title: Th1 differentiated CD4 T Cell - eGFP_neg, Control, cell 11


title: LacZ-3

status: Public on May 01 2021

type: SRA

city: Dallas

first: Miao

last: Cui

state: Texas

address: UT Southwestern Medical Center Department of Molecular Biology 6000 Harry Hines Blvd.

country: USA

institute: UTSW

library_source: transcriptomic

data_processing:
For data analysis, quality control of RNA-seq data was performed using FastQC Tool (Version 0.11.4).
Sequencing reads were aligned to rat Rnor 6.0 reference genome using HiSAT2 (Version 2.0.4) with default settings and --rna-strandness F. Duplicate reads using Samtools v0.1.18
Aligned reads were counted using featurecount (Version 1.6.0) per gene ID
Differential gene expression analysis was performed with the R package edgeR (Version 3.20.5).
Genome_build: Rnor 6.0
Supplementary_files_format_and_content: Gene expressin fold-change (output from edgeR)

molecule: total RNA

organism: Mus musculus

source_name: Cardiomyocytes

growth_protocol: Primary cardomyocytes were isolated from 1- or 2-day-ols Sprague-Dawley rats with the Isolation System for Neonatal Rat/Mouse Cardiomyocytes (Cellutron, nc-6031) according to the manufacturer’s instructions. Cells were plated at a density of 3x105 cells/well to gelatin-coated 12-well plates and were maintained in DMEM/199 medium (3:1, 3% FBS) for 48 h before adenoviral infection.

extract_protocol:
RNA was harvested using Trizol reagent and ectracted using RNeasy Mini Kit (QIAGEN, 74104).
Libraries were prepared using KAPA mRNA HyperPrep Kits

treatment_protocol: Adenoviruses were diluted in DMEM/199 medium containing 3% FBS and were added to cells at an MOI of 50.

tag: cell type

value: primary rat neonatal ventricular cardiomyocytes

tag: strain

value: Sprague Dawley

tag: gene overexpression

value: Adeno-LacZ


title: LacZ-2


title: Nrf1-1


title: LacZ-1


title: Th1 differentiated CD4 T Cell - eGFP_neg, Control, cell 5

city: Berkeley

first: David

last: DeTomaso

state: CA

address: Stanley Hall

department: Computational Biology

institute: UC Berkeley

data_processing:
Sequencing reads were initially filtered with Trimmomatic 0.32 to remove low quality ends and adapter sequences
Bowtie2 v2.2.9  was used to align reads to a reference transcriptome derived from the GRCm38 Mus musculus genome (NCBI Refseq).
RSEM 1.2.31 was used to compile the transcriptome and estimate transcript abundance following read alignment.
Prior to alignment, the GRCm38 reference was augmented to include the modified eGFP+IL23R functional knockout transcript.
Genome_build: GRCm38
Supplementary_files_format_and_content: gene_expression_tpm.txt.gz: tab-delimited text table (genes x cells) of TPM-normalized gene expression values.  cell_metadata.txt: tab-delimited table which contains GFP label, genotype, and stimulation for each cell.

molecule: polyA RNA

source_name: Th1 differentiated CD4 T Cell - eGFP_neg_Control

extract_protocol:
Spleen and lymph nodes were extracted and then a single-cell suspension was prepared by homogenization with a syringe plunger and debris was filtered with a 70um filter. Red blood cells were lysed with ACK lysis buffer from Lonza. CD4+ T cells were enriched using a bead protocol from Miltenyi Biotech (cat #. 130-117-043) and then stained with anti-CD44 (clone IM7), anti-CD62L (clone Mel14), anti-CD4 (clone RM4-5) and anti-CD25 (clone PC61) in preparation for FACS. Naive T cells were sorted based on CD44lowCD62LhiCD4+CD25-.
T cells were sorted into 96-well plates containing 5 μl TCL Buffer (QIAGEN) with 1% 2-mercaptoethanol, centrifuged and frozen at −80 °C.  Single-cell sequencing libraries were then created using the Smart-seq2 protocol (Picelli et al., Nature Methods 2014) and carried out as described in Singer et. al, Cell 2016.

treatment_protocol: 1x106/ml naive T cells were plated in 6 well dishes and incubated for 4 days with (1) IL-12+IL-21+IL-23 (Th1 cell condition) and (2) IL-1b+IL-6+IL-23 (pathogenic Th17 cell condition). Viable CD4+ T cells were sorted with FACS into eGFP+ (i.e. IL-23R+) and eGFP- (i.e. IL-23R-) population as single cells into 96 well plates pre-loaded with 5ul TCL buffer (containing b-mercaptoethanol according to the manufacturer's instructions, Qiagen). cDNA libraries were prepared as outlined in Pawlak M., DeTomaso, D. et al. 2021 and sequenced.

tag: differentiation

tag: gfp

tag: genotype

value: Control

value: CD4 T Cells


title: Sham-KO

description: single-nucleus RNAseq

data_processing:
The Cell Ranger Single-Cell Software Suit was used to perform sample demultiplexing, barcode processing and single-cell 3′ gene counting.
The cDNA reads were aligned to the mm10/GRCm38 premRNA reference genome.
Further analyses for quality filtering was performed using the Seurat R package
Doublets were identified using Scrublet and removed from downstream analysis
For quantity filtering, we removed cells that had more than 2500 or fewer than 200 detected genes. We then normalized the data by the total expression, multiplied by a scale factor of 10,000 and log-transformed the result. To account for variation in the number of genes and UMI detected in each cell, we normalized the scaled expression matrix using the ScaleData function and set the vars.to.regress to nUMI and nGenes.  IntegrateData function implemented in Seurat V3 was used to correct batch effects between samples and to merge samples into one Seurat object.
Genome_build: mm10
Supplementary_files_format_and_content: count matrix

source_name: cadiac nuclei

extract_protocol:
Hearts from wildtype and Nrf1 knockout mice were dissected in ice-cold PBS and a transverse cut on the ligation plane (for MI hearts) or similar level (for sham hearts) was made. Heart tissue below the ligation plane was collected and pooled from 4-6 hearts of the same genotype for nuclei isolation.  Cardiac nuclei isolation was performed as previously described (PMID:33111095)
Total cardiac nuclei were used to generate single nucleus RNA-seq libraries using Single Cell 3’ Reagent Kits v2 (10xGenomics) according to the manufacturer’s protocol
single-nucleus RNA-seq

treatment_protocol: Timed-pregnant Nrf1fl/fl:aMHC-Cre and Nrf1fl/fl mice in the C57BL/6 background were used to deliver pups for neonatal MI surgery at P3. Neonatal mice were anesthetized by hypothermia, and neonatal MI was performed as described previously. Briefly, neonatal mice were anesthetized by hypothermia on an ice bed. Lateral thoracotomy at the fourth intercostal space was performed by blunt dissection of the intercostal muscles after skin incision. A tapered needle attached to a non-absorbable 8-0 suture (PROLENE, Ethicon) was passed through the mid-ventricle below the origin of the left anterior descending artery (LAD) coronary artery and tied to induce infarction. The pericardial membrane remained intact after LAD ligation. Myocardial ischemia was indicated by the light pallor of the myocardium below the ligature after suturing. After LAD ligation, neonates were removed from the ice, thoracic wall incisions were sutured with a 7-0 non-absorbable suture, and the skin wound closed by using skin adhesive. Sham-operated mice underwent the same procedure involving thoracotomy without LAD ligation.

value: purified nuclei from heart

value: Nrf1 cardiomyocyte knockout


title: WT_M_05

city: Baton Rouge

first: Carrie

middle: M

last: Elks

state: LA

address: 6400 Perkins Road

institute: Pennington Biomedical Research Center

data_processing:
Lexogen Quantseq pipeline V1.8.8 on the Bluebee platform, quality control, mapping, and read count tables
Annotation and preparation for analysis, R V3.4.3, Rstudio V1.1.423 and biomaRt V2.24.1
DESeq2  V1.18.1 differential gene expression
JMP Genomics V9 quality control, principal components, ANOVA, hierarchical clustering
Genome_build: mouse- GRCm38.84
Supplementary_files_format_and_content: tab-delimited text containing DESeq2 normalized counts

source_name: inguinal fat

extract_protocol:
Total RNA was prepared using Trizol, RNA concentration was determined using a NanoDrop spectrophotometer, and 1ug RNA per sample was used for sequencing library construction.
Lexogen Quant-Seq
24 samples Pooled, 75bp forward read

treatment_protocol: Inguinal fat pads were dissected from 6 male + 6 female wild-type and 6 male + 6 female adipocyte-specific STAT5 knockout mice at 6 weeks of age

tag: Sex

value: male

value: wild type (floxed)

tag: diet

value: 5015 chow


title: KO_M_05

value: STAT5 knockout


title: WT_M_03


title: KO_M_04


title: WT_M_02


title: KO_M_03


title: KO_M_02


title: KO_M_06


title: WT_M_06


title: WT_M_01


title: WT_F_06

value: female


title: KO_M_01


title: WT_M_04


title: WT_F_05


title: WT_F_04


title: KO_F_06


title: KO_F_05


title: KO_F_02


title: KO_F_03


title: WT_F_03


title: KO_F_04


title: WT_F_02


title: WT_F_01


title: S6-KO_S12_R1_001

city: Wuerzburg

first: Franziska

last: Faber

state: Bavaria

address: Josef-Schneider-Straße 2

department: Institute for Molecular Infection Biology

country: Germany

institute: University of Wuerzburg

description:
dRNA-Seq
dRNA-seq  {delta}jag  stationary-phase
C. difficile grown to stationary phase

data_processing:
Grad-seq: Reads were trimmed and filtered with cutadapt (-q 20 -m 1 -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC). For read mapping and calculation of read counts the READemption pipeline version 4.3 was used with READemption align (-r, -a 95, -l 20 --fastq) and READemption coverage (default).
RIP-Seq: Reads were trimmed and filtered with BBDuk (ktrim=r, k=23, mink=11, hdist=1, tpe, tbo, qtrim=r, trimq=20, minlength=25). For read mapping and calculation of read counts the READemption pipeline version 4.5 was used with READemption align (-r, -a 95, -l 20 --fastq) and READemption coverage (default).
dRNA-Seq: Reads were trimmed and filtered with cutadapt (--nextseq-trim=20, -m 1 -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCA). For read mapping and calculation of read counts the READemption pipeline version 4.5 was used with READemption align (-r, -a 95, -l 20 --fastq) and READemption coverage (default).
Genome_build: CP010905.2
Supplementary_files_format_and_content: wiggle

organism: Clostridioides difficile

source_name: C. difficile 630 cell lysate

growth_protocol: Cultures were grown in BHI to late-exponential or stationary phase (see sample description)

