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Status |
Public on Oct 11, 2024 |
Title |
MOC_1779:nitro:100.00000uM:90min:PA14:MOC_1779_2:r1 |
Sample type |
SRA |
|
|
Source name |
PA14
|
Organism |
Pseudomonas aeruginosa UCBPP-PA14 |
Characteristics |
cell line: PA14 pert id: nitro pert iname: Nitrofurantoin pert dose: 100 pert dose_unit: uM pert idose: 100.00000uM pert time: 90 pert time_unit: min pert itime: 90min pert type: poscon category: antimicrobial_reference_set project id: MOC_1779 replicate id: r1 project plate_id_384well: MOC_1779_2 strain id: PA14 weak_signal: FALSE
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Treatment protocol |
Cells were treated at 37°C without shaking in a humidity chamber. In the case of the RNA-Seq Time Trial, cells were treated for 30, 60, 90, and 120 minutes. In the case of all other RNA-Seq experiments, cells were treated for 90 minutes.
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Growth protocol |
Bacterial cultures were grown with shaking (37°C, 250rpm) to mid-log phase, and 30uL of cultures were mixed with equal volume of 2X compound working solution, yielding final conditions containing 1x108 CFU/mL bacteria and with compounds (or vehicle) in 0.5% (v/v) DMSO or water in LB.
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Extracted molecule |
total RNA |
Extraction protocol |
At the end of the incubation period, samples were mixed with 30uL of 3X RNAgem Blue Buffer (Zygem, Charlottesville VA), and chemically lysed by incubation in a thermocycler at 75°C for 10 minutes. Total RNA was then extracted using the Direct-zol kit (Zymo Research, Irvin CA), and RNA quality and quantity were analyzed using the RNA ScreenTape with the 2200 TapeStation (Agilent, Santa Clara CA). RNA-Seq libraries were prepared using the RNA TagSeq protocol previously described (Shishkin, A. et al. Nature methods. 2015. https://doi.org/10.1038/nmeth.3313)
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Library strategy |
RNA-Seq |
Library source |
transcriptomic |
Library selection |
cDNA |
Instrument model |
Illumina NovaSeq 6000 |
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Description |
moc1430_1779_0066_0110_2kd_counts_manuscript.csv moc1430_1779_0066_0110_2kd_modzscore_ref_set.csv
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Data processing |
BWA to align reads to reference QC & filter samples with low counts and genes with low counts DESeq2 to identify weak and robust transcriptional signals VST function from DESeq2 for variance stabilization of counts Calculate moderated z-scores using custom scripts, based on previously published scripts (Subramanian, A. et al. Cell. 2017. https://doi.org/10.1016/j.cell.2017.10.049) Assembly: NC_0088463.1 (UCBPP-PA14) Supplementary files format and content: comma separated file with counts for each sample Supplementary files format and content: comma separated file with moderated z-score values for each treatment condition (replicates collapsed)
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Submission date |
Dec 20, 2023 |
Last update date |
Oct 11, 2024 |
Contact name |
Keith Romano |
Organization name |
Massachusetts General Hospital
|
Department |
Molecular Biology
|
Lab |
Hung Lab
|
Street address |
185 Cambridge St.
|
City |
Boston |
ZIP/Postal code |
02114 |
Country |
USA |
|
|
Platform ID |
GPL27892 |
Series (1) |
GSE251671 |
Perturbation-Specific Transcriptional Mapping for unbiased target elucidation of antibiotics |
|
Relations |
BioSample |
SAMN38978074 |
SRA |
SRX22977804 |