title: Scr_sh_Input_r2

description: CHIPSEQ

tag: developmental stage

value: shScrambled Ctr


title: P3_Blood_CD8_0013

status: Public on Jul 13 2021

type: SRA

city: Noda

first: Hiroyasu

last: Aoki

state: Chiba

address: 2669 Yamazaki

department: Research Institute for Biomedical Sciences

country: Japan

institute: Tokyo University of Science

library_source: transcriptomic

data_processing:
Library strategy: TCR-seq
Adapter trimming and quality filtering of sequencing data were performed by using Cutadapt and PRINSEQ-0.20.4. Sequencing data were processed by MiXCR-3.0.5. In MiXCR, Filtered reads were aligned to reference human TCR V/D/J sequences with the following parameters: -vParameters.geneFeatureToAlign = VTranscript -vjAlignmentOrder = JThenV, then identical sequences were assembled and grouped in clones with PCR and sequencing error correlation with the following parameters: -badQualityThreshold=10, –separateByV=true, --only-productive=true, –region-of-interest=CDR3. The Variable (V) and Joining (J) segment of TCRs were represented in IMGT gene nomenclature.
List of final clones were analyzed by VDJtools-1.2.1. Then, the sequencing reads of sample was normalized to the cell count in each sample for PBMC, and 50,000 reads in tumor biopsy samples by “DownSample” command of VDJtools. T-cell clones were determined as TCR reads with the same TCR Variable (V) segment, Joining (J) segment and CDR3 nucleotide sequence.
If multiple biopsies are obtained at the same time point in patient, TCRseq and subsequent data processing were performed separately, then clonotype tables are pooled by “PoolSamples” command of VDJtools.
Genome_build: IMGT reference sequences of human TRB
Supplementary_files_format_and_content: text files in VDJtools format include read count and frequency, complementarity determining region 3 (CDR3) nucleotide and amino acid sequences, Variable(V)/ Diversity(D)/ Joining(J) segment names, and V, D and J segment boundaries within CDR3 nucleotide sequence of individual clones

molecule: polyA RNA

organism: Homo sapiens

source_name: peripheral blood

extract_protocol:
10 ml of patient’s blood was taken at four time point (P1; day0, P2; day21-27, P3; day39-55, P4; disease progression). PBMCs were prepared by density gradient centrifugation by Ficoll®-Paque Plus (GE Healthcare Japan). The PBMCs were separated in two samples, then CD8+ T cell and CD4+ T cell were enriched by using CD8 Microbeads or CD4 Microbeads for human (Milteny Biotec Inc., Bergisch Gladbach, Germany). The purity was checked by FACSCanto II (BD Biosciences) with staining with CD8a-APC (clone RPA-T8, TONMO Biosciences, San Diego, CA, USA ), CD3-FITC (clone UCHT1, TONBO Biosciences, San Diego, CA, USA), CD4-PE (clone SK3, BioLegend), and Ghost Dye Red780 (TONBO Biosciences). The purity of enriched cells was routinely more than 95%.
Tumor biopsy was conducted on before (day0) and after the treatment (day39-day55). Presence or absence of tumor tissue in biopsies was determined pathologically. Tumor biopsies were homogenized in TRIzol (Ambion, Carlsbad, CA, USA). RNA was extracted from each sample using the RNeasy mini kit (QIAGEN, Hilden, Germany, # 74106) and amounts and purity measured with the Agilent 2200 TapeStation (Agilent Technologies, Santa Clara, CA, USA). Five-micrograms of total RNA was diluted with 1 mL of cell lysis buffer and used for TCR sequencing.
TCR sequencing libraries for next-generation sequencing were prepared according to the previous report (GSE115425) with some modifications. PolyA RNAs were isolated according to a previous report with some modifications (GSE110711). To amplify the TCR cDNA containing complementarity determining region 3 (CDR3), nested PCR of the TCR locus was performed as follows. Beads with containing cDNA was resuspended with the first PCR mixture comprised 0.8 μL of 10 μM primer mix (trP1, Trac_ex and Trbc_ex), 4.2 μL of dH2O, and 5 μL of KAPA Hifi Hotstart ReadyMix (KAPA Biosystems, Wilmington, MA, USA, #KK2602). The thermal cycling conditions were programmed as follows: denaturation at 95°C for 3 min, 5 cycles of denaturation for 20 sec at 98°C, annealing for 15 sec at 65°C and extension for 30 sec at 72°C, followed by a final extension at 72°C for 2 min. Next, 10 μL of the first-PCR products were used for purification with an Agencort AM Pure XP kit (Bexkman-Coulter, CA, USA, #A63881) at a 0.7:1 ratio of beads to sample, and eluted with 15 μL of DW. The second PCR mixture consisted of 1.75 μL of 10 μM primer mix (5’ WTA, Trac_in-Bio, and Trbc_in-Bio), 10.75 μL of the template and 12.5 μL of KAPA Hifi Hotstart ReadyMix. The thermal cycling conditions were the same as the first PCR except for the cycle number; 21 cycles. Next, 25 μL of the second-PCR products were purified using Agencort AM Pure XP kit (Bexkman-Coulter) at a 0.7:1 ratio of beads to sample, and eluted in 18 μL of DW. Fragmentasion and adaptor ligation were performed using 10-20ng of second-PCR product as a template with NEBNext FS DNA Library Prep Kit (New England Biolabs, #E7805) with some modifications; NEBNext Adaptor for Illumina in “Adaptor Ligation” step was substituted by 5uM Adaptor P1, and the reaction volume was 1/4 of the recommended in all steps. Adaptor-ligated DNA was purified using Agencort AM Pure XP kit (Bexkman-Coulter) at a 0.8:1 ratio of beads to sample, and eluted in 20 μL of DW. The third PCR was carried out using barcoded primers to enrich the TCR cDNA flanked with sequencing adapters. The third PCR mixture consisted of 1 μL of 10 μM trP1 primer, 1 μL of 10 μM IonA-BC-Trbc primer, 3 μL of template and 5 μL of NEBNext Ultra II Q5 Master Mix (accessory of NEBNext FS DNA Library Prep Kit). The thermal cycling conditions were programmed as follows: denaturation at 98°C for 30 sec, 10 cycles of denaturation for 10 sec at 98°C, annealing and extension for 1min 15 sec at 65°C, followed by a final extension at 65°C for 5 min. The PCR product was purified and subjected to size selection using Agencort AM Pure XP kit (Bexkman-Coulter) at a 1.0:1 ratio of beads to sample, and eluted with 20 μL of Tris-HCl (pH 8.0). Amplified TCR libraries were quantified using a KAPA SYBR Fast qPCR Kit (KAPA Biosystems, #KK4621) and size distribution was analyzed by agarose electrophoresis and SYBR Gold staining (Thermo Fisher Scientific, #S11494).   Final TCR libraries, whose lengths were 200–300 base pairs, were pooled and sequenced using an Ion 540 Kit Chef, an Ion 540 Chip kit, and an Ion Genestudio S5 Sequencer (Thermo Fisher Scientific, # A27759, #A27766, # A38194) according to the manufacturer’s instructions, except the input library concentration (65 pM) and flow number (500).

treatment_protocol: Patients with advanced gastrointestinal cancers with high TMB confirmed by Guardant360 (Guardant Health, Inc. Redwood City, CA), a 74-gene sequencing circulating tumor DNA assay, received intravenous nivolumab monotherapy of 360 mg every 3 weeks.

tag: cell type

value: CD4- CD8+ T cells

tag: age

tag: Sex

value: male

tag: cancer type

value: esophageal

tag: tissue

value: peripheral blood

tag: date


title: tot_30m-mqsr2

city: Boston

first: Peter

last: Culviner

state: MA

address: 677 Huntington Ave

department: IID

country: USA

institute: Harvard School of Public Health

data_processing:
The inline barcode was clipped from read 2 and used to separate reads into their source samples using cutadapt (v3.2). Adapter sequences were also trimmed at this stage – the provided fastq files are both demultiplexed and trimmed.
Reads were mapped to the MG1655 genome using bowtie2 (v2.4.1) using the --very-sensitive argument set.
For processed files ending in “5_prime_ends”: for each proper pair, 1 count was added to the 5’-end of the fragment (corresponding to a free RNA 5’-end). For processed files ending in “density”: for each proper pair, 1 count was added to every position aligning to and between the two reads.
Genome_build: U00096.3
Supplementary_files_format_and_content: Processed files are gzipped csv files. The first column corresponds to the forward strand and the second two the reverse strand. Each row corresponds to the counts at a genomic position from 1 to len(genome). Counts are defined in the data processing steps. Integer counts are raw and not normalized to library depth.

molecule: total RNA

organism: Escherichia coli

source_name: bacterial liquid culture

growth_protocol: Escherichia coli was grown in M9 (10x stock made with 64 g/L Na2HPO4-7H2O, 15 g/L KH2PO4, 2.5 g/L NaCl, 5.0 g/L NH4Cl) medium supplemented with 0.1% casamino acids, 0.4% glycerol, 2 mM MgSO4, and 0.1 mM CaCl2. Single colonies on an LB + 0.4% glucose plate were picked and grown overnight in M9 media + 0.4% glucose on a roller drum then grown at 37°C on a Synergy H1 plate reader (BioTek) in 24-well plates using double orbital rotation at 307 CPM for the course of the experiment. Overnight cultures grown with 0.4% glucose were back-diluted to 0.02 OD600 in 1.5 mL of M9 media with added glucose and grown to OD 0.2 – 0.35 in 24-well plates. Cultures were pelleted by spinning 5 minutes on a benchtop centrifuge at 4000 G. Pellets were washed once with M9 media lacking glucose, respun, and resuspended in M9 media lacking glucose. On a fresh 24-well plate, individual cultures were back-diluted to 0.1 and returned to the plate reader. After 30 minutes of growth, toxin was induced by adding arabinose to 0.2% and cells were returned to the plate reader. After induction for either 5 or 30 minutes, 1 mL of the culture was mixed with 110 uL of stop solution (95% ethanol and 5% phenol), spun for 30 seconds at 13k RPM to pellet cells, and flash frozen in liquid nitrogen.

extract_protocol:
RNA was extracted using Trizol (Invitrogen) and Direct-zol RNA MiniPrep kit (Zymo).
See publication for complete details. For samples with rRNA depletion, rRNA was removed by hybridization to custom biotinylated oligos. Next, if samples were fragmented, RNA was mixed with reverse transcription buffer and heated to 95°C for 3 minutes before placing on ice. All samples were then reverse transcribed using reverse transcription primers with a 5’-overhang with a PCR amplification handle and 1 of 24 inline barcodes. As cDNA now contained a barcode, samples were pooled and those with a tRNA size selection step were loaded onto a denaturing TBE urea gel and the tRNA region of the gel was extracted for cDNA purification. For all other samples, pools were cleaned up with a modified AMPure XP bead protocol to remove leftover primers. Next, all samples were subjected to a second round of reverse transcription with a template switching oligo to add a second PCR amplification handle to the 3’-end of the cDNA. After a bead cleanup, samples were PCR amplified with primers adding the i5 and i7 handles for Illumina sequencing. PCR reactions were size selected with a 2-sided bead cut and sequenced using a custom read 1 primer on a NovaSeq instrument. The combination of the i7 index and the inline read 2 barcode were used to separate out biological samples into their own fastq files using cutadapt.

tag: strain

value: MG1655 pBAD30-mqsr

tag: induction time

value: 30 minutes

tag: rrna depletion

value: No

tag: trna size selection

tag: fragmentation time

value: 0 minutes


title: P1_Blood_CD8_0023

value: female

value: colorectal


title: tot_30m-mqsr1


title: C646_Brd4_r2

city: Daejeon

first: Seung-Kyoon

last: Kim

address: 99 Daehak-ro, Yuseong-gu,

department: Convergent Bioscience and Informatics

country: South Korea

institute: Chungnam National University

description: CHIPSEQ

library_source: genomic

data_processing:
The sequencing reads were mapped to the mm10 UCSC annotation using TOPHAT (Trapnell, et al., 2009) with the parameters (-a 8 -m 0 -I 500000 -p 8 -g 20 --library-type fr-firststrand --no-novel-indels  --segment-mismatches 2 for RNA-Seq.
For GRO-Seq, the raw fastq reads were trimmed using cutadapt with parameters -a AAAAAAAAAAAAAAAAAAAA -z -e 0.10 -f fastq -m 32 (Chae et al., 2015).
The CHIPSEQ FASTQ reads were aligned to UCSC's mm10 genome using Bowtie2 with default parameters (Langmead, et al., 2012). Reads with mapping quality less than 10 were removed using SAMtools (Li, et al., 2009) for ChIP-seq. To normalize the differences induced from sequencing depths, the mapped reads were “down sampled” to the lowest number of the uniquely mapped reads with duplicates followed by duplicate reads removal using our inhouse perl scripts. The downsampled, duplicates removed mapped reads were extended to the fragment estimate from MACS using inhouse perl and R scripts. Wig files followed by bigWig files were generated through perl scripts for visualization on UCSC using these extended reads. The coverage values in bigWig files were normalized to 10million.
RPKM calculations were done using the HOMER package by normalizing to 10 million reads
Tracks for visualization on UCSC browser are generated using HOMER.
Genome_build: mm10
Supplementary_files_format_and_content: Strand specific bigWig files for visualizing expression levels on a genome browser.

