Melatonin promotes Arabidopsis root growth in an IAA dependent manner [7d]
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ABSTRACT:
ORGANISM(S): Arabidopsis thaliana
PROVIDER: GSE167524 | GEO | 2021/08/11
REPOSITORIES: GEO
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Project description:Purpose: We aimed to compare transcriptomic changes after melatonin (MT) and IAA treatments in Arabidopsis and dissected cross-talk between MT and IAA Methods: A total amount of 1 μg RNA was used for generation of sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. After cluster generation, the library preparations were sequenced on an Illumina Hiseq2000 platform and paired-end reads were generated. Clean reads were obtained by removing low quality reads, reads containing adapter and ploy-N from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. Index of the Arabidopsis genome was built using Bowtie v2.2.3 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then FPKM (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of drought stress versus control condition was performed using the DESeq R package (1.18.0). Results:In total, six samples with two biological replicates per genotype/treatment combination were used for RNA sequencing analysis. At least 2 G clean bases were generated for each sample. Comparative analysis identified coregulated genes by melatonin and IAA in Arabidopsis seedlings
2021-12-21 | GSE152813 | GEO
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2020-01-08 | GSE134945 | GEO
Project description:Purpose: We aimed to dissect transcriptomic responses of Tulipa gesneriana during petal senescence and characterize function of senescence related genes. Methods: A total amount of 3 µg RNA was used for generation of sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated. Clean reads were obtained by removing low quality reads, reads containing adapter and ploy-N from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. Index of the Arabidopsis genome was built using Bowtie v2.2.3 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then FPKM (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis between each two different stages was performed using the DESeq R package (1.18.0). Results:In total, 15 samples with three biological replicates per stage combination were used for RNA sequencing analysis. At least 6 G clean bases were generated for each sample. Comparative analysis identified genes involved in flower development and/or fllower senescence.
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2021-12-21 | GSE136182 | GEO
Project description:Purpose: We aimed to identify ZAT18 target genes and characterize functions of ZAT18 during plant drought tolerance Methods: A total amount of 3 μg RNA was used for generation of sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated. Clean reads were obtained by removing low quality reads, reads containing adapter and ploy-N from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. Index of the Arabidopsis genome was built using Bowtie v2.2.3 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then FPKM (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of drought stress versus control condition was performed using the DESeq R package (1.18.0). Results:In total, eight samples with two biological replicates per genotype/treatment combination were used for RNA sequencing analysis. At least 2 G clean bases were generated for each sample. Comparative analysis revealed that 1777 genes were transcriptionally affected by AtZAT18 trasngene or drought treatment. The results showed that overexpression of AtZAT18 modulated expression level changes of 423 and 561genes under control and drought stress conditions, respectively. Drought stress treatment changed expression of 971 genes with 768 up-regulated and 203 down-regulated.
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Project description:Purpose: Cucumber (Cucumis sativus L.) is an economically important vegetable crop worldwide, and cucumber fruit spine density has an important impact on the commercial value. However, little is known about the regulatory mechanism for the fruit spine formation.In this study, the transcriptome analyses of ovaries and pericarps from numerous-spine parent and few-spine parent were conducted to identify the gene regulatory networks involved in the formation and development of numerous fruit spines in cucumber. Methods: Cucumber mRNA profiles of ovaries and pericarps from numerous-spine parent and few-spine parent were generated by deep sequencing, in triplicate, using Illumina HiSeq 4000. Then, clean data (clean reads) were obtained by removing reads containing adapters, reads containing poly-N sequences and low-quality reads from the raw data. Simultaneously, the Q20, Q30 and GC contents of the clean data were calculated. All of the downstream analyses were based on the high-quality clean data. Clean paired-end reads were mapped to the reference genome using TopHat v2.0.12 (Trapnell et al. 2012). Then, the FPKM (fragments per kilobase of transcript sequence per million base pairs sequenced) value of each gene was calculated to estimate gene expression levels (Trapnell et al. 2010). Genes with an adjusted P-value < 0.05 identified by DESeq were assigned as differentially expressed genes(DEGs). Results: We generated 42.96-57.53 million raw reads from each library, and 39.85-54.02 million clean reads were obtained after the removal of low-quality reads and adapter sequences. Among the clean reads, 79.03-80.94% were mapped to the gene database . Based on the KEGG database, pathway enrichment analysis was performed to identify significantly enriched metabolic pathways or signal transduction pathways in DEGs. Plant hormone signal transduction was significantly enriched in up-regulated genes in both F_6DBF compared with M_6DBF and F_0DAA compared with M_0DAA. Conclusions: Based on the transcriptome analysis, we excavated possible biological regulatory networks involved in the formation and development of numerous fruit spines in cucumber. This work will promote the exploration of molecular mechanisms that regulate cucumber fruit spine density.
2018-01-27 | GSE109615 | GEO
Project description:Purpose: To obtain an overview of the metabolic changes associated to the absence of KpsM in Synechocystis, in particular on carbon-related metabolic pathways Methods: The trancriptomes of Synechocystis wild type and kpsM mutant were analyzed by RNA sequencing, using three biological replicates. Cultures were grown until reaching an OD730nm of 1.5. Sequencing libraries were generated using NEBNext® Ultra TM RNA Library Prep Kit for Illumina®. PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina). Raw data (raw reads) of FASTQ format were firstly processed through in-house scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter and poly-N sequences and reads with low quality from raw data. Paired-end clean reads were mapped to the reference genome using HISAT2 software.HTSeq was used to count the read numbers mapped of each gene, including known and novel genes. And then RPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. RPKM, Reads Per Kilobase of exon model per Million mapped reads, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels. Differential expression analysis between two conditions/groups (three biological replicates per condition) was performed using DESeq2 R package. DESeq2 provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P values were adjusted using the Benjamini and Hochberg’s approach for controlling the False Discovery Rate (FDR). Genes with an adjusted P value < 0.05 found by DESeq2 were assigned as differentially expressed. Results: Approximately 700 genes (out of the 3636 identified and 215 quantified, representing a coverage of 85,12%) were significantly differentially expressed (P 216 value < 0.05) in the kpsM mutant compared to the wild type. Of those, 406 were down217 regulated, while 297 were up-regulated. Conclusions: Overall, our transcriptomic data indicates alterations in the mechanisms of energy production and conversion in the kpsM mutant compared to the wild type. The approach resulted in the identification of altered levels of transcripts belonging to a variety of functional categories and highlighting a number of key metabolic processes affected by the disruption of kpsM, namely photosynthesis, oxidative phosphorylation and carbon metabolism.
2021-01-20 | GSE165073 | GEO