extract_protocol:
5 OD per culture were harvested by subsequent addition of 1/5 volume ice-cold stop mix (95% EtOH, 5% phenol). Tubes were immediately frozen with liquid nitrogen and stored at -80 °C. RNA was extracted via hot phenol extraction. The mixture was slowly thawed on ice and centrifuged for 20 min at 4500 g and 4 °C. The supernatant was completely discarded. Cells were resuspended in 600 µl lysozyme (10 mg/ml) in TE buffer (pH 8.0) and incubated for 10 min at 37 °C. 60 µl w/v SDS was added and mixed by inversion. The samples were heated to 64 °C in a water bath for 1-2 minutes. Subsequently, 66 µl 3 M sodium acetate (pH 5.2) and 750 µl acid phenol (Roti Aqua Phenol, Roth, #A980.1) was added. The mixture was incubated for another 5 min at 64 °C. Samples were cooled down on ice before centrifugation for 15 min at 13,000 rpm and 4 °C. The aqueous layer was transferred to a phase lock gel tube (Eppendorf). 750 µl chloroform (Roth, #Y015.2) was added before centrifugation for 12 min at 13,000 rpm at room temperature. The aqueous layer was transferred to a new tube and 2 volumes of EtOH:3 M NaOAc (30:1, pH 5.2) was added. RNA precipitation was done overnight at -20 °C. RNA was pelleted by centrifugation for 45 mins, 16,000 rpm at 4°C. Pellet was washed twice with EtOH (80% EtOH, 100% EtOH) and air-dried. RNA was resuspended in 50 µl nuclease free water (65 °C, 1000 rpm, 5 min). DNA contaminations were removed via DNase I digestion as described previously above.
RNA-sequencing was conducted by Vertis Biotechnology AG. In brief, quality of RNA samples was checked on a Shimadzu MultiNA microchip electrophoresis system. Fragmentation was done using ultrasound (1 pulse of 30 sec each at 4 °C). Adapter was ligated to the 3’end. First-strand cDNA synthesis was performed using M-MLV transcriptase and the 3’ adapter as primer. cDNA was purified and 5’ Illumina TruSeq adapter was ligated to the 3’ end of the antisense strand. Amplification was done via PCR to 10-20 ng/µl using high fidelity protocol and 14 PCR cycles. The cDNA was then purified with Agencourt AMPure XP kit (Beckman Coulter Genomics) and analyzed by capillary electrophoresis as stated above. For sequencing, the samples were pooled in equimolar amounts and sequenced on a Illumina NextSeq 500 system using 75 bp read length.

tag: phase

value: stationary-phase

value: {delta}jag


title: S3-WT_S9_R1_001

description:
dRNA-Seq
dRNA-seq WT  stationary-phase
C. difficile grown to stationary phase

value: WT


title: S4-KO_S10_R1_001


title: S1-WT_S7_R1_001

description:
dRNA-Seq
dRNA-seq WT
C. difficile grown to stationary phase


title: S2-WT_S8_R1_001

description:
dRNA-Seq
dRNA-seq WT stationary-phase
C. difficile grown to stationary phase


title: E6-KO_S6_R1_001

description:
dRNA-Seq
dRNA-seq  {delta}jag exponential-phase
C. difficile grown to late-exponential phase

value: late-exponential phase


title: KO_F_01


title: WT-4_S41_R1_001

description:
RIP-Seq
RIP-seq control
C. difficile grown to late-exponential phase, RNA from RIP-seq

extract_protocol:
Pellets were resuspended in 800 µl lysis buffer (20 mM Tris-HCl (pH 8.0), 1 mM MgCl2, 150 mM KCl, 1 mM DTT) and 1 µl DNase I (Fermentas, 1 U/µl) was added to each sample. Cells were mixed with 800 µl of 0.1 mm glass beads and lysed in a RETSCH's Mixer Mill (30 Hz, 10 min, 4°C). Lysates were cleared by centrifugation for 10 min at 14,000 g and 4 °C. The supernatant was transferred to a new tube and the volume was adjusted to 900 µl with lysis buffer.25 µl of anti-FLAG antibody (clone M2, Merck/Sigma-Aldrich #F1804) was added to each sample before rocking at 4 °C for 1 hour. In the meantime, 75 µl Protein A Sepharose beads (Merck/Sigma-Aldrich #P6649) per sample were washed 3x with 1 ml lysis buffer. The lysates were added to the beads and rocked for another hour. Beads were washed 5x with 500 µl lysis buffer and finally resuspended in 550 µl lysis buffer. 50 µl of beads were transferred to a fresh tube and mixed with 1x protein loading buffer for Western blot analysis. 500 µl Phenol:Chloroform:Isoamylalcohol (25:24:1, pH 4.5, Roth) was added to the beads. The solution was mixed for 20 s, transferred to phase lock tubes (Eppendorf) and incubated at room temperature for 3 min. Tubes were spun for 30 min at 15,000 g and 15 °C. The aqueous phase was transferred to a new tube, 3 µl Glycoblue (Thermo Fisher Scientific) and 800 µl isopropanol was added. RNA precipitation was done overnight at -20 °C. Samples were centrifuged for 45 min at 15,200 rpm and 4 °C. Supernatant was discarded. RNA was washed 1x with 80% Ethanol and 1x with 100% Ethanol (centrifugation for 10 min, 15,200 rpm, 4 °C). The pellets were air-dried at room temperature and resuspended in 15.5 µl nuclease free water (65 °C, 5 min, 600 rpm). For DNAse I treatment, 2 µl DNase I (Thermo Fisher Scientific), 0.5 µl RNase inhibitor and 2 µl DNase buffer (Thermo Fisher Scientific ) was added to each sample. Incubation was done for 30 min at 37 °C. To isolate the RNA, 100 µl nuclease free water and 120 µl Phenol:Chloroform:Isoamylalcohol (25:24:1, pH 4.5, Roth) was added to the samples. RNA isolation and precipitation was done as described before. RNA was resuspended in 20 µl nuclease free water.
RNA-sequencing was conducted by Vertis Biotechnology AG. In brief, quality of RNA samples was checked on a Shimadzu MultiNA microchip electrophoresis system. Fragmentation was done using ultrasound (4 pulses of 30 sec each at 4 °C). Adapter was ligated to the 3’end. First-strand cDNA synthesis was performed using M-MLV transcriptase and the 3’ adapter as primer. cDNA was purified and 5’ Illumina TruSeq adapter was ligated to the 3’ end of the antisense strand. Amplification was done via PCR to 10-20 ng/µl using high fidelity protocol and 11 PCR cycles. The cDNA was then purified with Agencourt AMPure XP kit (Beckman Coulter Genomics) and analyzed by capillary electrophoresis as stated above. For sequencing, the samples were pooled in equimolar amounts and sequenced on a Illumina NextSeq 500 system using 75 bp read length.

tag: antibody

value: control


title: E1-WT_S1_R1_001

description:
dRNA-Seq
dRNA-seq WT
C. difficile grown to late-exponential phase


title: WT-1_S39_R1_001


title: FLAG-3_S44_R1_001

description:
RIP-Seq
RIP-seq FLAG
C. difficile grown to late-exponential phase, RNA from RIP-seq

value: FLAG


title: FLAG-2_S43_R1_001


title: E4-KO_S4_R1_001


title: E2-WT_S2_R1_001

description:
dRNA-Seq
dRNA-seq WT exponential-phase
C. difficile grown to late-exponential phase


title: Grad_01_Fraction_19_Rep_1

description:
Grad-Seq
C. difficile grown to late-exponential phase

extract_protocol:
Glycerol gradient fractionation was performed as previously described (Hor et al., 2020). C. difficile was grown in 2x100 ml BHI to late-exponential phase (OD 1.6). The cultures were cooled down for 15 minutes on ice-water slurry before harvesting by centrifugation for 20 min at 4 °C and 4,000 g. Pellets were washed three times with ice-cold 1x TBS before resuspension in 500 µl ice-cold 1x lysis buffer (20 mM Tris-HCl (pH 7.5), 150 mM, KCl, 1 mM DTT, 1 mM PMSF, 0.2% Triton X 100, 20 U/ml DNase I (Thermo Fisher Scientific), 200 U/ml RNase inhibitor. Cell lysis was performed in 2 ml tubes containing Lysing matrix E (MP Biomedicals, 1.4 mm ceramic spheres, 0.1 mm silica spheres and one 4 mm glass bead) by using a FastPrep-24 (MP Biomedicals) machine at 6 m/s for 30 seconds. The lysate was centrifuged for 30 minutes at 4 °C and 4,000 g to remove beads and debris. For input controls 10 µl lysate was mixed with 1 ml TRIzol and 20 µl was mixed with 5x protein loading buffer. The 10 – 40% (w/v) glycerol gradient (in lysis buffer without DNase I or RNase inhibitor) was formed in an open-top polyallomer ultracentrifugation tube (Seton Scientific) by using a Gradient Station model 153 (Biocomp).  200 µl of the cleared cell lysate was layered on top of the gradient. The gradient was centrifuged for 17 h at 4 °C and 100,000 rcf (23,700 rpm) with an SW 40 Ti rotor (Beckman Coulter). Fractionation was done manually into 20 fractions with a volume of 590 µl.. For quality control, all fractions were analyzed with a NanoDrop (Thermo Fisher Scientific) Spectrophotometer at 260 nm. 90 µl of each fraction was mixed with 5x protein loading buffer to perform LC-MS/MS.500 µl of the fractions was used for total RNA isolation. Therefore, 50 µl 10% SDS (25 µl for the pellet) and 600 µl of acidic phenol/chloroform/isoamyl alcohol (P/C/I, Roth, 300 µl for the pellet) was added to the fractions. After mixing for 30 seconds the mixture was incubated for 5 minutes at room temperature. Additionally, 400 µl chloroform was added to the Trizol-dissolved lysate and mixed. The lysate was also incubated for 5 minutes at room temperature. Samples were centrifuged for 15 minutes at 4 °C and 13,000 rpm. The aqueous phase of all samples was collected. For RNA precipitation 1.4 ml ice-cold 30:1 Ethanol:3 M NaOAc (pH 6.5) and 1 µl GlycoBlue (Thermo Fisher Scientific) co-precipitant was added. RNA was precipitated over night at -20 °C. RNA was pelleted at 4 °C for 30 minutes and 13,000 rpm and washed with 70% ice-cold Ethanol, followed by centrifugation at 4 °C for 15 minutes and 13,000 rpm. The pellet was air-dryed and subsequently dissolved in 40 µl DEPC-H2O. To digest DNA 0.5 µl RNase inhibitor, 5 µl DNase I buffer with MgCl2 (Thermo Fisher Scientific), 4 µl DNase I (Thermo Fisher Scientific) and 0.5 µl DEPC-H2O was added to each RNA sample. Digestion was performed for 45 minutes at 37 °C. RNA was again purified as described above. This time 150 µl DEPC-H2O and 200 µl acidic P/C/I was added to the samples. RNA was finally dissolved in 35 µl DEPC-H2O and stored at -80 °C.
5 µl of the gradient samples were diluted in 45 µl DEPC-H2O. 10 µl of the diluted RNA was mixed with a 1:100 dilution of ERCC spike-in mix 1 (Thermo Fisher Scientific). Library preparation and sequencing was carried out by Vertis Biotechnology AG. In brief, the RNA was fragmented using ultrasound (4 pulses of 30 seconds each at 4 °C) followed by 3’ adapter ligation. First-strand cDNA synthesis was performed using M-MLV reverse transcriptase and the 3’-adapter as primer. After purification of the cDNA the 5’ Illumina TruSeq sequencing adapter was ligated to the 3’ end of the antisense cDNA. Amplification of cDNA (10-20 ng/µl) was done via PCR using high fidelity RNA polymerase and 12 sequencing cycles. cDNA purification was performed with Agencourt AMPure XP kit (Beckman Coulter Genomics). The samples were pooled for sequencing. cDNA pool was eluted in the size range of 200 – 500 bp from a preparative agarose gel. Quality check of RNA was performed in between steps with capillary electrophoresis using a Shimadzu MultiNA microchip. The cDNA pool was sequenced on an Illumina NextSeq 500 system using 75 bp read length.

