The need for a Sample to Data standard

The experimental design is a cornerstone of modern science, especially for data scientists.  But, How experimental design can be captured for better reuse, reproducibility, and understanding of the original results? How we can write in a file the complexity of our experimental design? 

This post is about that

ThermoRAWFileParser: A small step towards cloud proteomics solutions

Proteomics data analysis is in the middle of a big transition. We are moving from small experiments (e.g. a couple of RAW files, samples) to big large scale experiments. While the average number of RAW files per datasets in PRIDE hasn't grown in the last 6 years (Figure 1), we can see multiple experiments with more than 1000 RAW files (Figure 1 - right).

Figure 1: The boxplot of the number of files per dataset in PRIDE (left - outliers removed; right - outliers included) 

On the other side, File size shows a trend towards large RAW files (Figure 2).

Figure 2: Box plot of file size by datasets in PRIDE (outliers removed)

Then, how proteomics data analysis can be moved towards large scale and elastic compute architectures such as Cloud infrastructures or High-performance computing (HPC) clusters?

First Scrum Board

Here, my first Scrum board to guide the release of OmicsDI project.

Team members update the task board continuously each sprint; if someone thinks of a new task (“test a new machine learning algorithm”), she writes a new card and puts it on the wall. Either during or before the daily scrum, estimates are changed (up or down), and cards are moved around the board.

Each row on the Scrum board is a user story, which is the unit of work we encourage teams to use for their product backlog.

During the sprint planning meeting, the team selects the product backlog items they can complete during the next Spring. Each product backlog item is turned into multiple sprint backlog items. Each of these is represented by one task card that is placed on the Scrumboard.

• Story (User Story): The story description (“As a user we want to…”) shown on that row.
• Ongoing:  Any card being worked on goes here. The programmer who chooses to work on it moves it over when she's ready to start the task. Often, this happens during the daily scrum when someone says, “I'm going to work on the boojum today.”
• Testing: A lot of tasks have corresponding test task cards. So, if there's a “Code the boojum class” card, there is likely one or more task cards related to testing: “Test the boojum”, “Write FitNesse tests for the boojum,” “Write FitNesse fixture for the boojum,” 
• Done: Cards pile up over here when they're done. They're removed at the end of the sprint. Sometimes we remove some or all during a sprint if there are a lot of cards.

Optionally, depending on the team, the culture, the project and other considerations:

• Notes: Just a place to jot a note or two.
• Tests Specified: We like to do “Story Test-Driven Development,” or “Acceptance Test-Driven Development,” which means the tests are written before the story is coded. Many teams find that it helps to have acceptance tests identified before coding begins on a particular story. This column just contains a checkmark to indicate the tests are specified.

An API for all MS-based File formats

We recently released and published our first Java API (Application Programming Interface) for the most common file formats in proteomics, not only ms files but also identification files such as mzIdentML and mztab. 

ms-data-core-api (https://github.com/PRIDE-Utilities/ms-data-core-api)

The library allow the end-users and the developers to use a common data structure for proteomics independently of the file types, and .. But first lets try to understand what is a API.

What is an API?

Imagine you are a builder or civil engineering and your are building your bridge, different components, blocks and different teams needs to be coordinated and plugged for the final results. Wrong communications between the members of the teams, different block sizes or building plans only produced strange results. 

In the simplest terms, APIs are sets of requirements, data structures, objects that govern how applications and software components can talk each other. An API, is a set of routines and protocols that provide building blocks for computer programmers and web developers to build software applications. In the past, APIs were largely associated with computer operating systems and desktop applications. In recent years though, we have seen the emergence of Web APIs (Web Services).

What is ms-data-core-api?

The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. 

The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library.

The future of Proteomics: The Consensus

After the Big Nature papers about the Human Proteome [1][2] the proteomics community has been divided by the same well-known topics than genomics had before: same reasons, same discussions [3-7]. No one discusses about the technical issues, the instrument settings, nothing about the samples processing, even anything about the analytical method (Most of both projects are "common" bottom-up experiments). Main issues are data-analysis problems and still Computational Proteomics Challenges.  

