• Cell-free genome-wide transcri...

Treffer: Cell-free genome-wide transcriptomics through machine learning optimization

Title:
Cell-free genome-wide transcriptomics through machine learning optimization
Authors:
Wagner, Léa, Hoang, An, Rue, Olivier, Delumeau, Olivier, Loux, Valentin, Faulon, Jean-Loup, Jules, Matthieu, Borkowski, Olivier
Contributors:
MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Society for Biological Engineering, Jeffrey Tabor, Le Song, Tara Deans, Michael Clear, Harris Wang, Nelson Barton, Marilene Pavan, Emma Chory, Gozde Demirer, Keith Keitz, Ingmar Riedel-Kruse, Katie Galloway, Junghae Suh, Tara Karimi, ANR-24-CE44-4467,RNADarkMatter,Exploration des paysages transcriptionnels des bactéries cultivables et non cultivables de l'intestin humain à l'aide d'émulateurs in vitro de la transcription.(2024), ANR-11-IDEX-0003,IPS,Idex Paris-Saclay(2011), ANR-22-PEBB-0008,Galaxy-BioProd,Galaxy-BioProd: An operating portal for the production of biosourced products(2022)
Source:
SEED 2025, Jun 2025, Houston, United States
Publisher Information:
CCSD, 2025.
Publication Year:
2025
Collection:
collection:AGROPARISTECH
collection:UNIV-PARIS-SACLAY
collection:AGREENIUM
collection:AGROPARISTECH-ORG
collection:AGROPARISTECH-SVS
collection:INRAE
collection:INRAE-UPSACLAY
collection:UNIVERSITE-PARIS-SACLAY
collection:ANR
collection:GS-MATHEMATIQUES
collection:GS-COMPUTER-SCIENCE
collection:GS-BIOSPHERA
collection:GS-LIFE-SCIENCES-HEALTH
collection:GS-HEALTH-DRUG-SCIENCES
collection:MAIAGE
collection:MICA-UNITES
collection:MICALIS
collection:MATHNUM
collection:BBEST
collection:TEST-MATHNUM
collection:BRS-MICALIS
collection:SYBER-MICALIS
Subject Terms:
Cell-free, Escherichia coli, Phage T7, Active learning, Machine learning, Genome expression, Transcriptomics, Direct RNA sequencing, Synthetic biology, Microbiology, [SDV]Life Sciences [q-bio], [INFO]Computer Science [cs]
Subject Geographic:
Houston, United States
Original Identifier:
HAL: hal-05155548
Document Type:
Konferenz conferenceObject<br />Conference poster
Language:
English
Relation:
https://doi.org/10.1101/2025.06.04.657821
Access URL:
https://hal.inrae.fr/hal-05155548
https://hal.inrae.fr/hal-05155548v1/document
https://hal.inrae.fr/hal-05155548v1/file/Poster_LW_SEED_portrait_vf.pdf
Rights:
info:eu-repo/semantics/OpenAccess
URL: http://creativecommons.org/licenses/by/
Accession Number:
edshal.hal.05155548v1
Database:
HAL

Weitere Informationen

Despite advances in transcriptomics, understanding of genome regulation remains limited by the complex interactions within living cells. To address this, we performed cell-free transcriptomics by developing a platform using an active learning workflow to explore over 1,000,000 buffer conditions. This enabled us to identify a buffer that increased mRNA yield by 20-fold, enabling cell-free transcriptomics. By employing increasingly complex conditions, our approach untangles the regulatory layers controlling genome expression.