tag: fraction

value: Fraction 20


title: Grad_01_Fraction_21P_Rep_1

value: Pellet


title: Grad_01_Fraction_18_Rep_1

value: Fraction 19


title: Grad_01_Fraction_20_Rep_1

value: Fraction 21


title: E5-KO_S5_R1_001


title: FLAG-1_S42_R1_001


title: Grad_01_Fraction_17_Rep_1

value: Fraction 18


title: Grad_01_Fraction_16_Rep_1

value: Fraction 17


title: Grad_01_Fraction_15_Rep_1

value: Fraction 16


title: Grad_01_Fraction_14_Rep_1

value: Fraction 15


title: Grad_01_Fraction_13_Rep_1

value: Fraction 14


title: S5-KO_S11_R1_001


title: Grad_01_Fraction_12_Rep_1

value: Fraction 13


title: E3-WT_S3_R1_001


title: WT-2_S40_R1_001


title: Grad_01_Fraction_09_Rep_1

value: Fraction 10


title: Grad_01_Fraction_11_Rep_1

value: Fraction 12


title: Grad_01_Fraction_10_Rep_1

value: Fraction 11


title: Grad_01_Fraction_08_Rep_1

value: Fraction 9


title: Grad_01_Fraction_07_Rep_1

value: late-exponential phase

value: Fraction 8


title: Grad_01_Fraction_05_Rep_1

value: Fraction 6


title: Grad_01_Fraction_06_Rep_1

value: Fraction 7


title: Grad_01_Fraction_04_Rep_1

value: Fraction 5


title: Grad_01_Fraction_03_Rep_1

value: Fraction 4


title: Maize_Incompatible_24 hai_Rep 1

city: Urbana

first: Tiffany

last: Jamann

state: IL

address: 1102 S Goodwin Ave, Turner Hall

department: Crop Sciences

data_processing:
Fastq files were generated and demultiplexed with with the bcl2fastq v2.20 Conversion Software (Illumina). Adaptors were trimmed from the 3'-end of the reads.
The maize and sorghum reads were aligned to their respective genomes using the splice-aware aligner STAR v2.7
The read counts per gene were then quantified from the alignments using ‘featureCounts’ in the subread package. Multi-mapping reads and reads with ambiguous assignments were removed.
The Bioconductor package “edgeR” was used for quality control and normalization. The count per million (CPM) was calculated using the package "edgeR".
The genes with less than one count per million (CPM) in more than three samples were filtered out. The trimmed mean of M-values (TMM) normalization factors were calculated to correct for different library sizes and composition bias and to obtain normalized log2CPM values with a prior count of three.
The package “limma”  was used to calculate differentially expressed genes (DEGs).
Genome_build: GCF_000005005.2_B73_RefGen_v4 from NCBI
Genome_build: Sbicolor_454v3 from Phytozome
Supplementary_files_format_and_content: tab-delimited text files include raw gene counts for every gene and for every sample.
Supplementary_files_format_and_content: summary files include a summary statistic table for each gene.
Supplementary_files_format_and_content: csv files include log normalized count data for every gene and for every sample.

organism: Zea mays

source_name: leaf

growth_protocol: S. turcica strains (Et28A and 15st008) were cultured inlactose-caesin hydrolysate agar (LCA) media for 2 weeks under 12hr/12hr light/dark environment at room temperature. Maize (B73) and sorghum (BTx623) were grown in the greenhouse at the Plant Care Facility at the University of Illinois at Urbana-Champaign.

extract_protocol:
RNA was extracted using a combination of TriZol (ThermoFisher Scientific, Waltham, MA) and an RNAeasy miniElute cleanup kit (QIAGEN, Germantown, MD).
The libraries were prepared using a TruSeq Stranded mRNAseq Sample Prep kit (Illumina, San Diego, California) and quantified using qRT-PCR.

treatment_protocol: Each maize and sorghum plant was treated with either mock, maize-specific S. turcica (Et28A) or sorghum-specific S. turcica (15st008) when plants were approximately V3 stage. Fungal concentrations were 4^10^3 spores/ml.

tag: tissue

value: leaf

tag: cultivar

tag: timepoint

value: 24 hours after inoculation

tag: replication

tag: group

value: Incompatible interaction


title: Maize_Incompatible_24 hai_Rep 3

institute: University of Illinois at Urbana-Champaign


title: Maize_Incompatible_24 hai_Rep 2


title: Maize_Compatible_72 hai_Rep 2

value: 72 hours after inoculation

value: Compatible interaction


title: Maize_Incompatible_72 hai_Rep 3


title: Maize_Compatible_72 hai_Rep 1


title: Maize_Compatible_72 hai_Rep 3


title: Grad_01_Fraction_00L_Rep_1

value: Lysate


title: Maize_Compatible_24 hai_Rep 1


title: Grad_01_Fraction_01_Rep_1

value: Fraction 2


title: Maize_Compatible_24 hai_Rep 2


title: Maize_Compatible_24 hai_Rep 3


title: Maize_Mock_24 hai_Rep 3

value: mock

value: No interaction


title: Maize_Mock_72 hai_Rep 2


title: Maize_Mock_72 hai_Rep 3


title: Maize_Mock_24 hai_Rep 2


title: Maize_Mock_24 hai_Rep 1


title: Maize_Mock_72 hai_Rep 1


title: Grad_01_Fraction_02_Rep_1

value: Fraction 3


title: RBEtreat3

city: Shanghai

first: Fei

last: Song

state: Shanghai

address: 180 Fenglin Road Shanghai China

country: China

institute: Zhongshan Hospital

data_processing:
Reads filtering under criteria removing reads with 20% of the base quality lower than 13
HISAT2.1.0 mapping to reference genome
Reads Per Kilobase of exon per Megabase of library size (RPKM) were calculated using a protocol from Chepelev et al., Nucleic Acids Research, 2009. In short, exons from all isoforms of a gene were merged to create one meta-transcript. The number of reads falling in the exons of this meta-transcript were counted and normalized by the size of the meta-transcript and by the size of the library.
Genome_build: GRCh38
Supplementary_files_format_and_content: tab-delimited text files include RPKM values for each Sample

organism: Homo sapiens

source_name: Anlotinib 5μM treatment of RBE cell line

growth_protocol: The cell lines were grown in RPMI 1640 with 10% fetal bovine serum (FBS) (Gibco) and antibiotics (penicillin 100 U/ml, streptomycin 100 mg/ml) in a 37℃ humidified atmosphere with 5% CO2.

extract_protocol:
HCCC9810 and RBE cells were cultured with or without anlotinib for 24 hours. The total RNA of the cultured cells was extracted using the RNeasy kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. RNA was quantified using the Qubit® RNA HS Assay Kit by Qubit®2.0 Fluorometer (Life Technologies, Grand Island, NY, USA). RNA quality was analyzed using an Agilent 2100 bioanalyzer (Agilent Technologies, Palo Alto, CA. USA). RNA-seq library construction for next-generation sequencing and paired-end deep sequencing was performed on an Illumina PE150 platform (Illumina, San Diego, CA), according to the manufacturer’s protocol.
RNA libraries were prepared for sequencing using standard Illumina protocols

treatment_protocol: Anlotinib was provided by Zhengda Tianqing Pharmaceutical Group Co., Ltd. Dimethyl sulfoxide (Sigma, St. Louis, MO, USA) was used to dissolve the drug. Working solutions were prepared by diluting the stock solution with RPMI 1640. The final DMSO concentration was 0.1%.

value: Intrahepatic cholangiocarcinoma cell

tag: cell line

value: RBE cell line

tag: drug treatment

value: Anlotinib 5μM


title: RBEtreat2

tag: cell type


title: Maize_Incompatible_72 hai_Rep 1

tag: strain


title: Maize_Incompatible_72 hai_Rep 2


title: RBEtreat1


title: RBEcontrol6

source_name: control of RBE cell line

value: control


title: 9810treat8

source_name: Anlotinib 5μM treatment of HCCC9810 cell line

value: HCCC9810 cell line


title: 9810control12

source_name: control of HCCC9810 cell line


title: 9810treat7


title: 9810treat9


title: RBEcontrol4


title: RBEcontrol5


title: H446 Hi-ChIP

first: Jane

middle: E

last: Johnson

state: TX

address: 5323 Harry Hines Blvd

department: Neuroscience

institute: UT Southwestern Medical Center

library_source: genomic

data_processing:
HiChIP
Reads were mapped to hg19 assembly using HiC-pro
Interaction loops were called using HiChipper
Genome_build: hg19
Supplementary_files_format_and_content: Loop coordinates in bed format

molecule: genomic DNA

extract_protocol:
HiChIP protocol based on Mumbach et al., 2016, Nature Methods
Illumina Nextera Library preparation

value: Small cell lung cancer cell line

value: H3K27ac, Abcam, ab4729


title: H524 Hi-ChIP


title: SHP77 Hi-ChIP


title: H69 Hi-ChIP


title: 9810control11


title: H2066 Hi-ChIP


title: Hypothalamus_GSK-J4_30mg/kg_rep3

type: RNA

first: Xu

last: Wang

address: No. 639, Zhizaoju Road

institute: Shanghai Jiao Tong university, School of Medicine

description: Gene expression in GSK-J4 treated hypothalamus

hyb_protocol: 0.6 μg of Cy3-labelled cRNA (specific activity >10.0 pmol Cy3/μg cRNA) was fragmented at 60°C for 30 minutes in a reaction volume of 22.5μl containing 1x Agilent fragmentation buffer and 2x Agilent blocking agent following the manufacturers' instructions. On completion of the fragmentation reaction, 22.5μl of 2x Agilent hybridization buffer was added to the fragmentation mixture and hybridized to Agilent Mouse Gene Expression(8*60K)for 17 hours at 65°C in a rotating Agilent hybridization oven. After hybridization, microarrays were washed 1 minute at room temperature with GE Wash Buffer 1 (Agilent) and 1 minute with 37°C GE Wash buffer 2 (Agilent), then dried immediately by brief centrifugation.

scan_protocol: Slides were scanned immediately after washing on the Agilent DNA Microarray Scanner (G2505C) using one color scan setting for 4x180k array slides (Scan Area 61x21.6 mm, Scan resolution 3um, Dye channel is set to Green and Green PMT is set to 100%).