Brazil: A place for Science and Friendship

Búzios

It's really difficult to break stereotypes, especially for developing countries, like Brazil. If you mention its name around the world they are immediately associated with: sports, music, beaches, rum and "País do Carnaval". If you ask to someone in the streets of Germany or China about personalities from Brazil, they will mention Pelé. Breaking stereotypes is a task for years or centuries but we are going in the right direction.

Hotel Ferradura/ Ferradura Resort

Last December I attended to the 2nd Proteomics Meeting of the Brazilian Proteomics Society jointly with the 2nd Pan American HUPO Meeting in Hotel Ferradura/ Ferradura Resort, Búzios, Rio de Janeiro State, Brazil. The venue was gorgeous, mountains close to a small bay that offers calm, clear waters and the open sea. We arrived after 2 hours by car from Rio international airport. My plans, give a talk about PRIDE and ProteomeXchange but more than that, my talk was about "if we really need to share our proteomics data".  

HUPO-PSI Meeting 2014: Rookie’s Notes

Standardisation: the most difficult flower to grow.

The PSI (Proteomics Standard Initiative) 2014 Meeting was held this year in Frankfurt (13-17 of April) and I can say I’m now part of this history. First, I will try to describe with a couple of sentences (for sure I will fai) the incredible venue, the Schloss Reinhartshausen Kempinski. When I saw for the first time the hotel, first thing came to my mind was those films from the 50s. Everything was elegant, classic, sophisticated - from the decoration to a small latch. The food was incredible and the service is first class from the moment you set foot on the front step and throughout the whole stay. 

  

Standardization is the process of developing and implementing technical standards. Standardization can help to maximize compatibility, interoperability, safety, repeatability, or quality. It can also facilitate commoditization of formerly custom processes. In bioinformatics, the standardization of file formats, vocabulary, and resources is a job that all of us appreciate but for several reasons nobody wants to do. First of all, standardization in bioinformatics means that you need to organize and merge different experimental and in-silico pipelines to have a common way to represent the information. In proteomics for example, you can use different sample preparation, combined with different fractionation techniques and different mass spectrometers; and finally using different search engines and post-processing tools. The diversity and possible combinations is needed because allow to explore different solutions for complex problems. (Standarization in Proteomics: From raw data to metadata files).

Which journals release more public proteomics data!!!

I'm a big fan of data and the -omics family. Also, I like the idea of make more & more our data public available for others, not only for reuse, but also to guarantee the reproducibility and quality assessment of the results (Making proteomics data accessible and reusable: Current state of proteomics databases and repositories). I'm wondering which of these journals (list - http://scholar.google.co.uk/) encourages their submitters and authors to make their data publicly available:

Journal	h5-index	h5-median
Molecular & Cellular Proteomics	74	101
Journal of Proteome Research	70	91
Proteomics	60	76
Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics	52	78
Journal of Proteomics	49	60
Proteomics - Clinical Applications	35	43
Proteome Science	23	32

After a simple statistic, based on PRIDE data:

Number of PRIDE projects by Journal

Analysis of histone modifications with PEAKS 7: A respond to Search Engines comparison from PEAKs Team

Recently we posted a comparison of different search engines for PTMs studies (Evaluation of Proteomic Search Engines for PTMs Identification). After some discussion of the mentioned results in our post the  PEAKS Team just published a blog post with the reanalysis of the dataset. Here the results:

Originally Posted in Peaks Blog:

The complex nature of histone modification patterns has posed as a challenge for bioinformatics analysis over the years. Yuan et al. [1] conducted a study using two datasets from human HeLa histone samples, to benchmark the performance of current proteomic search engines. This article was published in J Proteome Res. 2014 Aug 28 (PubMed), and the data from the two datasets, HCD_Histone and CID_Histone (PXD001118), was made publically available through ProteomeXchange. With this data, the article uses eight different proteomic search engines to compare and evaluate the performance and capability of each. The evaluated search engines in this study are: pFind, Mascot, SEQUEST, ProteinPilot, PEAKS 6, OMSSA, TPP and MaxQuant. 

In this study, PEAKS 6 was used to compare the performance capabilities between search engines. However, PEAKS 7, which was released November 2013, is the latest version available of the PEAKS Studio software. PEAKS 7 not only includes better performance than PEAKS 6, but a lot of additional and improved features. Our team has reanalyzed the two datasets HCD_Histone and CID_Histone with PEAKS 7 to update the ID results presented in the publication by Yuan et al.  These updated results showed that instead, it is PEAKS, pFind and Mascot that identify the most confident results.

