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Record W2810335964 · doi:10.1038/s41598-018-28473-z

Genome Sequencing and analyses of Two Marine Fungi from the North Sea Unraveled a Plethora of Novel Biosynthetic Gene Clusters

2018· article· en· W2810335964 on OpenAlexaff
Abhishek Kumar, Jens Laurids Sørensen, Frederik Teilfeldt Hansen, Mikko Arvas, Muhammad Fahad Syed, Lara Hassan, J. Philipp Benz, Éric Record, Bernard Henrissat, Stefanie Pöggeler, Frank Kempken

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsMcGill University
FundersAgence Nationale de la RechercheEuropean Commission
KeywordsGenomeBiologyGeneComputational biologyGeneticsDNA sequencing

Abstract

fetched live from OpenAlex

Marine Fungi are potent secondary metabolite producers. However, limited genetic information are available their biosynthetic gene clusters (BGCs) and their biotechnological applications. To overcome this lack of information, herein, we used next-generation sequencing methods for genome sequencing of two marine fungi, isolated from the German Wadden Sea, namely Calcarisporium sp. KF525 and Pestalotiopsis sp. KF079. The assembled genome size of the marine isolate Calcarisporium sp. KF525 is about 36.8 Mb with 60 BGCs, while Pestalotiopsis sp. KF079 has a genome size of 47.5 Mb harboring 67 BGCs. Of all BGCs, 98% and 97% are novel clusters of Calcarisporium sp. and Pestalotiopsis sp., respectively. Only few of the BGCs were found to be expressed under laboratory conditions by RNA-seq analysis. The vast majority of all BGCs were found to be novel and unique for these two marine fungi. Along with a description of the identified gene clusters, we furthermore present important genomic features and life-style properties of these two fungi. The two novel fungal genomes provide a plethora of new BGCs, which may have biotechnological applications in the future, for example as novel drugs. The genomic characterizations will provide assistance in future genetics and genomic analyses of marine fungi.

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 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)0.001
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 score0.017
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.047
GPT teacher head0.294
Teacher spread0.247 · 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 teacher head, 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

Citations45
Published2018
Admission routes1
Has abstractyes

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