title: RNA-seq of mouse liver + LNA-miR802_3 days_female5_G152_M5

source_name: live_LNA-miR802_3 days_female

value: LNA inhibition of miR-802-5p for 3 days


title: wt KSHV infected BR2

status: Public on Jan 25 2018

type: SRA

city: Gainesville

first: Rolf

last: Renne

address: 2033 Mowry Road

country: USA

institute: University of Florida

description: Biological Replicate 2

library_source: transcriptomic

data_processing:
Library strategy: qCLASH
Adapter sequences were removed and low-quality reads were discarded using Trimmomatic.
Reads were analyzed using Hyb, a program which aligns reads to a transcript database and identifies RNA-RNA hybrids.
miRNA-mRNA hybrids were analyzed for the origin of the mRNA (5' UTR, CDS, or 3' UTR) using a custom script.
The binding characteristics of the miRNA portion of the hybrid with the mRNA portion were analyzed using a custom script.
Supplementary_files_format_and_content: .hyb files contain the read sequence, the energy of the hybrid folding, the read and genomic location of each RNA component of the hybrid, the alignment score of each  RNA component of the hybrid, and the identity of each RNA.  Column headers are (from left to right): Unique sequence identifier, read sequence, predicted binding energy in kcal/mol, name of matched transcript (first fragment), read start site (first fragment), read end  site (first fragment), transcript start site (first fragment), transcript end site (first fragment), mapping score (first fragment), name of matched transcript (second fragment), read start site (second fragment), read end  site (second fragment), transcript start site (second fragment), transcript end site (second fragment), mapping score (second fragment).

molecule: total RNA

organism: Homo sapiens

source_name: TIVE-EX-LTC cells

growth_protocol: TIVE-EX-LTC cells were were maintained in DMEM containing 10% FBS and 1% Penicillin/Streptomycin

extract_protocol:
Protein G Dynabeads were coated with 2A8, a mouse monoclonal antibody against Argonaute (Ago).  Cells were lysed and treated with DNase and RNase T1.  The cell lysates were added to the antibody-coated beads and allowed to incubate overnight.  After immunoprecipitation, Ago-bound RNAs were phosphorylated.  Ago-RNA complexes were incubated overnight with RNA ligase.  This was followed by dephosphorylation and ligation of the miRCat-33 3’-linker (5’-TGGAATTCTCGGGTGCCAAGG-3’).  After the complexes were eluted from the beads, the samples were treated with proteinase K.  The RNA was phosphorylated and ligated to the Illumina TruSeq® Small RNA Sample Prep Kits RA5 5' adapter.
RNA was reverse-transcribed using Illumina TruSeq® Small RNA Sample Prep Kits RTP and then amplified through 19-24 PCR cycles with Illumina TruSeq® Small RNA Sample Prep Kits RP1 and one of the following barcoded primers: RPI1, RPI2, or RPI3.  WT_BR1, UI_BR1, and D11_BR1 received RPI1, WT_BR2, UI_BR2, and D11_BR2 received RPI2, and WT_BR3, UI_BR3, and D11_BR3 received RPI3.  PCR products were incubated with PEG-8000 and DNA was extracted from the supernatants.

treatment_protocol: TIVE-EX-LTC cells were expanded in 15 cm plates until they reached approximately 80-90% confluence. Cells were trypsinized, resuspended in medium, and rinsed twice with PBS. After the second rinse the cells were resuspended in 10 mL PBS and transferred to a clean 10 cm cell culture plate on ice. Cells were UV-irradiated in a crosslinker at a wavelength of 250 nm, receiving a total energy of 600 joules/cm2. After crosslinking, the cells were counted using a hemocytometer and distributed into aliquots of 50 million cells. The cells were pelleted at 4° C, supernatants were removed, and the cells were frozen at -80° C until further use.

tag: cell type

value: derived from Telomerase-Immortalized human umbilical Vein Endothelial cells, Long-Term Culture (TIVE-LTC)

tag: infection status

value: infected with wild type KSHV


title: RNA-seq of mouse liver + LNA-miR802_3 days_female4_G152_M4

city: Boston

first: David

middle: J.

