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PGreen

The pGreen plasmids are vectors for plant transformation. They were first described in 2000 as components of a novel T-DNA binary system. The supporting web page provides supplementary information and ongoing support to researchers to request their plasmid resources. As these plasmids have been taken up by the research community, the plasmids have been…

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Measurement, T-DNA regions & PSoup

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Topics to explore

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Overview

PGreenI and pGreenII

T-DNA regions

PSoup

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.

PGreen

Nodes21
Edges20
Triples25
Avg. degree1.9
Density0.095238
Components1

How this topic connects Entity context

Quick relationship hints grouped by predicate. Useful for spotting recurring semantic connections around the current entity.

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PGreen

Top relations

related to No transformation selection · 8
PGreen → LacZ, Left, MCS, MCS-CaMV, Right, SK, T-DNA, The
related to Bialaphos selection · 6
PGreen → HpaI, LacZ, Left Border, MCS-CaMV, SK, The MCS
related to Kanamycin selection · 6
PGreen → HpaI, LacZ, Left Border, MCS-CaMV, SK, The MCS
related to Hygromycin selection · 2
PGreen → HpaI, Left Border
related to pSoup · 2
PGreen → Agrobacterium, This
related to External links · 1
PGreen → The

Important terminology Word statistics

Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

derived pgreenii selection plasmid site transformation cassette pbluescript 0000 cloning plant providing research t-dna plasmids left border lacz gene blue

Entity relationships Subject–Predicate–Object triples

Extracted RDF-like relationships with confidence and source. The table includes structured facts and lower-confidence contextual relations.
SubjectPredicateObjectConfidenceSrc
PGreenrelated to Bialaphos selectionHpaI0.60section
PGreenrelated to Bialaphos selectionLeft Border0.60section
PGreenrelated to Bialaphos selectionSK0.60section
PGreenrelated to Bialaphos selectionLacZ0.60section
PGreenrelated to Bialaphos selectionMCS-CaMV0.60section
PGreenrelated to Bialaphos selectionThe MCS0.60section
PGreenrelated to External linksThe0.60section
PGreenrelated to Hygromycin selectionHpaI0.60section
PGreenrelated to Hygromycin selectionLeft Border0.60section
PGreenrelated to Kanamycin selectionHpaI0.60section
PGreenrelated to Kanamycin selectionLeft Border0.60section
PGreenrelated to Kanamycin selectionSK0.60section

Related concept clusters Concept neighborhoods

Clusters of nearby vocabulary surrounding the topic. Scan them for adjacent concepts and language you may have missed.

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

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