<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/40221?offset=10 https://bioinformaticsonline.com/bookmarks/view/33887/gview-a-java-application-for-viewing-and-examining-prokaryotic-genomes-in-a-circular-or-linear-context Fri, 14 Jul 2017 07:47:03 -0500 https://bioinformaticsonline.com/bookmarks/view/33887/gview-a-java-application-for-viewing-and-examining-prokaryotic-genomes-in-a-circular-or-linear-context <![CDATA[GView: A Java application for viewing and examining prokaryotic genomes in a circular or linear context]]> GView is a Java application for viewing and examining prokaryotic genomes in a circular or linear context. It accepts standard sequence file formats and an optional style specification file to generate customizable, publication quality genome maps in bitmap and scalable vector graphics formats. GView features an interactive pan-and-zoom interface, a command-line interface for incorporation in genome analysis pipelines, and a public Application Programming Interface for incorporation in other Java applications.

Availability: GView is a freely available application licensed under the GNU Public License. The application, source code, documentation, file specifications, tutorials and image galleries are available at http://gview.ca

Contact: ac.cg.cpsa-cahp@raalesmod.nav.yrag

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/27076/ale-a-generic-assembly-likelihood-evaluation-framework-for-assessing-the-accuracy-of-genome-and-metagenome-assemblies Tue, 26 Apr 2016 03:38:43 -0500 https://bioinformaticsonline.com/bookmarks/view/27076/ale-a-generic-assembly-likelihood-evaluation-framework-for-assessing-the-accuracy-of-genome-and-metagenome-assemblies <![CDATA[ALE: a Generic Assembly Likelihood Evaluation Framework for Assessing the Accuracy of Genome and Metagenome Assemblies]]> Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences' own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.

More at http://www.ncbi.nlm.nih.gov/pubmed/23303509

Address of the bookmark: http://sc932.github.io/ALE/about.html

]]> Neel https://bioinformaticsonline.com/bookmarks/view/38457/pilongrid-parallel-wrapper-around-the-pilon-framework Thu, 13 Dec 2018 09:35:40 -0600 https://bioinformaticsonline.com/bookmarks/view/38457/pilongrid-parallel-wrapper-around-the-pilon-framework <![CDATA[PilonGrid: parallel wrapper around the Pilon framework]]> The distribution is a parallel wrapper around the Pilon framework The pipeline is composed of bash scripts, an example mapping.fofn which shows how to input your fastq files (you give paths to the R1 file), and how to launch the pipeline.

 

Address of the bookmark: https://github.com/skoren/PilonGrid

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/40140/alf-a-simulation-framework-for-genome-evolution Tue, 22 Oct 2019 22:05:58 -0500 https://bioinformaticsonline.com/bookmarks/view/40140/alf-a-simulation-framework-for-genome-evolution <![CDATA[ALF--a simulation framework for genome evolution.]]> Artificial Life Framework (ALF) simulates a root genome into a number of related genomes. Result files include the resulting gene sequences, true tree and true MSAs. A description of ALF can be found in the following article:

Daniel A Dalquen, Maria Anisimova, Gaston H Gonnet, Christophe Dessimoz: ALF - A Simulation Framework for Genome Evolution. Mol Biol Evol, 29(4):1115-1123, April 2012.
http://mbe.oxfordjournals.org/content/29/4/1115

Address of the bookmark: http://alfsim.org/#index

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38646/visnetwork-an-r-package-for-network-visualization-using-visjs-javascript-library Wed, 09 Jan 2019 11:00:32 -0600 https://bioinformaticsonline.com/bookmarks/view/38646/visnetwork-an-r-package-for-network-visualization-using-visjs-javascript-library <![CDATA[visNetwork: an R package for network visualization, using vis.js javascript library]]>

visNetwork is an R package for network visualization, using vis.js javascript library (http://visjs.org/). All remarks and bugs are welcome on github : https://github.com/datastorm-open/visNetwork.

Features

Based on htmlwidgets, so :

  • compatible with shiny, R Markdown documents, and RStudio viewer

The package proposes all the features available in vis.js API, and even more with special features for R :

  • easy to use
  • custom shapes, styles, colors, sizes, …
  • works smooth on any modern browser for up to a few thousand nodes and edges
  • interactivity controls (highlight, collapsed nodes, selection, zoom, physics, movement of nodes, tooltip, events, …)
  • visualize rpart tree

Address of the bookmark: https://datastorm-open.github.io/visNetwork/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/40969/leaflet-javascript-libraries-for-interactive-maps Mon, 10 Feb 2020 01:35:07 -0600 https://bioinformaticsonline.com/bookmarks/view/40969/leaflet-javascript-libraries-for-interactive-maps <![CDATA[Leaflet: JavaScript libraries for interactive maps]]> Leaflet is one of the most popular open-source JavaScript libraries for interactive maps.

Features

  • Interactive panning/zooming
  • Compose maps using arbitrary combinations of:
    • Map tiles
    • Markers
    • Polygons
    • Lines
    • Popups
    • GeoJSON
  • Create maps right from the R console or RStudio
  • Embed maps in knitr/R Markdown documents and Shiny apps
  • Easily render spatial objects from the sp or sf packages, or data frames with latitude/longitude columns
  • Use map bounds and mouse events to drive Shiny logic
  • Display maps in non spherical mercator projections
  • Augment map features using chosen plugins from leaflet plugins repository

https://rstudio.github.io/leaflet/

Address of the bookmark: https://rstudio.github.io/leaflet/

]]> 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:

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