data_processing: The scanned images were analyzed with Feature Extraction Software 10.7.1.1 (Agilent) using default parameters to obtain background subtracted and spatially detrended Processed Signal intensities as the raw data. Raw data were normalized in quantile algorithm with Genespring 13.0(Agilent). Probe that at least 1 out of 2 samples flagged as Detected were maintained.

label_protocol: Cyanine-3 (Cy3) labeled cRNA was prepared from 0.2 μg RNA using the One-Color Low RNA Input Linear Amplification PLUS kit (Agilent) according to the manufacturer's instructions, followed by RNAeasy column purification (QIAGEN, Valencia, CA). Dye incorporation and cRNA yield were checked with the NanoDrop ND-1000 Spectrophotometer.

growth_protocol: Mice were housed in a temperature-controlled (22 °C) room with a 12 h light/dark cycle, fed a HFD (60 kcal% fat content; Research Diets formulas D12331; Research Diets, Inc., New Brunswick, NJ)

extract_protocol: RNA was extracted by mirVanaTM RNA Isolation Kit (Applied Biosystem p/n AM1556 ) following the manufacturer's instructions.

treatment_protocol: Male DIO mice were administrated with vehicle or GSK-J4, the mice were sacrificed and the hypothalamus tissues were obtained

value: Hypothalamus

tag: gender

value: Male

value: C57BL/6J DIO


title: H2029 Hi-ChIP


title: Hypothalamus_Vehicle_rep2


title: H2171 Hi-ChIP


title: 9810control10


title: Hypothalamus_GSK-J4_30mg/kg_rep1


title: Hypothalamus_GSK-J4_30mg/kg_rep2


title: H1876 Hi-ChIP


title: H1836 Hi-ChIP


title: H1694 Hi-ChIP


title: Hypothalamus_Vehicle_rep3


title: H1105 Hi-ChIP


title: DMS273 Hi-ChIP


title: Hypothalamus_Vehicle_rep1


title: CORL88 Hi-ChIP


title: CORL279 Hi-ChIP


title: H146 Hi-ChIP


title: human sperm_Sample H10

city: Providence

first: Susan

last: Hall

state: RI

address: Box G-E5

institute: Brown University

data_processing:
The sequencing configuration used was 2 x 150 bp, single index, per lane with estimated data output of 350M raw paired-end reads per lane.
RNA-sequencing data from each species was aligned to the genome of the corresponding species (human; hg19 version, mouse; mm10 version, rat; rn6 version) using Bowtie
Raw counts of comparable transcripts from all three species were combined and normalized using the upper quartile method  to compare abundance across species. The average normalized counts to the nearest integer were calculated for each transcript for each species, and the number of transcripts that had each abundance was plotted to find the distribution of counts. The linear fit curve (lm function in R) was calculated on the natural log of the variables to find the goodness of fit of the exponential curve and start values for the non-linear fit curve (nls function in R). The exponential equation y = ae(-bx) was used to determine p = 0.05 and p= 0.01, where a and b constants were derived from the non-linear fit curve.
Genome_build: human; hg19 version, mouse; mm10 version, rat; rn6 version
Supplementary_files_format_and_content: normalized expression

source_name: human sperm

growth_protocol: Semen specimens were collected from men aged 18 to 55 years with proven fertility. Rat and mouse semen specimens were collected via needle punctures of the cauda epididymides.

extract_protocol:
RNA was collected from human ejaculated sperm, rat and mouse (cauda) epididymal sperm using the method previously described by E. Bianchi et al, 2018.
PolyA-purified mRNA-sequencing: Poly A selection libraries were prepared for sequencing using standard Illumina protocols

treatment_protocol: No treatment involved

value: sperm


title: human sperm_Sample H12


title: human sperm_Sample H7


title: human sperm_Sample H19


title: RNAseq Sample 4 MacroH2A1.2 overexpressed cells

city: Singapore

first: Azhar

middle: B

last: Ali

address: 14 Medical Drive MD6 #11-01

department: CSI Singapore

country: Singapore

institute: National University of Singapore

description:
RNA-seq
C17

data_processing:
For RNA-seq data quality control, FASTp was used to filter reads containing adapter or low quality.
Reads were mapped to reference genome using STAR and quantified using HTSeq.
Differential expression of genes between 2 samples was done using DESeq2.
GO enrichment analysis, KEGG pathway enrichment analysis, reactome pathway enrichment analysis, DO enrichment analysis were done using cluster profiler.
Genome_build: hg19
Supplementary_files_format_and_content: txt file recoding FPKM values

source_name: PC-9 lung cancer cell line

growth_protocol: Vector and macroH2A1.2 overexpressed PC-9 cells were cultured with RPMI supplemented with 10% fetal bovine serum. The cells were passage every 3 to 4 days.

extract_protocol:
RNA was extracted using GeneJet RNA purification kit.
RNA-seq library were prepared by sequencing provider, Novogene IT.  250-300bp insert stranded-specific cDNA library (Eukaryotic mRNA) was used for sequencing.

treatment_protocol: 200µg/ml Hygromycin selection antibody

value: Lung cancer cell line

tag: mutation

value: EGFR mutant, macroH2A1.2 transfected

tag: passage


title: RNAseq Sample 6 MacroH2A1.2 overexpressed cells

description:
RNA-seq
C17


title: RNAseq Sample 3 vector transfected cells

description:
RNA-seq
C1

value: EGFR mutant, pCMV3 transfected


title: methocel pool-d15 Quad F vs. Cerivastatin 1.0mkd-d15 Quad F3

city: West Point

first: Keith

last: Tanis

address: 770 Sumnetown Pike

institute: Merck & Co, Inc

hyb_protocol: Purified Cy3 or Cy5 complementary RNA was hybridized to two microarrays with fluor reversal for 24 hr in a hybridization chamber, washed, and scanned. Sample studies utilized GPL15569 and others GPL3631 as indicated in the sample annotations. Each GEO sample represents the mean of the 2 fluor-reversed hybridizations.

scan_protocol: Arrays were scanned using a laser confocal scanner.

data_processing:
Arrays were quantified on the basis of spot intensity relative to background, adjusted for experimental variation between arrays using average intensity over multiple channels using the Rosetta Resolver error model as described in PMID: 16522673.
The submitted raw data files include the mean intensity values from the Cy3,Cy5 fluor-reversed hybridizations.

organism: Rattus norvegicus

source_name: methocel pool-d15 Quad F

label_protocol: Total RNA was reverse transcribed using an oligo-dT primer containing a T7 RNA polymerase promoter site, followed by complementary strand synthesis using random hexamers. In vitro transcription (IVT) was then performed using RNA polymerase and the RNA was then labeled with one of two fluorescent dyes, Cy3 and Cy5.

extract_protocol: Trizol reagent (1 ml per 100 mg of tissue) was added to the tissue, and samples homogenized immediately. An aliquot of the homogenate was then transferred to a separate vial and chloroform extracted to remove proteins. The remaining tissue homogenate was stored at –70C for future use. The supernatant was then transferred to a separate vial, and RNA isolated using Qiagen RNeasy columns or 96-well plates as described by the manufacturer. Following RNA isolation, samples were treated with DNase to remove contaminating DNA, and quantified.

treatment_protocol: The compounds, vehicles, frequency of administration, dose levels and time points collected are presented in the sample annotations. In general, the study details were consistent between protocols and were as follows: male and/or female Sprague-Dawley CD (SD) or rats were obtained from Charles River Laboratories, Inc. (Raleigh, North Carolina). The rats were approximately seven to eight weeks of age and weighed from 125 to 325 grams at the start of the study. The animals were acclimated for approximately one week and randomized into the treatment and control groups. In general, five rats were included in each treatment group. Doses were calculated based on animal body weight, and the last dose was given 24 hours prior to necropsy in studies with daily dosing. All animals were fasted overnight prior to necropsy. Tissue samples (liver, kidney, heart, and skeletal muscle) were collected from each animal at necropsy. Tissue sections were removed and processed for histopathology assessment, and an additional adjacent tissue section removed and placed immediately on dry ice for genomic analyses, allowing for direct comparison of the gene expression response to histopathology outcome. For the skeletal muscle, the quadriceps muscle group was selected as a representative muscle, consisting of 3 muscles (vastus lateralis, vastus medialis, and rectus femoris) comprised of predominantly Type II (glycolytic) muscle fibers, and 1 muscle (vastus intermedius) comprised of predominantly Type I (oxidative) muscle fibers. Tissue samples were stored at –70C until RNA extraction.

tag: sample type

value: Study Compound Control Pool

tag: study

source_name: Skeletal Muscle (Quad)

value: Skeletal Muscle (Quad)

tag: compound

value: Cerivastatin

tag: necropsy day

tag: dose

tag: dose unit

value: mkd

tag: animal number


title: RNAseq Sample 2 vector transfected cells

description:
RNA-seq
C1

tag: tissue


title: human sperm_Sample H18


title: methocel pool-d15 Quad F vs. Cerivastatin 1.0mkd-d15 Quad F4


title: RNAseq Sample 1 vector transfected cells

description:
RNA-seq
C1


title: methocel pool-d15 Quad F vs. Cerivastatin 0.5mkd-d15 Quad F1


title: methocel pool-d15 Quad F vs. Cerivastatin 0.5mkd-d15 Quad F4


title: human sperm_Sample H13


title: RNAseq Sample 5 MacroH2A1.2 overexpressed cells

description:
RNA-seq
C17


title: methocel pool-d15 Quad F vs. methocel-d15 Quad F10


title: THP1-IL34

city: tianjin

first: lv

middle: xiao

last: qian

address: nanjinglu

institute: Tsinghu university

data_processing:
After reads filtering, we map clean reads to reference genome using HISAT software; We map clean reads to reference transcripts using Bowtie2, then calculate gene expression level for each sample with RSEM. The genome build is GRCh38(hg38)
Genome_build: GRCh38(hg38)
Supplementary_files_format_and_content: FPKM

source_name: Leukemia cell

extract_protocol:
sorted leukemia were harvested and resuspended in 350ul RLT and 3.5ul β-Me.Total RNA is exacted by QIAGEN Rneasy Mini Kit(CAT 74103). 1 ug of total RNA for the construction of sequencing libraries.
RNA libraries were prepared for sequencing using standard Illumina protocols

treatment_protocol: MoLm13 and THP1 cells were infected by lentivirus vector pCDH-GFP-IL-34 and CON, then they were sent to sort.

value: monocyte

value: peripheral blood

tag: disease state

value: acute monocytic leukemia

tag: genotype

value: IL34 overexpression


title: methocel pool-d15 Quad F vs. methocel-d15 Quad F8


title: methocel pool-d15 Quad F vs. Cerivastatin 1.0mkd-d15 Quad F2


title: methocel pool-d15 Quad F vs. methocel-d15 Quad F5


title: methocel pool-d15 Quad F vs. methocel-d15 Quad F4


title: methocel pool-d15 Quad F vs. Cerivastatin 0.5mkd-d15 Quad F3


title: methocel pool-d15 Quad F vs. Cerivastatin 0.5mkd-d15 Quad F5


title: methocel pool-d15 Quad F vs. methocel-d15 Quad F2


title: methocel pool-d15 Quad F vs. methocel-d15 Quad F9


title: methocel pool-d15 Quad F vs. methocel-d15 Quad F1


title: Six2-Cre dyewap

city: Suita

first: Daisuke

last: Okuzaki

state: Osaka

address: Yamadaoka 3-1

department: Immunology Frontier Research Center

country: Japan

institute: Osaka univ.