last: Waxman

state: MA

address: 5 Cummington Mall

department: Department of Biology and Bioinformatics Program

institute: Boston University

data_processing:
Sequence reads were mapped to the mouse genome (mm9) using TopHat2 (Trapnell et al, Nat. Biotechnol, 2010)
FeatureCounts (Liao, 2014, Bioinformatics) was used to obtain read counts for RefSeq genes. Mature (spliced) mRNAs were quantified by counting sequence reads that map to a concatenated sequence comprised of the union of the exonic regions for all isoforms of a given gene. Primary RNA transcripts (hnRNA) were quantified by counting reads that map to the intersection of all intronic sequences across all isoforms of a given gene; it thus excludes sequences found in an exonic region of at least one isoform of the gene.
Differential expression analysis for RefSeq genes was conducted using edgeR (Robinson, 2010, Bioinformatics),
Genome_build: mm9
Supplementary_files_format_and_content: Microsoft Excel file containing normalized read counts and 12 pair-wise differential expression results.

organism: Mus musculus

extract_protocol:
RNA was extracted from flash fozen mouse liver using Trizol per the manufacturer's instructions.
RNA was depleted of rRNA using the NEBNext Poly(A) mRNA Magnetic isolation module (NEB, Ipswich, MA, Cat# E7490S). Libraries were prepared with the NEBNext Ultra Directional RNA Library Prep kit for Illumina (NEB, Cat# E7420) and multiplexed using NEBNext Multiplex Oligos for Illumina (NEB, Cat# E7335, E7500), according to the manufacturer's instructions.

treatment_protocol: To inhibit miR-1948-5p and miR-802-5p function in mouse liver in vivo, LNA constructs complementary to these miRNAs and a negative control were purchased from Exiqon and injected into male and female 9 wk ICR mice (mmu-miR-1948-5p:GGCAGAATACTCATA; mmu-miR-802-5p: GAATCTTTGTTACTG; negative control: ACGTCTATACGCCCA).  Mice were given two subcutaneous injections of the LNA in 1X PBS solution at 25 mg/kg on Day 0 and again on Day 1. Mice were killed and euthanized on Day 3 or Day 6, to give a total of three days or six days of LNA exposure in vivo.  Injections were carried out in the morning between 10 am - 12 pm. LNA-miR-1948 was injected into male mice; LNA-miR-802 was injected into female mice; LNA-negative control was injected into both male and female mice for both 3 day and 6 day exposures, and served as controls.  At the end of the 3 day or 6 day treatments, mice were killed by cervical dislocation, and livers were harvested for RNA extraction and RNA-seq library construction from poly(A) enriched mRNAs.  For each sex and at each time point, N=5 mice received LNA-anti-miRNA, and N=3 mice received scrambled LNA (negative controls).

tag: strain

tag: age

value: 10 weeks

tag: Sex

value: Female

tag: treatment

tag: tissue

value: liver


title: wt KSHV infected BR1

description: Biological Replicate 1


title: RNA-seq of mouse liver + LNA-miR802_3 days_female2_G152_M2


title: RNA-seq of mouse liver + LNA-miR1948_3 days_male1_G152_M9

source_name: live_LNA-miR1948_3 days_male

value: Male

value: LNA inhibition of miR-1948-5p for 3 days


title: uninfected BR2

description: Biological Replicate 2

value: uninfected


title: uninfected BR3

description: Biological Replicate 3


title: uninfected BR1

description: Biological Replicate 1


title: Female liver 5_small RNA-seq_G108_M11

description: Female5: G108_M11

data_processing:
Adapter sequences were removed from reads using TrimGalore software
Sequence reads were mapped to the mouse genome (mm9) using Bowtie2 (Langmead et al, Genome Biology, 2009)
Bedtools (Quinlan, 2014, Curr Protoc Bioinformatics) was used to obtain read counts at 2,044 mature mouse miRNA loci derived from miRBase version 21  (Griffiths-Jones, Nuclei Acids Research, 2008).
Differential expression analysis of mature microRNAs  was conducted using edgeR (Robinson, 2010, Bioinformatics),
Genome_build: mm9
Supplementary_files_format_and_content: Microsoft Excel file containing normalized read counts and differential expression results  for 2,044 mature mouse microRNAs between male and female liver.

