A flexible Transcriptomic Analysis Pipeline (TAP) for quality control and functional assessment of transcriptomes
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA954449 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR24136626_1.fastq.gz | Fastqsanger.gz | |||
| SRR24136626_2.fastq.gz | Fastqsanger.gz | |||
| SRR24136627_1.fastq.gz | Fastqsanger.gz | |||
| SRR24136627_2.fastq.gz | Fastqsanger.gz | |||
| SRR24136628_1.fastq.gz | Fastqsanger.gz |
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Project description:BackgroundRNA-sequencing (RNA-seq) has revolutionized the exploration of biological mechanisms, shedding light on the roles of non-coding RNAs, including long non-coding RNAs (lncRNAs), across various biological processes, including stress responses. Despite these advancements, there remains a gap in our understanding of the implications of different RNA-seq library protocols on comprehensive lncRNA expression analysis, particularly in non-mammalian organisms.ResultsIn this study, we sought to bridge this knowledge gap by investigating lncRNA expression patterns in Drosophila melanogaster under thermal stress conditions. To achieve this, we conducted a comparative analysis of two RNA-seq library protocols: polyA + RNA capture and rRNA-depletion. Our approach involved the development and application of a Transcriptome Analysis Pipeline (TAP) designed to systematically assess both the technical and functional dimensions of RNA-seq, facilitating a robust comparison of these library protocols. Our findings underscore the efficacy of the polyA + protocol in capturing the majority of expressed lncRNAs within the Drosophila melanogaster transcriptome. In contrast, rRNA-depletion exhibited limited advantages in the context of D. melanogaster studies. Notably, the polyA + protocol demonstrated superior performance in terms of usable read yield and the accurate detection of splice junctions.ConclusionsOur study introduces a versatile transcriptomic analysis pipeline, TAP, designed to uniformly process RNA-seq data from any organism with a reference genome. It also highlights the significance of selecting an appropriate RNA-seq library protocol tailored to the specific research context.BackgroundAdvances in next generation sequencing (NGS) technologies enable the comprehensive analysis of genetic sequences of organisms in a relatively cost-effective manner [1, 2]. Among these technologies, RNA-sequencing (RNA-seq) has emerged as a preeminent method to study fundamental biological mechanisms at the level of cells, tissues, and whole organisms. RNA-seq enables the detection and quantification of various RNA populations, including messenger RNA (mRNA) and various species of non-coding RNA, such as long non-coding RNA (lncRNA), as well as an assessment of features including splice junctions in RNA.
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Project description:This study examined how transcriptomics tools can be included in a Triad-based soil quality assessment to assess the toxicity of soils from river banks polluted by metals. To that end we measured chemical soil properties and used the standardized ISO guideline for ecotoxicological tests and a newly developed microarray for gene expression in the indicator soil arthropod, Folsomia candida. Microarray analysis revealed that the oxidative stress response pathway was significantly affected in all soils except one. The data indicate that changes in cell redox homeostasis are a significant signature of metal stress. Finally, 32 genes showed significant dose-dependent expression with metal concentrations. They are promising genetic markers providing an early indication of the need for higher tier testing in soil quality. One of the least polluted soils showed toxicity in the bioassay that could be removed by sterilization. The gene expression profile for this soil did not show a metal-related signature, confirming that another factor than metals (most likely of biological origin) caused the toxicity. This study demonstrates the feasibility and advantages of integrating transcriptomics into Triad-based soil quality assessment. Combining molecular and organismal life-history traitM-bM-^@M-^Ys stress responses helps identifying causes of adverse effect in bioassays. Further validation is needed for verifying the set of genes with dose-dependent expression patterns linked with toxic stress. We used a one-color microarray design where each sample was hybridized to a single array
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