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SRP051084
Mus musculus
10 Downloadable Samples
Illumina HiSeq 2000
Description
Purpose: The aim of this study is to identify genes that are under the transcriptional control of the epigenetic modifier Smchd1 in mouse lymphoma cell lines. Methods: Total RNA was extracted using QIAGEN RNeasy Minikit from sorted lymphoma cell lines derived from mice either wild-type or null for Smchd1. 1µg total RNA was used to generate sequencing libraries for whole transcriptome analysis with Illumina's TruSeq RNA Sample Preparation Kit v2 as per standard protocols. Libraries were sequenced on HiSeq 2000 with Illumina TruSeq SBS Kit v3-HS reagents as either 100 bp single-end or paired-end reads at the Australian Genome Research Facility (AGRF), Melbourne. Reads were aligned to the mouse reference genome mm10 and mapped to known genomic features at the gene level using the Rsubread package (version 1.10.5) (Liao et al. 2013). Mapped reads were then summarized into gene-level counts using FeatureCounts (Liao et al. 2014). Overall design: Total RNA was extracted and purified from each cell line and their transcriptomes analysed by RNA-Seq.
Publication Title
Transcriptional profiling of the epigenetic regulator Smchd1.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 6 Downloadable Samples
- Illumina HiSeq 2000
Description
Purpose: The aim of this study is to identify genes that are under the transcriptional control of the epigenetic regulator Smchd1 in neural stem cells (NSCs) derived from E14.5 mouse brain Methods: Total RNA was extracted using an AllPrep DNA/RNA Mini Kit (Qiagen) from cultured neural stem cells derived from male mouse E14.5 brains either wild-type or null for Smchd1. 1 µg total RNA was used to generate sequencing libraries for whole transcriptome analysis with Illumina's TruSeq RNA Sample Preparation Kit v2 as per standard protocols. Libraries were sequenced on HiSeq 2000 with Illumina TruSeq SBS Kit v3-HS reagents as either 100 bp single-end or paired-end reads at the Australian Genome Research Facility (AGRF), Melbourne. Reads were aligned to the mouse reference genome mm10 and mapped to known genomic features at the gene level using the Rsubread package (version 1.10.5) (Liao et al. 2013). Mapped reads were then summarized into gene-level counts using FeatureCounts (Liao et al. 2014). Overall design: Total RNA was extracted and purified from each cell line and their transcriptomes analyzed by RNA-Seq.
Publication Title
Transcriptional profiling of the epigenetic regulator Smchd1.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 6 Downloadable Samples
- Illumina HiSeq 2000
Description
Hypomorphic mutations of PAX5 occur in one third of B-progenitor acute lymphoblastic leukemias (B-ALLs), however their functional consequences remain undefined. Here we employ advanced transgenic RNAi in mice to suppress endogenous Pax5 expression in the hematopoietic compartment in vivo, which co-operates with activated STAT5 to induce B-ALL. In this model, restoring endogenous Pax5 expression in established B-ALL induces transcriptional and immunophenotypic changes reminiscent of normal B cell differentiation, disabling leukemia-initiating capacity and ultimately causing leukemia clearance. Overall design: Comparison of leukemias harvested from triplicate untreated mice versus triplicate Dox-treated (3 days) mice
Publication Title
limma powers differential expression analyses for RNA-sequencing and microarray studies.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 24 Downloadable Samples
- IlluminaHiSeq2000
Description
This experiment aims to identify the biological pathways and diseases associated with the cytokine Interleukin 13 (IL-13) using gene expression measured in peripheral blood mononuclear cells (PBMCs). Overall design: The experiment comprised of samples obtained from 3 healthy donors. The expression profiles of in vitro IL-13 stimulation were generated using RNA-seq technology for 3 PBMC samples at 24 hours. The transcriptional profiles of PBMCs without IL-13 stimulation were also generated to be used as controls. An IL-13R-alpha antagonist (Redpath et al. Biochemical Journal, 2013) was introduced into IL-13 stimulated PBMCs and the gene expression levels after 24h were profiled to examine the neutralization of IL-13 signaling by the antagonist.