Who is a senior developer anyway?

Originally posted in Java Code Geeks

by David Green on October 28th, 2013 | Filed in: Software Development

What makes you a “senior developer”? Everyone and their dog calls themselves a senior developer these days. From fresh graduates to the CTO, everyone is a senior developer. But what the hell does it even mean?

Technologists

Some developers are avid technologists. They got into programming really because they like tinkering. If it hadn’t been 7 languages in 7 weeks, it would have been a box of meccano or they’d be in their shed busy inventing the battery operated self-tieing tie. These people are great to have on your team, they’ll be the ones continually bombarding you with the latest and greatest shiny. If you ever want to know if there’s an off the shelf solution to your problem, they’ll know the options, have tried two of them, and currently have a modified version of a third running on their raspberry pi.

The trouble with technologists is more technology is always the answer. Why have a HTTP listener when you can have a full stack application server? Why use plain old TCP when you can introduce an asynchronous messaging backbone? Why bother trying to deliver software when there’s all these toys to play with!

Installing standalone SpectraST in linux

Some tips to install SpectraST in linux in standalone:

1. -  Download TTP latest version.
2. -  go to SpectraST folder and run make:
     
    cd TPP-x.x.x/trans_proteomic_pipeline/src/Search/SpectraST
    make -f Makefile_STANDALONE_LINUX

ProteoWizard: The chosen one in RAW file conversion

I'm the chosen one.

After five years in proteomics and a quick walk through different computational proteomics topics such as: database analysis, proteomics repositories and databases or identification algorithms I'm sure that the most painful and no grateful job is work with file formats: writing, reading, and dealing with end-users. 

File formats (the way that we use to represent, storage and exchange our data) are fundamentals piece in bioinformatics, more than that, are one of the milestone of the Information Era. In some fields the topic is more stable than others, but the topic is still in the table for most of us. To have a quick idea see the evolution of general standards in recent years like XML, JSON and recently YAML.

Evaluation of Proteomic Search Engines for PTMs Identification

The peptide-centric MS strategy is called bottom-up, in which proteins are extracted from cells, digested into peptides with proteases, and analyzed by liquid chromatography tandem mass spectrometry (LC−MS/MS). More specifically, peptides are resolved by chromatography, ionized in mass spectrometers, and scanned to obtain full MS spectra. Next, some high-abundance peptides (precursor ions) are selected and fragmented to obtain MS/MS spectra by high- energy C-trap dissociation (HCD) or collision-induced dissociation (CID). 

Then, peptides are commonly identified by searching the MS/MS spectra against a database and finally assembled into identified proteins. Database searching plays an important role in proteomics analysis because it can be used to translate thousands of MS/MS spectra into protein identifications (IDs). 

Many database search engines have been developed to quickly and accurately analyze large volumes of proteomics data. Some of the more well-known search engines are Mascot,  SEQUEST, PEAKS DB, ProteinPilot, Andromeda, and X!Tandem. Here a list of commonly use search engines in proteomics and mass spectrometry.

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.

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). 

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.

SWATH-MS and next-generation targeted proteomics

For proteomics, two main LC-MS/MS strategies have been used thus far. They have in common that the sample proteins are converted by proteolysis into peptides, which are then separated by (capillary) liquid chromatography. They differ in the mass spectrometric method used.

The first and most widely used strategy is known as shotgun proteomics or discovery proteomics. For this method, the MS instrument is operated in data-dependent acquisition (DDA) mode, where fragment ion (MS2) spectra for selected precursor ions detectable in a survey (MS1) scan are generated (Figure 1 - Discovery workflow). The resulting fragment ion spectra are then assigned to their corresponding peptide sequences by sequence database searching (See Open source libraries and frameworks for mass spectrometry based proteomics: A developer's perspective).

The second main strategy is referred to as targeted proteomics. There, the MS instrument is operated in selected reaction monitoring (SRM) (also called multiple reaction monitoring) mode (Figure 1 - Targeted Workflow). With this method, a sample is queried for the presence and quantity of a limited set of peptides that have to be specified prior to data acquisition. SRM does not require the explicit detection of the targeted precursors but proceeds by the acquisition, sequentially across the LC retention time domain, of predefined pairs of precursor and product ion masses, called transitions, several of which constitute a definitive assay for the detection of a peptide in a complex sample (See Targeted proteomics) .

