<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/43101?offset=20 https://bioinformaticsonline.com/bookmarks/view/44414/reconplot-an-r-package-for-the-visualization-and-interpretation-of-genomic-rearrangements Thu, 14 Dec 2023 12:33:19 -0600 https://bioinformaticsonline.com/bookmarks/view/44414/reconplot-an-r-package-for-the-visualization-and-interpretation-of-genomic-rearrangements <![CDATA[ReConPlot: an R package for the visualization and interpretation of genomic rearrangements]]> ReConPlot (REarrangement and COpy Number PLOT), an R package that provides functionalities for the joint visualization of SCNAs and SVs across one or multiple chromosomes. ReConPlot is based on the popular ggplot2 package, thus allowing customization of plots and the generation of publication-quality figures with minimal effort.

Address of the bookmark: https://academic.oup.com/bioinformatics/article/39/12/btad719/7460198?login=false

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/38487/betsy-a-new-backward-chaining-expert-system-for-automated-development-of-pipelines-in-bioinformatics Mon, 17 Dec 2018 18:46:51 -0600 https://bioinformaticsonline.com/bookmarks/view/38487/betsy-a-new-backward-chaining-expert-system-for-automated-development-of-pipelines-in-bioinformatics <![CDATA[BETSY: A new backward-chaining expert system for automated development of pipelines in Bioinformatics]]> The BETSY provides a command-line interface and available at https://github.com/jefftc/changlab. A user first searches in the knowledge base for desired output and then BETSY develops an initial workflow to produce that data which is later examined by the user. The user can optimize the parameters, the algorithm to preprocess the data, and normalize it depending on the task.

Currently, BETSY consists of modules required for the microarray and next-generation sequencing data [4] such as expression analysis, classification, peak calling, and visualization.

Address of the bookmark: https://github.com/jefftc/changlab

]]> Jit https://bioinformaticsonline.com/news/view/1467/biopython-cookbook Thu, 08 Aug 2013 06:43:02 -0500 https://bioinformaticsonline.com/news/view/1467/biopython-cookbook <![CDATA[BioPython Cookbook]]> If you are planning to start learning BioPython ( it does not bite but swallow :P just kidding) then this online cookbook will be really helpful for you.

http://biopython.org/DIST/docs/tutorial/Tutorial.html

]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/17926/orange-bioinformatics-2534 Mon, 06 Oct 2014 12:51:37 -0500 https://bioinformaticsonline.com/bookmarks/view/17926/orange-bioinformatics-2534 <![CDATA[Orange-Bioinformatics 2.5.34]]> Orange Bioinformatics extends Orange, a data mining software package, with common functionality for bioinformatics. The provided functionality can be accessed as a Python library or through a visual programming interface (Orange Canvas). The latter is also suitable for non-programmers.

Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.

Address of the bookmark: https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34

]]> Robert M Willioms https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn Mon, 29 Feb 2016 17:39:24 -0600 https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn <![CDATA[scikit-learn]]> Machine Learning in Python

Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license

More at http://scikit-learn.org/stable/index.html

 

Address of the bookmark: http://scikit-learn.org/stable/auto_examples/index.html

]]> Jitendra Prajapati 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/40221/dash-a-web-application-framework-that-provides-pure-python-abstraction-around-html-css-and-javascript Tue, 05 Nov 2019 06:39:48 -0600 https://bioinformaticsonline.com/bookmarks/view/40221/dash-a-web-application-framework-that-provides-pure-python-abstraction-around-html-css-and-javascript <![CDATA[Dash: a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript.]]> Dash is a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript.

Dash Bio is a suite of bioinformatics components that make it simpler to analyze and visualize bioinformatics data and interact with them in a Dash application.

The source can be found on GitHub at plotly/dash-bio.

These docs are using Dash Bio version 0.1.4.

Address of the bookmark: https://dash.plot.ly/dash-bio

]]> Jit 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/pages/view/14091/subprocess-pkg Sun, 17 Aug 2014 07:59:32 -0500 https://bioinformaticsonline.com/pages/view/14091/subprocess-pkg <![CDATA[Subprocess pkg]]> Subprocess is one of simplest way of running linux command from within python code

Example:

if you want to run fastqc for QC of fastq file:

from subprocess import Popen,PIPE,call

p=Popen(["fastqc","-f","fastq","-o", "/home/name/result/","/dev/stdin"],stdin=fopen("read.fastq","r") ,stdout=PIPE,stderr=PIPE)

print p.stderr

p.stdout.close()

More:

http://pymotw.com/2/subprocess/

]]> Rahul Agarwal 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/

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