Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1070560 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27776549_1.fastq.gz | Fastqsanger.gz | |||
| SRR27776549_2.fastq.gz | Fastqsanger.gz | |||
| SRR27776550_1.fastq.gz | Fastqsanger.gz | |||
| SRR27776550_2.fastq.gz | Fastqsanger.gz |
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Project description:Brown algae (Phaeophyceae) are complex photosynthetic organisms with a very different evolutionary history to green plants, to which they are only distantly related. These seaweeds are the dominant species in rocky coastal ecosystems and they exhibit many interesting adaptations to these, often harsh, environments. The brown algae are also important because they are one of only a very small number of eukaryotic lineages that have evolved complex multicellularity. This work used whole genome tiling array approach to generate a comprehensive transcriptome map of the filamentous seaweed Ectocarpus siliculosus (Dillwyn) Lyngbye, a model organism for the brown algae. Keywords: high-resolution tiling array, brown algae, ectocarpus The slides were hybridised with two, labelled samples: 1) a mixture of labelled cDNA corresponding to RNA samples from mature sporophytes and gametophytes and from immature sporophytes stressed either in high salt medium or by addition of hydrogen peroxide and 2) genomic DNA as a control.
2010-04-07 | E-GEOD-19912 | biostudies-arrayexpress
Project description:Background: Microalgae can make a significant contribution towards meeting global renewable energy needs in both lipid-based liquid biofuel and hydrogen biofuel. The development of energy-related products and chemicals from algae could be accelerated with improvements in systems biology tools, and recent advances in sequencing technology provide a platform for enhanced transcriptomic analyses. However, these techniques are still heavily reliant upon available genomic sequence data. We have developed a de novo sequencing, annotation, and quantitation pipeline that can be applied to unsequenced organisms for effective quantitative gene expression profiling. Chlamydomonas moewusii is a unicellular green alga capable of evolving molecular hydrogen (H2) under both dark and light anaerobic conditions, and has high hydrogenase activity that can be rapidly induced. However, to date, there is no systematic investigation of transcriptomic profiling during induction of hydrogen photoproduction in this organism. Results: In this work, we measured rates of hydrogen production and extracted RNA from samples of C. moewusii following various lengths of dark anaerobic incubation. RNA-Seq was applied to investigate transcriptomic profiles during the dark anaerobic induction of hydrogen photoproduction. One hundred fifty six million reads generated from seven samples were then used for de novo assembly after data trimming. The BlastX results against NCBI database and Blast2GO results were used to interpret the functions of the assembled 39,136 contigs, which were then used as the reference transcripts for RNA-Seq analysis. Nearly 98% transcripts had Blast hits, although more than one-third were annotated as hypothetical proteins. The expression value of RNA-Seq results was imported into statistical software for data quality control, normalization, and subsequent statistical analyses such as One-way ANOVA and K-means Clustering. Our results indicated that more transcripts were differentially expressed during the period of early and higher hydrogen photoproduction, and fewer transcripts were differentially expressed when rates of hydrogen photoproduction decreased. Conclusions: Herein, we have described a workflow to analyze RNA-Seq data without reference genome sequence information, which can be applied to the rapid development of other unsequenced microorganisms (both prokaryotic and eukaryotic) with the potential for development as fuel production strains. This study provided the first transcriptomic RNA-Seq dataset, as well as biological insights into the metabolic changes that occur concomitant with induction of hydrogen photoevolution in C. moewusii, which can help further development of this organism as a hydrogen photoproduction strain. Examine time course gene differential expression post anaerobic induction for hydrogen production
2013-09-04 | E-GEOD-46225 | biostudies-arrayexpress
Project description:Algal photo-bio hydrogen production, a promising method for producing clean and renewable fuel in the form of hydrogen gas, has been studied extensively over the last few decades. In this study, microarray analyses were used to obtain a global expression profile of mRNA abundance in the green alga Chlamydomonas reinhardtii at five different time points before the onset and during the course of sulphur depleted hydrogen production. The present work confirms previous findings on the impacts of sulphur deprivation but also provides new insights into photosynthesis, sulphur assimilation and carbon metabolism under sulphur starvation towards hydrogen production. For instance, while a general trend towards repression of transcripts encoding photosynthetic genes was observed, the abundance of Lhcbm9 (encoding a major light harvesting polypeptide) and LhcSR1 (encoding a chlorophyll binding protein) was strongly elevated throughout the experiment, suggesting remodeling of the photosystem II light harvesting complex as well as an important function of Lhcbm9 under sulphur starvation. This study presents the first global transcriptional analysis of C. reinhardtii during hydrogen production using five major time points at Peak Oxygen, Mid Oxygen, Zero Oxygen, Mid Hydrogen and Peak Hydrogen. Keywords: Time course, sulfur deprivation, hydrogen production.