molecule: genomic DNA

organism: Mus musculus

source_name: Cortical neurons

growth_protocol: Primary cortical neurons from E16/18 mice were cultured in Neurobasal media with B-27 and Glutamax until DIV7

extract_protocol:
RNA was extracted using Trizol reagent for mRNA- or GRO-seq. Protein was crosslinked with DNA, cross-linked samples incubated with antibodies, and genomic DNA was purified for ChIP-seq.
mRNA-seq libraries were prepared using the TruSeq RNA Library Preparation Kit (Illumina) according to the manufacturer's instructions. GRO-seq libraries was prepared as previously described (C. G. Danko, Molecular cell 50, 212-222, 2013). ChIP-seq libraries were prepared using NEBNext ChIP-Seq Library Prep Master Mix Set (NEB).

treatment_protocol: DIV7 Neurons neurons were made quiescent overnight in 1 μM tetrodotoxin (TTX). In the BDNF experiment, neurons were incubated with BDNF (10 ng/ml) for different length of time. For JQ1 experiment, TTX treated neurons were pre-treated with -/+ JQ1 (0.5 µM) for 5 min, then followed by treating with 55 mM KCl for different length of time. For C646 experiment, TTX treated neurons were pre-treated with C646 (20 µM) for 48 hr, then followed by treating with 55 mM KCl for 30 min.

value: Cortical neurons

tag: treatment


title: WT_Brd4_r2

description: CHIPSEQ

value: Vehicle


title: 10umCad_input_rep1

city: Aurora

first: Joshua

middle: C

last: Black

state: CO

address: 12801 East 17th Ave

department: Pharmacology

institute: University of Colorado

data_processing:
ChIP-seq experiments were aligned to a custom genome combining human hg38 and drosophila dr3 genomes using Bowtie2 (2.3.4.1).
Sam files were transformed into bam files and sorted using samtools (version 1.8).
Reads aligned to chr1-22 and chrX were isolated and an equal number of reads were randomly selected from each biological replicate for H3K27me3 and Input.
Replicates were merged and indexed using samtools (version 1.8).
Alignment coverage bigwig files were created with deeptools bamCoverage (version 3.1.3) using CPM with 50 bp binning.
Genome_build: HG38
Supplementary_files_format_and_content: bigwig

source_name: 10uM cadmium resistance

growth_protocol: MDA-MB-231 female breast cancer cell lines were maintained at 37°C in Dulbecco’s Modified Essential Medium (DMEM) with 10% FB essence (VWR), 1% penicillin/streptomycin, and L-glutamine.

extract_protocol:
Cells were crosslinked by adding 1% formaldehyde to media for 10 minutes at 37°C. Crosslinking was stopped by addition of Glycine to 0.125M and incubating for 5 minutes at room temperature. Cells were washed with PBS and collected and centrifuged. Cells were resuspended in cellular lysis buffer (5mM PIPES pH 8, 85 mM KCl, 0.5% NP40), incubated on ice for 5 minutes and centrifuged. Samples were resuspended in nuclear lysis buffer (50 mM Tris pH 8, 10 mM EDTA pH 8, 1% SDS) and sonicated for 45 minutes using 30 second on/off cycle repeats at 70% intensity (using a Qsonica Q800R). 10 g crosslinked DNA and 2 g crosslinked Drosophila control spike-in chromatin was used per immunoprecipitation. Chromatin was pulled down overnight using Invitrogen Protein A Dynabeads with antibodies for H3 (Cell Signaling Technology), H3K27me3 (Abcam) or IgG (Cell Signaling Technology). Beads were washed twice with IP buffer (16.7 mM Tris pH 8, 1.2 mM EDTA pH 8, 167 mM NaCl, 0.1% SDS, 1.84% Triton X100), once with TSE buffer (20 mM Tris pH 8, 2 mM EDTA pH 8, 500 mM NaCl, 1% Triton X100, 0.1% SDS) and LiCl buffer (100mM Tris pH 8, 500 mM LiCl, 1% deoxycholic acid, 1% NP40), and twice with TE (10mM Tris pH 8, 1 mM EDTA pH 8). DNA was eluted from beads with proteinase K and purified with Promega Wizard SV PCR clean up kit. DNA was analyzed with qPCR and compared to 10% input collected prior to pulldown.
ChIP-seq libraries were prepared using the Illumina NEBNext Ultra II ChIP-Seq sample kit, according to the manufacturer’s protocol. Libraries were validated using the Agilent Technologies 2100 Bioanalyzer.

treatment_protocol: Cadmium-resistant cell (MDA-MB-231cadR) cell lines were selected by passage in medium containing CdSO4. The CdSO4 concentration was increased by increments of 5-25 μM after 10 passages at the current concentration and when cell count increased at least 1.8 times average daily over at least four passages consistently. Cells used in this study were adapted to CdSO4 containing medium for 7 months up to 10 μM CdSO4.

value: 10uM cadmium resistance

tag: chip target

value: input

tag: cell line


title: tot_30m-rele2

value: MG1655 pBAD30-rele


title: 10umCad_H3K27me3_rep3


title: tot_30m-mazf2

value: MG1655 pBAD30-mazf


title: P3_Blood_CD4_0013

value: CD4+ CD8- T cells


title: C646_Brd3_r2

description: CHIPSEQ


title: P2_Blood_CD8_0013

value: CD4- CD8+ T cells


title: WT_Brd3_r2

description: CHIPSEQ


title: 10umCad_H3K27me3_rep1


title: tot_30m-mazf1


title: 10umCad_H3K27me3_rep2


title: C646_Brd2_r2

description: CHIPSEQ


title: tot_30m-higb2

value: MG1655 pBAD30-higb


title: tot_30m-higb1


title: WT_Brd2_r2

description: CHIPSEQ


title: tot_30m-hica2

value: MG1655 pBAD30-hica


title: single-cell_RNA-seq_P55_KD(CoV2-)

city: Paris

first: Mickael

middle: Mathieu

last: Menager

address: 24 Boulevard du Montparnasse

country: France

institute: Institut Imagine

data_processing:
Sequencing reads were demultiplexed and aligned to the human reference genome (GRCh38, release 98, built from ensembl sources), using the CellRanger Pipeline v3.1.
Unfiltered RNA UMI counts were loaded into Seurat v3.1 (Stuart et al., 2019) for quality control, data integration and downstream analyses. Apoptotic cells and empty sequencing capsules were excluded by filtering out cells with fewer than 500 features or a mitochondrial content higher than 20%.
Data from each sample were log-normalized and scaled, before batch correction using Seurat’s FindIntegratedAnchors. For computational efficiency, anchors for integration were determined using all control samples as reference and patient samples were projected onto the integrated controls space.
On this integrated dataset, we computed the principal component analysis on the 2000 most variable genes. UMAP was carried out using the 20 most significant PCs, and community detection was performed using the graph-based modularity-optimization Louvain algorithm from Seurat’s FindClusters function with a 0.8 resolution.
Cell types labels were assigned to resulting clusters based on a manually curated list of marker genes as well as previously defined signatures of the well-known PBMC subtypes (Monaco et al., 2019).  Despite filtering for high quality cells, five clusters out of the twenty-six stood out as poor quality clusters and were removed from further analysis, namely: one erythroid-cell contamination; one low UMI cluster from a single control ; two clusters of proliferating cells originating from a patient with EBV co-infection  and one megakaryocytes cluster. In total 152,201 cells were kept for further analysis.
After extraction of high quality cluster, reclustering was performed
Genome_build: GRCh38, release 98
Supplementary_files_format_and_content: h5 files contain the raw count matrix for each sample
Supplementary_files_format_and_content: the rds file contains the Seurat object of all integrated samples

source_name: PBMC

extract_protocol:
Peripheral blood samples were collected on lithium heparin. PBMCs were isolated by density gradient centrifugation (2200 rpm without break for 30 minutes) using Ficoll (Eurobio Scientific, Les Ulis, France). After centrifugation, cells were washed with Phosphate-buffered saline (PBS) (Thermo Fisher scientific, Illkirch, France). The pellet was resuspended in PBS and cells were centrifuged at 1900 rpm for 5 minutes. Finally, the PBMC pellet was frozen in a medium containing 90% of Fetal Bovine Serum (FBS) (Gibco, Thermo Fisher scientific, Illkirch, France) and 10% of dimethyl sulfoxide (DMSO) (Sigma Aldrich, St. Quentin Fallavier, France). PBMCs were thawed according to 10X Genomics protocol.
The SC-RNA-SEQ libraries were generated using Chromium Single Cell 3′ Library & Gel Bead Kit v.3 (10x Genomics) according to the manufacturer’s protocol. Briefly, cells were counted, diluted at 1000 cells/µL in PBS+0,04% and 20,000 cells were loaded in the 10x Chromium Controller to generate single-cell gel-beads in emulsion. After reverse transcription, gel-beads in emulsion were disrupted. Barcoded complementary DNA was isolated and amplified by PCR. Following fragmentation, end repair and A-tailing, sample indexes were added during index PCR

value: PBMC

tag: disease group

value: KD (CoV2-)

tag: age group

value: pediatric

tag: batch


title: single-cell_RNA-seq_P53_KD(CoV2-)

tag: cell type


title: CBP_sh3_Brd4_new_r2

description: CHIPSEQ

tag: developmental stage

tag: knockdown

value: shCBP


title: P1_Tumor_0013

source_name: tumor biopsy

value: tumor biopsy

tag: age

tag: Sex

value: male

tag: cancer type

value: esophageal


title: tot_30m-hica1

tag: strain

value: 30 minutes

value: No

value: 0 minutes


title: P1_Blood_CD4_0013

tag: tissue

value: peripheral blood

tag: date


title: single-cell_RNA-seq_P47_KD(CoV2-)


title: tot_30m-ev2

value: MG1655 pBAD30-ev

tag: induction time

tag: rrna depletion

tag: trna size selection


title: Scr_sh_Brd4_new_r2

description: CHIPSEQ

value: Cortical neurons

value: shScrambled Ctr


title: P4_Blood_CD8_0011

value: CD4- CD8+ T cells


title: tot_30m-ev1

tag: fragmentation time


title: CBP_sh3_Brd3_new_r2

description: CHIPSEQ


title: P2_Blood_CD4_0013

value: CD4+ CD8- T cells


title: P4_Blood_CD4_0011


title: Scr_sh_Brd3_new_r2

description: CHIPSEQ


title: tot_30m-chpb2

value: MG1655 pBAD30-chpb


title: single-cell_RNA-seq_P39_MIS-C_MYO(CoV2+)

value: PBMC

value: MIS-C_MYO (CoV2+)


title: P1_Blood_CD8_0013


title: tot_30m-chpb1


title: CBP_sh3_Brd2_new_r2

description: CHIPSEQ


title: P3_Tumor_0011


title: P3_Blood_CD8_0011


title: Scr_sh_Brd2_new_r2

description: CHIPSEQ


title: P3_Blood_CD4_0011


title: single-cell_RNA-seq_P37_MIS-C_MYO(CoV2+)

value: pediatric


title: single-cell_RNA-seq_P33_MIS-C_MYO(CoV2+)


title: WT_Input_r1

description: CHIPSEQ

tag: treatment

value: Vehicle


title: single-cell_RNA-seq_P23_MIS-C_MYO(CoV2+)

tag: disease group


title: Scr_sh_Input_r1

description: CHIPSEQ


title: rz_5m_trna-yoeb2

description: rRNA depleted total RNA

value: MG1655 pBAD30-yoeb

value: 5 minutes

value: Yes


title: rz_5m_trna-yoeb1

description: rRNA depleted total RNA


title: P2_Blood_CD8_0011

value: male

value: esophageal


title: rz_5m_trna-yhav2

description: rRNA depleted total RNA

value: MG1655 pBAD30-yhav

value: 0 minutes


title: single-cell_RNA-seq_P20_MIS-C(CoV2+)

value: MIS-C (CoV2+)

tag: age group


title: C646_Brd4_r1

description: CHIPSEQ

value: Cortical neurons


title: single-cell_RNA-seq_P43_MIS-C_MYO(CoV2+)

tag: batch


title: rz_5m_trna-yhav1

description: rRNA depleted total RNA


title: P1_Tumor_0011

tag: cell type

value: tumor biopsy


title: single-cell_RNA-seq_P17_MIS-C(CoV2+)


title: WT_Brd4_r1

description: CHIPSEQ

tag: developmental stage


title: single-cell_RNA-seq_P13_Acute-Inf(CoV2+)

value: Acute-Inf (CoV2+)


title: C646_Brd3_r1

description: CHIPSEQ


title: single-cell_RNA-seq_P12_Acute-Inf(CoV2+)


title: rz_5m_trna-yafo1

description: rRNA depleted total RNA

value: MG1655 pBAD30-yafo


title: single-cell_RNA-seq_P31_MIS-C_MYO(CoV2+)


title: P4_Blood_CD8_0007

value: CD4- CD8+ T cells

value: colorectal

value: peripheral blood


title: single-cell_RNA-seq_P8_Acute-Inf(CoV2+)

value: PBMC

value: pediatric


title: P2_Blood_CD4_0011

value: CD4+ CD8- T cells


title: C646_Brd2_r1

description: CHIPSEQ


title: rz_5m_trna-rele2

description: rRNA depleted total RNA

value: MG1655 pBAD30-rele

value: 5 minutes


title: single-cell_RNA-seq_P5_Acute-Inf(CoV2+)