Publication Title
Combining multiple tools outperforms individual methods in gene set enrichment analyses.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 6 Downloadable Samples
Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Multiple Myeloma (MM) is an hematological malignancy. MM cells are resistant to X-ray irradiations. We irradiated RPMI 8226 cancer cells with C-ions, which are more energetic than X-ray irradiations. We found that MM cells, RPMI 8226, are also resistant to C-ion irradiations.
Publication Title
HIF-1α and rapamycin act as gerosuppressant in multiple myeloma cells upon genotoxic stress.
Sample Metadata Fields
Cell line
View Samples- Mus musculus
- 3 Downloadable Samples
- Illumina HiSeq 2000
Description
Purpose: The aim of this study is to determine the relative expresson levels of mRNA transcripts in wild type platelets Methods: Total RNA was extracted and purified from purified platelets from BALB/c male mice (3 independent samples). Platelet purification was performed as described in Josefsson EC et al, Journal of Experimental Medicine (2011) 208:2017-31. Total RNA (100 ng) was used to generate sequencing libraries for whole transcriptome analysis following Illumina’s TruSeq RNA v2 sample preparation protocol. Completed libraries were sequenced on HiSeq 2000 with TruSeq SBS Kit v3- HS reagents (Illumina) as 100 bp paired-end reads at the Australian Genome Research Facility (AGRF), Melbourne. Reads were aligned to the mouse reference genome mm10 and counts for known genes were obtained using the Rsubread package (version 1.18.0) (Liao et al. 2013; Liao et al. 2014). Overall design: Total RNA was extracted and purified from purified platelets from BALB/c male mice (3 independent samples per population).
Publication Title
Loss of PUMA (BBC3) does not prevent thrombocytopenia caused by the loss of BCL-XL (BCL2L1).
Sample Metadata Fields
Age, Specimen part, Cell line, Subject
View Samples- Homo sapiens
- 5 Downloadable Samples
- NextSeq 500
Description
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and brings new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods available in a systematic manner. Here, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms and varying levels of biological noise. We used this dataset to compare different protocols and data analysis methods. We found that different protocols have different data quality and ERCC spike-in works independently to endogenous RNA. We found significant differences in the results from the methods compared and we associated the results with data characteristics to identify methods that perform well in different situations. Our dataset and analysis provide a valuable resource for algorithm selection in different biological settings. Overall design: our experiment utilized the 3 human lung adenocarcinoma cell lines H2228, H1975 and HCC827. The experiment included mixtures of RNA and single cells from these cell lines. For the single cell designs, the three cell lines were mixed equally and processed by 10X chromium, Drop-seq and CEL-seq2, referred to as sc_10X, sc_Drop-seq and sc_CEL-seq2 respectively in analysis that follows. For the mixture designs, we used plate-based protocols to mix and dilute samples in 2 different ways. 9 cell mixtures from the 3 cell lines were sorted in different combinations in the cell mixture experiment and data were generated by CEL-seq2, the material after pooling from 384 wells were subsampled in either 1/9 or 1/3 to simulate cells of different sizes, with different PCR product clean up ratios ranging from 0.7 to 0.9, referred to as cellmix1 to cellmix4. For the cell mixture experiment, we also sorted wells with 10 times more cells (90 cells) to provide a pseudo bulk reference for each mixture (referred to as cellmix5). Distinct RNA mixtures which were diluted down to create single cell equivalents (ranging from 3.75, 7.5, 15 to 30 pg per well) were generated using CEL-seq2 and SORT-seq (referred to as RNAmix_CEL-seq2 and RNAmix_Sort-seq. This is the 9 cell mixture dataset.
Publication Title
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 3 Downloadable Samples
- NextSeq 500
Description
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and brings new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods available in a systematic manner. Here, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms and varying levels of biological noise. We used this dataset to compare different protocols and data analysis methods. We found that different protocols have different data quality and ERCC spike-in works independently to endogenous RNA. We found significant differences in the results from the methods compared and we associated the results with data characteristics to identify methods that perform well in different situations. Our dataset and analysis provide a valuable resource for algorithm selection in different biological settings. Overall design: our experiment utilized the 5 human lung adenocarcinoma cell lines H2228, H1975, A549, H838 and HCC827. For the single cell designs, the five cell lines were mixed equally and processed by 10X chromium and CEL-seq2, referred to as sc_10X_5cl, and sc_CEL-seq2_5cl respectively in analysis that follows. For CEL-seq2, three plates were sorted and processed.
