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.

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). 

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 must provide (as supporting information) all “in-house” tools that have been used to reach a result we intend to publish. This applies especially to manuscripts dealing with software packages, tools, etc. I am willing to cut some slack to journals such as Analytical Chemistry or Molecular Cell Proteomics, whose editorial staffs are –and rightly so- more concerned about quality issues involving raw data and experimental reproducibility, but in instances like Bioinformatics, BMC Bioinformatics, several members of the Nature family and others at the forefront of bioinformatics, methinks we should hold them to a higher standard. Some of these journals would greatly benefit from implementing a review system from the point of view of Software Production, moving bioinformatics and science in general one step forward in terms of reproducibility and software reusability. What do you think would happen if the following were checked during peer reviewing?

Perl Proteomics & InSilicoSpectro

In contrast with genomics, bioinformaticians in proteomics don’t have a "big" and "complete" perl library for proteomics data analysis. It could be related with the "heterogeneity" in proteomics. A lot of different instruments, protocols, properties. Also genomic have a huge community (bioinformaticians) and standardize tools (instruments and software’s). In 2006 Collinge and Masselot published an open-source perl library named InSilicoSpectro. The aim was provide a set of recurrent functions that are necessary for proteomics data analysis.

Some of the Illustrative functions are: mz list file format conversions, protein sequence digestion, theoretical peptide and fragment mass computations, graphical display, matching with experimental data, isoelectric point estimation (with different methods), and peptide retention time prediction.