title: rz_5m_trna-rele1

description: rRNA depleted total RNA

value: Yes


title: single-cell_RNA-seq_P3_Acute-Inf(CoV2-)

value: Acute-Inf (CoV2-)


title: single-cell_RNA-seq_C39

value: CTL

value: adult


title: P4_Blood_CD4_0007

value: male


title: P3_Blood_CD4_0007


title: CBP_sh3_Brd4_new_r1

description: CHIPSEQ

value: Cortical neurons

tag: knockdown

value: shCBP


title: P3_Blood_CD8_0007

tag: age


title: Scr_sh_Brd4_new_r1

description: CHIPSEQ

value: shScrambled Ctr


title: rz_5m_trna-yafo2

description: rRNA depleted total RNA

value: MG1655 pBAD30-yafo

value: 0 minutes


title: rz_5m_trna-mazf2

description: rRNA depleted total RNA

value: MG1655 pBAD30-mazf


title: P1_Blood_CD8_0011

value: CD4- CD8+ T cells

tag: Sex

value: esophageal

value: peripheral blood


title: P2_Blood_CD8_0007

tag: cancer type

value: colorectal


title: single-cell_RNA-seq_C32

value: PBMC

value: pediatric


title: WT_Brd3_r1

description: CHIPSEQ

tag: strain

value: Vehicle


title: CBP_sh3_Brd3_new_r1

description: CHIPSEQ


title: P1_Blood_CD4_0011

value: CD4+ CD8- T cells

tag: tissue


title: rz_5m_trna-mazf1

description: rRNA depleted total RNA

value: 5 minutes

value: Yes


title: single-cell_RNA-seq_C31

value: CTL

value: adult


title: P2_Blood_CD4_0007

value: male

tag: date


title: WT_Brd2_r1

description: CHIPSEQ

value: Cortical neurons


title: rz_5m_trna-mqsr2

description: rRNA depleted total RNA

value: MG1655 pBAD30-mqsr

tag: induction time


title: P1_Tumor_0007

value: tumor biopsy


title: single-cell_RNA-seq_C30_bis


title: P1_Blood_CD8_0007


title: Scr_sh_Brd3_new_r1

description: CHIPSEQ

value: shScrambled Ctr


title: CBP_sh3_Brd2_new_r1

description: CHIPSEQ

value: shCBP


title: rz_5m_trna-mqsr1

description: rRNA depleted total RNA

tag: rrna depletion

value: 0 minutes


title: P1_Blood_CD4_0007

value: peripheral blood


title: single-cell_RNA-seq_C30


title: rz_5m_trna-higb2

description: rRNA depleted total RNA

value: MG1655 pBAD30-higb

tag: trna size selection


title: rz_5m_trna-higb1

description: rRNA depleted total RNA

tag: fragmentation time


title: single-cell_RNA-seq_C29

value: PBMC


title: rz_5m_trna-hica2

description: rRNA depleted total RNA

value: MG1655 pBAD30-hica


title: rz_5m_trna-hica1

description: rRNA depleted total RNA


title: P4_Blood_CD8_0005

value: CD4- CD8+ T cells

value: female

value: gastric


title: BDNF_2h_Brd4

description: CHIPSEQ

tag: treatment

value: BDNF


title: single-cell_RNA-seq_C28

tag: disease group

value: CTL

value: pediatric


title: rz_5m_trna-ev2

description: rRNA depleted total RNA

value: MG1655 pBAD30-ev

value: 5 minutes

value: Yes


title: P4_Blood_CD4_0005

value: CD4+ CD8- T cells


title: P3_Tumor_0005

value: tumor biopsy


title: BDNF_30m_Brd4

description: CHIPSEQ

value: Cortical neurons


title: single-cell_RNA-seq_C27_bis

tag: age group


title: rz_5m_trna-ev1

description: rRNA depleted total RNA

value: 0 minutes


title: single-cell_RNA-seq_C27

tag: batch


title: BDNF_10m_Brd4

description: CHIPSEQ

tag: cell type


title: Unst_Brd4

description: CHIPSEQ

tag: developmental stage


title: Unst_Input

description: CHIPSEQ


title: P3_Blood_CD8_0005

value: peripheral blood


title: P3_Blood_CD4_0005


title: single-cell_RNA-seq_C26

value: adult


title: rz_5m_trna-chpb1

description: rRNA depleted total RNA

value: MG1655 pBAD30-chpb


title: rz_frg_5m-yoeb2

description: rRNA depleted total RNA

value: MG1655 pBAD30-yoeb

value: No

value: 3 minutes


title: BDNF_30m_Brd3

description: CHIPSEQ


title: RNA_P3_Tumor_2_0001

data_processing:
Adapter trimming and quality filtering of sequencing data were performed using cutadapt v1.18. Filtered reads were mapped to hg38 Refseq RNA using Bowtie2-3.4.2 with the following parameters: -t -N 1 -D 200 -R 20 -L 20 -i S,1,0.50 --nofw. Tag numbers of each gene were quantified as the expression level of each gene.
Between-sample normalization was performed against raw count data using Microsoft R open 3.6.1 (https://mran.microsoft.com/open/) and TCC package (DDD-D method). Genes with fold change ≥4 and maximum expression ≥20 were identified as differentially expressed genes.
Co-expressed gene modules among differentially expressed genes were detected using the WGCNA package. For WGCNA, the power value was 19, the merge threshold value was 0.1, the threshold value for the output of co-expression interactions was 0.25, and other calculation settings were set to defaults. The genes in PR associated gene modules detected using WGCNA were further clustered into upregulated and downregulated genes in PR patients using hclust function of stats package.
Among genes upregulated in PR (1,606 genes), genes included in the term “T cell activation” in GO biological processes (GO) was extracted using Cytoscape 3.7.2 with ClueGO plugin (v2.5.5). We used versions of the GO term database and KEGG pathway term database that were current on Feb 27, 2019. If multiple biopsies are obtained at the same time point in patient, RNAseq and subsequent data processing were performed separately.
Genome_build: GRCh38
Supplementary_files_format_and_content: tab-delimited text files include raw tag-count values for each sample

extract_protocol:
Tumor biopsy was conducted on before (day0) and after the treatment (day39-day55). Presence or absence of tumor tissue in biopsies was determined pathologically. Tumor biopsies were homogenized in TRIzol (Ambion, Carlsbad, CA, USA). RNA was extracted from each sample using the RNeasy mini kit (QIAGEN, Hilden, Germany, # 74106) and amounts and purity measured with the Agilent 2200 TapeStation (Agilent Technologies, Santa Clara, CA, USA). Five-micrograms of total RNA was diluted with 1 mL of cell lysis buffer and used for RNA sequencing.
PolyA RNAs were isolated according to a previous report with some modifications (GSE110711). Beads with containing cDNA was resuspended with whole transcript amplification mixture comprised 2 μL of 10 μM primer mix (trP1 and 3’WTA), 10.5 μL of dH2O, and 12.5 μL of KAPA Hifi Hotstart ReadyMix (KAPA Biosystems). The thermal cycling conditions were programmed as follows: denaturation at 95°C for 3 min, 12 cycles of denaturation for 20 sec at 98°C, annealing for 15 sec at 65°C and extension for 5 min at 72°C, followed by a final extension at 72°C for 5 min. Next, 25 μL of the first-PCR products were used for purification with an Agencort AM Pure XP kit (Bexkman-Coulter) at a 0.7:1 ratio of beads to sample, and eluted with 15 μL of DW. Fragmentasion and adaptor ligation were performed using 10-60ng of second-PCR product as a template with NEBNext FS DNA Library Prep Kit (New England Biolabs) with some modifications; NEBNext Adaptor for Illumina in “Adaptor Ligation” step was substituted by 1.5uM CS1 adaptor, and the reaction volume was 1/4 of the recommended in all steps. Adaptor-ligated DNA was added with 7.125 uL of 10x TE, then purified using Agencort AM Pure XP kit (Bexkman-Coulter) at a 0.4:1 ratio of beads to sample to remove large fragments, a 0.7:1 ratio of beads to sample to remove smaller fragments, and eluted with 20 μL of Tris-HCl (pH 8.0). The second PCR was carried out using barcoded primers to enrich the TCR cDNA flanked with sequencing adapters. The second PCR mixture consisted of 2.5 μL of 10 μM trP1 primer, 2.5 μL of 10 μM IonA-BC primer, 7.5 μL of template and 12.5 μL of NEBNext Ultra II Q5 Master Mix (accessory of NEBNext FS DNA Library Prep Kit). The thermal cycling conditions were programmed as follows: denaturation at 98°C for 30 sec, 11 cycles of denaturation for 10 sec at 98°C, annealing and extension for 1min 15 sec at 65°C, followed by a final extension at 65°C for 5 min. The PCR product was purified and subjected to size selection using Agencort AM Pure XP kit (Bexkman-Coulter) at a 0.7:1 ratio of beads to sample, and eluted with 20 μL of Tris-HCl (pH 8.0). Amplified TCR libraries were quantified using a KAPA SYBR Fast qPCR Kit (KAPA Biosystems, #KK4621) and size distribution was analyzed by agarose electrophoresis and SYBR Gold staining (Thermo Fisher Scientific, #S11494).   Final RNAseq libraries, whose lengths were 200–300 base pairs, were pooled and sequenced using an Ion 540 Kit Chef, an Ion 540 Chip kit, and an Ion Genestudio S5 Sequencer (Thermo Fisher Scientific, # A27759, #A27766, # A38194) according to the manufacturer’s instructions, except the input library concentration (65 pM) and flow number (550).

value: male

value: esophageal


title: Scr_sh_Brd2_new_r1

description: CHIPSEQ

tag: knockdown

value: shScrambled Ctr


title: P56_KD(CoV2-)

data_processing:
Paired-end RNA-seq reads were aligned to the human Ensembl genome [GRCh38.91] reference using Hisat2 (v2.0.4)(Kim et al., 2019) and counted using featureCounts from the Subread R package.
The raw count matrix was analyzed using DESeq2 (version 1.28.1) (Love et al., 2014). No pre-filtering was applied to the data.
Standard DESeq2 protocol (DESeqDataSetFromMatrix + DESeq) was performed on the raw counts with “~Sex + groups” as the design formula .
For visualisation and scoring purposes, the counts were first vst (variance stabilizing transformations ) transformed and the batch effect (in the form of the sex of the patients) was then corrected using removeBatchEffect from the limma package
The vst transformed were log transformed (log2(vst+1))
Genome_build: GRCh38.91
Supplementary_files_format_and_content: vst_normalised_matrix.csv contains the vst-transformed, log2-normalized counts for each sample

extract_protocol:
Peripheral blood samples were collected on lithium heparin. PBMCs were isolated by density gradient centrifugation (2200 rpm without break for 30 minutes) using Ficoll (Eurobio Scientific, Les Ulis, France). After centrifugation, cells were washed with Phosphate-buffered saline (PBS) (Thermo Fisher scientific, Illkirch, France). The pellet was resuspended in PBS and cells were centrifuged at 1900 rpm for 5 minutes. Finally, the PBMC pellet was frozen in a medium containing 90% of Fetal Bovine Serum (FBS) (Gibco, Thermo Fisher scientific, Illkirch, France) and 10% of dimethyl sulfoxide (DMSO) (Sigma Aldrich, St. Quentin Fallavier, France).
RNA was extracted from PBMCs following the instructions of RNeasyR Mini kit (Qiagen, Courtaboeuf, France). The optional step with the DNase was performed. RNA integrity and concentration were assessed by capillary electrophoresis using Fragment Analyzer (Agilent Technologies). RNAseq libraries were prepared starting from 100 ng of total RNA using the Universal Plus mRNA-Seq kit (Nugen) as recommended by the manufacturer.

