Research any topic before you write.
Find related topics. | Discover entities. | See connections. | Build a topical map.
HMMER is a free and commonly used software package for sequence analysis written by Sean Eddy. Its general usage is to identify homologous protein or nucleotide sequences, and to perform sequence alignments. It detects homology by comparing a profile-HMM (a Hidden Markov model constructed explicitly for a particular search) to either a single sequence or…
The analysis highlights Art and Products as prominent areas in the source structure around HMMER.
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 HMMER shows recurring relationship patterns in the source. For example, HMMER → HMM, HMMs, In, Pfam, Programs, Protein Data Bank, Search, SUPERFAMILY, SwissProt, The, TIGRFAMs, UniProt Another extracted example is HMMER → BLAST, BLAST-based, DNA-based, E-values, Further, HMM-based, HMMER3, The, This, While. 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.
sequence sequences alignment alignments profile hmmer3 states model package search used speed also protein homologous major profile-hmm hmms hmm searches
TTTA extracted 65 structured relationships around HMMER. Examples in this analysis include HMMER → Available in → English and HMMER → Developers → Sean Eddy, Travis Wheeler, HMMER development team. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| HMMER | Available in | English | 1.00 | infobox |
| HMMER | Developers | Sean Eddy, Travis Wheeler, HMMER development team | 1.00 | infobox |
| HMMER | License | BSD-3 | 1.00 | infobox |
| HMMER | Repository | github.com/EddyRivasLab/hmmer | 1.00 | infobox |
| HMMER | Stable release | 3.4 / 15 August 2023; 3 years ago (15 August 2023) | 1.00 | infobox |
| HMMER | Type | Bioinformatics tool | 1.00 | infobox |
| HMMER | Website | hmmer.org | 1.00 | infobox |
| HMMER | Written in | C | 1.00 | infobox |
| HMMER | is a | free and commonly used software package for sequence analysis written by Sean Eddy | 0.90 | text |
| HMMER | is a | console utility ported to every major operating system | 0.90 | text |
| Pfam | instance of | and macOS.HMMER is the core utility that protein family databases | 0.80 | text |
| InterPro are based upon | instance of | and macOS.HMMER is the core utility that protein family databases | 0.80 | text |
The concept neighborhoods around HMMER bring nearby vocabulary together. In this analysis, examples include Package, Software and Web. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For HMMER, one of the stronger structural bridges in this analysis connects HMMER 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.
TTTA analyzes the structure around HMMER to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Art & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — HMMER · EN edition · Analysis: TopicsToTalkAbout