source_name: female_liver tissue

extract_protocol:
RNA was extracted from flash fozen mouse liver using Trizol per the manufacturer's instructions.
strand-specific, small RNA library;
1 μg of total RNA was used in NEBNext Small RNA library prep set for Illumina (NEB, Cat# E7330) and multiplexed using NEBNext Multiplex Oligos for Illumina (NEB, Cat# E7335), according to the manufacturer's instructions.

treatment_protocol: Livers were excised from male and female CD1 mice, 8 weeks of age,  purchased from Charles River Laboratories  (mouse strain Crl:CD1(ICR)).

value: female

value: 8 weeks

tag: molecule subtype

value: small RNA (~21 bp)


title: Female liver 4_small RNA-seq_G108_M10

description: Female4: G108_M10


title: RNA-seq of mouse liver + LNA-miR802_3 days_female1_G152_M1


title: Female liver 3_small RNA-seq_G108_M9


title: Male liver 3_small RNA-seq_G108_M3

description: Male3: G108_M3

source_name: male_liver tissue

value: male


title: Male liver 2_small RNA-seq_G108_M2

description: Male2: G108_M2


title: Male liver 1_small RNA-seq_G108_M1

description: Male1: G108_M1


title: Male liver 4_small RNA-seq_G108_M4

description: Male4: G108_M4


title: Female liver 1_small RNA-seq_G108_M7

description: Female1: G108_M7


title: Male liver 6_small RNA-seq_G108_M6

description: Male6: G108_M6


title: RNA-seq of mouse liver + LNA-miR802_3 days_female3_G152_M3


title: Resistant RNASeq rep1

city: Stanford

first: Anthony

last: Oro

state: CA

address: 269 Campus Drive, CCSR 2145c

department: Dermatology

institute: Stanford University

data_processing:
For RNA-Seq, alignment was performed using Tophat
DESeq was used for differential expression analysis
Normalized counts were obtained using DESeq
Genome_build: mm9
Supplementary_files_format_and_content: Text file containing normalized read counts.

source_name: Mouse BCC tumor

growth_protocol: Tumors were generated in Ptch+/-, K14 Cre-ER, p53 fl/fl mice. Mice were treated with Smo inhibitor to generate sensitive and resistant basal cell carcinomas.

extract_protocol:
Qiagen blood and tissue kit was used to extract DNA and Qiagen Rneasy kit was used for RNA extration.
Standard library construction was used to isolate fragments between 150 and 350 base pairs in length.
Standard Illumina paired-end library generation kit was used for RNA and Exome-seq samples.

treatment_protocol: Mice were treated with Smo inhibitor by oral gavauge.

tag: genotype

value: Ptch+/-, K14 Cre-ER, p53 fl/fl mice

tag: tumor stage

value: Resistant


title: Resistant RNASeq rep4


title: Sensitive RNASeq rep1

value: Sensitive


title: Sensitive RNASeq rep2


title: Resistant RNASeq rep2


title: Sensitive RNASeq rep4


title: Female liver 6_small RNA-seq_G108_M12

description: Female6: G108_M12


title: Male liver 5_small RNA-seq_G108_M5

description: Male5: G08_M5


title: Female liver 2_small RNA-seq_G108_M8


title: Resistant ExomeSeq rep3

library_source: genomic

molecule: genomic DNA


title: Resistant  ExomeSeq rep2


title: Sensitive  ExomeSeq rep3


title: Sensitive ExomeSeq rep2


title: RIC_day11_r2

city: Koto-ku

first: Yutaka

last: Saito

state: Tokyo

country: Japan

institute: National Institute of Advanced Industrial Science and Technology (AIST)

description: RNA-seq for ribosome-induced cell clusters (RICs) day 11 replicate 2

