Unidentified
Ontology highlight
ABSTRACT:
PROVIDER: PRJDB19259 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| DRR670519_1.fastq.gz | Fastqsanger.gz | |||
| DRR670519_2.fastq.gz | Fastqsanger.gz | |||
| DRR670520_1.fastq.gz | Fastqsanger.gz | |||
| DRR670520_2.fastq.gz | Fastqsanger.gz | |||
| DRR670521_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 24 |
Similar Datasets
Project description:In this study we used single cell multi-omics profiling to create an atlas of the human YS to gain insights into its haematopoietic, metabolic and nutritive functions during early embryonic development. This contains CITE-seq data (surface protein and cytosolic RNA content) data from two biological replicates. Pooled lanes were demultiplexed using SoupOrCell (for alignment and demultiplexing software and version numbers, please see accompanying manuscript and protocols within this accession). Raw count files provided are directly as output by alignment software, without any quality control applied. Quality control is described in accompanying manuscript methods. Metadata by barcode are provided as supplementary tables in accompanying manuscript.
2022-12-08 | E-MTAB-11549 | biostudies-arrayexpress
Project description:In this study we used single cell multi-omics profiling to create an atlas of the human YS to gain insights into its haematopoietic, metabolic and nutritive functions during early embryonic development. This contains embryonic liver CITE-seq (surface protein and cytosolic RNA content) data from three biological replicates. Pooled lanes were demultiplexed using SoupOrCell (for alignment and demultiplexing software and version numbers, please see accompanying manuscript and protocols within this accession). Raw count files provided are directly as output by alignment software, without any quality control applied. Quality control is described in accompanying manuscript methods. Metadata by barcode are provided as supplementary tables in accompanying manuscript.
2022-12-08 | E-MTAB-11618 | biostudies-arrayexpress
Project description:In this study we used single cell multi-omics profiling to create an atlas of the human YS to gain insights into its haematopoietic, metabolic and nutritive functions during early embryonic development. This contains fetal liver CITE-seq (surface protein and cytosolic RNA content) data from six biological replicates. Pooled lanes were demultiplexed using SoupOrCell (for alignment and demultiplexing software and version numbers, please see accompanying manuscript and protocols within this accession). Raw count files provided are directly as output by alignment software, without any quality control applied. Quality control is described in accompanying manuscript methods. Metadata by barcode are provided as supplementary tables in accompanying manuscript.
2022-12-08 | E-MTAB-11613 | biostudies-arrayexpress
Project description:Multi-Omics Data of Gastric cancer cell lines (KAP220462)
| PRJDB19413 | ENA
Project description:Omics research targets biomolecules such as proteins, lipids, and metabolites, providing a comprehensive view of biological systems. The field has seen significant advancements with the development of mass spectrometry (MS). However, the accuracy of quantitative analyses is crucial due to the expansion of omics applications. Accurate comparisons depend on sample preparation and normalization methods. Some biological samples, like tissues and feces, present inherent variations that challenge accuracy. Normalization aims to reduce these variations through pre-acquisition methods that equalize biomolecule content and post-acquisition methods that adjust instrument signals. Most research has focused on single-omics data and post-acquisition normalization. However, multi-omics research, essential for understanding complex biological systems, has not thoroughly evaluated normalization methods. This study evaluates three pre-acquisition normalization methods using methanol-chloroform-water extraction to enhance multi-omics data analysis. Our multi-omics data shows that the data is significantly different depending on the normalization methods and our optimized normalization method showed that there were significant multi-omics profile changes even with young mice tissue samples. Based on these findings, we suggest sample normalization based on total protein amount to minimize the sample variation and get an accurate comparison for multi-omics.
2024-11-26 | MSV000096533 | MassIVE
Project description:Comparative analysis and its application using multi-dimensional agricultural omics information (KAP220331)
| PRJDB19384 | ENA
Project description:Omics research targets biomolecules such as proteins, lipids, and metabolites, providing a comprehensive view of biological systems. The field has seen significant advancements with the development of mass spectrometry (MS). However, the accuracy of quantitative analyses is crucial due to the expansion of omics applications. Accurate comparisons depend on sample preparation and normalization methods. Some biological samples, like tissues and feces, present inherent variations that challenge accuracy. Normalization aims to reduce these variations through pre-acquisition methods that equalize biomolecule content and post-acquisition methods that adjust instrument signals. Most research has focused on single-omics data and post-acquisition normalization. However, multi-omics research, essential for understanding complex biological systems, has not thoroughly evaluated normalization methods. This study evaluates three pre-acquisition normalization methods using methanol-chloroform-water extraction to enhance multi-omics data analysis. Our multi-omics data shows that the data is significantly different depending on the normalization methods and our optimized normalization method showed that there were significant multi-omics profile changes even with young mice tissue samples. Based on these findings, we suggest sample normalization based on total protein amount to minimize the sample variation and get an accurate comparison for multi-omics.
2024-11-26 | MSV000096533 | GNPS
Project description:Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. These emerging multi-omics techniques help the investigation of cell-type resolved gene regulatory mechanisms. Here, we developed in situ SHERRY after ATAC-seq (ISSAAC-seq), a highly sensitive and flexible single cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single cell. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from thousands of nuclei from the mouse cerebral cortex, we uncovered major and rare cell types together with their cell-type specific regulatory elements and expression profiles. Finally, we revealed distinct dynamics and relationships of transcription and chromatin accessibility during an oligodendrocyte maturation trajectory.
2022-06-15 | E-MTAB-11264 | biostudies-arrayexpress
Project description:Development of novel microbiome-driven therapies based on the meta-omics analysis (KAP220130)
| PRJDB19252 | ENA
Project description:Deep proteome data in the single-cell multi-omics analysis of single mouse oocytes.
2023-07-20 | PXD043935 |