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Neuroproteomics

Overview of attention for book
Cover of 'Neuroproteomics'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Neuroproteomics Studies: Challenges and Updates
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    Chapter 2 Progress and Potential of Imaging Mass Spectrometry Applied to Biomarker Discovery
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    Chapter 3 Biofluid Proteomics and Biomarkers in Traumatic Brain Injury
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    Chapter 4 Degradomics in Neurotrauma: Profiling Traumatic Brain Injury
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    Chapter 5 Evolving Relevance of Neuroproteomics in Alzheimer’s Disease
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    Chapter 6 Genome to Phenome: A Systems Biology Approach to PTSD Using an Animal Model
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    Chapter 7 Photoaffinity Labeling of Pentameric Ligand-Gated Ion Channels: A Proteomic Approach to Identify Allosteric Modulator Binding Sites
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    Chapter 8 Quantitative Phosphoproteomic Analysis of Brain Tissues
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    Chapter 9 Glycoproteins Enrichment and LC-MS/MS Glycoproteomics in Central Nervous System Applications
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    Chapter 10 A Novel 2-DE-Based Proteomic Analysis to Identify Multiple Substrates for Specific Protease in Neuronal Cells
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    Chapter 11 Neuroproteomic Profiling of Cerebrospinal Fluid (CSF) by Multiplexed Affinity Arrays
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    Chapter 12 Isolation and Proteomic Analysis of Microvesicles and Exosomes from HT22 Cells and Primary Neurons
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    Chapter 13 Combined MALDI Mass Spectrometry Imaging and Parafilm-Assisted Microdissection-Based LC-MS/MS Workflows in the Study of the Brain
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    Chapter 14 De Novo and Uninterrupted SILAC Labeling of Primary Microglia
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    Chapter 15 Spike-In SILAC Approach for Proteomic Analysis of Ex Vivo Microglia
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    Chapter 16 A Proteomic Evaluation of Sympathetic Activity Biomarkers of the Hypothalamus-Pituitary-Adrenal Axis by Western Blotting Technique Following Experimental Traumatic Brain Injury
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    Chapter 17 Efficient and Accurate Algorithm for Cleaved Fragments Prediction (CFPA) in Protein Sequences Dataset Based on Consensus and Its Variants: A Novel Degradomics Prediction Application
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    Chapter 18 Effect of Second-Hand Tobacco Smoke on the Nitration of Brain Proteins: A Systems Biology and Bioinformatics Approach
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    Chapter 19 An Advanced Omic Approach to Identify Co-Regulated Clusters and Transcription Regulation Network with AGCT and SHOE Methods
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    Chapter 20 AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study
  22. Altmetric Badge
    Chapter 21 An Integration of Decision Tree and Visual Analysis to Analyze Intracranial Pressure
Attention for Chapter 17: Efficient and Accurate Algorithm for Cleaved Fragments Prediction (CFPA) in Protein Sequences Dataset Based on Consensus and Its Variants: A Novel Degradomics Prediction Application
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Chapter title
Efficient and Accurate Algorithm for Cleaved Fragments Prediction (CFPA) in Protein Sequences Dataset Based on Consensus and Its Variants: A Novel Degradomics Prediction Application
Chapter number 17
Book title
Neuroproteomics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6952-4_17
Pubmed ID
Book ISBNs
978-1-4939-6950-0, 978-1-4939-6952-4
Authors

Atlal El-Assaad, Zaher Dawy, Georges Nemer, Hazem Hajj, Firas H. Kobeissy, El-Assaad, Atlal, Dawy, Zaher, Nemer, Georges, Hajj, Hazem, Kobeissy, Firas H.

Editors

Firas H. Kobeissy, Stanley M. Stevens, Jr.

Abstract

Degradomics is a novel discipline that involves determination of the proteases/substrate fragmentation profile, called the substrate degradome, and has been recently applied in different disciplines. A major application of degradomics is its utility in the field of biomarkers where the breakdown products (BDPs) of different protease have been investigated. Among the major proteases assessed, calpain and caspase proteases have been associated with the execution phases of the pro-apoptotic and pro-necrotic cell death, generating caspase/calpain-specific cleaved fragments. The distinction between calpain and caspase protein fragments has been applied to distinguish injury mechanisms. Advanced proteomics technology has been used to identify these BDPs experimentally. However, it has been a challenge to identify these BDPs with high precision and efficiency, especially if we are targeting a number of proteins at one time. In this chapter, we present a novel bioinfromatic detection method that identifies BDPs accurately and efficiently with validation against experimental data. This method aims at predicting the consensus sequence occurrences and their variants in a large set of experimentally detected protein sequences based on state-of-the-art sequence matching and alignment algorithms. After detection, the method generates all the potential cleaved fragments by a specific protease. This space and time-efficient algorithm is flexible to handle the different orientations that the consensus sequence and the protein sequence can take before cleaving. It is O(mn) in space complexity and O(Nmn) in time complexity, with N number of protein sequences, m length of the consensus sequence, and n length of each protein sequence. Ultimately, this knowledge will subsequently feed into the development of a novel tool for researchers to detect diverse types of selected BDPs as putative disease markers, contributing to the diagnosis and treatment of related disorders.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 20%
Researcher 1 20%
Other 1 20%
Student > Master 1 20%
Unknown 1 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 40%
Computer Science 1 20%
Immunology and Microbiology 1 20%
Unknown 1 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 March 2020.
All research outputs
#15,962,021
of 25,257,066 outputs
Outputs from Methods in molecular biology
#4,721
of 14,164 outputs
Outputs of similar age
#244,079
of 433,158 outputs
Outputs of similar age from Methods in molecular biology
#387
of 1,086 outputs
Altmetric has tracked 25,257,066 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,164 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 63% 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 433,158 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,086 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.