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Record W2085814327 · doi:10.1186/1471-2164-13-202

Comparing thousands of circular genomes using the CGView Comparison Tool

2012· article· en· W2085814327 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Genomics · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Livestock and Meat Agency
KeywordsGenomeBiologyVisualizationComputational biologyScripting languageSequence (biology)Variety (cybernetics)Computer scienceData miningGeneticsGeneArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Continued sequencing efforts coupled with advances in sequencing technology will lead to the completion of a vast number of small genomes. Whole-genome comparisons represent an important part of the analysis of any new genome sequence, as they can provide a better understanding of the biology and evolution of the source organism. Visualization of the results is important, as it allows information from a variety of sources to be integrated and interpreted. However, existing graphical comparison tools lack features needed for efficiently comparing a new genome to hundreds or thousands of existing sequences. Moreover, existing tools are limited in terms of the types of comparisons that can be performed, the extent to which the output can be customized, and the ease with which the entire process can be automated. RESULTS: The CGView Comparison Tool (CCT) is a package for visually comparing bacterial, plasmid, chloroplast, or mitochondrial sequences of interest to existing genomes or sequence collections. The comparisons are conducted using BLAST, and the BLAST results are presented in the form of graphical maps that can also show sequence features, gene and protein names, COG (Clusters of Orthologous Groups of proteins) category assignments, and sequence composition characteristics. CCT can generate maps in a variety of sizes, including 400 Megapixel maps suitable for posters. Comparisons can be conducted within a particular species or genus, or all available genomes can be used. The entire map creation process, from downloading sequences to redrawing zoomed maps, can be completed easily using scripts included with the CCT. User-defined features or analysis results can be included on maps, and maps can be extensively customized. To simplify program setup, a CCT virtual machine that includes all dependencies preinstalled is available. Detailed tutorials illustrating the use of CCT are included with the CCT documentation. CONCLUSION: CCT can be used to visually compare a reference sequence to thousands of existing genomes or sequence collections (next-generation sequencing reads for example) on a standard desktop computer. It provides analysis and visualization functionality not available in any existing circular genome visualization tool. By visually presenting sequence conservation information along with functional classifications and sequence composition characteristics, CCT can be a useful tool for identifying rapidly evolving or novel sequences, horizontally transferred sequences, or unusual functional properties in newly sequenced genomes. CCT is freely available for download at http://stothard.afns.ualberta.ca/downloads/CCT/.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.288
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it