Test-Driven Web Development with Python (3.3 - 3.x)
Text Processing in Python - David Mertz (2.3 - 2.x)
The Coder's Apprentice: Learning Programming with Python 3 - Pieter Spronck (PDF) (3.x)
The Definitive Guide to Jython, Python for the Java Platform - Josh Juneau, Jim Baker, Victor Ng, Leo Soto, Frank Wierzbicki (2.5)
The Little Book of Python Anti-Patterns (Source)
The Programming Historian - William J. Turkel, Adam Crymble and Alan MacEachern
The Python Ecosystem: An Introduction
The Python GTK+ 3 Tutorial
The Standard Python Library - Fredrik Lundh
Think Complexity - Allen B. Downey (2nd Edition) (PDF, HTML)
Web2py: Complete Reference Manual, 6th Edition (pre-release) (2.5 - 2.x)
Wikibooks: Python Programming (2.7)

Five unique traits of effective computational biologist

Jitendra Narayan — Thu, 11 Jul 2013 13:12:51 -0500

Bioinformatics research is driven by large set of software, scripts, and tools to analyse gigantic biological data. Being a great biological programmer or bioinformatician involves more than writing code that works. The biological programmers who rise to the top ranks of their profession are not only good programmer but also expert in biological stuff. Moreover, In order to be a good and effective biological programmer, you need to possess a combination of traits that allow your computational as well as biological skill, experience, and knowledge to produce working code. There are some technically skilled biological programmers who will never be effective because they lack the other important traits needed. Here are top five traits that are necessary to become a great biological programmer.

1. Learn and get updated

Some of the bad biological programmers only learn new technical or non-technical things when it’s absolutely necessary. The good biological programmers learn new technical skills proactively. But great biological programmers not only learn new technical skills on their own but also learn non-technical skills, and have an open mind to sources of knowledge that others may shut out.

In other concrete term, the bad biological programmer learn Perl's regular expression when they started a project on comparative genomics; the good biological programmer learned it a year before because it looked interesting; and the great biological programmer also read about the BioPerl packages, genomics, DNA string, genomic theories, or some similar course of study so that they could understand the results and explain it biologically.

2. Not a merely coder!!!

I often encountered with biological programmer who call themself a hard-core computer programmer and avoid biology. I can almost guarantee that if you are one of them then you are not doing research but merely writing "dry" codes.

According to my supervisor most of the computational biologist, don't know what they are doing biologically. Even they struggle to explain their own programs output and results. Therefore, It is highly advisable to learn basic of biology which can assist you to explain the result and understand your discovery. Always remember you are a researcher not a coder.

3. Be Social with biologist

The computational biologist spends most of the time in from of computers, writing codes. They always think their job is to produce working codes, not technical research perfections. But, they are completely wrong. You should not forget that apart from your computational skills you also need some biologist, other than your supervisor, to explain and make you understand the complex biological mechanism.

I highly recommend your to interact with biotech researchers and learn how do they explain their one graph (which they generally produce after one year of work) biologically. Remember, the origin of your research project is complex biological phenomenon, which is more complex than that of your limited programming rules.

4. Do not search, research for answers

Researching for answers means more than typing several keywords into a search engine or posting a question at Stack Overflow or the BioStars forums. I have entered problems into search engines that generate no results, and every question I posted on Stack Overflow or the BioStars forums never got anything resembling an answer, yet I solved the issues and moved on. I’m not a magician — I just know how to find answers or discover root causes.

Many problems are situational, and if you depend on search engines and forums, you can waste a lot of time going down a rabbit hole and possibly never getting a solution. Learn to perform root cause analysis, learn enough about the underlying system to look for other clues and solutions, and learn to take a long distance view of an issue before deep diving into it.

5. Love and defend your research

You cannot rise to the top in this research profession without loving your work. There are some very good “it’s just a job” biological programmers (I’ve been one at times), but if that is your outlook, you won’t be willing to do whatever it takes to succeed. This idea gets a lot of folks in a huff, because they feel it is a personal insult. “I’m a good programmer, but I have other priorities and can’t make work my life.” I understand completely; I have other priorities too. As much as I hate to say it, when I am passionate about my work, I am willing (though not eager) to abandon my other priorities to finish the job. It is not an insult to say that if you aren’t willing to pull out all the stops you can’t be the best, it is a fact.

You must be passionate about more than programming — you must also be excited about your research, the tools and technology you are using, and so on. I have seen very good and even great biological programmers operating at mediocre levels because something was not a good fit, such as they hated the project or were using a technology they disliked. Therefore, like your research project and get excited about your discoveries. You have not only to discover but also defend your finding with scientific words.

Thanks to all of you for reading.

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