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Genomics Protocols

Overview of attention for book
Cover of 'Genomics Protocols'

Table of Contents

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    Book Overview
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    Chapter 1 Whole genome amplification with Phi29 DNA polymerase to enable genetic or genomic analysis of samples of low DNA yield.
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    Chapter 2 Scanning for DNA Variants by Denaturant Capillary Electrophoresis
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    Chapter 3 Identification of SNPs, or Mutations in Sequence Chromatograms
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    Chapter 4 BeadArray-Based Genotyping
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    Chapter 5 Microsatellite-Based Candidate Gene Linkage Analysis Studies
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    Chapter 6 Full Complexity Genomic Hybridization on 60-mer Oligonucleotide Microarrays for Array Comparative Genomic Hybridization (aCGH)
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    Chapter 7 Detection of Copy Number Changes at Multiple Loci in DNA Prepared from Formalin-Fixed, Paraffin-Embedded Tissue by Multiplex Ligation-Dependent Probe Amplification
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    Chapter 8 Application of Microarrays for DNA Methylation Profiling
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    Chapter 9 Genomewide Identification of Protein Binding Locations Using Chromatin Immunoprecipitation Coupled with Microarray
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    Chapter 10 Transcriptional Profiling of Small Samples in the Central Nervous System
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    Chapter 11 Quantitative Expression Profiling of RNA from Formalin-Fixed, Paraffin-Embedded Tissues Using Randomly Assembled Bead Arrays
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    Chapter 12 Expression Profiling of microRNAs in Cancer Cells: Technical Considerations
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    Chapter 13 Identification of Disease Biomarkers by Profiling of Serum Proteins Using SELDI-TOF Mass Spectrometry
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    Chapter 14 The Applicability of a Cluster of Differentiation Monoclonal Antibody Microarray to the Diagnosis of Human Disease
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    Chapter 15 Protein Profiling Based on Two-Dimensional Difference Gel Electrophoresis
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    Chapter 16 Quantitative Protein Profiling by Mass Spectrometry Using Isotope-Coded Affinity Tags
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    Chapter 17 Quantitative protein profiling by mass spectrometry using label-free proteomics.
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    Chapter 18 Using 2D-LC-MS/MS to Identify Francisella tularensis Peptides in Extracts from an Infected Mouse Macrophage Cell Line
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    Chapter 19 Baculovirus Expression Vector System: An Emerging Host for High-Throughput Eukaryotic Protein Expression
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    Chapter 20 Coimmunoprecipitation and Proteomic Analyses
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    Chapter 21 Tandem Affinity Purification Combined with Mass Spectrometry to Identify Components of Protein Complexes
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    Chapter 22 Mammalian Two-Hybrid Assay for Detecting Protein-Protein Interactions in Vivo
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    Chapter 23 Detection of protein-protein interactions in live cells and animals with split firefly luciferase protein fragment complementation.
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    Chapter 24 Subcellular Localization of Intracellular Human Proteins by Construction of Tagged Fusion Proteins and Transient Expression in COS-7 Cells
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    Chapter 25 GeneFAS: GeneFAS: A Tool for the Prediction of Gene function Using Multiple Sources of Data
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    Chapter 26 Comparative Genomics-Based Prediction of Protein Function
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    Chapter 27 Design, Manufacture, and Assay of the Efficacy of siRNAs for Gene Silencing
Attention for Chapter 17: Quantitative protein profiling by mass spectrometry using label-free proteomics.
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Chapter title
Quantitative protein profiling by mass spectrometry using label-free proteomics.
Chapter number 17
Book title
Genomics Protocols
Published in
Methods in molecular biology, March 2008
DOI 10.1007/978-1-59745-188-8_17
Pubmed ID
Book ISBNs
978-1-58829-871-3, 978-1-59745-188-8
Authors

Arsalan S. Haqqani, John F. Kelly, Danica B. Stanimirovic

Abstract

"Gel-free," or mass spectrometry (MS)-based, proteomics techniques are emerging as the methods of choice for quantitatively comparing proteins levels among biological proteomes, since they are more sensitive and reproducible than two dimensional gel (2-DE)-based methods. Currently, the MS-based methods utilize mainly stable isotope labels (e.g., ICAT, iTRAQ) that enable easy identification of differentially expressed proteins in two or more samples. "Label-free" MS-based methods would alleviate several limitations of the labeling methods, provided that relative quantitative profiling of proteins among multiple MS runs is achievable. However, comparisons of multiple MS runs of highly complex biological samples are very challenging and time consuming. To alleviate this problem, several laboratories and MS vendors have developed software for computer-assisted comparisons of multiple label-free MS runs to allow profiling of differentially expressed proteins. In this chapter, we describe the use of custom-developed MatchRx software in quantitative comparison of multiple label-free MS runs. We also describe details of sample preparation, fractionation, statistical analysis, and protein database searching for label-free comparative quantitative proteomics, as well as the application of a "targeted" MS approach, which includes quantification of the samples using MS followed by selective identification of only the differentially expressed peptides using tandem MS (MS/MS).

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
China 1 1%
France 1 1%
Canada 1 1%
Unknown 72 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 32%
Student > Ph. D. Student 15 20%
Student > Master 9 12%
Professor > Associate Professor 6 8%
Student > Bachelor 5 7%
Other 12 16%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 42%
Chemistry 12 16%
Biochemistry, Genetics and Molecular Biology 10 13%
Medicine and Dentistry 6 8%
Immunology and Microbiology 4 5%
Other 7 9%
Unknown 5 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 31 March 2008.
All research outputs
#7,453,827
of 22,787,797 outputs
Outputs from Methods in molecular biology
#2,318
of 13,094 outputs
Outputs of similar age
#28,505
of 81,498 outputs
Outputs of similar age from Methods in molecular biology
#3
of 25 outputs
Altmetric has tracked 22,787,797 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,094 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 76% 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 81,498 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.