description: - / -(Six2-Cre)(cy3)vs+/+(Six2-Cre)(cy5) dyeswap

hyb_protocol: Before hybridization, 825ng labeled cRNA of each product was fragmented and mixed with control targets and hybridization buffer according to the manufacturer's protocol (Agilent Technologies). Hybridizations were done for approximately 17h at 65°C. The slides were washed according to the manufacturer's manual, and scanning of microarrays was performed with 5µm resolution using a DNA microarray laser scanner (Agilent Technologies).

scan_protocol: Scanning of microarrays was performed with 5µm resolution using a DNA microarray laser scanner (Agilent Technologies). The array was scanned using Agilent G2505C DNA microarray scanner.

data_processing: Images were quantified using Agilent Feature Extraction Software (version 10.5.1.1).

source_name: Kidneys dissected from 2-month-old Cnnm2+/+;Six2-Cre mice

label_protocol: 200 ng of total RNA was labeled using low RNA Input Linear Amp kit (Agilent Technologies, Pato Alto, CA, USA). In brief, cDNA was reverse transcribed from 500ng of total RNA with an oligo-dT-T7 promoter primer and MMLV-RT. Second strand synthesis was carried out with random hexamers. Fluorescent antisense cRNA was synthesized with cyanine 5-CTP (Cy5-CTP) or 3-CTP (Cy3-CTP), and T7 polymerase. The fluorescent-labeled antisense cRNA was purified with RNeasy columns (Qiagen) according to manufacturer's protocols and resuspended in water. The purified products were quantified at A650nm for Cy5-CTP and  at A552nm for Cy3-CTP with Agilent 2100 Bioanalyzer (Agilent Technologies).

extract_protocol: Kidneys were dissected from 2-month-old Cnnm2+/+;Six2-Cre or Cnnm2fl/fl;Six2-Cre mice, and total RNAs were extracted using the RNeasy Extraction Kit (Qiagen) following manufacturer's instructions.

treatment_protocol: no teatment

tag: age

tag: genotype/variation

value: Kidneys

source_name: Kidneys dissected from 2-month-old Cnnm2fl/fl;Six2-Cre mice


title: THP1-CON

tag: cell line

tag: cell type

value: control


title: methocel pool-d15 Quad F vs. methocel-d15 Quad F3


title: Molm13-CON

value: acute myeloid leukemia


title: Molm13-IL34


title: HepG2.siCTRL

city: Beijing

first: Guan

last: Guiwen

state: Beijing

address: 38 Xueyuan Road, Haidian District

institute: Peking University

data_processing:
Base calling was performed using software provided with the Ion Torrent Proton sequencer.
Sequenced reads were mapped to hg19 whole genome using tophat2 with default settings.
Cufflinks was used to caculate gene expression level, and FPKM was used to express expression level.
Genome_build: hg19
Supplementary_files_format_and_content: Matrix table with FPKM for every gene for this sample.

extract_protocol:
HepG2 cells were transfected with control or AUF1-targeting siRNAs using Lipofectamine RNAiMAX for 48?h. Total RNA was isolated using TRIzol reagent (Invitrogen) and RNA sequencing was completed by VAZYME (Beijing, China).
After the samples pass the test, use the Ribo-zero kit to remove rRNA. Subsequently, high temperature and metal ions are used to achieve RNA fragmentation. Using Ribosomal-depleted RNA as a template, use six-base random primers (random hexamers) to synthesize one-strand cDNA, then add buffer, dNTPs (dUTP, dATP, dGTP and dCTP) and enzymes to synthesize two-strand cDNA, and then use VAHTSTM DNA Clean Beads purify double-stranded cDNA. The purified double-stranded cDNA is first repaired, A-tailed and connected to a sequencing adapter. VAHTSTM DNA Clean Beads are used for fragment size sorting. Then, UDG enzyme is used to degrade the cDNA chain containing U, and finally PCR enrichment is performed, and the PCR product is purified with VAHTSTM DNA Clean Beads to obtain the final chain-specific cDNA library. After the library is constructed, Qubit 3.0 is used to initially determine the concentration, and then the Agilent 2100 Bioanalyzer is used to detect the inserted fragments in the library. After this item meets expectations, the ABI Step One Plus Real-Time PCR system is used to accurately quantify the effective concentration of the library.

value: HCC cell line

tag: treatment

value: siCtrl


title: HepG2.siAUF1


title: Six2-Cre

description: +/+(Six2-Cre)(cy3)vs- / -(Six2-Cre)(cy5)


title: PC_8 matched normal tissue

first: Yanyang

last: Zhao

state: Not required for this country

address: No.1, Dahua Road,Dongdan

institute: Genetics Medicine

data_processing:
The paired-end reads obtained from HiSeq 2000 were aligned to the human reference genome and transcriptome (hg19) using SOAP2 program. No more than 5 mismatches were allowed in the alignment for each read. The gene expression level is calculated by using RPKM, and the formula is shown as follows: RPKM = 106 C/(NL/103)
Genome_build: hg19
Supplementary_files_format_and_content: an excel file includes RPKM values for each Sample

source_name: Prostate

extract_protocol:
RNA was harvested from samples using Trizol reagent. NEBNext Ultra RNA Library Prep Kit (Cat#E7530) for Illumina was used with 1 ug of total RNA for the construction of sequencing libraries
RNA libraries were prepared for sequencing using standard Illumina protocols

value: prostate

value: 69 years old

tag: isolate

value: PC_8 matched normal tissue


title: PC_4 matched normal tissue

value: 74 years old

value: PC_4 matched normal tissue


title: PC_2 matched normal tissue

value: 58 years old

value: PC_2 matched normal tissue


title: PC_9 prostate tumor

value: 66 years old

value: PC_9 tumor


title: PC_7 matched normal tissue

value: 73 years old

value: PC_7 matched normal tissue


title: PC_3 prostate tumor

value: 70 years old

value: PC_3 tumor


title: PC_1 matched normal tissue

value: 71 years old

value: PC_1 matched normal tissue


title: PC_6 prostate tumor

value: 72 years old

value: PC_6 tumor


title: PC_5 matched normal tissue

value: prostate

value: 63 years old

value: PC_5 matched normal tissue


title: PC_3 matched normal tissue

value: PC_3 matched normal tissue


title: PC_5 prostate tumor

value: PC_5 tumor


title: PC_9 matched normal tissue

value: PC_9 matched normal tissue


title: PC_2 prostate tumor

value: 58 years old

value: PC_2 tumor


title: PC_6 matched normal tissue

value: PC_6 matched normal tissue


title: PC_10 matched normal tissue

value: PC_10 matched normal tissue


title: PC_10 prostate tumor

value: PC_10 tumor


title: PC_8 prostate tumor

value: 69 years old

value: PC_8 tumor


title: PC_7 prostate tumor

value: 73 years old

value: PC_7 tumor


title: PC_4 prostate tumor

value: 74 years old

value: PC_4 tumor


title: PC_1 prostate tumor

value: prostate

value: 71 years old

value: PC_1 tumor


title: HiDEP-1 treated with Valproic acid, biological rep1

city: Kumamoto

first: Kenichi

last: Miharada

address: 2-2-1 Honjo, Chuo-ku

institute: Kumamoto University

hyb_protocol: Following fragmentation, 10 ug of cRNA were hybridized for 16 hr at 45C on GeneChip rat Genome Array. GeneChips were washed and stained in the Affymetrix Fluidics Station 450.

scan_protocol: GeneChips were scanned using the Affymetrix GeneArray Scanner 3000.

data_processing: The data were analyzed using expression console software version 1.1.2 using default analysis settings and RMA as normalization method.

label: Biotin

source_name: HiDEP-1 treated with 10 μM Valproic acid for 24 hours

label_protocol: Biotinylated cRNA were prepared according to the standard Affymetrix protocol from 6 ug total RNA (Expression Analysis Technical Manual, 2001, Affymetrix).

growth_protocol: HiDEP-1 was maintained in StemSpan SFEM supplemented with 1 % penicillin/streptomycin, 3 IU/ml erythropoietin, 1 μM dexamethason and 1 μg/ml doxycycline, in 5% CO2 incubator at 37°C

extract_protocol: Total RNA was extracted using RNeasy Mini Kit (QIAGEN) by following the manufacturer’s protocol. RNA concentration and integrity were measured using Agilent RNA Pico Kit on a Bioanalyzer Instrument (Agilent Genomics).

treatment_protocol: Ten (10) μM of Fluoro-SAHA, Valproic acid or M344 was added and HiDEP was cultured for 24 hours. Equivalent volume (0.1 %) of DMSO was added as a control.

value: Immortalized erythroblast cell line HiDEP-1

value: Valproic acid


title: HiDEP-1 treated with Valproic acid, biological rep2


title: HiDEP-1 treated with M344, biological rep3

source_name: HiDEP-1 treated with 10 μM M344 for 24 hours


title: HiDEP-1 treated with Fluoro-SAHA, biological rep1

source_name: HiDEP-1 treated with 10 μM Fluoro-SAHA for 24 hours

value: Fluoro-SAHA


title: HiDEP-1 treated with Valproic acid, biological rep3


title: HiDEP-1 treated with M344, biological rep2


title: HiDEP-1 treated with DMSO, biological rep3

source_name: HiDEP-1 treated with 0.1% (v/v) DMSO for 24 hours

value: DMSO


title: HiDEP-1 treated with M344, biological rep1


title: HiDEP-1 treated with Fluoro-SAHA, biological rep3


title: HiDEP-1 treated with Fluoro-SAHA, biological rep2


title: SCI+Gsx1-35d-04

city: Piscataway

first: Li

last: Cai

state: NJ

address: 599 Taylor Road, Rutgers, Rutgers University

department: Biomedical Engineering

institute: Rutgers University

data_processing:
Hisat2 for seq tag alignment.
samtools to generate BAM files.
HTSeq to generate data matrix.
DESeq2 to detect differentially expressed genes.
Genome_build: mm10 (GRCm38)
Supplementary_files_format_and_content: 19003-HTseqcount-matrix.txt: Matrix table with raw gene counts for every gene and every sample.

source_name: spinal cord

extract_protocol:
Spinal cord tissues of 3, 14, 35 DPI were isolated and segments containing injured/injected (parenchymal segments spanning ~2-3 mm from each side of the lesion) were rapidly snap-frozen in liquid nitrogen. Total RNA was isolated from spinal cord tissues using the RNeasy Lipid Tissue Mini kit (Qiagen, #74804) following the manufacturer’s protocol. The concentration of the total RNA was determined using Qubit RNA BR Assay Kit (Life Technologies) and quality of the total RNA was determined using the RNA 6000 Nano chip on the 2100 Bioanalyzer automated electrophoresis system (Agilent Technologies).
The libraries were prepared using an Illumina MiSeq paired-end kit and sequenced as paired-end, 2x150 bp on the Illumina MiSeq.
Total RNA was used for library preparation of each sample, which was subsequently bar-coded and prepared according to the manufacturer’s instructions (Illumina). The sequencing run was performed according to the manufacturer’s instructions and generated a total of 40 million reads per sample.