Publication Title
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Sample Metadata Fields
Subject
View Samples- Homo sapiens
- 1 Downloadable Sample
- NextSeq 500
Description
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and brings new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods available in a systematic manner. Here, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms and varying levels of biological noise. We used this dataset to compare different protocols and data analysis methods. We found that different protocols have different data quality and ERCC spike-in works independently to endogenous RNA. We found significant differences in the results from the methods compared and we associated the results with data characteristics to identify methods that perform well in different situations. Our dataset and analysis provide a valuable resource for algorithm selection in different biological settings. Overall design: our experiment utilized the 3 human lung adenocarcinoma cell lines H2228, H1975 and HCC827. The experiment included mixtures of RNA and single cells from these cell lines. For the single cell designs, the three cell lines were mixed equally and processed by 10X chromium, Drop-seq and CEL-seq2, referred to as sc_10X, sc_Drop-seq and sc_CEL-seq2 respectively in analysis that follows. For the mixture designs, we used plate-based protocols to mix and dilute samples in 2 different ways. 9 cell mixtures from the 3 cell lines were sorted in different combinations in the cell mixture experiment and data were generated by CEL-seq2, the material after pooling from 384 wells were subsampled in either 1/9 or 1/3 to simulate cells of different sizes, with different PCR product clean up ratios ranging from 0.7 to 0.9, referred to as cellmix1 to cellmix4. For the cell mixture experiment, we also sorted wells with 10 times more cells (90 cells) to provide a pseudo bulk reference for each mixture (referred to as cellmix5). Distinct RNA mixtures which were diluted down to create single cell equivalents (ranging from 3.75, 7.5, 15 to 30 pg per well) were generated using CEL-seq2 and SORT-seq (referred to as RNAmix_CEL-seq2 and RNAmix_Sort-seq. This is the RNAmix_CEL-seq2 dataset.
Publication Title
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 1 Downloadable Sample
- NextSeq 500
Description
Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years and brings new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq to address various biological questions. However, the current lack of gold-standard benchmarking datasets makes it difficult for researchers to evaluate the performance of the many methods available in a systematic manner. Here, we designed and generated a cross-platform benchmark dataset that has in-built truth in various forms and varying levels of biological noise. We used this dataset to compare different protocols and data analysis methods. We found that different protocols have different data quality and ERCC spike-in works independently to endogenous RNA. We found significant differences in the results from the methods compared and we associated the results with data characteristics to identify methods that perform well in different situations. Our dataset and analysis provide a valuable resource for algorithm selection in different biological settings. Overall design: our experiment utilized the 3 human lung adenocarcinoma cell lines H2228, H1975 and HCC827. The experiment included mixtures of RNA and single cells from these cell lines. For the single cell designs, the three cell lines were mixed equally and processed by 10X chromium, Drop-seq and CEL-seq2, referred to as sc_10X, sc_Drop-seq and sc_CEL-seq2 respectively in analysis that follows. For the mixture designs, we used plate-based protocols to mix and dilute samples in 2 different ways. 9 cell mixtures from the 3 cell lines were sorted in different combinations in the cell mixture experiment and data were generated by CEL-seq2, the material after pooling from 384 wells were subsampled in either 1/9 or 1/3 to simulate cells of different sizes, with different PCR product clean up ratios ranging from 0.7 to 0.9, referred to as cellmix1 to cellmix4. For the cell mixture experiment, we also sorted wells with 10 times more cells (90 cells) to provide a pseudo bulk reference for each mixture (referred to as cellmix5). Distinct RNA mixtures which were diluted down to create single cell equivalents (ranging from 3.75, 7.5, 15 to 30 pg per well) were generated using CEL-seq2 and SORT-seq (referred to as RNAmix_CEL-seq2 and RNAmix_Sort-seq. This is the RNAmix_CEL-seq2 dataset.
Publication Title
scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.
Sample Metadata Fields
Specimen part, Subject
View Samples