Ontology highlight
ABSTRACT:
PROVIDER: PRJNA491462 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7852250.fastq.gz | Fastqsanger.gz | |||
| SRR7852251.fastq.gz | Fastqsanger.gz | |||
| SRR7852252.fastq.gz | Fastqsanger.gz | |||
| SRR7852253.fastq.gz | Fastqsanger.gz | |||
| SRR7852244.fastq.gz | Fastqsanger.gz |
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Project description:Pulse chase measurements using thiouracil (DTU) labeling via UPRT and chasing with uracil Data from tachyzoites is labeled "DTU Pulse Chase". Two independent pulse chase experiments were performed in tachyzoites, pulse chase 1 and 2. Duplicate arrays at each timepoint were performed for pulse chase 2 (2 a and b). Data from bradyzoites are labeled "DTU Bradyzoite Pulse Chase". Two independent pulse chase experiments were performed in bradyzoites and a single set of arrays were performed for each experiment. Just one chase timepoint was used in the bradyzoite experiments, the 2 hour chase. An RNA stablity experiment design type examines stability and/or decay of RNA transcripts. Keywords: RNA_stability_design
2005-07-16 | GSE2947 | GEO
Project description:How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. Here, we present OPEX, an optimal experimental design method to identify informative omics experiments for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's cross-behavior potential, when exposed to novel biocide and antibiotic combinations, led to accelerated knowledge discovery with predictive models that are more accurate while needing 44% fewer data to train. Selecting experiments favoring broader exploration followed by fine-tuning emerged as the optimal strategy. This led to the discovery of 29 cross-protection and 4 cross-vulnerability conditions, with further validation revealing the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to automate omics data collection for training accurate predictive models, evidence-driven decision making and accelerated knowledge discovery in life sciences.
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Project description:RNA metabolic labeling using 4-thiouridine (s4U) can be used to capture the dynamics of RNA synthesis and decay. The power of this approach is dependent on appropriate quantification of labeled and unlabeled sequencing reads, which can be compromised by the apparent loss of s4U-labeled reads in a process we refer to as dropout. Here we show that s4U-containing transcripts can be selectively lost when RNA samples are handled under sub-optimal conditions, but that this loss can be minimized using an optimized protocol. We demonstrate a second cause of dropout in nucleotiderecoding and RNA sequencing (NR-seq) experiments that is computational and downstream of library preparation. NR-seq experiments involve chemically converting s4U from a uridine analog to a cytidine analog and the apparent T-to-C mutations are then used to identify the populations of newly synthesized RNA. We show that high levels of T-to-C mutations can prevent read alignment with some computational pipelines, but that this bias can be overcome using improved alignment pipelines. Importantly, kinetic parameter estimates are affected by dropout independent of the NR chemistry employed, and all chemistries are practically indistinguishable in bulk, short-read RNA-seq experiments. Dropout is an avoidable problem that can be identified by including unlabeled controls, and mitigated through improved sample handing and read alignment that together improve the robustness and reproducibiltiy of NR-seq experiments.
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Project description:In molecular biology, the design of mechanistic experiments has to be optimized by considering statistical and biological principles. In contrast to statistical principles, biological principles of experimental design are not universally formulated. In an attempt to pinpoint generally acceptable rules, we investigated the importance of determining the optimal ranges of scale of i.e. dose and time in gene expression experiments. We propose a protocol for executing small scale, genome wide, range finding studies, covering a wide range of the potentially relevant part of the design space to find the optimal ranges of experimentation. This protocol is executed and a proof-of-concept is presented, where this approach is tested for both an in-vitro and an in-vivo study that aim to unravel DNA repair mechanisms provoked after UV radiation. We identified four challenges of range finding studies in omics experimentation; (1) the modularity of biological processes, (2) their dynamics, (3) the extent to which end-points indicate biological processes, and (4) the costs associated with the assays, which are all addressed by our approach.
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Project description:In molecular biology, the design of mechanistic experiments has to be optimized by considering statistical and biological principles. In contrast to statistical principles, biological principles of experimental design are not universally formulated. In an attempt to pinpoint generally acceptable rules, we investigated the importance of determining the optimal ranges of scale of i.e. dose and time in gene expression experiments. We propose a protocol for executing small scale, genome wide, range finding studies, covering a wide range of the potentially relevant part of the design space to find the optimal ranges of experimentation. This protocol is executed and a proof-of-concept is presented, where this approach is tested for both an in-vitro and an in-vivo study that aim to unravel DNA repair mechanisms provoked after UV radiation. We identified four challenges of range finding studies in omics experimentation; (1) the modularity of biological processes, (2) their dynamics, (3) the extent to which end-points indicate biological processes, and (4) the costs associated with the assays, which are all addressed by our approach.
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Project description:Analysis and design of RNA sequencing experiments for identifying mRNA isoform regulation
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Project description:Accelerated knowledge discovery from omics data by optimal experimental design
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Project description:Conventional prokaryotic RNA labeling method usually requires large amounts of starting materials and tends to generate high background signals. Recently, two novel methods based on amplification systems were introduced. Here, we compared three alternative strategies: direct labeling method, ployadenylation-involved oligo-dT priming amplification method and random priming amplification method (hereafter referred to as DL, PAOD and RPA method in this article) for prokaryotic RNA labeling employing the expression profiling investigation in Escherichia coli (E. coli) heat shock model. To identify an optimal RNA labeling method for prokaryotic microarray analysis, experiments were performed with starting RNA obtained from E.coli growing at control (37°C) or heat shock (43°C) condition. We employed 0.5 μg of total RNA for a single round of amplification using PAOD and RPA methods coupling to IVT to generate cDNA targets. In addition, an approach using DL method was also performed. Three types of cDNA mixtures containing Cy5-labeled (or Cy3-labeled) control DNA and Cy3-labeled (or Cy5-labeled) DNA targets were hybridized with E.coli microarrays in a dye-swap strategy. Replicate experiments were conducted from the same batch of total RNA to assess technique reproducibility of each approach.
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