Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

TDR Mycobacterium Tuberculosis

ABSTRACT: The Special Programme for Research and Training in Tropical Diseases (TDR) strain collection

PROVIDER: PRJEB31023 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	ERR3132080_1.fastq.gz	Fastqsanger.gz
	ERR3132080_2.fastq.gz	Fastqsanger.gz
	ERR3132090_1.fastq.gz	Fastqsanger.gz
	ERR3132090_2.fastq.gz	Fastqsanger.gz
	ERR3132100_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 462

Similar Datasets

Mycobacterium tuberculosis strain:TDR

Project description:Mycobacterium tuberculosis genome sequencing

| PRJNA287858 | ENA

Project description:Cinnamomum research special

| PRJNA970043 | ENA

Mycobacterium tuberculosis subsp. tuberculosis

Project description:Whole genome sequencing of isogenic collection of antibiotic-resistant Mycobacterium tuberculosis strains

| PRJNA265080 | ENA

Mycobacterium tuberculosis H37Ra

Project description:An attenuated strain used in mycobacterial virulence research

| PRJNA18649 | ENA

Transcriptional profiling of Mycobacterium tuberculosis exposed to caseum mimics

Project description:We profiled the transcriptional response of Mycobacterium tuberculosis exposed to different caseum mimic conditions using RNA-seq. We also hypothesized that an improved Mtb TRN could be inferred using a large, diverse compendium of RNA-seq data and therefore sought to supplement the existing repository of Mtb expression data available on SRA with additional conditions that could inform on Mtb's underlying regulatory programs. We went on to infer Mtb's TRN using a collection of inference methods in combination with this compendium, and employed it to build an interpretable machine learning model of Mtb fitness under stress.

2025-03-19 | GSE292410 | GEO

Merget2013 - Mycobacterium tuberculosis permeability prediction tool

Project description:MycPermCheck predicts potential to permeate the Mycobacterium tuberculosis cell membrane based on physicochemical properties. Due to the lack of reliable experimental datapoints, the authors defined the training set using molecules that are active against M.tb. Model Type: Predictive machine learning model. Model Relevance: Probability of permeability across the M.tb cell wall. Model Encoded by: Miquel Duran-Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos8d8a

2024-05-21 | MODEL2405210004 | BioModels

Transcriptional profiling of Mycobacterium tuberculosis strains exposed to antibiotic treatment.

Project description:We profiled the transcriptional response of different, phylogenetically distinct Mycobacterium tuberculosis (Mtb) strains to isoniazid (INH) and rifampicin (RIF) using RNA-seq. We also hypothesized that an improved Mtb TRN could be inferred using a large, diverse compendium of RNA-seq data and therefore sought to supplement the existing repository of Mtb expression data available on SRA with additional conditions that could inform on Mtb's underlying regulatory programs. We went on to infer Mtb's TRN using a collection of inference methods in combination with this compendium, and employed it to build an interpretable machine learning model of Mtb fitness under stress.

2025-03-19 | GSE292409 | GEO

Developing gene signature against Mycobacterium bovis infection in Cattle

Project description:The bTB research programme is centred on identifying the genetic basis for resistance against bovine tuberculosis and towards diagnostics

2016-10-25 | GSE74050 | GEO

SLCC-WGS

Project description:Historical "Special Listeria Culture Collection" whole genome sequencing

| PRJEB11656 | ENA

Developing gene signature against Mycobacterium bovis infection in Cattle

Project description:The bTB research programme is centred on identifying the genetic basis for resistance against bovine tuberculosis and towards diagnostics Agilent Whole Genome Bovine 8x60k Gene Expression (AMADID: 29411) , Labeling kit: Agilent Quick-Amp labeling Kit (p/n5190-0442)

2016-10-25 | E-GEOD-74050 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data