Project description:This is a Random Forest algorithm-based machine learning model to predict lncRNAs from coding mRNAs in plant transcriptomic data. The model assigns 1 for coding sequences and 2 for long non-coding sequences. The prediction is performed using a combination of Open Reading Frame (ORF) based, Sequence-based and Codon-bias features. Users need to download the curated ONNX model and also need to convert the sequences into feature matrix as mentioned in PLIT paper (Deshpande et al. 2019) to make predictions on sequences from Zea Mays sequence data.
Project description:This dataset represents woody plants recorded in 16 1-ha forest plots in an elevational gradient in Madidi National Park, Bolivia, ranging from lowland Amazonian moist forest and lowland dry forest to the treeline of the Andean Altiplano. This work was carried out by David Henderson and Jonathan Myers (Washington University in St. Louis), Sebastian Tello (Missouri Botanical Garden and University of Missouri, St. Louis), and Brian Sedio (University of Texas at Austin and Smithsonian Tropical Research Institute).
Project description:An Infinium microarray platform (GPL28271, HorvathMammalMethylChip40) was used to generate DNA methylation data from blood samples from yellow-bellied marmots (Marmota flaviventris). DNA methylation data from n=159 blood samples. All samples were collected as part of a long-term study of a free-living population of yellow-bellied marmots in the Gunnison National Forest, Colorado (USA), where marmots were captured and blood samples collected biweekly during the their active season (May to August). Genomic DNA was extracted with Qiagen DNeasy blood and tissue kit.
Project description:Understanding of mechanisms of resistance of forest trees against microbial pathogens is an essential prerequisite for the development of sustainable forestry practices and for the improvement of commercially-grown trees via either conventional breeding or rational genetic engineering. We have studied the transcriptional response of Scots pine trees to Heterobasidion annosum infection under field conditions. By comparing responses of trees to wounding and to fungal inoculation we could identify a set of genes that were specifically responding to fungal infection. We have also investigated a contribution of Scots pine antimicrobial protein Sp-AMP2 to the host antimicrobial defense to evaluate the potential of Sp-AMP genes as molecular markers for resistance breeding.