FAANG pig RNA-seq and RRBS datasets
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR789169.fastq.gz | Fastqsanger.gz | |||
| ERR789170.fastq.gz | Fastqsanger.gz | |||
| ERR789171.fastq.gz | Fastqsanger.gz | |||
| ERR789172.fastq.gz | Fastqsanger.gz | |||
| ERR789173.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 106 |
Similar Datasets
Project description:We reported the retinal transcriptome and methylome profiles of spaceflight and habitat control mice. The retinal transcrtiome was generated including 764 million paired-end 75x2 bp RNA-seq data. The methylome was generated including 788 million 75-bp RRBS DNA methylation data. We identified 177 differentially expressed genes (DEGs) induced by spaceflight based on the RNA-seq dataset, and 643 differentially methylated genes (DMGs) based on RRBS dataset. Functional enrichement analysis based on DEGs and DMGs revealed that spaceflight altered extracellular matrix component (EMC), cell to cell junction, inflammation pathway, wound-healing process, etc. Integration analysis based on transcriptome and methylome suggested that modifications on DNA methylation were correlated with transcriptome changes for several significant biological processes and/or pathways.
2021-06-30 | GSE159771 | GEO
Project description:We reported the kidney transcriptome and methylome profiles of rat Brown Norway (BN) and Sprague Dawley (CD) strains at late pregnancy (day 18/21) compared to non-pregnancy. The renal transcriptome was characterized from 326 million single-end 100bp RNA-seq data. The methylome dataset included 234 million 75-bp RRBS DNA methylation reads. We identified 297 differentially expressed genes (DEGs) induced by pregnancy in CD rats and 174 DEGs induced in BN rats, based on the RNA-seq dataset. 237 of the 297 DEGs in CD pregnant rats and 114 of the 174 DEGs in BN pregnant rats are strain specific, indicating differential adaptation to pregnancy. Methylome data reveals that strain differences is the major factor in determining methylation differences. We also found that pregant BN showed higher methylation than non-pregnant BN at transcription starter site (TSS) and promoter regions. Functional analysis based on DEGs and DMGs revealed that pregnant BN rats showed renal transcriptomic changes that induce vitamin D deficiency and renal pro-inflammatory gene expression that can partly explain pregnancy-specific proteinuria. Integrated analysis based on transcriptome and methylome suggested that modifications on DNA methylation were correlated with transcriptome changes for several significant biological processes and/or pathways.
2022-06-22 | GSE173380 | GEO
Project description:RNA-seq datasets from hippocampus samples collected from 4 week old control and PRRSV infected piglets.
| PRJEB11625 | ENA
Project description: Not available
| S-BSST1277 | biostudies-other
Project description: Not available
| S-BSST1278 | biostudies-other
Project description:The ubiquity of RNA-seq has led to many methods that use RNA-seq data to analyze variations in RNA splicing. However, available methods are not well suited for handling heterogeneous and large datasets. Such datasets scale to thousands of samples across dozens of experimental conditions, exhibit increased variability compared to biological replicates, and involve thousands of unannotated splice variants resulting in increased transcriptome complexity. We describe here a suite of algorithms and tools implemented in the MAJIQ v2 package to address challenges in detection, quantification, and visualization of splicing variations from such datasets. Using both large scale synthetic data and GTEx v8 as benchmark datasets, we assess the advantages of MAJIQ v2 compared to existing methods. We then apply MAJIQ v2 package to analyze differential splicing across 2,335 samples from 13 brain subregions, demonstrating its ability to offer insights into brain subregion-specific splicing regulation.
| S-EPMC9984406 | biostudies-literature
Project description:Single-cell RNA sequencing has led to unprecedented levels of data complexity. Although several computational platforms are available, performing data analyses for multiple datasets remains a significant challenge. Here, we provide a comprehensive analytical protocol to interrogate multiple datasets on SingCellaR, an analysis package in R. This tool can be applied to general single-cell transcriptome analyses. We demonstrate steps for data analyses and visualization using bespoke pipelines, in conjunction with existing analysis tools to study human hematopoietic stem and progenitor cells. For complete details on the use and execution of this protocol, please refer to Roy et al. (2021).
| S-EPMC8980964 | biostudies-literature
Project description:Since CNVs play a vital role in genomic studies, it is an imperative need to develop a comprehensive, more accurate and higher resolution porcine CNV map with practical significance in follow-up CNV functional analyses To detect CNV of pigs, we performed high density aCGH data of diverse pig breeds in the framework of the pig draft genome sequence (Sscrofa10.2)
2013-05-15 | GSE46847 | GEO
Project description:Since CNVs play a vital role in genomic studies, it is an imperative need to develop a comprehensive, more accurate and higher resolution porcine CNV map with practical significance in follow-up CNV functional analyses To detect CNV of pigs, we performed high density aCGH data of diverse pig breeds in the framework of the pig draft genome sequence (Sscrofa10.2)
2015-06-29 | GSE49299 | GEO
Project description:Single-cell RNA sequencing (scRNA-seq) technologies allow numerous opportunities for revealing novel and potentially unexpected biological discoveries. scRNA-seq clustering helps elucidate cell-to-cell heterogeneity and uncover cell subgroups and cell dynamics at the group level. Two important aspects of scRNA-seq data analysis were introduced and discussed in the present review: relevant datasets and analytical tools. In particular, we reviewed popular scRNA-seq datasets and discussed scRNA-seq clustering models including K-means clustering, hierarchical clustering, consensus clustering, and so on. Seven state-of-the-art scRNA clustering methods were compared on five public available datasets. Two primary evaluation metrics, the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI), were used to evaluate these methods. Although unsupervised models can effectively cluster scRNA-seq data, these methods also have challenges. Some suggestions were provided for future research directions.
| S-EPMC7549635 | biostudies-literature