tag: strain

value: spinal cord

value: 8-12 weeks

tag: time point

value: 35 DPI


title: SCI+Nkx6.1-35d-01


title: SCI+Ctrl-35d-03

value: SCI+Ctrl


title: SCI+Nkx6.1-35d-02


title: SCI+Ctrl-35d-02


title: SCI+Gsx1-35d-01


title: miRNA_MW1_S1_BM_treated

city: El Cerrito

first: Christapher

middle: S

last: Morrissey

address: 7447 seaview Pl

institute: DNAnexus

data_processing:
R1 fastq files were imported into Galaxy, groomed for QC, adapters clipped and then MiRNA expreesion was assessed using miRanalyzer
The Norm- Expressed-Mapped data from the miRanalyzer Mature Unique Reads output was used to assess differential expression in R1 data sets (ADM vs BM treated)
Values of 0 for miRNAs that had values in the other condition were replaced with 1/2 the minimum value for the 0 value condition
Genome_build: mm10
Supplementary_files_format_and_content: xlsx

source_name: Cultured WT FAPs

growth_protocol: Growth was in 20%FBS, 10%HS, 5ng/ml FGF, 1XPS, DMEM (GM)

extract_protocol:
Small RNA was extracted using a miRVana kit following the manufacturors protocol
Libraries were prepared using the Illumina TruSeq Small RNA Sample Prep kit per the manufacturers protocol and additonal size selection for miRNAs was perfomred on a TBE PAGE gel

treatment_protocol: Cells were cultured for 24 hours in BM (10% HS, 1xPS, DMEM) or ADM

tag: organ source

value: Skeletal Muscle

tag: cell source

value: Sorted Sca1+ FAPs

value: BM treated


title: sham-35d-02

value: Sham


title: SCI+Ctrl-35d-01


title: sham-35d-03


title: SCI+Gsx1-35d-03


title: SCI+Gsx1-14d-03

value: 14 DPI


title: SCI+Gsx1-14d-02


title: HiDEP-1 treated with DMSO, biological rep2


title: HiDEP-1 treated with DMSO, biological rep1


title: SCI+Ctrl-35d-04


title: SCI+Nkx6.1-35d-03


title: SCI+Gsx1-35d-02


title: miRNA_MW3_S3_ADM_treated

value: ADM treated


title: sham-35d-01


title: SCI+Ctrl-14d-02


title: SCI+Ctrl-14d-03


title: SCI+Gsx1-14d-01


title: SCI+Ctrl-14d-01


title: SCI+Nkx6.1-3d-02

value: 3 DPI


title: SCI+Gsx1-3d-02


title: SCI+Ctrl-3d-02


title: SCI+Gsx1-3d-03


title: SCI+Gsx1-3d-01


title: SCI+Ctrl-3d-03


title: SCI+Nkx6.1-3d-01


title: sham-3d-02


title: SCI+Nkx6.1-3d-03


title: sham-3d-03


title: sham-3d-01


title: miR_206KO (06S2-GCCAAT)

data_processing:
Raw sequencing reads were first analyzed with FastQC from the Babraham Institute
RNA-seq data meeting the FastQC quality standards were then quantified (counts and TPMs) using Kallisto
Values for genes with an average TPM of <1 for replicates were designated as 0 TPMs. Pseudogenes and Riken transcripts were also removed.
Genome_build: mm10
Supplementary_files_format_and_content: xlsx

source_name: Cultured FAPs

extract_protocol:
Total RNA was extracted using the Qiagen RNeasy Mini kit per the manufacturors protocol
Libraries were prepared by the Stanford Functional Genomics Center using the Illumina TruSeq Stranded mRNA LT kit

treatment_protocol: Cells were maintained for 48 hours in 10% HS, 1xPS, DMEM (BM)

value: miR-206 KO


title: SCI+Ctrl-3d-01


title: Wildtype (wtS2-ACTTGA)


title: TBI and Control

city: Boston

first: Brian

last: Kalish

state: MA

address: 220 Longwood Ave

department: Neurobiology

institute: Boston Children's Hospital

description: RNA-seq

data_processing:
Sequencing data were aligned to the genome and processed according to a previously published pipeline (https://github.com/indrops/indrops). Briefly, this pipeline was used to build a custom transcriptome from Ensembl GRCm38 genome and GRCm38.84 annotation using Bowtie 1.1.1, after filtering the annoatation gtf file ((gencode.v17.annotation.gtf filtered for feature_type=”gene”, gene_type="protein_coding" and gene_status="KNOWN"). Unique molecular identifiers (UMIs) were used to link sequence reads back to individual captured molecules. All steps of the pipeline were run using default parameters unless explicitly stated. Quality control, dimensionality reduction, and clustering of cells All cells were combined into a single dataset. All mitochondrial genes were removed from the dataset. Cells with fewer than 500 or more than 15,000 UMI counts were excluded. Cells were then clustered using the Seurat R package. The data were log normalized and scaled to 10,000 transcripts per cell. Variable genes were identified using the following paramenters: x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5. We limited the analysis to the top 30 principal components (PCs). Clustering resolution was set to 0.6. Clusters containing fewer than 100 cells were discarded. The Seurat FindMarkers function was used to identify genetic markers of cellular subtypes. The expression of known marker genes was used to assign each cluster to one of the main cell types. Doublets were removed using Scrublet. Clusters from each cell type were combined and the Seurat-based clustering repeated to characterize subtype diversity.
Genome_build: mm10
Supplementary_files_format_and_content: counts

source_name: Mouse brain

extract_protocol:
Frozen brain tissue was thawed in 500 uL Buffer HB (0.25M sucrose, 25mM KCl, 5mM MgCl2, 20mM Tricine-KOH pH 7.8, 0.15mM spermine tetrahydrochloride, 0.5mM spermidine trihydrochloride, 1mM DTT). The tissue was transferred to a 2 mL dounce. 500ul 5% IGEPAL CA-630 (Sigma) and 1 mL HB were added to the tissue and the tissue was homogenized with a tight pestle 10-15 times. The sample was transferred to a 15 mL tube and total solution brought to 9 mL with HB. In a Corex tube (Fisher Scientific), 1 mL 30% iodixanol layered on top of 1 mL 40% iodixanol. The 9 mL sample was layered on top of the iodixanol cushion. The sample was spun at 10,000 xg for 18 minutes. 1 mL of sample at the 30-40% iodixanol interface was collected. After counting nuclear with a hemocytometer, the sample was diluted to 100,000 nuclear/mL with 30% iodixanol (with RNAsin) and subjected to single nuclear droplet encapsulation with inDrops. Individual nuclei were captured and barcoded using the inDrops platform as previously described.1 Briefly, single-cell suspensions were fed into a microfluidic device that packaged the cells with barcoded hydrogel microspheres and reverse transcriptase/lysis reagents.
After cell encapsulation, primers were photo-released by UV exposure. Two libraries of approximately 3000 cells each were collected for each sample. Indexed libraries were pooled and sequenced on a Nextseq 500 (Illumina).

value: brain

tag: protocol

value: mixed sample (non-demultiplexed) mild repetitive brain injury and Control


title: miR_206KO (06S1-ACAGTG)


title: Sample 14_Ikaros_Ikzf3_G158Rhomo_thymus_rep2

city: Bunkyo

first: Motoi

last: Yamashita

state: Tokyo

address: 1-5-45, Yushima

institute: Tokyo Medical and Dental University

data_processing:
Basecalls were performed using Illumina offline basecaller (OLB) version 1.9.4
RNA-seq sequences were aligned on mm10 using Hisat2 (version 2.1.0) with default parameters, and read counts were calculated using Htseq (version 0.12.4) using RefGenes as the reference.
ChIP-seq sequences were aligned on mm9 using BWA (Burrows-Wheeler Aligner, version 0.7.17)  with default parameters.
Peaks were called using MACS2 (version 2.1.1) with default parameters.
Genome_build: mm10 for RNA-seq and mm9 for ChIP-seq
Supplementary_files_format_and_content: tab-delimited text file include raw read count for each RNA-seq sample. BED6+3 format file for each ChIP-seq sample.

extract_protocol:
Sample 1-6: Bone marrow B220+ cells were purified using anti-CD45R coated MACS beads (Miltenyi Biotec) following manufacturer’s instruction. Pre-pro B cell (B220+AA4.1+IgM-CD43+CD24-) and pre-B cell (B220+AA4.1+IgM-CD43-CD24+) were isolated by flow cytometry. Total RNA was extracted by TRIzol reagent (Thermo Fisher Scientific), followed by DNA digestion by RNA Clean & Concentrator-5 (ZYMO RESEARCH), and mRNA was obtained by poly (A) selection with mRNA DIRECT kit (Thermo Fisher Scientific). RNA-seq libraries were prepared using NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs) following manufacturer’s protocol.
Sample7-16: Lysates were purified from sonicated nuclei and protein-DNA complexes were isolated with antibody.
Sample 1-6: Libraries were prepared using NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs)
Sample7-16: Libraries were prepared using NEBNext ChIP-Seq Library Prep Master Mix Set for Illumina (New England Biolabs)

value: thymus

value: thymocytes


title: Sample 13_Ikaros_Ikzf3_G158Rhomo_thymus_rep1


title: Sample 12_Aiolos_Ikzf3_G158Rhomo_thymus_rep2


title: P1MID7_rep2

city: Dallas

first: Miao

last: Cui

address: UT Southwestern Medical Center Department of Molecular Biology 6000 Harry Hines Blvd.

institute: UTSW

description: Visium spatial transcriptome  RNAseq

data_processing:
Library strategy: Visium Spatial Transcriptome RNAseq
The Space Ranger Single-Cell Software Suit was used to perform sample demultiplexing, reads alignment, and to generate feature-spot matrices.
The cDNA reads were aligned to the mm10/GRCm38 mRNA reference genome. The H&E staining image was supplemented to Spatial Ranger for tissue detection, fiducial detection, and alignment of the spatial position of sequenced data points.
Genome_build: mm10
Supplementary_files_format_and_content: count matrix
Supplementary_files_format_and_content: H&E tissue staining image

source_name: heart transverse section

extract_protocol:
Hearts at 3-day or 7-day post the surgery were dissected and immediately embedded in a tissue embedding mold filled with OCT and placed in a bath of isopentane and liquid nitrogen. OCT embedded heart tissue blocks were cryosectioned in a cryostat to generate appropriately sized sections for Visium Spatial slides. 10 um sections at 200 um below the ligation suture were collected and placed on the Visum Spatial slides capture areas.
Heart sections were permeabilized for 18 min followed by RNA reverse transcription. The optimized permeabilization time was determined using the Visum Spatial Tissue Optimization Kit (10xGenomics) according to the manufacturer’s protocol. Second strand cDNA was subsequently synthesized on tissue sections and was transferred to an Eppendorf tube after denaturation. The cDNA from the tissue slide that was spatially barcoded was next amplified via PCR and enzymatically fragmentated. P5, P7, i7 and i5 sample indexes are added via End Repair, A-tailing, Adaptor ligation and PCR to make the final sequencing library.

