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.