value: KD (CoV2+)

value: pediatric

value: PBMC


title: P2_Blood_CD8_0005

value: CD4- CD8+ T cells

tag: age

value: female

value: gastric


title: rz_5m_trna-chpb2

description: rRNA depleted total RNA

value: 5 minutes

value: Yes


title: P2_Blood_CD4_0005

value: CD4+ CD8- T cells

tag: Sex

value: peripheral blood


title: rz_frg_5m-yhav2

description: rRNA depleted total RNA

value: MG1655 pBAD30-yhav


title: P53_KD(CoV2-)


title: P1_Tumor_0005

value: tumor biopsy

tag: cancer type


title: Unst_Brd3

description: CHIPSEQ

tag: strain

value: Cortical neurons


title: single-cell_RNA-seq_C24

value: CTL

value: adult


title: rz_frg_5m-yoeb1

description: rRNA depleted total RNA

value: MG1655 pBAD30-yoeb

value: No

value: 3 minutes


title: rz_frg_5m-yhav1

description: rRNA depleted total RNA


title: P52_KD(CoV2-)

value: KD (CoV2+)

value: pediatric


title: P1_Blood_CD8_0005

value: CD4- CD8+ T cells

tag: tissue


title: P55_KD(CoV2-)

value: PBMC


title: rz_frg_5m-yafo2

description: rRNA depleted total RNA

value: MG1655 pBAD30-yafo


title: BDNF_2h_Brd3

description: CHIPSEQ

value: BDNF


title: BDNF_2h_Brd2

description: CHIPSEQ


title: BDNF_10m_Brd3

description: CHIPSEQ


title: P51_KD(CoV2-)


title: P1_Blood_CD4_0005

value: female

value: gastric

tag: date


title: rz_frg_5m-yafo1

description: rRNA depleted total RNA

tag: induction time

value: 5 minutes

value: Yes


title: BDNF_30m_Brd2

description: CHIPSEQ


title: P4_Blood_CD8_0003

value: male

value: esophageal

value: peripheral blood


title: P49_KD(CoV2-)


title: BDNF_10m_Brd2

description: CHIPSEQ

value: Cortical neurons


title: Unst_Brd2

description: CHIPSEQ


title: P48_KD(CoV2-)


title: rz_frg_5m-rele2

description: rRNA depleted total RNA

value: MG1655 pBAD30-rele

tag: rrna depletion


title: rz_frg_5m-rele1

description: rRNA depleted total RNA

tag: trna size selection

value: No


title: rz_frg_5m-mqsr2

description: rRNA depleted total RNA

value: MG1655 pBAD30-mqsr

tag: fragmentation time

value: 3 minutes


title: P3_Tumor_0003

value: tumor biopsy


title: P4_Blood_CD4_0003

value: CD4+ CD8- T cells


title: TTX_Unst_Input_r1

description: CHIPSEQ

tag: treatment

value: TTX


title: P45_KD(CoV2-)

tag: disease group

value: KD (CoV2+)

value: pediatric

value: PBMC


title: P44_KD(CoV2-)

tag: age group


title: P3_Blood_CD8_0003

value: CD4- CD8+ T cells


title: rz_frg_5m-mqsr1

description: rRNA depleted total RNA

value: 5 minutes


title: TTX_KCl_2hr_Brd4_r1

description: CHIPSEQ

value: TTX+KCl


title: P41_MIS-C_MYO(CoV2+)

value: MISC_MYO (CoV2+)


title: rz_frg_5m-mazf2

description: rRNA depleted total RNA

value: MG1655 pBAD30-mazf

value: Yes


title: P40_MIS-C_MYO(CoV2+)


title: P3_Blood_CD4_0003

value: male

value: esophageal

value: peripheral blood


title: P2_Blood_CD8_0003


title: TTX_KCl_1hr_Brd4_r1

description: CHIPSEQ

value: Cortical neurons


title: P38_MIS-C_MYO(CoV2+)


title: P2_Blood_CD4_0003


title: TTX_KCl_10m_Brd4_r1

description: CHIPSEQ


title: P1_Tumor_0003


title: TTX_Unst_Brd4_r1

description: CHIPSEQ


title: P36_MIS-C_MYO(CoV2+)


title: rz_frg_5m-mazf1

description: rRNA depleted total RNA

value: No

value: 3 minutes


title: rz_frg_5m-higb2

description: rRNA depleted total RNA

value: MG1655 pBAD30-higb


title: rz_frg_5m-higb1

description: rRNA depleted total RNA


title: P1_Blood_CD8_0003


title: P34_MIS-C_MYO(CoV2+)


title: P32_MIS-C_MYO(CoV2+)


title: P1_Blood_CD4_0003

value: CD4+ CD8- T cells


title: TTX_KCl_1hr_Brd3_r1

description: CHIPSEQ


title: rz_frg_5m-hica1

description: rRNA depleted total RNA

value: MG1655 pBAD30-hica


title: rz_frg_5m-hica2

description: rRNA depleted total RNA


title: P4_Blood_CD8_0002

value: female

value: colorectal


title: TTX_KCl_2hr_Brd3_r1

description: CHIPSEQ


title: P28_MIS-C_MYO(CoV2+)

value: pediatric

value: PBMC


title: TTX_KCl_30m_Brd4_r1

description: CHIPSEQ

value: TTX+KCl


title: TTX_KCl_30m_Brd3_r1

description: CHIPSEQ


title: P27_MIS-C_MYO(CoV2+)

value: MISC_MYO (CoV2+)


title: rz_frg_5m-ev2

description: rRNA depleted total RNA

value: MG1655 pBAD30-ev

value: 5 minutes


title: P4_Blood_CD4_0002


title: P3_Tumor_0002

value: tumor biopsy


title: P25_MIS-C_MYO(CoV2+)


title: rz_frg_5m-ev1

description: rRNA depleted total RNA

value: Yes


title: TTX_KCl_10m_Brd3_r1

description: CHIPSEQ


title: P22_MIS-C(CoV2+)

value: MISC (CoV2+)


title: TTX_Unst_Brd3_r1

description: CHIPSEQ

value: Cortical neurons

value: TTX


title: TTX_KCl_2hr_Brd2_r1

description: CHIPSEQ


title: rz_frg_5m-chpb2

description: rRNA depleted total RNA

value: MG1655 pBAD30-chpb

value: No

value: 3 minutes


title: P3_Blood_CD8_0002

value: CD4- CD8+ T cells

value: peripheral blood


title: rz_frg_5m-chpb1

description: rRNA depleted total RNA


title: rz_5m-yoeb2

description: rRNA depleted total RNA

value: MG1655 pBAD30-yoeb

value: 0 minutes


title: TTX_KCl_1hr_Brd2_r1

description: CHIPSEQ


title: P3_Blood_CD4_0002

value: CD4+ CD8- T cells

value: female

value: colorectal


title: TTX_Unst_Brd2_r1

description: CHIPSEQ


title: P20_MIS-C(CoV2+)

value: pediatric

value: PBMC


title: P19_MIS-C(CoV2+)


title: TTX_KCl_10m_Brd2_r1

description: CHIPSEQ

value: TTX+KCl


title: rz_5m-yhav2

description: rRNA depleted total RNA

value: MG1655 pBAD30-yhav

value: 5 minutes

value: Yes


title: P2_Blood_CD4_0002


title: rz_5m-yoeb1

description: rRNA depleted total RNA


title: P1_Tumor_0002

value: tumor biopsy


title: P2_Blood_CD8_0002


title: TTX_KCl_30m_Brd2_r1

description: CHIPSEQ

value: Cortical neurons


title: rz_5m-yhav1

description: rRNA depleted total RNA

value: No


title: rz_5m-yafo2

description: rRNA depleted total RNA

value: MG1655 pBAD30-yafo


title: P14_MIS-C(CoV2+)

value: MISC (CoV2+)


title: P1_Blood_CD8_0002

value: CD4- CD8+ T cells

value: peripheral blood


title: P1_Blood_CD4_0002


title: P16_MIS-C(CoV2+)


title: GRO_BRD234_sh_T_Unst_r3

description:
GROSEQ
nascent RNA

data_processing:
Library strategy: GRO-seq
The sequencing reads were mapped to the mm10 UCSC annotation using TOPHAT (Trapnell, et al., 2009) with the parameters (-a 8 -m 0 -I 500000 -p 8 -g 20 --library-type fr-firststrand --no-novel-indels  --segment-mismatches 2 for RNA-Seq.
For GRO-Seq, the raw fastq reads were trimmed using cutadapt with parameters -a AAAAAAAAAAAAAAAAAAAA -z -e 0.10 -f fastq -m 32 (Chae et al., 2015).
The CHIPSEQ FASTQ reads were aligned to UCSC's mm10 genome using Bowtie2 with default parameters (Langmead, et al., 2012). Reads with mapping quality less than 10 were removed using SAMtools (Li, et al., 2009) for ChIP-seq. To normalize the differences induced from sequencing depths, the mapped reads were “down sampled” to the lowest number of the uniquely mapped reads with duplicates followed by duplicate reads removal using our inhouse perl scripts. The downsampled, duplicates removed mapped reads were extended to the fragment estimate from MACS using inhouse perl and R scripts. Wig files followed by bigWig files were generated through perl scripts for visualization on UCSC using these extended reads. The coverage values in bigWig files were normalized to 10million.
RPKM calculations were done using the HOMER package by normalizing to 10 million reads
Tracks for visualization on UCSC browser are generated using HOMER.
Genome_build: mm10
Supplementary_files_format_and_content: Strand specific bigWig files for visualizing expression levels on a genome browser.


title: rz_5m-yafo1

description: rRNA depleted total RNA

value: 0 minutes


title: C28

value: CTL


title: GRO_BRD4_sh1_T_K3hr_r3

description:
GROSEQ
nascent RNA


title: C25


title: P4_Blood_CD8_0001

value: male

value: esophageal


title: P3_Tumor_0001


title: rz_5m-rele2

description: rRNA depleted total RNA

value: MG1655 pBAD30-rele


title: rz_5m-rele1

description: rRNA depleted total RNA


title: P21_MIS-C(CoV2+)

value: pediatric

value: PBMC


title: P17_MIS-C(CoV2+)


title: GRO_BRD3_sh1_T_K3hr_r3

description:
GROSEQ
nascent RNA


title: GRO_BRD234_sh_T_K3hr_r3

description:
GROSEQ
nascent RNA


title: P3_Blood_CD8_0001


title: GRO_BRD3_sh1_T_Unst_r3

description:
GROSEQ
nascent RNA


title: C24

value: adult


title: rz_5m-mqsr1

description: rRNA depleted total RNA

value: MG1655 pBAD30-mqsr

value: 5 minutes


title: rz_5m-mqsr2

description: rRNA depleted total RNA

value: Yes


title: C22


title: C21


title: GRO_BRD2_sh2_T_Unst_r3

description:
GROSEQ
nascent RNA

value: Cortical neurons


title: P4_Blood_CD4_0001

value: CD4+ CD8- T cells


title: C23


title: rz_5m-mazf2

description: rRNA depleted total RNA

value: MG1655 pBAD30-mazf

value: No


title: P3_Blood_CD4_0001


title: GRO_BRD2_sh2_T_K3hr_r3

description:
GROSEQ
nascent RNA


title: P2_Blood_CD4_0001


title: GRO_Scr_sh_T_K3hr_r3

description:
GROSEQ
nascent RNA

value: shScrambled Ctr


title: P2_Blood_CD8_0001

value: CD4- CD8+ T cells

value: peripheral blood


title: GRO_Scr_sh_T_Unst_r3

description:
GROSEQ
nascent RNA


title: C20

value: CTL


title: GRO_BRD4_sh1_T_Unst_r3

description:
GROSEQ
nascent RNA


title: P1_Tumor_0001

value: tumor biopsy

value: male


title: rz_5m-mazf1

description: rRNA depleted total RNA

value: 0 minutes


title: rz_5m-higb2

description: rRNA depleted total RNA

value: MG1655 pBAD30-higb


title: rz_5m-higb1

description: rRNA depleted total RNA


title: D67nk_lane1_20200703000

city: Iowa City

first: Michael

middle: S

last: Chimenti

state: IA

address: 431 Newton Road

department: Iowa Institute of Human Genetics

institute: University of Iowa

data_processing:
Reads were processed with ‘bcbio-nextgen’ (v1.2.2, https://github.com/chapmanb/bcbio-nextgen). This pipeline includes ‘best practices’ approaches for QC, alignment, and read quantitation. The bcbio-nextgen pipeline runs ‘MultiQC’ and ‘qualimap’ (2.2.2), computational tools that examines SAM/BAM alignment files and provides an overview of the data to detect common QC problems.  No QC problems were detected with the samples; mean sequence (Phred) quality scores were > 35 across all base positions.
Reads were aligned against the GRCh37 reference genome using ‘hisat2’ aligner (2.2.0)  and quantified with "featureCounts."
Genome_build: GRCh37 (Ensembl)
Supplementary_files_format_and_content: Flat text format; counts from hisat2 alignment and featureCounts

source_name: NK cells

growth_protocol: 1 million unfractionated or T cell-depleted PBMCs were co-cultured with 0.2 million Raji cells after addition of rituximab at a concentration of 1ug/ml in a total volume of 200ul in round bottom 96-well plates (Corning). RMPI was supplemented with 10% FBS, 100 U / ug/ml Penicillin / Streptomycin, 2mM L-Glutamine and 50mM b-mercaptoethanol to make complete cell culture medium. Complete medium including mAbs was refreshed every 2 days.

extract_protocol:
isolated from the coculture by negative selection and flow cytometric sorting. RNA was extracted from isolated NK cells using RNeasy Mini Kit (QIAGEN).
Transcription profiling using RNA-Seq was performed using manufacturer recommended protocols. Briefly, 500 ng of DNase I-treated total RNA was used to enrich for poly-A containing transcripts using oligo(dT) primers bound to beads. The enriched RNA pool was then fragmented, converted to cDNA and ligated to sequencing adaptors containing indexes using the Illumina TruSeq stranded mRNA sample preparation kit (Illumina). The molar concentrations of the indexed libraries were measured using the 2100 Agilent Bioanalyzer and combined equally into pools for sequencing.