Figure 1 - Discovery and Targeted proteomics workflows

Most read from the Journal of Proteome Research for 2013.

1- Protein Digestion: An Overview of the Available Techniques and Recent
    Developments

    Linda Switzar, Martin Giera, Wilfried M. A. Niessen

    DOI: 10.1021/pr301201x

2-  Andromeda: A Peptide Search Engine Integrated into the MaxQuant
     Environment

     Jürgen Cox, Nadin Neuhauser, Annette Michalski, Richard A. Scheltema, Jesper
     V. Olsen, Matthias Mann

     DOI: 10.1021/pr101065j

2- Evaluation and Optimization of Mass Spectrometric Settings during
     Data-dependent Acquisition Mode: Focus on LTQ-Orbitrap Mass Analyzers
 
     Anastasia Kalli, Geoffrey T. Smith, Michael J. Sweredoski, Sonja Hess

     DOI: 10.1021/pr3011588

3-  An Automated Pipeline for High-Throughput Label-Free Quantitative
     Proteomics

     Hendrik Weisser, Sven Nahnsen, Jonas Grossmann, Lars Nilse, Andreas Quandt,
     Hendrik Brauer, Marc Sturm, Erhan Kenar, Oliver Kohlbacher, Ruedi Aebersold,
     Lars Malmström

     DOI: 10.1021/pr300992u

4-  Proteome Wide Purification and Identification of O-GlcNAc-Modified Proteins
     Using Click Chemistry and Mass Spectrometry

     Hannes Hahne, Nadine Sobotzki, Tamara Nyberg, Dominic Helm, Vladimir S.
     Borodkin, Daan M. F. van Aalten, Brian Agnew, Bernhard Kuster

     DOI: 10.1021/pr300967y

5-  A Proteomics Search Algorithm Specifically Designed for High-Resolution
     Tandem Mass Spectra

     Craig D. Wenger, Joshua J. Coon
   
     DOI: 10.1021/pr301024c

6- Analyzing Protein–Protein Interaction Networks

    Gavin C. K. W. Koh, Pablo Porras, Bruno Aranda, Henning Hermjakob, Sandra E.
    Orchard

    DOI: 10.1021/pr201211w

7-  Combination of FASP and StageTip-Based Fractionation Allows In-Depth
     Analysis of the Hippocampal Membrane Proteome

     Jacek R. Wisniewski, Alexandre Zougman, Matthias Mann

     DOI: 10.1021/pr900748n

8-  The Biology/Disease-driven Human Proteome Project (B/D-HPP): Enabling
     Protein Research for the Life Sciences Community

     Ruedi Aebersold, Gary D. Bader, Aled M. Edwards, Jennifer E. van Eyk, Martin
     Kussmann, Jun Qin, Gilbert S. Omenn

     DOI: 10.1021/pr301151m

 9-  Comparative Study of Targeted and Label-free Mass Spectrometry Methods
      for Protein Quantification

       Linda IJsselstijn, Marcel P. Stoop, Christoph Stingl, Peter A. E. Sillevis Smitt,
       Theo M. Luider, Lennard J. M. Dekker

       DOI: 10.1021/pr301221f

In the ERA of science communication, Why you need Twitter, Professional Blog and ImpactStory?

Where is the information? Where are the scientifically relevant results? Where are the good ideas? Are these things (only) in journals? I usually prefer to write about bioinformatics and how we should include, annotate and cite our bioinformatics tools inside research papers (The importance of Package Repositories for Science and Research, The problem of in-house tools); but this post represents my take on the future of scientific publications and their dissemination based on the manuscript “Beyond the paper” (1).

In the not too distant future, today’s science journals will be replaced by a set of decentralized, interoperable services that are built on a core infrastructure of open data and evolving standards — like the Internet itself. What the journal did in the past for a single article, the social media and internet resources are doing for the entire scholarly output. We are now immersed in a transition to another science communication system— one that will tap on Web technology to significantly improves dissemination. I prefer to represent the future of science communication by a block diagram where the four main components: (i) Data, (ii) Publications, (iii) Dissemination and (iv) Certification/Reward are completely interconnected: