Identification of transcriptional networks controlling leaf sheath growth in Sorghum bicolor.

OBJECTIVES: The objective of this data set was to identify transcriptional networks that control elongation of seedling leaf sheaths in the C4 grass Sorghum bicolor. One motivation was that leaf sheaths are a primary constituent of stems in grass seedlings; therefore, genes that control growth of this organ are important contributors to successful transition from the seedling stage to the mature plant stage and, ultimately, crop success. Since diurnal rhythms contribute to regulation of signaling networks responsible for growth, a time course representing the late afternoon and early evening was anticipated to pinpoint important control genes for stem growth. Ultimately, the expected outcome was discovery of transcript networks that integrate internal and external signals to fine tune leaf sheath growth and, consequently, plant height. DATA DESCRIPTION: The data set is RNAseq profiling of upper leaf sheaths collected from wild type Sorghum bicolor (BTx623 line) plants at four-hour intervals from 12.5 h after dawn to 20 h after dawn. Global transcript levels in leaves were determined by deep sequencing of mRNA from four individual seedlings at each time point. This data set contains sequences representing the spectrum of mRNAs from individual genes. This data set enables detection of significant changes in gene-level expression caused by the progression of the day from late afternoon to the middle of the night. This data set is useful to identify gene expression networks regulating growth in the leaf sheath, an organ that is a major contributor to the sorghum seedling stem and defines seedling height.

Impact of the sickle mutant and temperature on the structure of transcripts and RNAs from Arabidopsis thaliana.

OBJECTIVES: The objective of this data set was to identify how interaction between temperature and the sickle-3 (sic-3) mutant alters the global messenger RNA (mRNA) content of Arabidopsis thaliana seedlings. The motivation was discovery of atypical mRNA splice variants in sic-3 that differed with seedling growth temperature. The expected outcome was identification of mRNA splice variants altered by sic-3, temperature, or the combination of temperature and genotype. DATA DESCRIPTION: The data set is RNAseq profiling of Arabidopsis (Col-0 ecotype) wild type and sic-3 seedlings under 16 °C or 28 °C. A comprehensive view of global mRNA sequences and their content was captured by deep sequencing of RNA pools made from sets of seedlings sampled every 4 h over 20 h. This data set contains sequences representing the spectrum of mRNA splice variants from individual genes, as well as from mRNA-related sequences like spliced introns. This data set enables detection of significant changes in gene-level expression and relative levels of mRNA splice variants caused by the different growth temperatures, the sic-3 mutation or both factors. This data set is useful to study production of mRNA splice variants and other mRNA-related RNAs in a range of plant species because Arabidopsis is a model plant.

72-h diurnal RNA-seq analysis of fully expanded third leaves from maize, sorghum, and foxtail millet at 3-h resolution.

OBJECTIVES: The purpose of this data set is to capture the complete diurnal (i.e., daily) transcriptome of fully expanded third leaves from the C4 panacoid grasses sorghum (Sorghum bicolor), maize (Zea mays), and foxtail millet (Setaria italica) with RNA-seq transcriptome profiling. These data are the cornerstone of a larger project that examined the conservation and divergence of gene expression networks within these crop plants. This data set focuses on temporal changes in gene expression to identify the network architecture responsible for daily regulation of plant growth and metabolic activities. The power of this data set is fine temporal resolution combined with continuous sampling over multiple days. DATA DESCRIPTION: The data set is 72 individual RNA-seq samples representing 24 time course samples each for sorghum, maize, and foxtail millet plants cultivated in a growth chamber under equal intervals of light and darkness. The 24 samples are separated by 3-h intervals so that the data set is a fine scale 72-h analysis of gene expression in the leaves of each plant type. FASTQ files from Illumina sequencing are available at the National Center for Biotechnology Information Sequence Read Archive.

A System Architecture for Efficient Transmission of Massive DNA Sequencing Data.

The DNA sequencing data analysis pipelines require significant computational resources. In that sense, cloud computing infrastructures appear as a natural choice for this processing. However, the first practical difficulty in reaching the cloud computing services is the transmission of the massive DNA sequencing data from where they are produced to where they will be processed. The daily practice here begins with compressing the data in FASTQ file format, and then sending these data via fast data transmission protocols. In this study, we address the weaknesses in that daily practice and present a new system architecture that incorporates the computational resources available on the client side while dynamically adapting itself to the available bandwidth. Our proposal considers the real-life scenarios, where the bandwidth of the connection between the parties may fluctuate, and also the computing power on the client side may be of any size ranging from moderate personal computers to powerful workstations. The proposed architecture aims at utilizing both the communication bandwidth and the computing resources for satisfying the ultimate goal of reaching the results as early as possible. We present a prototype implementation of the proposed architecture, and analyze several real-life cases, which provide useful insights for the sequencing centers, especially on deciding when to use a cloud service and in what conditions.