<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/44373?offset=620 https://bioinformaticsonline.com/bookmarks/view/44591/yamp-yet-another-metagenomic-pipeline Sat, 06 Jul 2024 04:26:00 -0500 https://bioinformaticsonline.com/bookmarks/view/44591/yamp-yet-another-metagenomic-pipeline <![CDATA[YAMP: Yet Another Metagenomic Pipeline]]> YAMP is constructed on Nextflow, a framework based on the dataflow programming model, which allows writing workflows that are highly parallel, easily portable (including on distributed systems), and very flexible and customisable, characteristics which have been inherited by YAMP. New modules can be added easily and the existing ones can be customised -- even though we have already provided default parameters deriving from our own experience.

Address of the bookmark: https://github.com/alesssia/YAMP

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/34391/taxoblast-taxoblast-is-a-pipeline-to-identify-contamination-in-genomic-sequence Thu, 23 Nov 2017 08:37:15 -0600 https://bioinformaticsonline.com/bookmarks/view/34391/taxoblast-taxoblast-is-a-pipeline-to-identify-contamination-in-genomic-sequence <![CDATA[Taxoblast : Taxoblast is a pipeline to identify contamination in genomic sequence]]> Modern genome sequencing strategies are highly sensitive to contamination making the detection of foreign DNA sequences an important part of analysis pipelines. Here we use Taxoblast, a simple pipeline with a graphical user interface, for the post-assembly detection of contaminating sequences in the published genome of the kelp Saccharina japonica. Analyses were based on multiple blastn searches with short sequence fragments. They revealed a number of probable bacterial contaminations as well as hybrid scaffolds that contain both bacterial and algal sequences. This or similar types of analysis, in combination with manual curation, may thus constitute a useful complement to standard bioinformatics analyses prior to submission of genomic data to public repositories. Our analysis pipeline is open-source and freely available at http://sdittami.altervista.org/taxoblast and via SourceForge (https://sourceforge.net/projects/taxoblast).

Address of the bookmark: https://sourceforge.net/projects/taxoblast/files/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38067/metaplotr-a-perlr-pipeline-for-plotting-metagenes-of-nucleotide-modifications-and-other-transcriptomic-sites Mon, 05 Nov 2018 08:12:45 -0600 https://bioinformaticsonline.com/bookmarks/view/38067/metaplotr-a-perlr-pipeline-for-plotting-metagenes-of-nucleotide-modifications-and-other-transcriptomic-sites <![CDATA[MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites]]> An increasing number of studies are mapping protein binding and nucleotide modifications sites throughout the transcriptome. Often, these sites cluster in certain regions of the transcript, giving clues to their function. Hence, it is informative to summarize where in the transcript these sites occur. A metagene is a simple and effective tool for visualizing the distribution of sites along a simplified transcript model. In this work, we introduce MetaPlotR, a Perl/R pipeline for creating metagene plots.

Address of the bookmark: https://github.com/olarerin/metaPlotR

]]> Neel https://bioinformaticsonline.com/bookmarks/view/39843/dnapipete-a-pipeline-designed-to-find-annotate-and-quantify-transposable-elements Mon, 12 Aug 2019 21:56:08 -0500 https://bioinformaticsonline.com/bookmarks/view/39843/dnapipete-a-pipeline-designed-to-find-annotate-and-quantify-transposable-elements <![CDATA[dnaPipeTE: a pipeline designed to find, annotate and quantify Transposable Elements]]> dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (< 1X).

https://github.com/clemgoub/dnaPipeTE/wiki/dnaPipeTE-WIKI-home

Address of the bookmark: https://github.com/clemgoub/dnaPipeTE

]]> Jit https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes Thu, 13 Aug 2020 10:06:19 -0500 https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes <![CDATA[PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes]]> PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring O(n2) resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.

Address of the bookmark: https://github.com/ryanmelnyk/PyParanoid

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/43353/judi-just-do-it Mon, 06 Sep 2021 02:44:35 -0500 https://bioinformaticsonline.com/bookmarks/view/43353/judi-just-do-it <![CDATA[JUDI: Just Do It]]> judi comes from the idea of bringing the power and efficiency of doit to execute any kind of task under many combinations of parameter settings.

https://github.com/ncbi/JUDI

Address of the bookmark: https://github.com/ncbi/JUDI

]]> 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