treatment_protocol: PBMCs were depleted of CD3+ T cells. The cell coculture was treated by rituximab at the concentration of 1ug/ml

tag: gender

value: female

value: naïve

value: NK cells


title: GRO_BRD234_sh_T_K3hr_r2

description:
GROSEQ
nascent RNA

tag: cell type


title: D67pbmc_lane1_20200703000

value: T cell present


title: rz_5m-hica2

description: rRNA depleted total RNA

value: MG1655 pBAD30-hica

value: 5 minutes

value: Yes


title: D67dep_lane1_20200703000

value: T cell depleted


title: GRO_BRD234_sh_T_Unst_r2

description:
GROSEQ
nascent RNA

value: Cortical neurons

tag: developmental stage


title: P1_Blood_CD4_0001

value: CD4+ CD8- T cells

value: esophageal


title: D63pbmc_lane1_20200703000

value: peripheral blood


title: GRO_BRD4_sh1_T_K3hr_r2

description:
GROSEQ
nascent RNA

tag: knockdown


title: rz_5m-hica1

description: rRNA depleted total RNA

value: No


title: P1_Blood_CD8_0001

value: CD4- CD8+ T cells

tag: age


title: D63nk_lane1_20200703000


title: GRO_BRD3_sh1_T_K3hr_r2

description:
GROSEQ
nascent RNA


title: D62pbmc_lane1_20200703000

value: male


title: rz_5m-ev1

description: rRNA depleted total RNA

value: MG1655 pBAD30-ev

value: 0 minutes


title: GRO_BRD4_sh1_T_Unst_r2

description:
GROSEQ
nascent RNA


title: D63dep_lane1_20200703000

value: female


title: rz_5m-chpb2

description: rRNA depleted total RNA

value: MG1655 pBAD30-chpb


title: D62nk_lane1_20200703000

value: naïve

value: NK cells


title: GRO_BRD3_sh1_T_Unst_r2

description:
GROSEQ
nascent RNA


title: KO2

city: Shanghai

first: Guangyan

last: Zhangyuan

state: Shanghai

address: 197 Ruijin Er Road

department: Department of General Surgery, Pancreatic Disease Center

country: China

institute: Ruijin Hospital, Shanghai Jiao Tong University School of Medicine

data_processing:
Basecalls performed using CASAVA version 1.8
Cellranger was utilized to calculate the raw counts and cell whitelist by default parameter
1. We applied the Seurat [https://satijalab.org/seurat/] package for cell normalization and cell filtering considering the MT percentage, minimum and maximum gene numbers under following criteria:   MT% < 20%;    Cell Gene Number < Median Gene Number * 2;   Cell Gene Number > 200.  PCA and tSNE analysis was used for the single cell to cell relation description. Graphcluster and K-mean was utilized for cell clustering and based on the marker gene achieved some cluster was combined. Wilcox rank sum test was then used for marker gene analysis.
Genome_build: mm10
Supplementary_files_format_and_content: TXT file contain counts for each sample

source_name: Mouse HCC tissue

extract_protocol:
Liver was digested and the cell suspension (300-600 living cells per microliter, determined by Count Star) was loaded onto the Chromium single cell controller (10xGenomics) to generate single-cell gel beads in the emulsion according to the manufacturer’s protocol
Single-cell RNA-seq libraries were constructed using 10xGenomics Single Cell 3’ Library Gel Bead Kit V2. Sequencing was performed on the Illumina HiSeq XTEN platform

treatment_protocol: For different groups of mice (Miz1F/F, Miz1ΔHep), HCC was induced by the combination of DEN (25 mg/kg i.p.) given at Week 2 postpartum followed by weekly injections of CCl4 (0.5 ml/kg i.p., dissolved in corn oil). Mice were sacrificed at the age of 5 months

value: HCC tissue

value: 5 months

tag: genotype


title: GRO_BRD2_sh2_T_Unst_r2

description:
GROSEQ
nascent RNA


title: GRO_BRD2_sh2_T_K3hr_r2

description:
GROSEQ
nascent RNA


title: KO1


title: rz_5m-chpb1

description: rRNA depleted total RNA

value: 5 minutes

value: Yes


title: D62dep_lane1_20200703000

value: T cell depleted


title: rz_5m-ev2

description: rRNA depleted total RNA


title: GRO_Scr_sh_T_K3hr_r2

description:
GROSEQ
nascent RNA

value: Cortical neurons

value: shScrambled Ctr


title: ctrl2


title: D61pbmc_lane1_20200703000

value: peripheral blood

value: T cell present


title: BCPAP Vemurafenib 20uM (2)

type: RNA

city: Milano

first: Chiara

last: Gargiuli

address: via Amadeo

department: Dept. of Applied Research and Technological Development

country: Italy

institute: Fondazione IRCCS Istituto Nazionale dei Tumori di Milano

description: BCPAP Vemurafenib 20uM (2)

hyb_protocol: Hybridization was performed according to manufacturer's standard protocol

scan_protocol: Microarrays were scanned with GeneChip Scanner 3000 7G Array (Thermo Fisher Scientific) and data were obtained using Affymetrix GeneChip Command Console Software (AGCC).

data_processing: Raw CEL files were imported in R and pre-processed using the robust multi-array average method implemented in the oligo Bioconductor package

label: biotin

source_name: PTC derived cell line

label_protocol: RNA labeling was performed according to manufacturer's standard protocol

growth_protocol: BCPAP cells were cultured in DMEM medium supplemented with 10% (v/v) heat-inactivated fetal bovine serum as monolayers at 37 °C in a 5% CO2 humidified atmosphere.

extract_protocol: Total RNA was extracted 48h after treatment by miRNeasy mini kit (Qiagen)

treatment_protocol: BCPAP cells were seeded in tissue culture plates 24h before treatements. The next day BRAF inhibitors working dilutions were freshly prepared from stock solutions; cells were treated with drugs or with DMSO and incubated for 48h at 37 °C in a 5% CO2 humidified atmosphere.

tag: tissue type

value: human papillary thyroid carcinoma

value: Vemurafenib

value: BRAFV600E mutation


title: BCPAP Dabrafenib 2uM (2)

description: BCPAP Dabrafenib 2uM (2)

value: Dabrafenib


title: BCPAP Vemurafenib 20uM (3)

description: BCPAP Vemurafenib 20uM (3)


title: GRO_Scr_sh_T_Unst_r2

description:
GROSEQ
nascent RNA

tag: strain


title: BCPAP DMSO (3)

description: BCPAP DMSO (3)

value: control


title: ctrl1

tag: tissue


title: BCPAP Vemurafenib 20uM (1)

description: BCPAP Vemurafenib 20uM (1)


title: BCPAP Dabrafenib 2uM (1)

description: BCPAP Dabrafenib 2uM (1)


title: D61dep_lane1_20200703000

value: male


title: D61nk2_lane1_20200703000

value: female

value: naïve


title: BCPAP Dabrafenib 2uM (3)

description: BCPAP Dabrafenib 2uM (3)


title: BCPAP DMSO (1)

description: BCPAP DMSO (1)


title: CFA_RZ1

status: Public on Jul 12 2021

type: genomic

city: Overton

first: Anil

last: Somenahally

state: Texas

department: Texas AgriLife Research

institute: Texas A&M University

description: Soil mciorbial DNA was extarcted, amplified and hybridized, microarray scanned

hyb_protocol: not provided

scan_protocol: After hybridization, arrays were scanned at full laser power and 100% photomultiplier tubes gain with a NimbleGen MS 200 Microarray Scanner (Roche NimbleGen). Scanned images were gridded by NimbleScan software using the gridding file containing GeoChip 4 probes and NimbleGen control probes to obtain the signal intensity for each probe. Probe spots with coefficient of variance (CV) >0.8 were removed. In general, a local background that represents the actual background signal for each spot is preferred for signal-to-noise ratio (SNR) calculations and false-positive filtering instead of the global background generated by NimbleScan; so a different method for background calculation was introduced here. To obtain a local background signal for each probe, a customized void gridding file targeting positions without probes was generated. The scanned images were gridded using the void gridding files. The local background signal for each probe was calculated as the mean signal intensity of the four neighbouring void spots. When all of the four neighbouring void spots were not valid, the set of eight void spots surrounding its closest neighbours was used

data_processing: Microarray hybridization array data was processed and normalized by following the data analysis pipeline detailed in Tu et al (37). We further processed the data by replacing all the biological replicates values by a zero value if any of the three replicates had no signal intensity. Further normalization of the data was performed by transforming absolute detection to relative abundance by estimating ratio of each probe to all genes detected by GeoChip for that specific sample. Relative signal intensity ratios were used for all comparative analysis and multivariate statistical analysis. We used specific gene probes for quantifying relative abundance of functional groups. For arsenate reducing bacteria (ARB) we used arsA, arsB and arsC gene probes. For sulfate reducing bacteria (SRB), we used aprA, APS_aprA, APS_aprB, dsrA, and dsrB gene probes. For sulfur oxidizing bacteria (SOB) we used sox gene probes. For iron reducing bacteria (IRB) we used cytochrome gene probes. Principal component analysis was performed using the PAST software (23). The PCA routine in PAST finds the eigenvalues and eigenvectors of the variance-covariance matrix or the correlation matrix. We used the variance-covariance matrix for the gene-probe relative abundance data. The Biplot option was used for projecting the predominant species (gene-probes) constraining the principal components. A one-way non-parametric multivariate analysis of variance (PERMANOVA) was used to test the significant differences between the experimental treatments for relative abundance of functional groups, based on Bray-Curtis similarity index. The canonical correspondence analysis for the relative abundance of arsenate reducing bacteria (ARBs) and sulfate reducing bacteria (SRBs) were compared with their functional associations (other gene probes detected in these organisms). The gene categories and their abundance were estimated for each sample, which was then used as variables (priori) within CCA and were plotted as biplots. Hierarchical clustering analysis and heat maps were created for the relative signal intensities for gene probes in GeoChip using Gplots package within R software. The individual probe data were then grouped by taxonomic phyla for graphing

organism: soil metagenome

source_name: Rice-rhizopshere

label_protocol: The purified DNA was labelled with Cy-3 using random primers and the Klenow fragment of DNA polymerase I (Wu et al. 2006). Labelled DNA was purified using the QIA quick purification kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions, measured on a NanoDrop ND-1000 spectrophotometer and then dried down in a SpeedVac (ThermoSavant, Milford, MA, USA) at 45 °C for 45 min. Dried DNA was rehydrated with 2.68 lL sample tracking control (NimbleGen) to confirm sample identity. The samples were incubated at 50 °C for 5 min, vortexed for 30 s and then centrifuged to collect all liquid at the bottom of the tube. Hybridization buffer (7.32 lL), containing 40% formamide, 25% SSC, 1% SDS, 2.38% Cy3-labelled alignment oligo (NimbleGen) and 2.8% Cy5-labelled CORS target, was added. The samples were then mixed by vortexing, spun down, incubated at 95 °C for 5 min and maintained at 42 °C until hybridization. An HX12 mixer (NimbleGen) was placed onto the array using NimbleGen’s precision mixer alignment tool, and then, the array was preheated to 42 °C on a hybridization station (MAUI, BioMicro Systems, Salt Lake City, UT, USA) for at least 5 min. Samples (6.8 lL) were then loaded onto the array surface and hybridized approximately 16 h with mixing

growth_protocol: Only soil genimic DNA was used I nthis study and did not involve any live culture growth

extract_protocol: the rhizosphere and the root-plaque samples were collected from each treatment plot. The microbial community DNA was extracted from the frozen rhizosphere and the root-plaque samples using MO BIO Power Max DNA extraction kits (Qiagen Inc.,).

treatment_protocol: The experimental treatments used in this study were (1) continuously flooded-As amended (CFA), (2) continuously flooded-control (As unamended) (CFC), and (3) continuously saturated-control (As unamended) (CSC).

tag: agent

value: continuously flooded-As

tag: microbiome

value: Soil microbiome


title: CSC_RP2

source_name: Rice-root plaque

value: continuously saturated-control


title: CFC_RZ2

value: continuously flooded-control


title: CFA_RP3


title: PPAR WT-T09-1

city: Charlottesville

first: Irina

last: Bochkis

state: VA

address: 1340 Jefferson Park Ave

institute: University of Virginia

data_processing:
The reads were aligned to mouse genome (mm10; NCBI Build 38) using BWA (Li and Durbin, 2009). Duplicate reads were removed from the analysis. Reads (phred score > 30) that aligned uniquely were used for subsequent analysis.
ATAC-Seq reads from two biological replicates were merged for each condition (WT Veh-GW, WT Veh-T09, WT GW, WT T09, Foxa2-KO Veh-GW, Foxa2-KO Veh-T09, Foxa2-KO GW, Foxa2-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks in WT GW against WT GW-Veh (ATAC_WT_GWonVeh), WT T09 against WT T09-Veh (ATAC_WT_T09onVeh), WT GW against Foxa2-KO GW (ATAC_WTonKO_GW), and WT T09 against Foxa2-KO T09 (ATAC_WTonKO_T09).
Foxa2 ChIP-Seq reads from three biological replicates were merged for each condition (WT Veh, WT GW, WT T09, LXRα-KO Veh, LXRα-KO GW, LXRα-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
LXRα ChIP-Seq reads from three biological replicates were merged for each condition (WT Veh, WT GW, WT T09, Foxa2-KO Veh, Foxa2-KO GW, Foxa2-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
FXR ChIP-Seq reads from two biological replicates were merged for each condition (WT Veh, WT GW4064, Foxa2-KO Veh, Foxa2-KO GW4064). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
PPAR ChIP-Seq details: Reads were aligned to the mouse genome (mm10; NCBI Build) using BWA.  Duplicate reads were removed. Reads (phred score > 30) that aligned uniquely were used for subsequent analysis. For PPARa ChIPs, data from two biological replicates were merged for each condition. PeakSeq was used to identify bound peaks in FXR ligand-activated condition against input controls (PPARα FDR 5^, q-value<0.07) and in LXR ligand-activated condition against input controls ( PPARα FDR 5%, q-value<0.07).
RNA-Seq reads from three or four biological replicates were aligned using STAR to mouse genome build mm10. Expression levels were calculated using RSEM. Differential expression analysis of RNA-seq (p-value <0.05) was performed in R using EdgeR package with a Benjamini-Hochberg FDR of 5%.
Genome_build: mm10
Supplementary_files_format_and_content: bed files with peak calls from PeakSeq with the following columns: chromosome, begin coordinate, end coordinate, q-value

source_name: Liver

extract_protocol:
Snap-frozen mouse liver (100 mg) from wild type, Foxa2-KO, and LXRα-KO mice was used to prepare chromatin. Foxa2‐specific rabbit antiserum (Seven Hills Bioreagents, WRAB‐1,200) and rabbit polyclonal antibody specific to LXRα (Active Motif, 61175) were used for immunoprecipitation. Details about PPAR ChIP antibody: rabbit polyclonal antibody specific to PPARα (Novus Biologicals, NB600-636)
Libraries were prepared according to Illumina's instructions. Briefly, DNAs were blunted with a combination of T4 DNA polymerase, Klenow polymerase, and T4 PNK, then a single 3′-end “A” base was added using Klenow exo (3′-to-5′ exo minus). multiplex adapters were then ligated to the ends of the modified DNA. After adapter ligation DNA was PCR amplified with Broad custom primers for 16 cycles and library fragments of ~280 bp (~150-bp mononucleosome insert plus ~130-bp adaptor and PCR primer sequences) were isolated using Pippin Prep agarose gel. The purified DNA was captured on an Illumina flow cell for cluster generation. All libraries were sequenced on Illumina NextSeq 500.