data_processing:
RTA software version 2.7.7 (Illumina) was used for base calling.
RNA-seq data were filtered using the following criteria: (1) remove reads containing adapters; (2) remove reads containing N > 10%, where N represents the base cannot be determined; (3) remove reads containing low quality (Qscore ≤ 5) base which is over 50% of the total base.
Then, RNA-seq reads were mapped to the human transcriptome using Tophat2 program version 2.1.1 where the genes.gtf file in the Illumina iGenome hg19 was used for the transcriptome annotation.
The number of reads mapped for each gene was counted using HTSeq package version 0.7.2.
For principal component analysis, read counts are transformed into counts per million (CPM) and log2-transformed using edgeR package version 3.16.5.
Genome_build: hg19
Supplementary_files_format_and_content: Tab-delimited text files that include the number of reads mapped for each gene.

molecule: polyA RNA

source_name: Ribosome-induced cell clusters (RICs) day 11 replicate 2

growth_protocol: HDFs purchased from Cell Applications were used within 3 passages from culturing of a new vial. HDFs were maintained at 37°C in a humidified atmosphere containing 5% CO2.

extract_protocol:
We used the TRIzol reagent (Thermo Fisher Scientific) with 1.0-mm glass beads (WAKENBTECH), a KONTES Homogenizer pestle (Kimble Chase), and a ReliaPrep RNA Cell Miniprep System (Promega) for purification. RNA quality was assessed using a Bioanalyzer (Agilent Technologies).
Sequencing libraries were generated using NEBNext Ultra RNA Library Prep Kit for Illumina (NEB) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3’ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 250-300 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter). Then 3 μl USER Enzyme (NEB) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.

treatment_protocol: Cells were collected using conventional trypsin digestion. The obtained cell suspensions were adjusted to approximately 2.5 × 105 cells/mL in PluriSTEM Human ES/iPS Medium (Merck) and then applied 2 ml to 6-well plate (Thermo Fisher Scientific) containing 20 µl of ribosome solution (1 µg/µl) that is extracted from Escherichia coli JE28. The plated cells were cultured for 14 days, with half the medium replaced every 3 or 4 days.

value: Ribosome-induced cell cluster (RIC)


title: Sensitive ExomeSeq rep1


title: Sensitive  ExomeSeq rep4


title: RIC_day8_r2

description: RNA-seq for ribosome-induced cell clusters (RICs) day 8 replicate 2

source_name: Ribosome-induced cell clusters (RICs) day 8 replicate 2


title: RIC_day8_r1

description: RNA-seq for ribosome-induced cell clusters (RICs) day 8 replicate 1

source_name: Ribosome-induced cell clusters (RICs) day 8 replicate 1


title: RIC_day11_r1

description: RNA-seq for ribosome-induced cell clusters (RICs) day 11 replicate 1

source_name: Ribosome-induced cell clusters (RICs) day 11 replicate 1


title: RIC_day14_r2

description: RNA-seq for ribosome-induced cell clusters (RICs) day 14 replicate 2

source_name: Ribosome-induced cell clusters (RICs) day 14 replicate 2


title: Resistant  ExomeSeq rep1


title: Sensitive RNASeq rep3


title: RIC_day5_r2

description: RNA-seq for ribosome-induced cell clusters (RICs) day 5 replicate 2

source_name: Ribosome-induced cell clusters (RICs) day 5 replicate 2


title: RIC_day5_r1

description: RNA-seq for ribosome-induced cell clusters (RICs) day 5 replicate 1

source_name: Ribosome-induced cell clusters (RICs) day 5 replicate 1


title: RIC_day2_r2

description: RNA-seq for ribosome-induced cell clusters (RICs) day 2 replicate 2

source_name: Ribosome-induced cell clusters (RICs) day 2 replicate 2


title: Tet2-KO

city: shang hai

first: Qian

last: Zhang

address: xiangyin road 800

country: China

institute: Second military medical university

data_processing:
Illumina Casava1.6 software used for basecalling.
Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to SOAPaligner/soap2. Mismatches no more than 2 bases were allowed in the alignment.
The gene expression level is calculated by using RPKM method (Reads Per kb per Million reads) as described in article: Mortazavi, A., B. A. Williams, et al. (2008).
Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods. 5(7): 621-8.
A strict algorithm was developed to identify differentially expressed genes refering to Audic, S. and J. M. Claverie (1997). The significance of digital gene expression profiles. Genome Res 7(10): 986-95.
Genome_build: mm10
Supplementary_files_format_and_content: Supplementary_files_format_and_content: tab-delimited text files include RPKM values for each Sample.

