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ChIP-on-chip: History, Works, Technology & Regions
ChIP-on-chip (also known as ChIP-chip) is a technology that combines chromatin immunoprecipitation ('ChIP') with DNA microarray ("chip"). Like regular ChIP, ChIP-on-chip is used to investigate interactions between proteins and DNA in vivo. Specifically, it allows the identification of the cistrome, the sum of binding sites, for DNA-binding proteins on a…
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ChIP-on-chip topic overview
The analysis highlights History, Works, Technology and Regions as prominent areas in the source structure around ChIP-on-chip.
Related topics
70
Source areas
6
Connected nodes
76
Extracted relationships
110
Concept neighborhoods
20
Bridge connections
76
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Overview · 23 topics
Workflow of a ChIP-on-chip experiment · 20 topics
Strengths and weaknesses · 11 topics
Technological platforms · 9 topics
History · 4 topics
Alternatives · 3 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How ChIP-on-chip connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around ChIP-on-chip shows recurring relationship patterns in the source. For example, ChIP-on-chip → Adrien, Andrau, Benoukraf, Bibcode, Bioinformatics, Brown, Carroll, Cauchy, ChIP-chip, CoCAS, Denis, Fenouil, Ferrier, Frederic, Gottardo, Imbert, ISSN, Jaeger, Jean, Jean-Christophe Another extracted example is ChIP-on-chip → Although, Brian Dynlacht, DNA, DNA-binding, E2F, However, III, In, Michael Zhang's, Myc, Nimblegen, ORFs, Other, Peggy Farnham's, Richard Young's, Saccharomyces, Since, The, The ChIP-on-chip, This. Use these groups to spot repeated connection types before inspecting the individual relationships.
ChIP-on-chip → Although, Brian Dynlacht, DNA, DNA-binding, E2F, However, III, In, Michael Zhang's, Myc, Nimblegen, ORFs, Other, Peggy Farnham's, Richard Young's, Saccharomyces, Since, The, The ChIP-on-chip, This
related to Strengths and weaknesses· 13
ChIP-on-chip → Although ChIP-on-chip, Another, As, DNA, DNA-binding, For, However, Most, Most ChIP-on-chip, So, The, These, Using
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 110 structured relationships around ChIP-on-chip. Examples in this analysis include Cy5 or Alexa 647.Finally → instance of → the single-stranded DNA fragments are labeled with a fluorescent tag and ChIP-on-chip → related to Analysis and software → CoCAS. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Cy5 or Alexa 647.Finally
instance of
the single-stranded DNA fragments are labeled with a fluorescent tag
0.80
text
the fragments are poured over the surface of the DNA microarray
instance of
the single-stranded DNA fragments are labeled with a fluorescent tag
0.80
text
which is spotted with short
instance of
the single-stranded DNA fragments are labeled with a fluorescent tag
0.80
text
single-stranded sequences that cover the genomic portion of interest
instance of
the single-stranded DNA fragments are labeled with a fluorescent tag
0.80
text
ChIP-on-chip
related to Analysis and software
CoCAS
0.60
section
ChIP-on-chip
related to Analysis and software
Analysis
0.60
section
ChIP-on-chip
related to Analysis and software
Agilent ChIP-on-Chip
0.60
section
ChIP-on-chip
related to Analysis and software
MAT
0.60
section
ChIP-on-chip
related to Analysis and software
ChIP-chip
0.60
section
ChIP-on-chip
related to Dry-lab portion of the workflow
After
0.60
section
ChIP-on-chip
related to Dry-lab portion of the workflow
Those
0.60
section
ChIP-on-chip
related to Dry-lab portion of the workflow
This
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around ChIP-on-chip bring nearby vocabulary together. In this analysis, examples include Genome, Experiments and First. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
ChIP-on-chip
Genome
Experiments
First
Maps
Dna
Analysis
Become
Technique
Used
Chromatin
Experiment
Antibodies
chip-on-chip
Genome
Experiments
First
Maps
Dna
Analysis
Become
Technique
Used
Chromatin
Experiment
Antibodies
genome
Become
Arrays
Mammalian
Regions
Used
One
Maps
Resolution
Sites
Tiled
Transcription
Protein
tiled arrays
Tiled
Genome
Become
Experiments
Resolution
Chip-on-chip
Maps
Yeast
Dna
Proteins
Data
Using
workflow of a chip-on-chip experiment
Genome
Experiments
Microarrays
First
Maps
Dna
Analysis
Become
Technique
Used
Chromatin
Experiment
dna microarray
Fragments
Microarrays
Used
Proteins
First
Interest
Step
Using
Array
Arrays
Technology
Experiment
dna
Fragments
Microarrays
Used
Proteins
First
Interest
Step
Using
Array
Arrays
Technology
Experiment
chromatin immunoprecipitation
Proteins
Technique
Technology
Factors
First
Maps
Binding
Dna
Determine
Modifications
Become
Like
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For ChIP-on-chip, one of the stronger structural bridges in this analysis connects ChIP-on-chip with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
ChIP-on-chip
Nodes77
Edges76
Triples110
Avg. degree1.97
Density0.025974
Components1
Source & methodology
TTTA analyzes the structure around ChIP-on-chip to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Works, Technology & Regions, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