tag: antibody

value: PPAR

value: WT

value: Liver

value: 3 months


title: PPAR WT-T09-2


title: PPAR WT-GW4064-1


title: PPAR WT-GW3965-2


title: PPAR WT-Veh-2

value: Veh


title: PPAR WT-GW3965-1


title: CSC_RZ2


title: PPAR WT-GW4064-2


title: CSC_RZ1


title: BCPAP DMSO (2)

description: BCPAP DMSO (2)

value: human papillary thyroid carcinoma


title: PPAR WT-Veh-1


title: CSC_RP1


title: CSC_RP3


title: CFA_RZ3


title: CFC_RP3


title: PPAR KO-Veh-2

value: Foxa2 KO (Foxa2loxP/loxP;Alfp.Cre)


title: CFC_RZ3


title: PPAR KO-Veh-1


title: CFA_RZ2


title: PPAR KO-T09-2


title: CFC_RP2


title: CSC_RZ3


title: CFC_RP1


title: CFA_RP2


title: CFA_RP1


title: CFC_RZ1


title: PPAR KO-GW4064-2


title: PPAR KO-GW4064-1


title: PPAR KO-T09-1


title: PPAR KO-GW3965-1


title: PPAR KO-GW3965-2


title: Single-Cell from the hypothalamus of 1 mouse  [3_R50E]

city: Copenhagen

first: Tune

middle: Hannes

last: Pers

address: Blegdamsvej 3B

department: Novo Nordisk Foundation Center for Basic Metabolic Research

country: Denmark

institute: University of Copenhagen

description:
r50_norm_genes.csv
r50_norm_counts.mtx
r50_raw_genes.csv
r50_raw_counts.mtx

data_processing:
Read alignment: Cell Ranger version 1.2 (10X Genomics, USA) was used to de-multiplex and quantify unique molecular identifiers (UMI). A count matrix was generated for each sample with default parameters and genes were mapped to the 10x reference for GRCm38 (mm10; for the scRNA-seq data set) or an adapted reference from 10x mm10-premrna to include introns (for the snRNA-seq data set), following the steps outlined on the 10x Genomics website  (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/advanced/references).
Barcode filtering: Barcodes were filtered to include those with a total UMI count >10% of the 99th percentile of the expected recovered cells.
Quality control: The Scrublet tool was run with default parameters to identify likely doublets (this step was omitted for the snRNA-seq data) which were subsequently removed from the expression matrix. Further processing was performed using the Seurat50 R package (version 2.3). Cells in which <500 or >4,000 (>5,500 for the snRNA-seq data) expressed genes were detected, as well any cell with >20% (>5% for snRNA-seq) mitochondrial and/or ribosomal transcripts were discarded. Genes detected in <10 cells (<3 cells in the snRNA-seq data) were also removed.
Normalization: Counts were normalized using Seurat SCTransform
Genome_build: Mus Musculus reference genome build Grch38 – release 77
Supplementary_files_format_and_content: r50_norm_genes.csv: comma separated. Genes to match nomralized and filtered mtx file.
Supplementary_files_format_and_content: r50_norm_counts.mtx: mtx formatted data. Contents: gene*cell matrix containing the filtered and normalized cell counts
Supplementary_files_format_and_content: r50_raw_genes.csv: comma separated. Genes to match  unfiltered mtx file.
Supplementary_files_format_and_content: r50_raw_counts.mtx:mtx formatted data. Contents: gene*cell matrix containing the unfiltered cell counts
Supplementary_files_format_and_content: r50_metadata.csv: comma-separated. Contents: cell metadata. Columns: orig.ident,nCount_RNA,nFeature_RNA,percent.mito,percent.ribo,nCount_SCT,nFeature_SCT,SCT_snn_res.0.1,seurat_clusters,trt,sample,batch,SCT_snn_res.0.6

source_name: hypothalamus

growth_protocol:
Individually housed under specific-pathogen free conditions in a temperature-controlled room with a 12:12h light:dark cycle and provided with ad libitum (ad-lib) access to water and Altromin 1310 chow (Brogaarden, Denmark)
see SAMPLES section

extract_protocol:
Tissue dissociation with Neural Tissue Dissociation Kit (Miltenyi Biotec) and fire-polished Pasteur pipettes. See article for details
see SAMPLES section
Single-cells cDNA libraries were generated using Chromium Single-cell platform and 3’ v2 Reagent Kit according to manufacturer’s protocol (10x Genomics, USA). Single-cell libraries were sequenced on a NextSeq 500 platform (3 samples (whole single-cells) and 2 samples (single-nuclei) on one flow cell)

treatment_protocol:
3 ug intracerebral ventricular injection of , FGF1 R50E. Euthanized 5d post injection
see SAMPLES section

value: ob/ob

tag: Sex

value: 11 weeks

tag: number of mice

value: hypothalamus


title: Single-Cell from the hypothalamus of 1 mouse  [34_R50E]

value: male


title: Single-Cell from the hypothalamus of 1 mouse  [30_R50E]


title: Single-Cell from the hypothalamus of 1 mouse  [37_R50E]


title: Veh treated plantaris muscle, 3D overloaded, rep2

city: Lexington

first: Vandre

last: Figueiredo

state: Kentucky

address: Rose street

institute: University of Kentucky

description: Gene expression data from plantaris muscle of control mice following 3 day of overload

hyb_protocol: standard Affymetrix protocol

scan_protocol: standard Affymetrix protocol

data_processing: Bioconductor RMA

source_name: Plantaris muscle of control mice following 3 day of overload

label_protocol: standard Affymetrix protocol

extract_protocol: Trizol extraction of total RNA was performed and further isolated using Direct-zol™ RNA Kits (Cat # R2072).

treatment_protocol: skeletal muscle was sanp freeze using liquid nitrogen and maintened at -80 °C until extraction.

value: female

value: skeletal muscle

value: 5 month-old

value: Pax7CreER/+; Rosa26DTA/+


title: Veh treated plantaris muscle, 3D overloaded, rep1


title: Single-Cell from the hypothalamus of 1 mouse  [12_R50E]


title: Tam treated plantaris muscle, 3D overloaded, rep2

description: Gene expression data from plantaris muscle of Satellite cell depleted mice following 3 day of overload

source_name: Plantaris muscle of Satellite cell depleted mice following 3 day of overload


title: LXR KO-Veh-3

value: LXR

tag: treatment


title: LXR KO-Veh-2


title: Veh treated plantaris muscle, sham operated, rep2

description: Gene expression data from plantaris muscle of control mice following sham operation, used as control sample.

source_name: Plantaris muscle of control mice following sham operation


title: Tam treated plantaris muscle, sham operated, rep2

description: Gene expression data from plantaris muscle of Satellite cell depleted mice following sham operation, used as control sample.

source_name: Plantaris muscle of Satellite cell depleted mice following sham operation


title: Veh treated plantaris muscle, 7D overloaded, rep2

description: Gene expression data from plantaris muscle of control mice following 7 day of overload

source_name: Plantaris muscle of control mice following 7 day of overload


title: Veh treated plantaris muscle, 1D overloaded, rep2

description: Gene expression data from plantaris muscle of control mice following 1 day of overload

source_name: Plantaris muscle of control mice following 1 day of overload


title: Tam treated plantaris muscle, 7D overloaded, rep1

description: Gene expression data from plantaris muscle of Satellite cell depleted mice following 7 day of overload

source_name: Plantaris muscle of Satellite cell depleted mice following 7 day of overload


title: LXR KO-T09-3


title: LXR KO-Veh-1


title: Veh treated plantaris muscle, 7D overloaded, rep1


title: Tam treated plantaris muscle, 1D overloaded, rep1

description: Gene expression data from plantaris muscle of Satellite cell depleted mice following 1 day of overload

source_name: Plantaris muscle of Satellite cell depleted mice following 1 day of overload


title: Tam treated plantaris muscle, 1D overloaded, rep2


title: Tam treated plantaris muscle, 7D overloaded, rep2


title: Single-Cell from the hypothalamus of 1 mouse  [4_R50E]


title: Veh treated plantaris muscle, 5D overloaded, rep1

description: Gene expression data from plantaris muscle of control mice following 5 day of overload

source_name: Plantaris muscle of control mice following 5 day of overload


title: Veh treated plantaris muscle, 5D overloaded, rep2


title: Veh treated plantaris muscle, 1D overloaded, rep1


title: Tam treated plantaris muscle, sham operated, rep1


title: Tam treated plantaris muscle, 5D overloaded, rep2

description: Gene expression data from plantaris muscle of Satellite cell depleted mice following 5 day of overload

source_name: Plantaris muscle of Satellite cell depleted mice following 5 day of overload


title: Tam treated plantaris muscle, 3D overloaded, rep1


title: Tam treated plantaris muscle, 5D overloaded, rep1


title: LXR KO-T09-1


title: LXR KO-GW-2

value: GW


title: LXR KO-GW-1


title: Veh treated plantaris muscle, sham operated, rep1


title: LXR KO-GW-3


title: LXR KO-T09-2


title: LXR WT-Veh


title: LXR WT-GW


title: LXR WT-T09


title: Ribosomal bound mRNAs from Purkinje cells [cc3_0]

first: Kellen

last: Winden

address: 300 Longwood Ave

department: Neurology

institute: Boston Children's Hospital

description:
processed data file: trap_genes.count_table
20171019_JD-19_JD4866-2_S18

data_processing:
Trim adapters using trimmomatic (v0.39)
Align to genome using STAR (v2.7.0f)
Assemble transcriptome using data from sorted PCs using cufflinks (v2.2.1)
Quantify genes/transcripts using cufflinks (v2.2.1)
Genome_build: GRCm38

source_name: Purkinje cells_CC

extract_protocol:
FACS: Qiagen RNAeasy for RNA isolation
TRAP: IP of GFP labeled ribosomes; Trizol/Qiagen RNAeasy for RNA isolation
SmartSeqv4 RNASeq kit, Clontech

tag: strain background

value: CC

value: Purkinje cells


title: Ribosomal bound mRNAs from Purkinje cells [cc2_rapa_0]

description:
processed data file: trap_genes.count_table
20171019_JD-14_JD4866-2_S14

source_name: Purkinje cells_CC_Rapamycin treated

value: Rapamycin treated


title: Ribosomal bound mRNAs from Purkinje cells [cc1_0]

description:
processed data file: trap_genes.count_table
20171019_JD-16_JD4866-2_S16


title: Ribosomal bound mRNAs from Purkinje cells [cc3_rapa_0]

description:
processed data file: trap_genes.count_table
20171019_JD-15_JD4866-2_S15


title: Ribosomal bound mRNAs from Purkinje cells [ww3_0]

description:
processed data file: trap_genes.count_table
20171019_JD-6_JD4866-2_S6

source_name: Purkinje cells_WW

value: WW


title: Ribosomal bound mRNAs from Purkinje cells [cc2_0]

description:
processed data file: trap_genes.count_table
20171019_JD-18_JD4866-2_S17


title: Ribosomal bound mRNAs from Purkinje cells [ww3_rapa_0]

description:
processed data file: trap_genes.count_table
20171019_JD-3_JD4866-2_S3

source_name: Purkinje cells_WW_Rapamycin treated


title: Ribosomal bound mRNAs from Purkinje cells [ww1_0]

description:
processed data file: trap_genes.count_table
20171019_JD-4_JD4866-2_S4


title: Ribosomal bound mRNAs from Purkinje cells [ww2_rapa_0]

description:
processed data file: trap_genes.count_table
20171019_JD-2_JD4866-2_S2


title: Ribosomal bound mRNAs from Purkinje cells [cc1_rapa_0]

description:
processed data file: trap_genes.count_table
20171019_JD-13_JD4866-2_S13


