Thursday, 28 August 2014

Git useful commands

In bioinformatics GitHub and its protocol git is getting more and more popular. Here you will find an introduction about git and also some useful commands when you use Git in your projects. 

What is Git

Git is a distributed version control system designed to handle everything from small to very large projects with speed and efficiency. Git is easy to learn and has a tiny footprint with lightning fast performance. It outclasses SCM tools like Subversion, CVS, Perforce, and ClearCase with features like cheap local branching, convenient staging areas, and multiple workflows.

Tuesday, 26 August 2014

Adding CITATION to your R package

Original post from Robin's Blog:

Software is very important in science – but good software takes time and effort that could be used to do other work instead. I believe that it is important to do this work – but to make it worthwhile, people need to get credit for their work, and in academia that means citations. However, it is often very difficult to find out how to cite a piece of software – sometimes it is hidden away somewhere in the manual or on the web-page, but often it requires sending an email to the author asking them how they want it cited. The effort that this requires means that many people don’t bother to cite the software they use, and thus the authors don’t get the credit that they need. We need to change this, so that software – which underlies a huge amount of important scientific work – gets the recognition it deserves.

Making Your Code Citable

Original post from GitHub Guides:

Digital Object Identifiers (DOI) are the backbone of the academic reference and metrics system. If you’re a researcher writing software, this guide will show you how to make the work you share on GitHub citable by archiving one of your GitHub repositories and assigning a DOI with the data archiving tool Zenodo.
ProTip: This tutorial is aimed at researchers who want to cite GitHub repositories in academic literature. Provided you’ve already set up a GitHub repository, this tutorial can be completed without installing any special software. If you haven’t yet created a project on GitHub, start first byuploading your work to a repository.

Wednesday, 20 August 2014

ProteoStats: Computing false discovery rates in proteomics

By Amit K. Yadav (@theoneamit) & Yasset Perez-Riverol (@ypriverol):

Perl is a legacy language thought to be abstruse by many modern programmers. I’m passionate with the idea of not letting die a programming language such as Perl. Even when the language is used less in Computational Proteomics, it is still widely used in Bioinformatics. I’m enthusiastic writing about new open-source libraries in Perl that can be easily used. Two years ago, I wrote a post about InSilicoSpectro and how it can be used to study protein databases like I did in “In silico analysis of accurate proteomics, complemented by selective isolation of peptides”. 

Today’s post is about ProteoStats [1], a Perl library for False Discovery Rate (FDR) related calculations in proteomics studies. Some background for non-experts:

One of the central and most widely used approach for shotgun proteomics is the use of database search tools to assign spectra to peptides (called as Peptide Spectrum Matches or PSMs). To evaluate the quality of the assignments, these programs need to calculate/correct for population wise error rates to keep the number of false positives under control. In that sense, the best strategy to control the false positives is the target-decoy approach. Originally proposed by Elias & Gygi in 2007, the so-called classical FDR strategy or formula proposed involved a concatenated target-decoy (TD) database search for FDR estimation. This calculation is either done by the search engine or using scripts (in-house, non-published, not benchmarked, different implementations). 

So far, the only library developed to compute FDR at spectra level, peptide level and protein level FDRs is MAYU [2]. But, while MAYU only uses the classical FDR approach, ProteoStats provides options for 5 different strategies for calculating the FDR. The only prerequisite being that you need to search using a separate TD database as proposed by Kall et al (2008) [3]. Also, ProteoStats provides a programming interface that can read the native output from most widely used search tools and provide FDR related statistics. In case of tools not supported, pepXML, which has become a de facto standard output format, can be directly read along with tabular text based formats like TSV and CSV (or any other well-defined separator). 

Friday, 20 June 2014

PepFinder™ Software: New Software Streamlines Protein Identification and Quantitation

PepFinder™ Software 

For biotherapeutic proteins to be effective, they must be produced in biologically active forms with proper folding and post-translation modifications (PTMs). Thermo Scientific™ PepFinder software provides accurate identification, in-depth characterization, and relative quantitation of biotherapeutic and other proteins from mass spectrometric data. It provides an automated workflows for glycopeptide identification, disulfide bond mapping, and quantification of PTMs. PepFinder software automates previously time-consuming manual processes, processing complex data and integrating the results into concise, informative reports. 

 - See more at:


Sunday, 8 June 2014

Thesis: Development of computational methods for analysing proteomic data for genome annotation

Thesis by Markus Brosch in 2009 about Computational proteomics methods for analysing proteomic data for genome annotation.

Notes from Abstract

Proteomic mass spectrometry is a method that enables sequencing of gene product fragments, enabling the validation and refinement of existing gene annotation as well as the detection of novel protein coding regions. However, the application of proteomics data to genome annotation is hindered by the lack of suitable tools and methods to achieve automatic data processing and genome mapping at high accuracy and throughput. 

In the first part of this project I evaluate the scoring schemes of “Mascot”, which is a peptide identification software that is routinely used, for low and high mass accuracy data and show these to be not sufficiently accurate. I develop an alternative scoring method that provides more sensitive peptide identification specifically for high accuracy data, while allowing the user to fix the false discovery rate. Building upon this, I utilise the machine learning algorithm “Percolator” to further extend my Mascot scoring scheme with a large set of orthogonal scoring features that assess the quality of a peptide-spectrum match. 

To close the gap between high throughput peptide identification and large scale genome annotation analysis I introduce a proteogenomics pipeline. A comprehensive database is the central element of this pipeline, enabling the efficient mapping of known and predicted peptides to their genomic loci, each of which is associated with supplemental annotation information such as gene and transcript identifiers.

In the last part of my project the pipeline is applied to a large mouse MS dataset. I show the value and the level of coverage that can be achieved for validating genes and gene structures, while also highlighting the limitations of this technique. Moreover, I show where peptide identifications facilitated the correction of existing annotation, such as re-defining the translated regions or splice boundaries. 

Moreover, I propose a set of novel genes that are identified by the MS analysis pipeline with high confidence, but largely lack transcriptional or conservational evidence.