<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/40834?offset=180 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/bookmarks/view/42923/flanker Sat, 27 Feb 2021 22:04:53 -0600 https://bioinformaticsonline.com/bookmarks/view/42923/flanker <![CDATA[Flanker]]> Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of mobile genetic elements (MGEs) without prior knowledge of their structure. Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence.

image

Address of the bookmark: https://github.com/wtmatlock/flanker

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/34567/jobtree-based-python-wrapper-to-run-the-genome-simulation-tool-suite-evolver Fri, 08 Dec 2017 16:26:32 -0600 https://bioinformaticsonline.com/bookmarks/view/34567/jobtree-based-python-wrapper-to-run-the-genome-simulation-tool-suite-evolver <![CDATA[jobTree based python wrapper to run the genome simulation tool suite Evolver]]> evolverSimControl (eSC) can be used to simulate multi-chromosome genome evolution on an arbitrary phylogeny (Newick format). In addition to simply running evolver, eSC also automatically creates statistical summaries of the simulation as it runs including text and image files. Also included are convenience scripts to: check on a running simulation and see detailed status and logging information; extract fasta sequence files from the leaf nodes of a completed simulation; extract pairwise multiple alignment files (.maf) from leaf and branch nodes from a completed simulation and with the help of mafJoin, join them together into a single maf covering the entire simulation.

Address of the bookmark: https://github.com/dentearl/evolverSimControl

]]> Jit 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 https://bioinformaticsonline.com/pages/view/44284/tools-for-geospatial-data-analysis Wed, 22 Mar 2023 02:10:28 -0500 https://bioinformaticsonline.com/pages/view/44284/tools-for-geospatial-data-analysis <![CDATA[Tools for Geospatial data analysis !]]>

Geospatial data is becoming increasingly important in many fields, including urban planning, environmental science, public health, and more. These tools can help you work with data from a variety of sources, including satellite imagery, GPS data, and other forms of spatial data. They can help you visualize data, perform complex analysis, and even create maps and other visualizations.

The list includes some of the most popular and widely used geospatial tools available in Python. These tools can help you work with data from a variety of sources and in a variety of formats. Some of the tools are focused on visualization, such as Cartopy, Folium, and Contextily, which allow you to create interactive maps and other visualizations. Other tools are more focused on data manipulation and analysis, such as Fiona, GeoPandas, and Rasterio, which allow you to manipulate and analyze spatial data in a variety of ways.

The list also includes some tools for working with specific types of geospatial data. For example, the H3 library is designed specifically for working with hexagonal grids, while PySAL is focused on spatial econometrics and spatial analysis. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.

In summary, this list is an excellent resource for anyone working with geospatial data in Python. It contains a wide range of tools for working with different types of data, and can help you visualize data, perform complex analysis, and create maps and other visualizations. If you're looking to enhance your skills in geospatial analysis, this list is definitely worth checking out.

These tools are:

These tools offer a wide range of capabilities for working with geospatial data, from visualizing and manipulating data to performing complex analysis and modeling. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/34404/sima-c-implementation-simultaneous-multiple-alignment-of-lcms-peak-lists Thu, 23 Nov 2017 17:15:52 -0600 https://bioinformaticsonline.com/bookmarks/view/34404/sima-c-implementation-simultaneous-multiple-alignment-of-lcms-peak-lists <![CDATA[SIMA C++ Implementation: Simultaneous Multiple Alignment of LC/MS Peak Lists]]> This is the c++ implementation for SIMA - Simultaneous Multiple Alignment of LC/MS Peak Lists. The package contains C++ source code as well as two binary files. The latter were tested under various operating systems, including Windows XP SP3 32bit, Windows Vista 32bit, Windows 2008 Server, Windows 7 32bit and 64bit, Ubuntu 10.04 64bit, Ubuntu 10.10 64bit, and gentoo AMD64.

The corresponding publication can be found here:

B. Voss*, M. Hanselmann*, B.Y. Renard, M.S. Lindner, U. Köthe, M. Kirchner, F.A. Hamprecht (2011). SIMA: Simultaneous Muliple Alignment of LC/MS Peak Lists, Bioinformatics 27(7):987-993. [doi] [techreport]

Address of the bookmark: https://hciweb.iwr.uni-heidelberg.de/hci/softwares/sima

]]> Neel https://bioinformaticsonline.com/bookmarks/view/39114/plumberan-r-package-that-converts-your-existing-r-code-to-a-web-api Wed, 13 Mar 2019 19:20:10 -0500 https://bioinformaticsonline.com/bookmarks/view/39114/plumberan-r-package-that-converts-your-existing-r-code-to-a-web-api <![CDATA[plumber:An R package that converts your existing R code to a web API]]> plumber allows you to create a REST API by merely decorating your existing R source code with special comments. Take a look at an example.

# plumber.R

#* Echo back the input
#* @param msg The message to echo
#* @get /echo
function(msg=""){
  list(msg = paste0("The message is: '", msg, "'"))
}

#* Plot a histogram
#* @png
#* @get /plot
function(){
  rand <- rnorm(100)
  hist(rand)
}

#* Return the sum of two numbers
#* @param a The first number to add
#* @param b The second number to add
#* @post /sum
function(a, b){
  as.numeric(a) + as.numeric(b)
}

Address of the bookmark: https://www.rplumber.io/

]]> BioJoker https://bioinformaticsonline.com/researchlabs/view/861/fiona-brinkman-laboratory Sun, 14 Jul 2013 12:46:31 -0500 <![CDATA[Fiona Brinkman Laboratory]]> Infectious disease control needs to be made more “sustainable”. We need to reduce selective pressure on pathogens to evolve antibiotic resistance. We need to control infectious disease outbreaks and associated immune disorders with a better understanding of the genetic, environmental and social factors that impact disease spread and severity.

Research Area

Investigating the role in disease of both the microbe and its host (i.e immune system failure), using genomics and systems biology-based approaches
Using genomics and network analysis to characterize disease outbreaks and their environmental/social/genetic causes, and
Identifying new anti-infective and immune modulating therapies/biomarkers.

Link @ http://www.brinkman.mbb.sfu.ca/

]]> https://bioinformaticsonline.com/pages/view/879/bioprogramming Sun, 14 Jul 2013 16:29:53 -0500 https://bioinformaticsonline.com/pages/view/879/bioprogramming <![CDATA[BioProgramming]]> The completion of the first human genome drafts was just a start of the modern DNA sequencing era which resulted in further invention, improved development toward new advanced strategies of high-throughput DNA sequencing, so called the “high-throughput next generation sequencing” (HT-NGS). The decreasing genome sequencing cost and desire to explore and understand biological machanism at genomic level, speed up the genomic sequencing projects. In the fast growing HT-NGS technologies, the main challenge is to cope with the analysis of vast production of sequencing database through advanced bioinformatics tools. In oder to develope sotware/tools bioinformatician/ biological programmers need to expertise in any one one the programming language. However, sometime one language are not enough to handle all sort of biological needs, which compel us to learn new biologically suitable language to handle ever growing genome or protein sequences.

The next step after reading genetic code is writing a script to analyse and explore the hidden information. This tutorial is aimed to introduce you new biological programming languages with their packages/libraries, and assist in your scripting work.

Navigate the sub-section of this page [ see right hand side of the page for it ]

]]> Jitendra Narayan