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Project description:The budding yeast, Saccharomyces cerevisiae, has emerged as an archetype of eukaryotic cell biology. Here we show that S. cerevisiae is also a model for the evolution of cooperative behavior by revisiting flocculation, a self-adherence phenotype lacking in most laboratory strains. Expression of the gene FLO1 in the laboratory strain S288C restores flocculation, an altered physiological state, reminiscent of bacterial biofilms. Flocculation protects the FLO1-expressing cells from multiple stresses, including antimicrobials and ethanol. Furthermore, FLO1+ cells avoid exploitation by non-expressing flo1 cells by self/non-self recognition: FLO1+ cells preferentially stick to one another, regardless of genetic relatedness across the rest of the genome. Flocculation, therefore, is driven by one of a few known â??green beard genesâ??, which direct cooperation towards other carriers of the same gene. Moreover, FLO1 is highly variable among strains both in expression and in sequence, suggesting that flocculation in S. cerevisiae is a dynamic, rapidly-evolving social trait. This dataset contains raw transcriptome data of flocculating cells (that express FLO1 driven by the GAL1 promoter) and non-flocculating cells (that do not express FLO1). Experiment Overall Design: Cultures of flocculating and non-flocculating cells were grown for 24 hours in YPGal medium. Subsequently, RNA was isolated from these cultures, converted into cDNA and analyzed using commercially available Afymetrix S98 arrays. For each culture, two biological replicates were analyzed.
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Project description:Background: Microalgae can make a significant contribution towards meeting global renewable energy needs in both lipid-based liquid biofuel and hydrogen biofuel. The development of energy-related products and chemicals from algae could be accelerated with improvements in systems biology tools, and recent advances in sequencing technology provide a platform for enhanced transcriptomic analyses. However, these techniques are still heavily reliant upon available genomic sequence data. We have developed a de novo sequencing, annotation, and quantitation pipeline that can be applied to unsequenced organisms for effective quantitative gene expression profiling. Chlamydomonas moewusii is a unicellular green alga capable of evolving molecular hydrogen (H2) under both dark and light anaerobic conditions, and has high hydrogenase activity that can be rapidly induced. However, to date, there is no systematic investigation of transcriptomic profiling during induction of hydrogen photoproduction in this organism. Results: In this work, we measured rates of hydrogen production and extracted RNA from samples of C. moewusii following various lengths of dark anaerobic incubation. RNA-Seq was applied to investigate transcriptomic profiles during the dark anaerobic induction of hydrogen photoproduction. One hundred fifty six million reads generated from seven samples were then used for de novo assembly after data trimming. The BlastX results against NCBI database and Blast2GO results were used to interpret the functions of the assembled 39,136 contigs, which were then used as the reference transcripts for RNA-Seq analysis. Nearly 98% transcripts had Blast hits, although more than one-third were annotated as hypothetical proteins. The expression value of RNA-Seq results was imported into statistical software for data quality control, normalization, and subsequent statistical analyses such as One-way ANOVA and K-means Clustering. Our results indicated that more transcripts were differentially expressed during the period of early and higher hydrogen photoproduction, and fewer transcripts were differentially expressed when rates of hydrogen photoproduction decreased. Conclusions: Herein, we have described a workflow to analyze RNA-Seq data without reference genome sequence information, which can be applied to the rapid development of other unsequenced microorganisms (both prokaryotic and eukaryotic) with the potential for development as fuel production strains. This study provided the first transcriptomic RNA-Seq dataset, as well as biological insights into the metabolic changes that occur concomitant with induction of hydrogen photoevolution in C. moewusii, which can help further development of this organism as a hydrogen photoproduction strain.
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