Blog Archives

One step ahead in Bioinformatics using Package Repositories

October 28, 2013
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One step ahead in Bioinformatics using Package Repositories

About a year ago I published a post about in-house tools in research and how using this type of software may end up undermining the quality of a manuscript and the reproducibility of its results.  While I can certainly relate to someone reluctant to release nasty code (i.e. not commented, not well-tested, not documented), I still think we...

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Little Book of R for Bioinformatics by Avril Coghlan

October 25, 2013
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Introduction to bioinformatics, with a focus on genome analysis, using the R statistics software. By Avril Coghlan(Wellcome Trust Sanger Institute, Cambridge,UK). Original Site: http://a-little-book-of-r-for-bioinformatics.readthedocs.org/en/lates...

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Creating an Open Source Revolution in Computational Proteomics

October 24, 2013
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Creating an Open Source Revolution in Computational Proteomics

First of all, I don’t want to discuss in this post about Open-Source, its strengths & strengths. This post is about the most useful Open-Source packages, frameworks or libraries in the field of computational proteomics (a short version of our manuscript “Open source libraries and frameworks for Mass Spectrometry based Proteomics: A developer’s perspective”). 

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Which are the best programming languages for a bioinformatician?

October 19, 2013
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This is a basic question when you (as a programmer or biologist or mass spectrometrist) start a career in bioinformatics. What is your favorite programming language in bioinformatics?. This pool will give you a short picture about which languages are m...

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Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection

October 17, 2013
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Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection

Bioinformatics is becoming more and more a Data Mining field. Every passing day, Genomics and Proteomics yield bucketloads of multivariate data (genes, proteins, DNA, identified peptides, structures), and every one of these biological data units are described by a number of features: length, physicochemical properties, scores, etc. Careful consideration of which features to select when trying...

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Why R for Mass Spectrometrist and Computational Proteomics

August 25, 2012
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Why R for Mass Spectrometrist and Computational Proteomics

Why R:Actually, It is a common practice the integration of the statistical analysis of the resulted data and in silico predictions of the data generated in your manuscript and your daily research. Mass spectrometrist, biologist and bioinformaticians c...

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