<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/40298?offset=230 https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times Sat, 02 Jun 2018 07:40:06 -0500 https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times <![CDATA[MCMCTREE: a phylogenetic program for Bayesian estimation of species divergence times]]> MCMCTREE is a phylogenetic program for Bayesian estimation of species divergence times using soft fossil constraints under various molecular clock models. This is part of the PAML package. In this tutorial I will analyze an easy example modified from dataset of Inoue et al. (2010). Here we conduct a commonly used time estimation method, "Approximate Likelihood Method", for the datasets including more than 10 species.

Address of the bookmark: http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html

]]> Poonam Mahapatra https://bioinformaticsonline.com/news/view/140/python-education-material Wed, 10 Jul 2013 16:38:23 -0500 https://bioinformaticsonline.com/news/view/140/python-education-material <![CDATA[Python Education Material]]> Google provide free class for people with a little bit of programming experience who want to learn Python. The google python class includes written materials, lecture videos, and lots of code exercises to practice Python coding. These materials are used within Google to introduce Python to people who have just a little programming experience.

Find more @ https://developers.google.com/edu/python/

]]> Jitendra Narayan 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/bookmarks/view/20471/bioinformatics-scripts Thu, 22 Jan 2015 22:29:39 -0600 https://bioinformaticsonline.com/bookmarks/view/20471/bioinformatics-scripts <![CDATA[Bioinformatics Scripts]]> Some of the useful bioinformatics scripts.

For example ... contig-stats.pl is a Perl script that will automatically describe features of a sequence assembly.

http://milkweedgenome.org/?q=scripts

Address of the bookmark: http://milkweedgenome.org/?q=scripts

]]> 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/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/40214/gooey-turn-almost-any-python-command-line-program-into-a-full-gui-application-with-one-line Fri, 01 Nov 2019 00:29:27 -0500 https://bioinformaticsonline.com/bookmarks/view/40214/gooey-turn-almost-any-python-command-line-program-into-a-full-gui-application-with-one-line <![CDATA[Gooey: Turn (almost) any Python command line program into a full GUI application with one line]]> Turn (almost) any Python command line program into a full GUI application with one line

The easiest way to install Gooey is via pip

pip install Gooey

Alternatively, you can install Gooey by cloning the project to your local directory

git clone https://github.com/chriskiehl/Gooey.git

run setup.py

python setup.py install

Address of the bookmark: https://github.com/chriskiehl/Gooey

]]> BioStar https://bioinformaticsonline.com/opportunity/view/41043/postdoctoral-scientist-genome-analytics-genome-bioinformatics-mf Sun, 16 Feb 2020 02:57:40 -0600 <![CDATA[Postdoctoral scientist genome analytics/ genome bioinformatics (m/f/*)]]> https://www.uksh.de/jobs/Stellenangebote-nr-20190570-p-8.html
Your profile:
Degree in bioinformatics, biostatistics, or equivalent
Experience in the processing and analysis of large-scale genomics data using compute clusters / high-performance computing
Strong competence in working in Unix/Linux environments (shell)
Strong programming skills (in particular: Python, R, Perl)
Experience with using git and snakemake
Fluent English language skills, both spoken and written
Strong communication skills and motivation to work in a young, interdisciplinary, dynamic team

Additional Information:

If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).

Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).

Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.

Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.

]]> https://bioinformaticsonline.com/blog/view/44633/learn-python-with-example Tue, 06 Aug 2024 23:51:51 -0500 https://bioinformaticsonline.com/blog/view/44633/learn-python-with-example <![CDATA[Learn python with example]]>

There are over 21 unique Python project walkthroughs in this content that range from beginner to advanced. See below for the timestamps for these projects:

00:00:00 | How To Navigate These Projects
---
00:01:46 | #1 - Quiz Game (Easy)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/quiz_game.py 
---
00:22:00 | #2 - Number Guessing Game (Easy)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/number_guesser.py 
---
00:39:49 | #3 - Rock, Paper, Scissors (Easy)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/rock_paper_scissors.py 
---
00:54:40 | #4 - Choose Your Own Adventure Game (Easy)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/choose_your_own_adventure.py 
---
01:06:47 | #5 - Password Manager (Medium)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/ 
Fernet Cryptography Documentation: https://cryptography.io/en/latest/fernet/ 
---
01:37:37 | #6 - PIG (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects/blob/main/project1.py 
---
01:59:07 | #7 - Madlibs Generator (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects/blob/main/project2.py 
---
02:15:04 | #8 - Timed Math Challenge (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects/blob/main/project3.py 
---
02:28:02 | #9 - Slot Machine (Medium)
Code: https://github.com/techwithtim/Python-Slot-Machine 
---
03:20:43 | #10 - Turtle Racing (Medium)
Code: https://github.com/techwithtim/Turtle-Racing-V2 
Turtle Docs: https://docs.python.org/3/library/turtle.html 
---
04:13:09 | #11 - WPM Typing Test (Medium)
Code: https://github.com/techwithtim/WPM_Typing_Test 
Curses Docs: https://docs.python.org/3/howto/curses.html 
05:09:43 | #12 - Alarm Clock (Easy)
Python Project Idea Blog: https://hackr.io/blog/python-projects 
Sound Effects: https://www.fesliyanstudios.com/royalty-free-sound-effects-download/alarm-203 
---
05:22:07 | #13 - Password Generator (Easy)
Python Project Idea Blog: https://hackr.io/blog/python-projects 
---
05:39:16 | #14 - Shortest Path Finder (Advanced)
Code: https://github.com/techwithtim/3-Mini-Python-Projects-For-Intermediates/blob/main/path-finder.py 
---
06:14:53 | #15 - NBA Stats & Current Scores (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects-For-Intermediates/blob/main/nba-scores.py 
---
06:38:22 | #16 - Currency Converter (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects-For-Intermediates/blob/main/currency-converter.py 
API: https://free.currencyconverterapi.com/
---
06:58:51 | #17 - YouTube Video Downloader (Medium)
Code:  https://github.com/techwithtim/Python-Beginner-Automation-Projects/blob/main/youtube.py 
---
07:09:50 | #18 - Automated File Backup (Medium)
Code: https://github.com/techwithtim/Python-Beginner-Automation-Projects/blob/main/backup.py 
---
07:21:18 | #19 - Mastermind/4 Color Match (Advanced)
---
07:48:20 | #20 - Aim Trainer (Advanced)
Code: https://github.com/techwithtim/Python-Aim-Trainer 
---
08:39:20 | #21 - Advanced Python Scripting (Advanced)
Code: https://github.com/techwithtim/Python-Scripting-Project 

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/40099/contiguator Fri, 04 Oct 2019 01:27:58 -0500 https://bioinformaticsonline.com/bookmarks/view/40099/contiguator <![CDATA[CONTIGuator !]]> CONTIGuator is a Python script for Linux environments whose purpose is to speed-up the bacterial genome assembly process and to obtain a first insight of the genome structure using the well-known artemis comparison tool (ACT).

 

Address of the bookmark: https://sourceforge.net/projects/contiguator/

]]> BioStar