title: RNA-Seq Foxa2-KO GW4064 1

data_processing:
The reads were aligned to mouse genome (mm10; NCBI Build 38) using BWA (Li and Durbin, 2009). Duplicate reads were removed from the analysis. Reads (phred score > 30) that aligned uniquely were used for subsequent analysis.
ATAC-Seq reads from two biological replicates were merged for each condition (WT Veh-GW, WT Veh-T09, WT GW, WT T09, Foxa2-KO Veh-GW, Foxa2-KO Veh-T09, Foxa2-KO GW, Foxa2-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks in WT GW against WT GW-Veh (ATAC_WT_GWonVeh), WT T09 against WT T09-Veh (ATAC_WT_T09onVeh), WT GW against Foxa2-KO GW (ATAC_WTonKO_GW), and WT T09 against Foxa2-KO T09 (ATAC_WTonKO_T09).
Foxa2 ChIP-Seq reads from three biological replicates were merged for each condition (WT Veh, WT GW, WT T09, LXRα-KO Veh, LXRα-KO GW, LXRα-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
LXRα ChIP-Seq reads from three biological replicates were merged for each condition (WT Veh, WT GW, WT T09, Foxa2-KO Veh, Foxa2-KO GW, Foxa2-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
FXR ChIP-Seq reads from two biological replicates were merged for each condition (WT Veh, WT GW4064, Foxa2-KO Veh, Foxa2-KO GW4064). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
RNA-Seq reads from three or four biological replicates were aligned using STAR to mouse genome build mm10. Expression levels were calculated using RSEM. Differential expression analysis of RNA-seq (p-value <0.05) was performed in R using EdgeR package with a Benjamini-Hochberg FDR of 5%.
Genome_build: mm10
Supplementary_files_format_and_content: bed files with peak calls from PeakSeq with the following columns: chromosome, begin coordinate, end coordinate, enrichment, q-value

extract_protocol:
Liver RNA was isolated from Foxa2loxP/loxP;Alfp.Cre mice and control litter-mates. Quality of RNA samples was analyzed using Agilent RNA 6000 Nano Kit (Bioanalyzer, Agilent Technologies). Samples with RIN scores above 9.5 were used in library preparation. 1 µg of total RNA was used to isolate mRNA (NebNext Poly(A) mRNA Magnetic Isolation Module).
Libraries of resulting mRNA were prepared using NebNext Ultra II RNA library preparation kit. All samples were sequenced on Illumina NextSeq 500.

treatment_protocol: 4 hours prior to harvest, T0901317 (T09) and GW4064 (GW) were administered to wildtype and mutant mice via oral gavage.

value: Liver

value: 8-12 weeks


title: RNA-Seq Foxa2-KO GW4064 4


title: RNA-Seq Foxa2-KO GW4064 2


title: RNA-Seq Foxa2-KO GW4064 3


title: RNA-Seq Foxa2-KO GW4064-Veh 1


title: RNA-Seq Foxa2-KO GW4064-Veh 4


title: RNA-Seq WT GW4064 4

value: WT


title: RNA-Seq Foxa2-KO GW4064-Veh 2


title: RNA-Seq WT GW4064-Veh 4


title: RNA-Seq WT GW4064-Veh 3


title: RNA-Seq WT GW4064 1


title: RNA-Seq WT GW4064-Veh 1


title: Ribosomal bound mRNAs from Purkinje cells [ww2_0]

description:
processed data file: trap_genes.count_table
20171019_JD-5_JD4866-2_S5


title: FXR ChIP Foxa2-KO GW4064 2

extract_protocol:
Snap-frozen mouse liver (100 mg) from wild type, Foxa2-KO, and LXRα-KO mice was used to prepare chromatin. Foxa2‐specific rabbit antiserum (Seven Hills Bioreagents, WRAB‐1,200) and rabbit polyclonal antibody specific to LXRα (Active Motif, 61175) were used for immunoprecipitation.
Libraries were prepared according to Illumina's instructions. Briefly, DNAs were blunted with a combination of T4 DNA polymerase, Klenow polymerase, and T4 PNK, then a single 3′-end “A” base was added using Klenow exo (3′-to-5′ exo minus). multiplex adapters were then ligated to the ends of the modified DNA.  After adapter ligation DNA was PCR amplified with Broad custom primers for 16 cycles and library fragments of ~280 bp (~150-bp mononucleosome insert plus ~130-bp adaptor and PCR primer sequences) were  isolated using Pippin Prep agarose gel. The purified DNA was captured on an Illumina flow cell for cluster generation. All libraries were sequenced on Illumina NextSeq 500.

value: FXR


title: FXR ChIP WT GW4064 2


title: FXR ChIP Foxa2-KO GW4064-Veh 2


title: FXR ChIP WT GW4064-Veh 2


title: Ribosomal bound mRNAs from Purkinje cells [ww1_rapa_0]

description:
processed data file: trap_genes.count_table
20171019_JD-1_JD4866-2_S1


title: FXR ChIP WT GW4064-Veh 1


title: Foxa2 ChIP WT GW4064


title: Foxa2 ChIP WT GW4064-Veh


title: RNA-Seq Foxa2-KO GW4064-Veh 3


title: ATAC Foxa2-KO GW4064 3

description: ATAC_Foxa2-KO_GW4064onVeh.bed, ATAC_WTonKO_GW4064.bed

extract_protocol:
Nuclei were isolated from frozen liver tissue (50 mg), homogenized in PBS with Complete Protease Inhibitor (PI, Roche) using a glass Dounce homogenizer.
Libraries were prepared according to Illumina's instructions. Briefly, DNAs were blunted with a combination of T4 DNA polymerase, Klenow polymerase, and T4 PNK, then a single 3′-end “A” base was added using Klenow exo (3′-to-5′ exo minus). multiplex adapters were then ligated to the ends of the modified DNA.  After adapter ligation DNA was PCR amplified with Broad custom primers for 16 cycles and library fragments of ~280 bp (~150-bp mononucleosome insert plus ~130-bp adaptor and PCR primer sequences) were  isolated using Pippin Prep agarose gel. The purified DNA was captured on an Illumina flow cell for cluster generation. All libraries were sequenced on Illumina NextSeq 500.


title: ATAC Foxa2-KO GW4064 2


title: ATAC Foxa2-KO GW4064-Veh 3


title: ATAC Foxa2-KO GW4064-Veh 1


title: ATAC Foxa2-KO GW4064 1


title: ATAC WT GW4064 2

description: ATAC_WT_GW4064onVeh.bed, ATAC_WTonKO_GW4064.bed


title: ATAC WT GW4064-Veh 3


title: ATAC WT GW4064 1


title: RNA-Seq WT GW4064-Veh 2


title: RNA-Seq WT GW4064 2


title: RNA-Seq WT GW4064 3


title: ATAC WT GW4064-Veh 1


title: FXR ChIP Foxa2-KO GW4064 1


title: RNA-Seq Foxa2-KO T09 2

data_processing:
The reads were aligned to mouse genome (mm10; NCBI Build 38) using BWA (Li and Durbin, 2009). Duplicate reads were removed from the analysis. Reads (phred score > 30) that aligned uniquely were used for subsequent analysis.
ATAC-Seq reads from two biological replicates were merged for each condition (WT Veh-GW, WT Veh-T09, WT GW, WT T09, Foxa2-KO Veh-GW, Foxa2-KO Veh-T09, Foxa2-KO GW, Foxa2-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks in WT GW against WT GW-Veh (ATAC_WT_GWonVeh), WT T09 against WT T09-Veh (ATAC_WT_T09onVeh), WT GW against Foxa2-KO GW (ATAC_WTonKO_GW), and WT T09 against Foxa2-KO T09 (ATAC_WTonKO_T09).
Foxa2 ChIP-Seq reads from three biological replicates were merged for each condition (WT Veh, WT GW, WT T09, LXRα-KO Veh, LXRα-KO GW, LXRα-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
LXRα ChIP-Seq reads from three biological replicates were merged for each condition (WT Veh, WT GW, WT T09, Foxa2-KO Veh, Foxa2-KO GW, Foxa2-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
RNA-Seq reads from three or four biological replicates were aligned using STAR to mouse genome build mm10. Expression levels were calculated using RSEM. Differential expression analysis of RNA-seq (p-value <0.05) was performed in R using EdgeR package with a Benjamini-Hochberg FDR of 5%.
Genome_build: mm10
Supplementary_files_format_and_content: bed files with peak calls from PeakSeq with the following columns: chromosome, begin coordinate, end coordinate, enrichment, q-value. RNA-seq: excel file with DE results

treatment_protocol: 4 hours prior to harvest, T0901317 (T09) and GW3965 (GW) were administered to wildtype and mutant mice via oral gavage.


title: ATAC WT GW4064-Veh 2


title: RNA-Seq Foxa2-KO T09 3


title: FXR ChIP Foxa2-KO GW4064-Veh 1


title: FXR ChIP WT GW4064 1


title: RNA-Seq Foxa2-KO T09 1


title: RNA-Seq Foxa2-KO GW3965 4


title: RNA-Seq Foxa2-KO GW3965 1


title: RNA-Seq Foxa2-KO Veh 4


title: RNA-Seq Foxa2-KO GW3965 2


title: RNA-Seq Foxa2-KO Veh 3


title: RNA-Seq Foxa2-KO Veh 2


title: RNA-Seq Foxa2-KO Veh 1


title: ATAC WT GW4064 3


title: ATAC Foxa2-KO GW4064-Veh 2


title: RNA-Seq WT T09 2


title: RNA-Seq WT T09 1


title: RNA-Seq WT T09 3


title: RNA-Seq WT Veh 4


title: RNA-Seq WT GW3965 3


title: RNA-Seq WT GW3965 2


title: RNA-Seq WT Veh 1


title: RNA-Seq WT Veh 2


title: Foxa2 ChIP WT T09

description:
3 replicates represented by a single raw file.
Foxa2-ChIP_WT_T09.bed

tag: chip antibody


title: Foxa2 ChIP WT GW3965

description:
3 replicates represented by a single raw file.
Foxa2-ChIP_WT_GW.bed


title: RNA-Seq WT Veh 3


title: RNA-Seq Foxa2-KO GW3965 3


title: ATAC Foxa2-KO GW3965 1


title: ATAC Foxa2-KO T09 1


title: ATAC Foxa2-KO T09-Veh 2


title: ATAC Foxa2-KO T09-Veh 1


title: ATAC Foxa2-KO GW3965 2


title: Foxa2 ChIP WT Veh

description:
3 replicates represented by a single raw file.
Foxa2-ChIP_WT_Veh.bed


title: ATAC Foxa2-KO T09 2


title: ATAC Foxa2-KO GW3965-Veh 2


title: ATAC Foxa2-KO GW3965-Veh 1


title: ATAC WT T09 1


title: ATAC WT GW3965 1


title: ATAC WT T09-Veh 2


title: ATAC WT GW3965-Veh 2


title: RNA-Seq WT GW3965 1


title: ATAC WT GW3965-Veh 1


title: ATAC WT GW3965 2


title: CRTH2-KO sample, set2

city: Ithica

first: Praveen

last: Sethupathy

state: NY

address: Cornell University College of Veterinary Medicine, Box 17

institute: Cornell University

description:
single cell RNA
Separated intestinal cells from CRTH2-KO mice - set2

data_processing:
Sequencing was demultiplexed, aligned to the genome, and quality checked using 10X Genomics Cell Ranger software.
Genome_build: mm10
Supplementary_files_format_and_content: Processed data contains barcodes, gene list, and counts matrix.

source_name: CRTH2-KO small intestine

extract_protocol: Libraries were prepped according to 10X Genomics Chromium Single Cell 3' kits (v2 chemistry)

value: small intestine


title: ATAC WT T09-Veh 1

source_name: Liver


title: CRTH2-KO sample, set3

description:
single cell RNA
Separated intestinal cells from CRTH2-KO mice - set3


title: Hippocampus, IgG Control_2

city: Jupiter

first: Sathyanarayanan

middle: V

last: Puthanveettil

state: Florida

address: 130 Scripps Way

department: Neuroscience

institute: Scripps Florida

data_processing:
All samples were processed and sequenced on a NextSeq 500. The quality of the reads overall was quite high with no decrease in quality over the course of sequencing.
After quality control, the reads were mapped to the mouse transcriptome using Salmon using paired end settings (Salmon reference was made using Ensembl: ftp://ftp.ensembl.org/pub/release-99/fasta/mus_musculus/cdna/Mus_musculus.GRCm38.cdna.abinitio.fa.gz)
Differential analysis was performed using DEseq
Genome_build: GRCm38
Supplementary_files_format_and_content: Excel file contaiining gene counts and differentially expressed genes

source_name: Hippocampus

growth_protocol: Hippocampi were isolated from adult mice for preparing lysates for Co-IP.