treatment_protocol: Myocardial infarction or Sham surgery was performed on 1-day-old mice by ligating the  left anterior descending artery

tag: position

value: 200 um below ligation suture

value: Wildtype

tag: condition

value: Myocardial infarction

tag: Stage

value: day 7 post myocardial infarction performed at postnatal day 1


title: P1ShamD7

description: Visium spatial transcriptome  RNAseq

value: Sham control

value: day 7 post Sham surgery performed at postnatal day 1


title: P1ShamD3

description: Visium spatial transcriptome  RNAseq

value: day 3 post Sham surgery performed at postnatal day 1


title: Wildtype (wtS1-CAGATC)

value: Skeletal Muscle

value: Sorted Sca1+ FAPs


title: Sample 11_Aiolos_Ikzf3_G158Rhomo_thymus_rep1

tag: tissue


title: Sample 8_Aiolos_Ikzf3_WT_thymus_rep2


title: Sample 10_Ikaros_Ikzf3_WT_thymus_rep2


title: Sample 9_Ikaros_Ikzf3_WT_thymus_rep1


title: Sample 7_Aiolos_Ikzf3_WT_thymus_rep1


title: P1MID3

description: Visium spatial transcriptome  RNAseq

value: day 3 post myocardial infarction performed at postnatal day 1


title: Sample 5_Ikzf3_G158Rhet_pre-B_rep2

source_name: Ikzf3+/G158R_pre-B cell

value: bone marrow

value: pre-B cell


title: Sample 6_Ikzf3_G158Rhet_pre-B_rep3


title: Sample 4_Ikzf3_G158Rhet_pre-B_rep1


title: S76590

city: Heidelberg

first: Axel

last: Roers

address: Im Neuenheimer Feld 305

department: Institute of Immunology

institute: Heidelberg University Hospital

data_processing:
Alignment of the samples with GSNAP (version 2018-07-04) to the mm10 reference, and using Ensembl 87 for the support of splicesite detection
featureCounts (version1.6.2) was used to assign the reads to genes with the help of Ensembl 87 annotation
Genome_build: mm10
Supplementary_files_format_and_content: featureCounts.mm10.E87.csv.gz This is a "," separated text file, containing the following files: GeneID, Strand, Length, Chromosome, Start, End position, followed by the 8 samples

source_name: total back skin tissue

extract_protocol:
RNA was extracted using  RNeasy Mini Kit+ (Qiagen), according to the manufacturer´s protocol.
Samples were subjected to the workflow for strand-specific RNA-Seq library preparation, according to manufacturer´s instruction (Ultra II Directional RNA Library Prep, NEB)

value: total back skin tissue

tag: flg genotype

value: ko/ko


title: single-cell RNA seq data of the dura from naive mice [dura2]

city: Muenster

first: I-Na

last: Lu

state: NRW

address: Domagkstraße 20

department: Department of Pediatric Hematology and Oncology

institute: University Children's Hospital Münster

description:
processed data file:
mouse_barcodes.tsv.gz
mouse_features.tsv.gz
mouse_matrix.mtx.gz
mouse_annotation_condition.csv
mouse_annotation_cluster.csv

data_processing:
Raw bcl files were de-multiplexed using the cellranger mkfastq pipeline (cellranger v3.1.0)
Subsequent reads alignments and transcript counting was done individually for each sample using the cellranger count and the cellranger vdj pipelines with standard parameters
 The cellranger aggr pipeline was employed, to generate a single cell-barcode matrix containing all the samples without normalization.
Genome_build: mm10 or Rattus_norvegicus.Rnor_6.0.dna.topleve
Supplementary_files_format_and_content: Standard cellranger output format

source_name: dura

extract_protocol:
Cells from CNS, SDM, dura, and CP were extracted as described before and sorted for  CD45+CD45iv- cells. CSF cells were not sorted. Blood cells were not sorted, but purified by  gradient centrifugation on Lymphoprep (Stemcell) in accordance with manufacturer's  instructions. After centrifugation, peripheral blood mononuclear cells enriched in the interface  were taken off and washed in 10 ml of PBS with 2% FCS media. Single cell suspensions  from all tissues (5 μl each) were manually counted in a Fuchs-Rosenthal chamber and the  cell concentration was adjusted to achieve a cell recovery of approx. 6’000-10’000 cells.  In total, 25 naive Lewis rats were used to generate single cell RNA sequencing data from rat  tissues. Per tissue, an average of 4,384 ± 736 SEM cells were used for analysis. 51,278 ±  2,859 SEM reads per cell were sequenced and this resulted in 733 ± 100 SEM median  genes detected per cell. In addition, we did single cell RNA sequencing on the cells isolated  from the skull dura of 3 pooled C57BL/6 mice at the peak of active EAE and 4 pooled  unimmunized C57BL/6 mice as ctrl. On average, 3,900 ± 1,100 SEM cells per sample were  used for analysis. 60,541 ± 4,644 SEM reads per cell were sequenced and resulted in 1,736  ± 852 SEM median genes detected per cell.
Single cell suspensions were loaded onto the Chromium Single Cell Controller using the Chromium Single Cell 3' Library & Gel Bead Kit (both from 10X Genomics). Rat experiments were done with v2 chemistry. The mouse experiments were done with v3 chemistry because of the manufacturer's reagent update. For BCR sequencing, we developed a method to sequence antigen receptor information from the 3’ scRNA-seq libraries by removing the constant region of antigen receptors during enrichment, while maintaining their cell barcode and unique molecular identifier (UMI) information attached to the 3' of the cDNA molecules. In summary, the method involves self-circulating the cDNA library, enriching the VDJ region and re-linearizing. Sample processing and library preparation was performed according to manufacturer's instructions using AMPure beads (Beckman Coulter). Sequencing was carried out on a local Illumina Nextseq 500 using the High-Out 75 cycle kit with a 26-8-0-57 read setup and Novaseq 150-8-0-150 read setup (NextSeq: read1-26bp, read2-57bp. Novaseq: PE150bp).

value: Dura-derived leukocytes

value: dura

tag: type of library

value: RNA expression


title: single-cell BCR seq data of the dura from naive mice [dura-BCR]

value: BCR


title: S76593

value: wt/wt


title: single-cell BCR seq data of the dura from EAE mice [dura-a-E-BCR]


title: S76592


title: S76588


title: S76586


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 381

city: Berkeley

first: David

last: DeTomaso

address: Stanley Hall

institute: UC Berkeley

data_processing:
Sequencing reads were initially filtered with Trimmomatic 0.32 to remove low quality ends and adapter sequences
Bowtie2 v2.2.9  was used to align reads to a reference transcriptome derived from the GRCm38 Mus musculus genome (NCBI Refseq).
RSEM 1.2.31 was used to compile the transcriptome and estimate transcript abundance following read alignment.
Prior to alignment, the GRCm38 reference was augmented to include the modified eGFP+IL23R functional knockout transcript.
Genome_build: GRCm38
Supplementary_files_format_and_content: gene_expression_tpm.txt.gz: tab-delimited text table (genes x cells) of TPM-normalized gene expression values.  cell_metadata.txt: tab-delimited table which contains GFP label, genotype, and stimulation for each cell.

source_name: Th17 differentiated CD4 T Cell - eGFP_pos_IL23R Knockout

extract_protocol:
Spleen and lymph nodes were extracted and then a single-cell suspension was prepared by homogenization with a syringe plunger and debris was filtered with a 70um filter. Red blood cells were lysed with ACK lysis buffer from Lonza. CD4+ T cells were enriched using a bead protocol from Miltenyi Biotech (cat #. 130-117-043) and then stained with anti-CD44 (clone IM7), anti-CD62L (clone Mel14), anti-CD4 (clone RM4-5) and anti-CD25 (clone PC61) in preparation for FACS. Naive T cells were sorted based on CD44lowCD62LhiCD4+CD25-.
T cells were sorted into 96-well plates containing 5 μl TCL Buffer (QIAGEN) with 1% 2-mercaptoethanol, centrifuged and frozen at −80 °C.  Single-cell sequencing libraries were then created using the Smart-seq2 protocol (Picelli et al., Nature Methods 2014) and carried out as described in Singer et. al, Cell 2016.

tag: differentiation

tag: gfp

tag: genotype

value: IL23R Knockout

value: CD4 T Cells


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 382

tag: cell type


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 378


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 379


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 384


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 377


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 376


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 373


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 375


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 380


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 374


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 371


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 370


title: S76595


title: single-cell RNA seq data of the dura from EAE mice [dura_a_E]


title: S76597


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 366


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 369


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 368


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 367


title: S76587


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 383


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 365


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 360


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 362


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 361


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 363


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 359


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 372


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 357


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 354


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 353


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 349


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 355


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 356


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 348


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 364


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 358


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 350


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 352


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 343


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 345


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 347


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 344


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 340


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 346


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 351


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 342


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 337


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 338


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 339


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 335


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 333


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 334


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 336


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 330


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 329


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 327


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 326


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 325


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 324


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 323


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 322


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 320


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 321


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 315


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 319


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 317


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 316


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 314


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 318


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 332


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 313


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 307


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 310


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 312


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 309


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 308


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 311


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 331


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 305


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 306


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 328


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 303


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 301


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 302


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 300


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 299


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 298


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 297


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 296


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 295


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 294


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 290


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 293


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 292


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 291


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 289


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 192

source_name: Th17 differentiated CD4 T Cell - eGFP_pos_Control

value: Control


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 187


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 191


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 304


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 188


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 189


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 186


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 190


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 181


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 180


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 179


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 182


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 183


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 178


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 177


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 176


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 174


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 175


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 173


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 171


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 172


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 170


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 185


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 184


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 166


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 162


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 164


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 167


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 168


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 163


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 161


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 159


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 158


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 160


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 152


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 157


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 151


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 154


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 150


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 148


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 153


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 149


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 156


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 155


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 146


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 144


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 145


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 143


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 142


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 169


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 140


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 165


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 141


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 138


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 137


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 136


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 139


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 135


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 131


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 133


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 134


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 147


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 132


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 130


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 129


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 127


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 124


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 122


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 123


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 126


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 119


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 120


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 121


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 118


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 117


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 116


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 115


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 114


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 112


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 111


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 110


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 109


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 113


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 108


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 107


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 104


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 106


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 103


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 102


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 100


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 128


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 125


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 101


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 99


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 97


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 286

source_name: Th17 differentiated CD4 T Cell - eGFP_neg_IL23R Knockout


title: Th17 differentiated CD4 T Cell - eGFP_pos, IL23R Knockout, cell 341


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 288


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 287


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 283


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 105


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 276


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 284


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 280


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 281


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 277


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 282


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 272


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 273


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 278


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 279


title: Th17 differentiated CD4 T Cell - eGFP_pos, Control, cell 98


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 271


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 274


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 275


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 269


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 270


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 266


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 267


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 285


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 264


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 265


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 263


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 268


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 261


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 260


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 258


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 259


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 256


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 257


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 254


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 253


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 252


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 251


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 250


title: liverTissue_ATAC-seq_sample670B

city: Chapel Hill

first: Karen

middle: L

last: Mohlke

state: NC

address: 120 Mason Farm Road

department: Genetics

institute: University of North Carolina at Chapel Hill

description:
The first three digits of raw and processed filenames represent the sample IDs. For each tissue sample, nuclei was extracted and divided into three portions prior to library preparation. This resulted in triplicate libraries for each tissue sample. If a library did not receive high enough sequencing depth, the same library was resequenced. Consequently, some libraries have two sets of raw fastq files, designated by RG1 and RG2. The fastq files are named as follows: 3 digits sample ID followed by a ., number indicating one of the three triplicate libraries followed by _, and RG1 or RG2 indicating the first round of sequencing (all samples) or second round of sequencing (only samples that didn't reach sufficient read depth on first sequencing run).
consensusNarrowPeaksWithPeakIDs-1bpOverlap-min3samples_liver-firstPass-20samples.bed
DESeq2Datasets_GC-correct_consensusNarrowPeaks-1bpOverlap-min3samples_liver-secondPass-20samples.RData

