Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR18683150_1.fastq.gz | Fastqsanger.gz | |||
| SRR18683150_2.fastq.gz | Fastqsanger.gz | |||
| SRR18683151_1.fastq.gz | Fastqsanger.gz | |||
| SRR18683151_2.fastq.gz | Fastqsanger.gz | |||
| SRR18683152_1.fastq.gz | Fastqsanger.gz |
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Similar Datasets
Project description:Protein kinases (PKs) are involved in plant growth and stress responses, and constitute one of the largest superfamilies due to numerous gene duplications. However, limited PKs have been functionally described in pecan, an economically important nut tree. Here, the comprehensive identification, annotation and classification of the entire pecan kinome was reported. A total of 967 PK genes were identified from pecan genome, and further classified into 20 different groups and 121 subfamilies using the kinase domain sequences, which were verified by the phylogenetic analysis. The receptor-like kinase (RLK) group contained 565 members, which constituted the largest group. Gene duplication contributed to the expansion of pecan kinome, 169 duplication events including 285 PK genes were found, and Ka/Ks ratio revealed they experienced strong negative selection. GO functional analysis indicated majority PKs involved in molecular functions and biological processes. The RNA-Seq data of PK genes in pecan were further analyzed at subfamily level, and different PK subfamilies performed various expression patterns across different conditions or treatments, suggesting PK genes in pecan involved in multiple biological functions and stress responses. Taken together, this study provided insight into the expansion, evolution and function of pecan PKs. Our findings regarding expansion, expression and co-expression analyses could lay a good foundation for future research to understand the roles of pecan PKs, and find the key candidate genes more efficiently.
2021-07-05 | GSE179336 | GEO
Project description:RNA-seq analysis of developing pecan (Carya illinoinensis) embryo
| PRJNA360120 | ENA
Project description:The first GSSM of V. vinifera was reconstructed (MODEL2408120001). Tissue-specific models for stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases.
2024-09-02 | MODEL2408160001 | BioModels
Project description:Kilian2024 - Immune cell dynamics in Cue-Induced Extended Human Colitis Model
Single-cell technologies such as scRNA-seq and flow cytometry provide critical insights into immune cell behavior in inflammatory bowel disease (IBD). However, integrating these datasets into computational models for dynamic analysis remains challenging. Here, Kilian et al., (2024) developed a deterministic ODE-based model that incorporates these technologies to study immune cell population changes in murine colitis. The model parameters were optimized to fit experimental data, ensuring an accurate representation of immune cell behavior over time. It was then validated by comparing simulations with experimental data using Pearson’s correlation and further tested on independent datasets to confirm its robustness. Additionally, the model was applied to clinical bulk RNA-seq data from human IBD patients, providing valuable insights into immune system dynamics and potential therapeutic strategies.
Figure 4c, obtained from the simulation of human colitis model is highlighted here.
This model is described in the article:
Kilian, C., Ulrich, H., Zouboulis, V.A. et al. Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease. npj Syst Biol Appl 10, 69 (2024). https://doi.org/10.1038/s41540-024-00395-9
Abstract:
Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.
This model was curated during the Hackathon hosted by BioMed X GmbH in 2024.
2025-03-07 | MODEL2502180001 | BioModels
Project description:To investigate the mechanisms of cell competition, we analysed the molecular differences between wild type and mild CncC (Nrf2) over-expressing causing the loser phenotype in Drosophila wing discs. We then compared the genes differentially expressed in these conditions to our previously identified ‘loser signature’ which reveal a significant overlap. From this later comparison, we raised a list of 121 up-regulated genes to conduct a cell competition screen using PECAn (Pipeline for Enhanced Clonal Analysis) for automating analysis of mosaic experiment.
2023-04-06 | GSE181165 | GEO