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Proteomis in Systems Biology

Overview of attention for book
Cover of 'Proteomis in Systems Biology'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Multiplexed Quantitative Proteomics for High-Throughput Comprehensive Proteome Comparisons of Human Cell Lines.
  3. Altmetric Badge
    Chapter 2 Sample Preparation Approaches for iTRAQ Labeling and Quantitative Proteomic Analyses in Systems Biology.
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    Chapter 3 Two Birds with One Stone: Parallel Quantification of Proteome and Phosphoproteome Using iTRAQ.
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    Chapter 4 Selected Reaction Monitoring to Measure Proteins of Interest in Complex Samples: A Practical Guide.
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    Chapter 5 Monitoring PPARG-Induced Changes in Glycolysis by Selected Reaction Monitoring Mass Spectrometry.
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    Chapter 6 A Targeted MRM Approach for Tempo-Spatial Proteomics Analyses.
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    Chapter 7 Targeted Phosphoproteome Analysis Using Selected/Multiple Reaction Monitoring (SRM/MRM).
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    Chapter 8 Testing Suitability of Cell Cultures for SILAC-Experiments Using SWATH-Mass Spectrometry.
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    Chapter 9 Combining Amine-Reactive Cross-Linkers and Photo-Reactive Amino Acids for 3D-Structure Analysis of Proteins and Protein Complexes.
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    Chapter 10 Tissue MALDI Mass Spectrometry Imaging (MALDI MSI) of Peptides.
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    Chapter 11 Ethyl Esterification for MALDI-MS Analysis of Protein Glycosylation.
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    Chapter 12 Characterization of Protein N-Glycosylation by Analysis of ZIC-HILIC-Enriched Intact Proteolytic Glycopeptides.
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    Chapter 13 Simple and Effective Affinity Purification Procedures for Mass Spectrometry-Based Identification of Protein-Protein Interactions in Cell Signaling Pathways.
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    Chapter 14 A Systems Approach to Understand Antigen Presentation and the Immune Response.
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    Chapter 15 Profiling of Small Molecules by Chemical Proteomics.
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    Chapter 16 Generating Sample-Specific Databases for Mass Spectrometry-Based Proteomic Analysis by Using RNA Sequencing.
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    Chapter 17 A Proteomic Workflow Using High-Throughput De Novo Sequencing Towards Complementation of Genome Information for Improved Comparative Crop Science.
  19. Altmetric Badge
    Chapter 18 From Phosphoproteome to Modeling of Plant Signaling Pathways.
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    Chapter 19 Interpretation of Quantitative Shotgun Proteomic Data.
  21. Altmetric Badge
    Chapter 20 A Simple Workflow for Large Scale Shotgun Glycoproteomics.
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    Chapter 21 Systemic Analysis of Regulated Functional Networks.
Attention for Chapter 16: Generating Sample-Specific Databases for Mass Spectrometry-Based Proteomic Analysis by Using RNA Sequencing.
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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Citations

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Chapter title
Generating Sample-Specific Databases for Mass Spectrometry-Based Proteomic Analysis by Using RNA Sequencing.
Chapter number 16
Book title
Proteomics in Systems Biology
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3341-9_16
Pubmed ID
Book ISBNs
978-1-4939-3339-6, 978-1-4939-3341-9
Authors

Luge, Toni, Sauer, Sascha, Toni Luge, Sascha Sauer

Editors

Jörg Reinders

Abstract

Mass spectrometry-based methods allow for the direct, comprehensive analysis of expressed proteins and their quantification among different conditions. However, in general identification of proteins by assigning experimental mass spectra to peptide sequences of proteins relies on matching mass spectra to theoretical spectra derived from genomic databases of organisms. This conventional approach limits the applicability of proteomic methodologies to species for which a genome reference sequence is available. Recently, RNA-sequencing (RNA-Seq) became a valuable tool to overcome this limitation by de novo construction of databases for organisms for which no DNA sequence is available, or by refining existing genomic databases with transcriptomic data. Here we present a generic pipeline to make use of transcriptomic data for proteomics experiments. We show in particular how to efficiently fuel proteomic analysis workflows with sample-specific RNA-sequencing databases. This approach is useful for the proteomic analysis of so far unsequenced organisms, complex microbial metatranscriptomes/metaproteomes (for example in the human body), and for refining current proteomics data analysis that solely relies on the genomic sequence and predicted gene expression but not on validated gene products. Finally, the approach used in the here presented protocol can help to improve the data quality of conventional proteomics experiments that can be influenced by genetic variation or splicing events.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 30%
Student > Doctoral Student 3 15%
Student > Ph. D. Student 2 10%
Other 1 5%
Student > Bachelor 1 5%
Other 4 20%
Unknown 3 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 35%
Agricultural and Biological Sciences 6 30%
Unspecified 1 5%
Neuroscience 1 5%
Unknown 5 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 October 2016.
All research outputs
#3,924,185
of 22,836,570 outputs
Outputs from Methods in molecular biology
#1,005
of 13,126 outputs
Outputs of similar age
#67,516
of 393,568 outputs
Outputs of similar age from Methods in molecular biology
#154
of 1,470 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,126 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,568 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 1,470 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.