data_processing:
We trimmed ATAC-seq reads to a uniform length of 126 bp using cutadapt and trimmed sequencing adapters using CTA (https://github.com/ParkerLab/cta)
We merged readgroups for the same library, aligned reads to the hg19 human genome using BWA-MEM, selected properly paired autosomal alignments with high mapping quality (mapq>30) with samtools, and removed duplicate alignments using Picard.
We merged the replicate bam files for each individual and used ataqv to generate ATAC-seq quality metrics and confirmed ATAC-seq libraries corresponded to the correct genotypes using verifyBamID.
To identify consensus peaks, we converted the merged .bam files for each individual to .bed files using BEDTools, called narrow peaks for each individual using MACS2 with parameters –nomodel –shift -100 –extsize 200 –keep-dup all, and removed peaks overlapping blacklisted regions (Duke-excluded and Dac-blacklist from ENCODE). We then merged peaks across individuals using BEDTools and defined consensus peaks as merged peaks that shared at least 1 base with a peak present in samples from at least 3 individuals.
Genome_build: hg19
Supplementary_files_format_and_content: .bam files contain ATAC-seq high-quality autosomal alignments (HQAA) to the hg19 genome in a compressed format
Supplementary_files_format_and_content: .narrowPeak files contain peaks called on individual ATAC-seq samples (after merging across replicates) using MACS2. The columns of the file are: peak_chromosome, peak_start, peak_stop, peak_name, score, strand, fold enrichment, -log10(p_value), -log10(q_value), and peak summit location relative to peak_start.
Supplementary_files_format_and_content: .bw files contain ATAC-seq signal values produced by MACS2 and can be loaded into the UCSC genome browser for visualization.
Supplementary_files_format_and_content: The .Rdata file 'DESeq2Datasets_GC-correct_consensusNarrowPeaks-1bpOverlap-min3samples_liver-secondPass-20samples.RData' contains R objects produced by DESeq2, known as DESeq datasets. The file contains two DESeq2 datasets: peaks.dds containing the raw peak counts across the 20 samples, and peaks.rlog containing the variance-stabilized peak counts. The raw peak counts can be extracted with the R command 'counts(peaks.dds,normalized=F)'. The normalization factors used are the combination of RASQUAL GC offsets per peak and DESeq2 size factors. Sample covariates can be obtained with 'colData(peaks.dds)'.
Supplementary_files_format_and_content: The .bed file 'consensusNarrowPeaksWithPeakIDs-1bpOverlap-min3samples_liver-firstPass-20samples.bed' contains the consensus peaks present in at least 3 samples. The file contains a header describing each column.

source_name: liver tissue

extract_protocol:
Omni-ATAC-seq protocol. We pulverized 50-mg pieces of frozen human liver tissue in liquid nitrogen using a Cell Crusher (CellCrusher, Cork Island), homogenized the tissue powder in ice cold nuclei isolation buffer (NIB: 20 mM Tris-HCl, 50 mM EDTA, 5 mM spermidine, 0.15 mM spermine, 0.1% mercaptoethanol, 40% glycerol, pH 7.5) using a 1-mL dounce for 40 strokes, and rotated for 5 minutes at 4oC. We filtered the solution through a Miracloth (Calbiochem, San Diego, Ca USA), centrifuged at 1100g for 10 minutes at 4oC, washed the pellet with 250-uL NIB containing 0.5% Triton-X, centrifuged at 500g for 5 minutes at 4oC, and resuspended the pellet in 250-uL of resuspension buffer (10 mM Tris-HCl, 10 mM NaCl, 3 mM MgCl2, pH 7.4). After counting isolated nuclei, we pelleted 50,000 nuclei at 500g for 5 minutes at 4oC for each of three replicate ATAC-seq libraries per sample.
Libraries were prepared using Nextera kits (Illumina)

tag: Sex

value: Female

tag: age

value: liver


title: liverTissue_ATAC-seq_sample767


title: liverTissue_ATAC-seq_sample683


title: liverTissue_ATAC-seq_sample651


title: liverTissue_ATAC-seq_sample662


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 248


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 249


title: liverTissue_ATAC-seq_sample794

value: Male


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 246


title: liverTissue_ATAC-seq_sample795


title: liverTissue_ATAC-seq_sample753


title: liverTissue_ATAC-seq_sample684B


title: liverTissue_ATAC-seq_sample786


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 245


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 247


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 244


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 262


title: liverTissue_ATAC-seq_sample469


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 255


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 243


title: liverTissue_ATAC-seq_sample365B


title: LNCaP_DHT_AcH3_siPSF

city: Itabashi-ku

first: Ken-ichi

last: Takayama

address: Sakaecho

institute: Tokyo Metropolitan Institute of Gerontology

description: 5_LD_siPSF_AcH3_siPSF_AcH3_DHT_R1_merged

data_processing:
We used Trimmomatic-033 to trim and crop of Illumina (fastq) data by removing the sequences of adapters.
ChIP-seq reads were aligned to the hg19 genome assembly using Bowtie-1.1.2 with parameters -a --best --strata -m 1 -v 2.
Generate alignments in the SAM format given single-end reads. Convert SAM to BAM files using samtools view command. samtools version is 0.1.19.
Signal scores in bed files were calculated using Model-based analysis of ChIP-seq (MACS; P-value < 10xE-4)
Genome_build: hg19
Supplementary_files_format_and_content: Bed files that summarized significant peak regions (P-value < 1.0xE-4)

source_name: prostate cancer cells

growth_protocol: LNCaP cells were cultured in RPMI 1640 containing 10% FBS.  Before hormone treatment, cells were incubated in phenol red-free RPMI 1640 containing 2.5% charcoal/dextran treated FBS for three days. LTAD cells were cultured in phenol red-free RPMI 1640 containing  10% charcoal/dextran treated FBS.

extract_protocol:
Lysates were clarified from sonicated nuclei and protein-DNA complexes were isolated with antibody.
The Chromatin immunoprecipitated DNA fragments (approx.10 ng in 30 µl water) are end repaired by using T4 DNA polymerase, Klenow DNA polymerase and T4 polynucleotide kinase in the presence of dNTPs. And an ‘A’ base is added to the 3’end of the blunt phosphorylated DNA fragments, using the polymerase activity of Klenow fragment. Then, adaptors (Adaptor oligo mix, Illumina) are ligated to the ends of the DNA fragments. The resulting DNA fragments are purified by MinElute column (QIAGEN).  300 ~ 500 bp DNA fragments are purified by using E-Gel SizeSelect agarose Gels (Invitrogen) according to manufacturer’s instruction.  Size selected adaptor-modified DNA fragments are amplifiedand the resulting PCR products are purified by MinElute column (QIAGEN).  The size and amount of DNA fragments (libraries) are validated by Bioanalyzer (Agilent).

treatment_protocol: Cells were treated with vehicle or 10 nM DHT for 24h. Cells were treated with No.10-3 or vehicle for 48 h. Cells were treated with siControl or siPSF (10 nM) for 48 h.

tag: cell line

value: LNCaP cells

value: prostate cancer cells

tag: treatment

value: DHT10 nM +siPSF

tag: chip antibody

value: AcH3 (Millipore06-599)


title: LNCaP_Et_AcH3_No.10-3_siCtrl

description: 6_LE_13_AcH3_PSF_inhibitor_AcH3_Et_R1_merged

value: ethanol (Vehicle) treatment + No.10-3 + siControl


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 240


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 239


title: LTAD_AcH3_No.10-3

value: LTAD cells

value: No.10-3 + siControl


title: LNCaP_DHT_siCtrl_AcH3

value: DHT10 nM + siControl


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 235


title: LTAD_AcH3_siPSF

description: 11_LT_13_AcH3_LTAD_PSF_inhibitor_AcH3_R1_merged

value: Vehicle + siPSF


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 238


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 242


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 236


title: LTAD_input

value: Vehicle + siControl

value: input


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 231

value: IL23R Knockout


title: Th17 differentiated CD4 T Cell - eGFP_neg, IL23R Knockout, cell 234

value: CD4 T Cells


title: LNCaP_DHT_siCtrl_input


title: LNCaP_Et_siCtrl_AcH3

value: ethanol (Vehicle) treatment + siControl


title: H889 hSCLC PROX1 ChIP

first: Jane

last: Johnson

address: 5323 Harry Hines Blvd

institute: UT Southwestern Medical Center

data_processing:
Reads were mapped to hg39 assembly using bowtie2
Low quality reads were filtered using samtools
Removed duplicates using picard
Bigwig files contain ChIP signal normalized to 10M reads
Genome_build: hg19
Supplementary_files_format_and_content: ChIP-seq signal in bigwig format

source_name: H889 hSCLC PROX1

growth_protocol: Cells were maintained in RPMI-1640 media (ThermoFisher Scientific, 11875135) supplemented with 5% heat deactivated fetal bovine serum (Sigma Aldrich) without antibiotics at 37 °C in a humidified atmosphere containing 5% CO2 and 95% air.

extract_protocol:
Cells (~7 x107 cells) in the exponential growth phase were fixed and nuclear extract were prepared. Lysates were clarified from sonicated nuclei and histone-DNA complexes were isolated with 10 μg antibody.
Libraries were constructed by using the NEBNext® ChIP-Seq Library Prep Master Mix (NEB,E6240) and NEBNext® Multiplex Oligos for Illumina® (Index Primers Set 1, E7335S, Index Primers Set 2, E7500S) with Agencourt AMPure XP magnetic beads (Beckman coulter, A63881) exactly as per manufacturer’s instructions.

treatment_protocol: no treatment

tag: tissue/cell line

tag: tissue/cell type

value: Small cell lung cancer cell line

tag: passage

tag: antibody

value: H3K27ac (Abcam, Ab4729)


title: LTAD_AcH3

description: 10_LT_siPSF_AcH3_LTAD_siPSF_AcH3_R1_merged