<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/40214?offset=10 https://bioinformaticsonline.com/opportunity/view/44407/mopga-2024-visiting-fellowship-program-for-early-career-researchers Fri, 10 Nov 2023 02:30:22 -0600 <![CDATA[MOPGA 2024 Visiting Fellowship Program for Early Career Researchers]]> France, as a major player in the fight against climate change and guarantor of the spirit of the Paris Agreement, is launching a new MOPGA visiting fellowship program geared towards strengthening scientific contributions to climate change issues raised by the COPs.

This seventh Make Our Planet Great Again (MOPGA) call for applications is intended to welcome at least 40 early career researchers wishing to carry out their research in France. The program is funded by the French Ministry for Europe and Foreign Affairs, in collaboration with the French Ministry for Higher Education and Research, and implemented by Campus France.

The MOPGA 2024 Visiting Fellowship Program for Early Career Researchers will support researchers working on topics listed in the "Research Themes" section.

More at https://www.campusfrance.org/en/mopga-2024

]]> https://bioinformaticsonline.com/bookmarks/view/34137/patristic-a-program-for-calculating-patristic-distances-and-graphically-comparing-the-components-of-genetic-change Mon, 07 Aug 2017 18:40:38 -0500 https://bioinformaticsonline.com/bookmarks/view/34137/patristic-a-program-for-calculating-patristic-distances-and-graphically-comparing-the-components-of-genetic-change <![CDATA[PATRISTIC: a program for calculating patristic distances and graphically comparing the components of genetic change]]> PATRISTICv1.0 is a java program that calculates patristic distances from large trees in a range of file formats and allows graphical and statistical interpretation of distance matrices calculated by other programs.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1352388/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37737/rebaler-program-for-conducting-reference-based-assemblies-using-long-reads Tue, 18 Sep 2018 07:52:41 -0500 https://bioinformaticsonline.com/bookmarks/view/37737/rebaler-program-for-conducting-reference-based-assemblies-using-long-reads <![CDATA[Rebaler: program for conducting reference-based assemblies using long reads.]]> Rebaler is a program for conducting reference-based assemblies using long reads. It relies mainly on minimap2 for alignment and Racon for making consensus sequences.

I made Rebaler for bacterial genomes (specifically for the task of testing basecallers). It should in principle work for non-bacterial genomes as well, but I haven't tested it.

Address of the bookmark: https://github.com/rrwick/Rebaler

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38625/croco-a-program-to-detect-potential-cross-contaminations-in-hts-assembled-transcriptomes-using-expression-level-quantification Mon, 07 Jan 2019 18:17:44 -0600 https://bioinformaticsonline.com/bookmarks/view/38625/croco-a-program-to-detect-potential-cross-contaminations-in-hts-assembled-transcriptomes-using-expression-level-quantification <![CDATA[CroCo: A program to detect potential cross contaminations in HTS assembled transcriptomes using expression level quantification]]> CroCo is a program to detect cross contamination events in assembled transcriptomes using sequencing reads to determine the true origin of every transcripts.
Such cross contaminations can be expected if several RNA-Seq experiments were prepared during the same period at the same lab, or by the same people, or if they were processed or sequenced by the same sequencing service facility.
Our approach first determines a subset of transcripts that are suspiciously similar across samples using a pairwise BLAST procedure. CroCo then combine all transcriptomes into a metatranscriptome and quantifies the "expression level" of all transcripts successively using every sample read data (e.g. several species sequenced by the same lab for a particular study) while allowing read multi-mappings.
Several mapping tools implemented in CroCo can be used to estimate expression level (default is RapMap).
This information is then used to categorize each transcript in the following 5 categories :


clean: the transcript origin is from the focal sample.

cross contamination: the transcript origin is from an alien sample of the same experiment.

dubious: expression levels are too close between focal and alien samples to determine the true origin of the transcript.

low coverage: expression levels are too low in all samples, thus hampering our procedure (which relies on differential expression) to confidently assign it to any category.

over expressed: expression levels are very high in at least 3 samples and CroCo will not try to categorize it. Indeed, such a pattern does not correspond to expectations for cross contaminations, but often reflect highly conserved genes such as ribosomal gene, or external contamination shared by several samples (e.g. Escherichia coli contaminations).

Address of the bookmark: https://gitlab.mbb.univ-montp2.fr/mbb/CroCo

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39869/mfannot-a-program-for-the-annotation-of-mitochondrial-and-plastid-genomes Mon, 26 Aug 2019 11:47:56 -0500 https://bioinformaticsonline.com/bookmarks/view/39869/mfannot-a-program-for-the-annotation-of-mitochondrial-and-plastid-genomes <![CDATA[MFannot : a program for the annotation of mitochondrial and plastid genomes]]> MFannot is a program for the annotation of mitochondrial and plastid genomes

MFannot is a program for the annotation of mitochondrial and plastid genomes. It is a PERL wrapper around a set of diverse, external independent tools.

It makes intense use of RNA/intron detection tools including HMMER, Exonerate, Erpin and others.

http://megasun.bch.umontreal.ca/cgi-bin/mfannot/mfannotInterface.pl

Address of the bookmark: https://github.com/BFL-lab/Mfannot

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43722/crossmap-program-for-genome-coordinates-conversion-between-different-assemblies Tue, 25 Jan 2022 17:59:32 -0600 https://bioinformaticsonline.com/bookmarks/view/43722/crossmap-program-for-genome-coordinates-conversion-between-different-assemblies <![CDATA[CrossMap: program for genome coordinates conversion between different assemblies]]> CrossMap is a program for genome coordinates conversion between different assemblies (such as hg18 (NCBI36) <=> hg19 (GRCh37)). It supports commonly used file formats including BAM, CRAM, SAM, Wiggle, BigWig, BED, GFF, GTF, MAF VCF, and gVCF.

Address of the bookmark: http://crossmap.sourceforge.net/

]]> Rahul Nayak 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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