Project description:Soon after fertilization of egg and sperm, plant genomes become transcriptionally activated and drive a series of coordinated cell divisions to form the basic body plan during embryogenesis. Early embryonic cells rapidly diversify from each other, and investigation of the corresponding gene expression dynamics can help elucidate underlying cellular differentiation programs. However, current plant embryonic transcriptome datasets either lack cell-specific information or have RNA contamination from surrounding non-embryonic tissues. We have coupled fluorescence-activated nuclei sorting together with single-nucleus mRNA sequencing to construct a gene expression atlas of Arabidopsis thaliana early embryos at single-cell resolution. In addition to characterizing cell-specific transcriptomes, we found evidence that distinct epigenetic and transcriptional regulatory mechanisms operate across emerging embryonic cell types. These datasets and analyses, as well as the approach we devised, are expected to facilitate the discovery of molecular mechanisms underlying pattern formation in plant embryos.
Project description:Small RNA libraries were constructed from total RNA from Jasminum sambac plants exhibiting virus-like symptoms. After sequencing, small RNAs were assembled into contigs with MetaVelvet and assembled contigs were aligned against the NR database of NCBI using BLASTx. Top hits that reported a virus as subject were considered putative viral sequences. Based on such alignments, the whole genome of a virus, we tentatively name Jasmine Virus H was recovered and cloned. Two more small RNA libraries were made in a confirmatory experiment. One from Jasminum sambac and another one from Nicotiana benthamiana plants infected with the newly-cloned virus. The small RNA libraries were aligned against the full-length sequence of Jasmine Virus H to determine the spacial distribution of virus-derived small RNAs along the virus genome.
Project description:Pattern discovery algorithms are methods for discovering recurrent, non-random motifs widely used in the analysis of biological sequences. Many algorithms exist but few comparisons have been made amongst them. We systematically profile eight representative methods at multiple parameter settings across 174 diverse experimental datasets, including ten novel ChIP-on-chip datasets. We executed 16,777 pattern discovery analyses to assess prediction accuracy, CPU usage and memory consumption. For 144 datasets we developed a gold-standard using machine-learning algorithms; cross-validation was used for the remaining datasets. Performance was highly disparate, with median accuracy ranging from 32% to 96%. Importantly we were unable to replicate previously reported algorithm-rankings, emphasizing the need to use many and diverse experimental datasets. We found deterministic algorithms like Projection and Oligo/Dyad had the highest prediction accuracy. Computational efficiency was not linearly related to dataset size and becomes critical: some algorithms are intractably slow on large datasets. This work provides the first combined assessment of the CPU, memory, and prediction accuracies of pattern discovery algorithms on real experimental datasets.
2009-11-24 | GSE15370 | GEO
Project description:Identification of unknown plant viruses
Project description:70mer probes were designed to detect plant viruses infection in genus level. This microarray platform is able to detect 169 plant virus species of 13 virus genera.
2010-06-30 | GSE15837 | GEO
Project description:Next generation sequencing for detection and discovery of plant viruses and viroids: comparison of two approaches