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Functional Genomics

Overview of attention for book
Cover of 'Functional Genomics'

Table of Contents

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    Book Overview
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    Chapter 1 Prediction of Protein Tertiary Structures Using MUFOLD.
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    Chapter 2 Prediction of protein functions.
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    Chapter 3 Genome-wide screens for expressed hypothetical proteins.
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    Chapter 4 Self-Custom-Made SFP Arrays for Nonmodel Organisms.
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    Chapter 5 Construction and analysis of full-length and normalized cDNA libraries from citrus.
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    Chapter 6 Assembling linear DNA templates for in vitro transcription and translation.
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    Chapter 7 Automated Computational Analysis of Genome-Wide DNA Methylation Profiling Data from HELP-Tagging Assays.
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    Chapter 8 Detection of RNA Editing Events in Human Cells Using High-Throughput Sequencing
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    Chapter 9 Comparative study of differential gene expression in closely related bacterial species by comparative hybridization.
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    Chapter 10 Whole-Genome RT-qPCR MicroRNA Expression Profiling
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    Chapter 11 Using Quantitative Real-Time Reverse Transcriptase Polymerase Chain Reaction to Validate Gene Regulation by PTTG.
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    Chapter 12 FRET-Based Real-Time DNA Microarrays
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    Chapter 13 2-D Gel Electrophoresis: Constructing 2D-Gel Proteome Reference Maps.
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    Chapter 14 The use of antigen microarrays in antibody profiling.
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    Chapter 15 Limited proteolysis in proteomics using protease-immobilized microreactors.
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    Chapter 16 Mass spectrometry for protein quantification in biomarker discovery.
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    Chapter 17 High-throughput microtitre plate-based assay for DNA topoisomerases.
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    Chapter 18 Microscale Thermophoresis as a Sensitive Method to Quantify Protein: Nucleic Acid Interactions in Solution
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    Chapter 19 Bioluminescence resonance energy transfer: an emerging tool for the detection of protein-protein interaction in living cells.
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    Chapter 20 LuMPIS: Luciferase-Based MBP-Pull-Down Protein Interaction Screening System.
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    Chapter 21 Yeast Two-Hybrid Screens: Improvement of Array-Based Screening Results by N- and C-terminally Tagged Fusion Proteins.
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    Chapter 22 Inducible microRNA-Mediated Knockdown of the Endogenous Human Lamin A/C Gene.
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    Chapter 23 Multiple-Gene Silencing Using Antisense RNAs in Escherichia coli.
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    Chapter 24 Functional screen of zebrafish deubiquitylating enzymes by morpholino knockdown and in situ hybridization.
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    Chapter 25 Silencing of gene expression by gymnotic delivery of antisense oligonucleotides.
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    Chapter 26 Polycistronic Expression of Interfering RNAs from RNA Polymerase III Promoters.
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    Chapter 27 Metabolite Analysis of Cannabis sativa L. by NMR Spectroscopy.
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    Chapter 28 Metabolome Analysis of Gram-Positive Bacteria such as Staphylococcus aureus by GC-MS and LC-MS.
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    Chapter 29 Metabolic Fingerprinting Using Comprehensive Two-Dimensional Gas Chromatography - Time-of-Flight Mass Spectrometry.
Attention for Chapter 7: Automated Computational Analysis of Genome-Wide DNA Methylation Profiling Data from HELP-Tagging Assays.
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Chapter title
Automated Computational Analysis of Genome-Wide DNA Methylation Profiling Data from HELP-Tagging Assays.
Chapter number 7
Book title
Functional Genomics
Published in
Methods in molecular biology, November 2011
DOI 10.1007/978-1-61779-424-7_7
Pubmed ID
Book ISBNs
978-1-61779-423-0, 978-1-61779-424-7
Authors

Jing Q, McLellan A, Greally JM, Suzuki M, Qiang Jing, Andrew McLellan, John M. Greally, Masako Suzuki

Editors

Michael Kaufmann, Claudia Klinger

Abstract

A novel DNA methylation assay, HELP-tagging, has been recently described to use massively parallel sequencing technology for genome-wide methylation profiling. Massively parallel sequencing-based assays such as this produce substantial amounts of data, which complicate analysis and necessitate the use of significant computational resources. To simplify the processing and analysis of HELP-tagging data, a bioinformatic analytical pipeline was developed. Quality checks are performed on the data at various stages, as they are processed by the pipeline to ensure the accuracy of the results. A quantitative methylation score is provided for each locus, along with a confidence score based on the amount of information available for determining the quantification. HELP-tagging analysis results are supplied in standard file formats (BED and WIG) that can be readily examined on the UCSC genome browser.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 1 6%
Unknown 16 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 24%
Student > Postgraduate 2 12%
Student > Ph. D. Student 2 12%
Professor > Associate Professor 2 12%
Lecturer 1 6%
Other 1 6%
Unknown 5 29%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 29%
Biochemistry, Genetics and Molecular Biology 2 12%
Computer Science 2 12%
Nursing and Health Professions 1 6%
Environmental Science 1 6%
Other 1 6%
Unknown 5 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 13 January 2012.
All research outputs
#18,303,566
of 22,661,413 outputs
Outputs from Methods in molecular biology
#7,810
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Outputs of similar age
#116,332
of 141,613 outputs
Outputs of similar age from Methods in molecular biology
#35
of 76 outputs
Altmetric has tracked 22,661,413 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,020 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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We're also able to compare this research output to 76 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.