Project description:The effects of two years' winter warming on the overall fungal functional gene structure in Alaskan tundra soil were studies by the GeoChip 4.2 Resuts showed that two years' winter warming changed the overall fungal functional gene structure in Alaskan tundra soil.
2019-03-07 | GSE127899 | GEO
Project description:Manistee National Forest ITS Fungal Sequences
Project description:Metaproteome analysis of a forest soil and a potting soil. Different protein extraction methods were compared to investigate protein extraction efficiency and compatibility with sample downstream processing.
Project description:Fungal necromass in soil represents the stable carbon pools. While fungi are known to decompose fungal necromass, how fungi decomopose melanin, remains poorly understood. Recently, Trichoderma species was found to be one of the most commonly associated fungi in soil, we have used a relevant fungal species, Trichoderma reesei, to characterized Genes involved in the decomposition of melanized and non-melanized necromass from Hyaloscypha bicolor.
2024-05-01 | GSE263516 | GEO
Project description:Fungal community in forest soil
Project description:Young Fagus sylvatica trees (approximately 7 to 8 years) were collected from a natural regeneration beech forest. The trees were excavated with intact soil cores, roots and top organic layer. The trees were then kept outdoors at the Department of Forest Botany, Georg-August-Universität Göttingen. Plants were protected from rain, and light conditions were matched to those of the natural stand using a shading net; otherwise, plants were exposed to natural climatic conditions. The soil moisture was regularly measured; plants were watered with deionized water as needed to keep soil moisture close to the original conditions. Trees was randomly relocated on a weekly basis throughout the experiment to avoid biasses caused by location or light effects. After 21 weeks, a treatment was applied to understand the physiological mechanisms of inorganic nitrogen uptake and assimilation under conditions of an inorganic nitrogen saturated forest simulation: Plants were fertilized with either a 20 mM solution of KNO3, a 20 mM solution of NH4Cl, or demineralized water (control) for 2 days. On the third day, the trees were harvested. Root tips were immediately shock-frozen in liquid nitrogen and used for RNA extraction.
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.