source_name: bone marrow derived mast cell

growth_protocol: Mouse bone marrow cells were isolated from femurs and cultured in RPMI1640 medium plus 10% (vol/vol) FBS (Gibco) with 10 ng/ml IL-3 and 5 ng/ml SCF. Half of the medium was replaced by fresh medium with cytokines every 2–3 d during culture. After 4–6 wk in culture, BMMCs were stained to confirm the surface expression of FcɛRI and c-Kit. Cells with purity >97.5% were used for subsequent experiments.

extract_protocol:
RNA was harvested using Trizol reagent, and isolated using an RNA isolation kit (QIAGEN). TruSeqRNASample Preparation Kit (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: bone marrow derived mast cell

tag: cell surface expression

value: FcɛRI+ c-Kit+, purity >97.5%


title: RIC_day2_r1

description: RNA-seq for ribosome-induced cell clusters (RICs) day 2 replicate 1

source_name: Ribosome-induced cell clusters (RICs) day 2 replicate 1


title: WT

value: wild type


title: Tet2 rep1 (CLIP-Seq)

data_processing:
After standard HiSeq demultiplexing, eCLIP libraries with distinct in-line barcodes were demultiplexed using custom scripts, and the random-mer was appended to the read name for later usage.
Reads were then adapter trimmed (cutadapt v1.9.dev1) and reads less than 18 bp were discarded
Reads were mapped against the full mouse genome (mm10) including a database of splice junctions with STAR (v 2.4.0i).
Uniquely mapping reads were then run through a custom-built PCR duplicate removal script, removing duplicate reads based on sharing identical Read1 start position, Read2 start position, and random-mer sequence to leave ‘Usable’ reads.
Peaks were identified using a "valley seeking" algorithm, in which a peak is called if the valley of certain depth are found on both sides
Supplementary_files_format_and_content: Common Peaks information in three biological replicates

extract_protocol:
BMMCs (4 × 10^7) were lysed in lysis buffer (50 mM Tris-HCl [pH 7.4], 100 mM NaCl, 1% NP-40, 0.1%SDS, 0.5% sodium deoxycholate and protease inhibitors) and sonicated.Lysates were treated with RNase I (Thermo Fisher Scientific) to fragment RNA and DNase I to remove DNA, while Tet2 antibody were bound to dynabeads in lysis buffer for 1 hour at room temp. Beads were washed three times using lysis buffer and incubated with proteins lyastes for 4 hour at 4℃. A 3` RNA adaptor was ligated onto the RNA with T4 RNA ligase (NEB). Protein-RNA complexes were run on a 4%–12% gradient Bis-Tris Gels (Invitrogen), transferred to PVDF membranes, and RNA was isolated off the membrane identically to standard eCLIP. A fraction of sample was used for western blot of CLIPed endogenous Tet2, indicating regions excised for CLIP library preparation. After purification, RNAs were reverse transcribed with Superscript III reverse transcriptase (Invitrogen), free primer was removed (ExoSap-IT, Affymetrix), and a 3’ DNA adaptor was ligated onto the cDNA product with T4 RNA ligase (NEB).
Libraries were then amplified with Premix Taq PCR mix (Takara). Adapters and primers were designed according to the commercial indexing and sequencing primers. Sequencing reads were processed and mapped according to CLIP procedure. Peaks were identified using a "valley seeking" algorithm, in which a peak is called if the valley of certain depth are found on both sides.

