Sunday, 6 April 2014

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

Monday, 3 March 2014

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

Wednesday, 19 February 2014

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:

Friday, 7 February 2014

Solving Invalid signature in JNLP

I have this error each time i run my jnlp:

invalid SHA1 signature file digest for

I found some discussions about possible solutions:

http://stackoverflow.com/questions/8176166/invalid-sha1-signature-file-digest

http://stackoverflow.com/questions/11673707/java-web-start-jar-signing-issue

But he problem was still there. I solved the problem using plugin option (<unsignAlreadySignedJars>true</unsignAlreadySignedJars>) and removing previous signatures to avoid possible signature duplications:



  <plugin>
     <groupId>org.codehaus.mojo.webstart</groupId>
       <artifactId>webstart-maven-plugin</artifactId>
         <executions>
           <execution>
             <id>jnlp-building</id>
             <phase>package</phase>
               <goals>
                 <goal>jnlp</goal>
               </goals>
            </execution>
         </executions>
         <configuration>
           <!-- Include all the dependencies -->
           <excludeTransitive>false</excludeTransitive>
           <unsignAlreadySignedJars>true</unsignAlreadySignedJars>
           <verbose>true</verbose>
           <verifyjar>true</verifyjar>
           <!-- The path where the libraries are stored -->
           <libPath>lib</libPath>
           <jnlp>
             <inputTemplate>webstart/jnlp-template.vm</inputTemplate>
             <outputFile>ProteoLimsViewer.jnlp</outputFile>
             <mainClass>cu.edu.cigb.biocomp.proteolims.gui.ProteoLimsViewer</mainClass>
           </jnlp>
           <sign>
             <keystore>keystorefile</keystore>
             <alias>proteolimsviewer</alias>
             <storepass>password</storepass>
             <keypass>password</keypass>
             <keystoreConfig>
               <delete>false</delete>
               <gen>false</gen>
             </keystoreConfig>
           </sign>
              <!-- building process -->
              <pack200>false</pack200>
              <verbose>true</verbose>
         </configuration>
     </plugin>

Wednesday, 22 January 2014

What is a bioinformatician

By Anthony Fejes originally posted in blog.fejes.ca

I’ve been participating in an interesting conversation on linkedin, which has re-opened the age old question of what is a bioinformatician, which was inspired by a conversation on twitter, that was later blogged.  Hopefully I’ve gotten that chain down correctly.

In any case, it appears that there are two competing schools of thought.  One is that bioinformatician is a distinct entity, and the other is that it’s a vague term that embraces anyone and anything that has to do with either biology or computer science.  Frankly, I feel the second definition is a waste of a perfectly good word, despite being a commonly accepted method.


Monday, 20 January 2014

Some of the most cited manuscripts in Proteomics and Computational Proteomics (2013)

Some of the most cited manuscripts in 2013 in the field of Proteomics and Computational Proteomics (no order):







     The PRoteomics IDEntifications (PRIDE, http://www.ebi.ac.uk/pride) database 
     at the European Bioinformatics Institute is one of the most prominent data 
     repositories of mass spectrometry (MS)-based proteomics data. Here, we 
     summarize recent developments in the PRIDE database and related tools. 
     First, we provide up-to-date statistics in data content, splitting the figures by 
     groups of organisms and species, including peptide and protein 
     identifications, and post-translational modifications. We then describe the 
     tools that are part of the PRIDE submission pipeline, especially the recently 
     developed PRIDE Converter 2 (new submission tool) and PRIDE Inspector 
     (visualization and analysis tool). We also give an update about the integration 
     of PRIDE with other MS proteomics resources in the context of the 
     ProteomeXchange consortium. Finally, we briefly review the quality control 
     efforts that are ongoing at present and outline our future plans.