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Record W2895244430 · doi:10.1038/s42003-018-0171-y

Assembling the genome of the African wild rice Oryza longistaminata by exploiting synteny in closely related Oryza species

2018· article· en· W2895244430 on OpenAlexfundno aff
Stefan Reuscher, Tomoyuki Furuta, Kanako Bessho‐Uehara, Michele Cosi, Kshirod K. Jena, Atsushi Toyoda, Asao Fujiyama, Nori Kurata, Motoyuki Ashikari

Bibliographic record

VenueCommunications Biology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
FundersInstitute of GeneticsNational Agriculture and Food Research OrganizationMinistry of Education, Culture, Sports, Science and Technology
KeywordsSyntenyOryzaOryza sativaGenomeBiologyBotanyEvolutionary biologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract The African wild rice species Oryza longistaminata has several beneficial traits compared to cultivated rice species, such as resistance to biotic stresses, clonal propagation via rhizomes, and increased biomass production. To facilitate breeding efforts and functional genomics studies, we de-novo assembled a high-quality, haploid-phased genome. Here, we present our assembly, with a total length of 351 Mb, of which 92.2% was anchored onto 12 chromosomes. We detected 34,389 genes and 38.1% of the genome consisted of repetitive content. We validated our assembly by a comparative linkage analysis and by examining well-characterized gene families. This genome assembly will be a useful resource to exploit beneficial alleles found in O. longistaminata. Our results also show that it is possible to generate a high-quality, functionally complete rice genome assembly from moderate SMRT read coverage by exploiting synteny in a closely related Oryza species.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.265
Teacher spread0.208 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations64
Published2018
Admission routes1
Has abstractyes

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