treatment_protocol: UV-crosslinked (first 400 mJ cm2 and then again at 200 mJ cm2)

tag: rip antibody

value: Tet2 (Millipore, MABE462, lot Q2141878)


title: Tet2 rep2 (CLIP-Seq)


title: Tet2 rep3 (CLIP-Seq)


title: PND 21-2

city: Beaverton

first: Hollis

last: Wright

state: OR

address: 505 NW 185th Avenue

department: DIvision of Neuroscience

institute: ONPRC/OHSU

data_processing:
Intial basecalling using Illumina RTA 1.18.64
Barcode and adapter sequence was removed using Trimmomatic v0.33 ILLUMINACLIP  TruSeq3-SE.fa:2:30:10 SLIDINGWINDOW:10:34 HEADCROP:13
Alignment using Tophat2 v2.1 /Bowtie2 v2.2.4
Mapping to reads using featureCounts function of Rsubread R package to Ensembl Rn6.08.83 with countMultiMappingReads, allowMultiOverlap, useMetaFeatures TRUE
Differential expression and normalized read counts using edgeR GLM modeling and voom estimation with batch correction
Genome_build: Rattus norvegicus rn6
Supplementary_files_format_and_content: .xlsx file, log2 counts per million per gene

organism: Rattus norvegicus

source_name: MBH_21 days

extract_protocol:
MBH tissue was removed, flash frozen on dry ice, and RNA was harvested using Qiagen's RNeasy Mini Kit (Valencia, CA). Samples were bioanalyzed on a RNA 6000 Nano chip kit to check for integrity and concentration before sending it to OHSU's Massively Parallel Sequencing Shared Resource for library preparation and sequencing. Illumina TruSeq RNA Sample Prep Kit (Cat#FC-122-1001) was used with 1 ug of total RNA depleted from ribosomal RNA for the construction of sequencing libraries.
RNA libraries were prepared for sequencing using standard Illumina protocols

value: sprague dawley (SD)

tag: gender

value: 21 days

value: MBH (Medial Basal hypothalamus)


title: PND 21-4


title: PND 21-3


title: PND 14-4

source_name: MBH_14 days

value: 14 days


title: PND 28-2

source_name: MBH_28 days

value: 28 days


title: PND 28-1

value: female


title: PND 14-3


title: LP 2

source_name: MBH_32 days

value: 32 days


title: LP 4

source_name: MBH_33 days

value: 33 days


title: PND 28-3


title: PND 14-2


title: PND 28-4


title: PND 21-1


title: LP 1

source_name: MBH_31 days

value: 31 days


title: PND 14-1


title: PND 7-3

source_name: MBH_7 days

value: 7 days


title: PND 7-4


title: RIC_day14_r1

description: RNA-seq for ribosome-induced cell clusters (RICs) day 14 replicate 1