extract_protocol:
Birefly lysates were prepared using a lysis buffer (0.50 % Nonidet P-40, 50 mM Tris-HCl pH 7.5, 125 mM NaCl, 1 mM EDTA pH 8.0, 1 mM DTT, 1 protease inhibitor cocktail tablet (Roche), 100 μl of phosphatase inhibitor cocktail 1 and 2 each (Sigma), 100 μl BSA). Following lysis for 30 minutes at 4 degreeC, lyzates were centrifuged to remove any debris and proceeded to IP.
RNA libraries were prepared for sequencing using standard Illumina protocols

value: 7-9 weeks old mice

value: Hippocampus

tag: rip antibody

value: Mouse IgG control Ab 08-6599 Thermofischer


title: Hippocampus, IgG Control_1


title: Hippocampus, Kif5C IP_2

value: Kinesin Ab MAB 6414 Millipore


title: Hippocampus, Kif5C IP_1


title: Mouse LGN Fn14 KO rep 1

first: Lucas

middle: Martin

last: Cheadle

address: 220 Longwood Ave; Goldenson Bldg 413

department: Neurobiology

institute: Harvard Medical School

data_processing:
Reads of fragment ends were nominally 75 bp in all cases, although for about one third of the reads not all sequencing cycles were completed.  Generally, over 99.9% of reported sequences were at least 70 bp long, so longer reads were uniformly 3ʹ-trimmed to that length and shorter ones were discarded, as were reads having any base with a sequencing Phred score below 13.  Most samples yielded a population of 35–80 million 70-bp reads.
Reads were aligned to the mouse genome (GRCm38/mm10 assembly, Dec. 2011) using the Burrows-Wheeler Aligner (bwa) tool.  Two sets of target sequences were incorporated into the bwa index in addition to the usual 21 chromosomal targets:  (1) the 16,299-bp mouse mitochondrial genome (GenBank accession NC_005089.1) and (2) a set of ~ 8 million short (≤ 138 bp) exon-exon splice-junction sequences (see below).  Typically ~70–75% of all LDR reads and ~80–90% of all developmental reads were mappable in each sample, allowing up to 2 mismatches, and of these ~75–90% aligned uniquely.
An in-house software tool, MAPtoFeatures, was used to quantify expression levels for individual genes as follows (Gray et al., 2014):  a database of genic features (CDSs and UTRs) was constructed from 95,023 genomic and 37 mitochondrial transcripts annotated in RefSeq for GRCm38.  Merged genes were constructed by unioning all exons in all transcripts assigned to each distinct gene; the resulting segments defined the gene’s exonic coordinates used here (with the gaps between them defining introns).  Genes with zero CDS exons were labeled “noncoding”.  These 33,102 genes were supplemented with 1,563 additional noncoding genes specified by the loci of all ribosomal RNA genes obtained from RepeatMasker (where the options Variations and Repeats, rmsk.repFamily=“rRNA” yielded 480 LSU-rRNA_Hsa elements; 45 SSU-rRNA_Hsa; and 1,038 5S).  The purpose of this step was to allow the filtering out of reads stemming from transcription of repeats and rRNA genes, which tend to get populated to inconsistent degrees from sample to sample depending on variability in the quality of rRNA depletion.
The splice-junction target sequences were based on the NCBI RefSeq database for GRCm38.  For each annotated transcript, we noted all subsets of two or more exons, ordered but not necessarily consecutive, that could be spliced together to produce a sequence at least as long as the read length (70 bp).  Each of these sequences was then trimmed to the maximum number of bases such that a read mapping to the sequence would necessarily cross these exons’ splice junction(s).  This procedure produced a library of all unique sets of exons whose intragenic splice junctions could possibly be covered by a read of the given length, based on the RefSeq annotation of exonic loci.  Aligned reads thus had the opportunity to align either to genomic (chromosomal) sequences or to exon-junction-crossing sequences found only in mature mRNA.  Multiple reads whose 5ʹ ends were assigned to the same locus on the same strand were not flattened to a single count.
For the developmental time course, our differential expression analysis calculated mean fold change ratios and their significance between (adjacent) pairs of time points for every expressed gene.  For the late-dark-rear study, we compared samples from mice re-exposed to light for one or eight hours to the unstimulated control using the three replicates for each pair. Statistical significance of each fold change was evaluated by Benjamini-Hochberg (BH) corrected p-values of all genes based on the biological variability implied by the replicates for each pair of conditions, with a significance threshold consistent with a false discovery rate of 5% (FDR=0.05). Differential-expression between sets of replicates was analyzed using the R Bioconductor package edgeR (version 3.14.0) (McCarthy et al., 2012).
Genome_build: Reads were aligned to the mouse genome (GRCm38/mm10 assembly, Dec. 2011) using the Burrows-Wheeler Aligner (bwa) tool
Supplementary_files_format_and_content: Excel tables for the Fn14 KO vs WT document differential expression (DE) ratios KO/WT.  Each row reports data for all annotated genes.  Columns report mean expression level (counts per million) for each sample as determined by edgeR; samples numbered for replicates 1-4; averages over replicates labeled WT and KO.

extract_protocol:
Mice were euthanized at P27. The LGNs of 4 WT and 4 KO mice were included in the analysis, and each mouse was processed as an independent bioreplicate.  After isoflurane anesthetization, mice were decapitated and the brain was isolated. Mouse brains were dissected and 300 um coronal sections were made on a Leica VT1000S vibratome. The dorsal LGNs were then microdissected in ice cold PBS after visual identification using a Nikon SMZ-10A brightfield dissection microscope, and flash frozen. Total RNA was trizol-extracted and purified using the Qiagen RNeasy Micro kit.
After RNA extraction, total RNA was depleted of ribosomal RNA, heat-fragmented to ~350 bp using the NEBNext kit, and processed with adapters to allow nonspecific sequencing on an Illumina NextSeq 500 System.

value: control (standard housing)

tag: time post-light stim

tag: replicate


title: Mouse LGN Fn14 KO rep 4


title: Mouse LGN Fn14 KO rep 3


title: Mouse LGN Fn14 WT rep 3


title: Mouse LGN Fn14 KO rep 2


title: Mouse LGN Fn14 WT rep 4


title: Mouse LGN Fn14 WT rep 2


title: Mouse LGN P32 control rep 4

description:
LC_DEG_DevP32vsP27_downregulated.xlsx
LC_DEG_DevP32vsP27_upregulated.xlsx

data_processing:
Reads of fragment ends were nominally 75 bp in all cases, although for about one third of the reads not all sequencing cycles were completed.  Generally, over 99.9% of reported sequences were at least 70 bp long, so longer reads were uniformly 3ʹ-trimmed to that length and shorter ones were discarded, as were reads having any base with a sequencing Phred score below 13.  Most samples yielded a population of 35–80 million 70-bp reads.
Reads were aligned to the mouse genome (GRCm38/mm10 assembly, Dec. 2011) using the Burrows-Wheeler Aligner (bwa) tool.  Two sets of target sequences were incorporated into the bwa index in addition to the usual 21 chromosomal targets:  (1) the 16,299-bp mouse mitochondrial genome (GenBank accession NC_005089.1) and (2) a set of ~ 8 million short (≤ 138 bp) exon-exon splice-junction sequences (see below).  Typically ~70–75% of all LDR reads and ~80–90% of all developmental reads were mappable in each sample, allowing up to 2 mismatches, and of these ~75–90% aligned uniquely.
An in-house software tool, MAPtoFeatures, was used to quantify expression levels for individual genes as follows (Gray et al., 2014):  a database of genic features (CDSs and UTRs) was constructed from 95,023 genomic and 37 mitochondrial transcripts annotated in RefSeq for GRCm38.  Merged genes were constructed by unioning all exons in all transcripts assigned to each distinct gene; the resulting segments defined the gene’s exonic coordinates used here (with the gaps between them defining introns).  Genes with zero CDS exons were labeled “noncoding”.  These 33,102 genes were supplemented with 1,563 additional noncoding genes specified by the loci of all ribosomal RNA genes obtained from RepeatMasker (where the options Variations and Repeats, rmsk.repFamily=“rRNA” yielded 480 LSU-rRNA_Hsa elements; 45 SSU-rRNA_Hsa; and 1,038 5S).  The purpose of this step was to allow the filtering out of reads stemming from transcription of repeats and rRNA genes, which tend to get populated to inconsistent degrees from sample to sample depending on variability in the quality of rRNA depletion.
The splice-junction target sequences were based on the NCBI RefSeq database for GRCm38.  For each annotated transcript, we noted all subsets of two or more exons, ordered but not necessarily consecutive, that could be spliced together to produce a sequence at least as long as the read length (70 bp).  Each of these sequences was then trimmed to the maximum number of bases such that a read mapping to the sequence would necessarily cross these exons’ splice junction(s).  This procedure produced a library of all unique sets of exons whose intragenic splice junctions could possibly be covered by a read of the given length, based on the RefSeq annotation of exonic loci.  Aligned reads thus had the opportunity to align either to genomic (chromosomal) sequences or to exon-junction-crossing sequences found only in mature mRNA.  Multiple reads whose 5ʹ ends were assigned to the same locus on the same strand were not flattened to a single count.
For the developmental time course, our differential expression analysis calculated mean fold change ratios and their significance between (adjacent) pairs of time points for every expressed gene.  For the late-dark-rear study, we compared samples from mice re-exposed to light for one or eight hours to the unstimulated control using the three replicates for each pair. Statistical significance of each fold change was evaluated by Benjamini-Hochberg (BH) corrected p-values of all genes based on the biological variability implied by the replicates for each pair of conditions, with a significance threshold consistent with a false discovery rate of 5% (FDR=0.05). Differential-expression between sets of replicates was analyzed using the R Bioconductor package edgeR (version 3.14.0) (McCarthy et al., 2012).
Genome_build: Reads were aligned to the mouse genome (GRCm38/mm10 assembly, Dec. 2011) using the Burrows-Wheeler Aligner (bwa) tool
Supplementary_files_format_and_content: Excel tables for the developmental time course and the late-dark-rear study document differential expression (DE) analyses between a pair of time points, each with multiple biological replicates.  Files labeled AvsB for times A and B report fold changes A/B.  Each row reports data for one merged gene (see above), excluding rRNA genes; only those genes are shown that meet a minimum threshold of 4 reads total over all listed samples, fewer being insufficiently informative for edgeR to meaningfully assess DE.  Columns include gene name and Entrez GeneID, genomic locus, exon number and total exonic (i.e., mRNA) length, and whether a coding gene; edgeR-calculated fold-change (FC>=1 for upregulated genes, FC<1 for downregulated), average expression level (Counts Per Million), unadjusted p value, and Benjamini-Hochberg-adjusted q value (FDR); read counts per sample (frds...), used as the input to edgeR; and normalized expression levels per sample (Dens...; units of Density = 0.35*RPKM).

extract_protocol:
Mice were euthanized on E18, P4, P10, P16, P20, P27 or P32. Each time point includes data from four mice total, processed as four independent samples.  After isoflurane anesthetization, mice were decapitated and the brain was isolated. Mouse brains were dissected and 300 um coronal sections were made on a Leica VT1000S vibratome. The dorsal LGNs were then microdissected in ice cold PBS after visual identification using a Nikon SMZ-10A brightfield dissection microscope, and flash frozen. Total RNA was trizol-extracted and purified using the Qiagen RNeasy Micro kit.
After RNA extraction, total RNA was depleted of ribosomal RNA, heat-fragmented to ~350 bp using the NEBNext kit, and processed with adapters to allow nonspecific sequencing on an Illumina NextSeq 500 System.


title: Mouse LGN P27 control rep 4

description:
LC_DEG_DevP27vsP20_downregulated.xlsx
LC_DEG_DevP27vsP20_upregulated.xlsx
LC_DEG_DevP32vsP27_downregulated.xlsx
LC_DEG_DevP32vsP27_upregulated.xlsx


title: Mouse LGN P32 control rep 3


title: Mouse LGN P32 control rep 1


title: Mouse LGN Fn14 WT rep 1


title: Mouse LGN P32 control rep 2


title: Mouse LGN P20 control rep 2

description:
LC_DEG_DevP20vsP16_downregulated.xlsx
LC_DEG_DevP20vsP16_upregulated.xlsx
LC_DEG_DevP27vsP20_downregulated.xlsx
LC_DEG_DevP27vsP20_upregulated.xlsx


title: ATAC WT T09 2


title: Mouse LGN P27 control rep 1


title: Mouse LGN P20 control rep 4


title: Mouse LGN P27 control rep 2


title: Mouse LGN P20 control rep 1


title: Mouse LGN P27 control rep 3


title: Mouse LGN P16 control rep 3

description:
LC_DEG_P16vsP10_downregulated.xlsx
LC_DEG_P16vsP10_upregulated.xlsx
LC_DEG_DevP20vsP16_downregulated.xlsx
LC_DEG_DevP20vsP16_upregulated.xlsx


title: Mouse LGN P16 control rep 2


title: Mouse LGN P20 control rep 3


title: Mouse LGN P16 control rep 1


title: Mouse LGN P10 control rep 1

description:
LC_DEG_DevP10vsP4_downregulated.xlsx
LC_DEG_DevP10vsP4_upregulated.xlsx
LC_DEG_DevP16vsP10_downregulated.xlsx
LC_DEG_DevP16vsP10_upregulated.xlsx


title: Mouse LGN P04 control rep 4

description:
LC_DEG_DevP4vsE18_downregulated.xlsx
LC_DEG_DevP4vsE18_upregulated.xlsx
LC_DEG_DevP10vsP4_downregulated.xlsx
LC_DEG_DevP10vsP4_upregulated.xlsx


title: Mouse LGN P10 control rep 4


title: Mouse LGN P10 control rep 2


title: Mouse LGN P16 control rep 4


title: Mouse LGN P10 control rep 3


title: Mouse LGN P04 control rep 2


title: Mouse LGN P04 control rep 1


title: Mouse LGN E18 control rep 3

description:
LC_DEG_DevP4vsE18_downregulated.xlsx
LC_DEG_DevP4vsE18_upregulated.xlsx


title: Mouse LGN E18 control rep 4


title: Mouse LGN E18 control rep 2


title: Mouse LGN P04 control rep 3