Project description:ATAC-seq of 79 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). Moreover, ATAC-seq of CD34+ HSPCs from 3 healthy donors are included. ATAC-seq was performed as described (Buenrostro et al., 2013) with a modification in the lysis buffer to reduce mitochondrial DNA contamination. Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011050 (dataset).
Project description:RNA preparation for sequencing was performed as described in (Fiorenza et al., 2016). Hippocampi derived from three different animals were pooled together for each sample and three independent samples were sequenced. RNA-seq datasets from Kdm5c null mice and control littermates at their home cages (HC) and after 1 h of novelty exploration (NE) were generated by next-generation sequencing (RNA-seq) using Illumina HiSeq 2500 apparatus, where the configuration was in conditional samples paired-end and in conventional samples single-end. RNA-Seq libraries were prepared from total RNA using poly(A) enrichment of the mRNA (mRNA-Seq). The libraries were stranded and multiplexed. Quality control of the raw data was performed with FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Library sizes can be found in Suppl Table 9 of the manuscript. RNA-Seq libraries were mapped to reference genome (Ensembl GRCm38) using STAR v2.5.0c (Dobin A. et al. 2013), and with corresponding gene model annotation (Mus_musculus.GRCm38.83.gtf). Samtools (v1.3) was used to further process BAM files (Li et al., 2009). For calling of differentially expressed genes (DEG), mapped reads were counted with HTSeq v0.6.1 (Anders et al., 2014) at gene level and count tables were analysed using DeSeq2 (v1.10.0) R-package (Love et al., 2014) and bioconductor v3.2. For consideration of differentially regulated genes between conditions, we used adjusted p-value < 0.1 as indicated in the manuscript.
Project description:Chromatin immunoprecipitation (ChIP) experiments were conducted as previously described (Ito et al, 2013) using anti-H3K4me3 (Millipore, #07-473), anti-H3K4me1 (Abcam, #ab8895), or anti-Kdm5C (Iwase et al., 2016). Hippocampi derived from two different animals were pooled together for each sample and two independent biological replicates per condition were sequenced according to manufacturer instructions in a HiSeq2500 apparatus (Illumina, Inc). Information on library preparation method, size of the libraries, and mapping to reference genome can be found in Supplementary Material accompanying the manuscript. ChIP-seq reads were aligned to the mouse genome (Mus_musculus.GRCm.38.83) using bowtie2 (v2.2.9) (Langmead and Salzberg, 2012) and further processed using samtools (v1.3.1) (Li et al., 2009). Peak calling was performed using MACS2 (v2.1.0) (Zhang et al., 2008) with default parameters except for Kdm5c that were as follows: -q 0.01 --nomodel --extsize 131 --broad --broad-cutoff 0.1. Read counts on aligned bam files were performed using Rsubread (v1.22.3) (Liao et al., 2014). Differential peak methylation analysis for H3K4me3 chromatin mark was performed using DESeq2 (v1.10.0) (Love et al., 2014) of the bioconductor suite (Huber et al., 2015) in the R (v3.3) statistical computing platform. For consideration of differentially methylated regions between conditions, we used adjusted p-value < 0.05 as indicated in the manuscript.
Project description:As described in our paper "Aspm knockout ferret reveals an evolutionary mechanism governing cerebral cortical size" (Johnson et al., Nature 2018), we used the standard Drop-seq method and analysis of Macosko et al. (2015) to capture, sequence, and analyze mRNA from single cells from Aspm wild-type, heterozygous, and knock-out littermate ferrets at embryonic day 35. Bulk reference samples were processed using standard Illumina mRNA-seq library prep and sequencing protocols, and the samples were described previously (Johnson, Wang et al., Nature Neurosci 2015)
Project description:We have quantified gene expression in five tissues (brain, heart, kidney, liver and testis) from humans, chimpanzees and rhesus macaques using the Illumina NlaIII Digital Gene Expression (DGE) protocol. This dataset extends a previous microarray study by Khaitovich et al. (Khaitovich et al. 2005) with the rhesus macaque outgroup and complements other previously generated tissue transcriptome profiles from primates (Enard et al. 2002; Khaitovich et al. 2006; Somel et al. 2009; Babbitt et al. 2010; Blekhman et al. 2010; Wetterbom et al. 2010). contributor: Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, D-04103 Leipzig, Germany
Project description:We profile single cells from patients with colorectum cancer using Chromium 3’ and 5’ single-cell RNA-sequencing. Patients EXT001, EXT009, and EXT012 from the KUL dataset were first analyzed by Lee et al., 2020, and the raw data are available in ArrayExpress under the accession codes E-MTAB-8410 and E-MTAB-8107. Patients EXT018, EXT048, EXT113, and EXT121 from KUL dataset were previously analyzed by Joanito et al., 2022. The raw data of those patients are available in EGA under the accession codes EGAD00001008584 and EGAD00001008585.
Project description:In order to identify relevant, molecularly defined subgroups in Multiple Myeloma (MM), gene expression profiling (GEP) was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/ GMMG-HD4 trial using Affymetrix GeneChip U133 plus 2.0 arrays. Hierarchical clustering identified 10 distinct subgroups. Using this dataset as training data, a prognostic signature was built. The dataset consists of 282 CEL files previously used in the hierarchical clustering study of Broyl et al (Blood, 116(14):2543-53, 2010) outlined above. To this set 8 CEL-files/gene expression profiles were added. Using this set of 290 CEL-files, a prognostic signature of 92 genes (EMC-92-genesignature) was generated by supervised principal components analysis combined with simulated annealing (Kuiper et al.).