source_name: Ribosome-induced cell clusters (RICs) day 14 replicate 1


title: RIC_day0_r1

description: RNA-seq for ribosome-induced cell clusters (RICs) day 0 replicate 1

source_name: Ribosome-induced cell clusters (RICs) day 0 replicate 1


title: RIC_day0_r2

description: RNA-seq for ribosome-induced cell clusters (RICs) day 0 replicate 2

source_name: Ribosome-induced cell clusters (RICs) day 0 replicate 2


title: PND 7-2


title: PND 7-1


title: Promoter Capture Hi-C TPO 30 minutes Rep 1

city: Cambridge

first: Federico

last: Comoglio

address: Hills Road

country: United Kingdom

institute: University of Cambridge

description:
processed data file:
CHiC_TPO_CHiCAGO_int_score5.ibed

data_processing:
Promoter Capture Hi-C (PCHi-C) reads were pre-processed with HiC-Pro v2.7.7 as described for Hi-C data.
Statistically significant PCHi-C interactions were computed with CHiCAGO v1.2.0 (Cairns et al. 2016). To this end, a restriction map file and a bait map file (both in BED format) were used to precompute auxiliary files using the makeDesignFiles.py script from chicagoTools. To generate input files for the CHiCAGO pipeline, HiC-Pro valid pairs in BAM format were mapped to restriction fragments using the HiC-Pro mapped_2hic_fragments.py script, de-duplicated using Sambamba v0.6.5, sorted by natural sort, and converted to CHiCAGO format using the bam2chicago.sh script provided by chicagoTools. CHiCAGO design files were generated using the makeDesignFiles.py script with parameters --minFragLen=150 --maxFragLen=40000 --maxLBrownEst=1500000 --binsize=20000 --removeb2b=True --removeAdjacent=True. An interaction score ≥ 5 was used to call statistically significant PCHi-C interactions.
genome build: mm10
processed data files format and content: Normalized (RPM) bigWig tracks were generated using the Homer v4.9 makeMultiWigHub.pl script.

source_name: HPC7 cells

growth_protocol: HPC7 cells were grown at 37ºC and 5% CO2 in IMDM (Invitrogen) supplemented with 10% fetal calf serum, 10% SCF conditioned media (produced by the BHK/MKL cell line), 1% L-Glutamine, 1% penicillin/streptomycin and 74.8uM monothioglycerol (Sigma). Cell density was maintained between 5*10^5 - 2*10^6 cells/mL.

extract_protocol:
Promoter Capture Hi-C libraries were generated essentially as described (Schoenfelder et al. 2015).
Libraries were prepared according to Illumina's instructions accompanying the DNA and RNA Sample Kit. The purified DNA was captured on an Illumina flow cell for cluster generation. Libraries were sequenced on a HiSeq4000 sequencer.

treatment_protocol: For serum starvation, cells were spun down at 1,000 rpm for 5 min and washed once in PBS. The cell pellet was resuspended at a density of 10^6 cells/mL in StemSpan SFEM medium (StemCell Technologies) and incubated for four hours. Serum-starved cells were then stimulated with recombinant murine thrombopoietin (TPO, Peprotech) at 100 ng/ml for 30 min. TPO was diluted in a small volume of fresh StemSpan SFEM medium prior to stimulation.

tag: cell line

value: ES-derived multipotent hematopoietic progenitor cells


title: Promoter Capture Hi-C serum-starved Rep 2

description:
processed data file:
CHiC_SS_CHiCAGO_int_score5.ibed


title: LP 3


title: Promoter Capture Hi-C TPO 30 minutes Rep 2


title: Hi-C serum-starved Rep 2

description:
processed data file:
diTAD_HiC_SS_mg.domains.bed

data_processing:
Hi-C reads were pre-processed with HiC-Pro v2.7.7 (Servant et al. 2015). The digest_genome.py utility was used to digest the mouse reference genome (GRCm38 primary assembly, release 82) in silico, generating a BED file containing all theoretical HindIII restriction fragments. HiC-Pro was run with Bowtie2 v2.2.3. The algorithm performs a two-step read alignment to the reference genome whereby reads are first independently aligned using the Bowtie2 end-to-end algorithm, followed by detection of ligation sites on unmapped chimeric reads and re-alignment of their 5' fractions.
Singletons, multi-mappers, duplicated reads, and read pairs with mapping quality <10 were discarded.
Aligned reads were assigned to restriction fragments and read pairs corresponding to invalid ligation products (e.g. self-ligations, dangling ends) or mapping outside the insert size range [150, 800], or anchored at unplaced scaffolds or the mitochondrial genome were discarded.
After a correlation analysis of Hi-C signals between individual samples, valid pairs from biological replicates were merged. These were used to compute raw and coverage-and-distance corrected ICE (iterative correction and eigenvector decomposition) contact matrices (Imakaev et al. 2012), and to generate Homer tag directories for which signals exceeding 10 times the average read counts in 10 kb bins were removed.
TADs were identified using the Homer Hi-C domain finding algorithm, which computes the ratio of upstream and downstream interaction counts (directionality index, DI) within a given fixed-size window (Dixon et al. 2012). A window size of 1 Mb, a bin size of 25 kb and a step size of 5 kb were used. Bins exhibiting coverage values smaller than 15% of the mean bin coverage or exceeding it by more than four standard deviations were excluded. DI values were smoothed using a running average over ±25 kb window. Domain boundary coordinates were defined based on smoothed DI profiles, requiring a minimum index score of 0.5.
genome build: mm10
processed data files format and content: Normalized (RPM) bigWig tracks were generated using the Homer v4.9 makeMultiWigHub.pl script.

