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Record W2132284327 · doi:10.1186/s13073-014-0100-8

YMAP: a pipeline for visualization of copy number variation and loss of heterozygosity in eukaryotic pathogens

2014· article· en· W2132284327 on OpenAlexfundno aff
Darren Abbey, Jason Funt, Mor N. Lurie-Weinberger, Dawn Thompson, Aviv Regev, Chad L. Myers, Judith Berman

Bibliographic record

VenueGenome Medicine · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesEuropean CommissionBroad InstituteNational Institute of General Medical SciencesCanadian Institute for Advanced ResearchBurroughs Wellcome FundUniversity of MinnesotaHoward Hughes Medical InstituteNational Cancer InstituteNational Institutes of HealthNational Science Foundation
KeywordsLoss of heterozygosityCopy-number variationVisualizationVariation (astronomy)Human geneticsBiologyPipeline (software)Computational biologyEvolutionary biologyGeneticsSystems biologyStructural variationGenomeBioinformaticsComputer scienceAlleleGeneData miningPhysicsAstronomy

Abstract

fetched live from OpenAlex

The design of effective antimicrobial therapies for serious eukaryotic pathogens requires a clear understanding of their highly variable genomes. To facilitate analysis of copy number variations, single nucleotide polymorphisms and loss of heterozygosity events in these pathogens, we developed a pipeline for analyzing diverse genome-scale datasets from microarray, deep sequencing, and restriction site associated DNA sequence experiments for clinical and laboratory strains of Candida albicans, the most prevalent human fungal pathogen. The YMAP pipeline (http://lovelace.cs.umn.edu/Ymap/) automatically illustrates genome-wide information in a single intuitive figure and is readily modified for the analysis of other pathogens with small genomes.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.037
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0370.010

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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations149
Published2014
Admission routes1
Has abstractyes

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