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PICRUSt is a bioinformatics software package. The name is an abbreviation for Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.
The analysis highlights Measurement, Genome prediction algorithm and Metagenome prediction algorithm as prominent areas in the source structure around PICRUSt.
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.
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.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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.
The extracted context around PICRUSt shows recurring relationship patterns in the source. For example, PICRUSt → Brownian Motion, However, In, Maximum Likelihood, More, Notably, OTUs, Relaxed Maximum Likelihood, These, This, Wagner Parsimony This Another extracted example is PICRUSt → Finally, Guerrero Negro, Human Microbiome Project, In, Langille, OTU, Test, The, This, When. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
gene 16s sequenced rrna tool prediction genomes copy sequences samples genome metagenome sample reference family numbers phylogenetic based nearest predictions
TTTA extracted 41 structured relationships around PICRUSt. Examples in this analysis include PICRUSt → Developers → Morgan Langille, Jesse Zaneveld, Daniel McDonald, Greg Caporaso, Gavin Douglas and PICRUSt → Original authors → Morgan Langille, Jesse Zaneveld, Dan Knights, Joshua A Reyes, Jose C Clemente, Deron E Burkepile, Rebecca L Vega Thurber, Rob Knight, Robert G Beiko, Curtis Huttenhower. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| PICRUSt | Developers | Morgan Langille, Jesse Zaneveld, Daniel McDonald, Greg Caporaso, Gavin Douglas | 1.00 | infobox |
| PICRUSt | Original authors | Morgan Langille, Jesse Zaneveld, Dan Knights, Joshua A Reyes, Jose C Clemente, Deron E Burkepile, Rebecca L Vega Thurber, Rob Knight, Robert G Beiko, Curtis Huttenhower | 1.00 | infobox |
| PICRUSt | Release | 29 July 2013; 13 years ago (2013-07-29) | 1.00 | infobox |
| PICRUSt | Website | picrust.github.io/picrust/ | 1.00 | infobox |
| PICRUSt | Written in | Python, R | 1.00 | infobox |
| PICRUSt | is a | bioinformatics software package | 0.90 | text |
| PICRUSt | is a | sample by functional-gene-count matrix | 0.90 | text |
| PICRUSt | related to Genome prediction algorithm | In | 0.60 | section |
| PICRUSt | related to Genome prediction algorithm | More | 0.60 | section |
| PICRUSt | related to Genome prediction algorithm | These | 0.60 | section |
| PICRUSt | related to Genome prediction algorithm | Brownian Motion | 0.60 | section |
| PICRUSt | related to Genome prediction algorithm | Maximum Likelihood | 0.60 | section |
The concept neighborhoods around PICRUSt bring nearby vocabulary together. In this analysis, examples include Sequenced, Samples and Genomes. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For PICRUSt, one of the stronger structural bridges in this analysis connects PICRUSt with Genome prediction algorithm. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around PICRUSt to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Measurement, Genome prediction algorithm & Metagenome prediction algorithm, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — PICRUSt · EN edition · Analysis: TopicsToTalkAbout