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Tiling Arrays

Overview of attention for book
Cover of 'Tiling Arrays'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 A Brief Introduction to Tiling Microarrays: Principles, Concepts, and Applications
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    Chapter 2 Design of Tiling Arrays and Their Application to Bacterial Transcriptome Analysis
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    Chapter 3 Transcript Profiling in Arabidopsis with Genome Tiling Microarrays
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    Chapter 4 Genome-Wide Analysis of Transcription Factor-Binding Sites in Skeletal Muscle Cells Using ChIP-Seq.
  6. Altmetric Badge
    Chapter 5 Analysis of Allele-Specific Gene Expression Using a Target-Oriented Tiling Microarray Assay
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    Chapter 6 Detection of Epigenetic Alterations Using Tiling Arrays
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    Chapter 7 Investigating Gene Promoter Methylation in a Mouse Model of Status Epilepticus
  9. Altmetric Badge
    Chapter 8 Integrative Analysis of ChIP-Chip and ChIP-Seq Dataset
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    Chapter 9 HAT: A Novel Statistical Approach to Discover Functional Regions in the Genome
  11. Altmetric Badge
    Chapter 10 Inference of Alternative Splicing from Tiling Array Data
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    Chapter 11 Analysis of In Vivo Occupancy of Aebp1, a Transcription Factor, Using High Resolution Tiling Array
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    Chapter 12 Application of the Simple and Efficient Mpeak Modeling in Binding Peak Identification in ChIP-Chip Studies
  14. Altmetric Badge
    Chapter 13 Evaluation of MeDIP-chip in the context of whole-genome bisulfite sequencing (WGBS-seq) in Arabidopsis. - PubMed - NCBI
  15. Altmetric Badge
    Chapter 14 Mapping Genomic Features of Tiling Microarray Data by TileMapper
Attention for Chapter 8: Integrative Analysis of ChIP-Chip and ChIP-Seq Dataset
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Chapter title
Integrative Analysis of ChIP-Chip and ChIP-Seq Dataset
Chapter number 8
Book title
Tiling Arrays
Published in
Methods in molecular biology, January 2013
DOI 10.1007/978-1-62703-607-8_8
Pubmed ID
Book ISBNs
978-1-62703-606-1, 978-1-62703-607-8
Authors

Lihua Julie Zhu, Zhu, Lihua Julie

Abstract

Epigenetic regulation and interactions between transcription factors and regulatory genomic regions play crucial roles in controlling transcriptional regulatory networks that drive development, environmental responses, and disease. Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) and ChIP followed by genomic tiling microarray hybridization (ChIP-chip) are the two of the most widely used technologies for genome-wide identification of DNA protein interactions and histone modification in vivo. Many algorithms and tools have been developed and evaluated that allow identification of transcription factor binding sites from ChIP-seq or ChIP-chip datasets. However, binding site identification is only the first step; the ultimate goal is to discover the regulatory network of the transcription factor (TF). Here, we present a common workflow for downstream analysis of ChIP-chip and ChIP-seq with an emphasis on annotating binding sites and integration with gene expression data to identify direct and indirect targets of the TF. These tools will help with the overall goal of unraveling transcriptional regulatory networks using datasets publicly available in GEO.

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The data shown below were collected from the profiles of 2 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Singapore 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 36%
Student > Master 10 17%
Researcher 9 16%
Professor 3 5%
Professor > Associate Professor 2 3%
Other 1 2%
Unknown 12 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 40%
Agricultural and Biological Sciences 10 17%
Immunology and Microbiology 3 5%
Neuroscience 3 5%
Medicine and Dentistry 3 5%
Other 2 3%
Unknown 14 24%
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 28 August 2013.
All research outputs
#14,632,302
of 22,719,618 outputs
Outputs from Methods in molecular biology
#4,602
of 13,079 outputs
Outputs of similar age
#173,431
of 280,759 outputs
Outputs of similar age from Methods in molecular biology
#163
of 341 outputs
Altmetric has tracked 22,719,618 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,079 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 64% 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 280,759 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 341 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 51% of its contemporaries.