extract_protocol:
Hi-C libraries were generated essentially as described (Schoenfelder et al. 2015).
Libraries were prepared according to Illumina's instructions accompanying the DNA and RNA Sample Kit. The purified DNA was captured on an Illumina flow cell for cluster generation. Libraries were sequenced on a HiSeq4000 sequencer.


title: Subcellular RNA-seq Cytoplasm STAT5 knockdown Rep 1

description:
processed data file:
CYTO_KD_merged_str1.bigWig
CYTO_KD_merged_str2.bigWig

data_processing:
Subcellular RNA-seq reads were quality and adapter trimmed using Trimmomatic v0.33 (Bolger et al. 2014) in palindrome mode (Illumina TruSeq universal adapter sequence).
Reads were aligned to the mouse reference genome (GRCm38 primary assembly, release 82) using STAR v2.4.2a (Dobin et al. 2013).
Gene-level count tables were generated while mapping based on Gencode vM7 annotations.
Downstream analyses were performed using R v3.3.2 and Bioconductor (Huber et al. 2015). Differentially transcribed and differentially expressed genes were identified using DESeq2 v1.14.1 (Love et al. 2014). False discovery rates were controlled at a 0.1% level by applying an independent hypothesis weighting (IHW) procedure (Ignatiadis et al. 2016) using the mean of normalized counts for each gene as the informative covariate. Conditional independence of p-values under the null hypothesis was verified prior to IHW.
genome build: mm10
processed data files format and content: Normalized (reads per million, RPM) strand-specific bedGraph tracks were generated while mapping and converted to bigWig format using BEDTools v2.17.0 (Quinlan and Hall 2010). For visualization, the RNA-seq coverage was log transformed with a pseudocount of 1 where indicated.

extract_protocol:
Subcellular fractionation and RNA preparation were performed essentially as described (Bhatt et al. 2012) with minor modifications. 10^6 cells were serum starved and stimulated with TPO as above. Cells were collected by centrifugation and washed once with PBS. The cell pellet was resuspended in ice-cold NP-40 lysis buffer (10 mM Tris-HCl pH 7.5, 0.15% NP40, 150 mM NaCl). The lysate was then layered on 2.5 volumes of a sucrose buffer and centrifuged for 10 min at 13,000 rpm at 4ºC. The supernatant (cytoplasmic fraction) was collected and added 3.5X volumes of RLT Buffer (Qiagen). The nuclei pellet was gently rinsed with ice-cold 1X PBS and resuspended in 200μl ice-cold glycerol buffer (20 mM Tris-HCl pH 7.9, 75 mM NaCl, 0.5 mM EDTA, 0.85 mM DTT, 0.125 mM PMSF, 50% glycerol) by gently flicking the tube. An equal volume of ice-cold nuclei lysis buffer (10 mM HEPES pH 7.6, 1 mM DTT, 7.5 mM MgCl2, 0.2 mM EDTA, 0.3 M NaCl, 1 M UREA, 1% NP-40) was added and gently vortexed twice for 2 sec, incubated for 2 min on ice, and then centrifuged at 13,000 rpm for 2 min at 4°C. The supernatant (nucleoplasmic fraction) was collected and added 3.5X volumes of RLT (Qiagen). The chromatin pellet was gently rinsed with cold 1X PBS and then dissolved in 500 μl TRIzol (Invitrogen). RNA purification from RLT-dissolved samples was performed using RNeasy columns (Qiagen). Chromatin-associated RNA was purified using Direct-zol (Zymo Research).
Libraries were prepared according to Illumina's instructions accompanying the DNA and RNA Sample Kit. The purified DNA was captured on an Illumina flow cell for cluster generation. Libraries were sequenced on a HiSeq4000 sequencer.


title: Hi-C serum-starved Rep 1


title: Hi-C TPO 30 minutes Rep 1

description:
processed data file:
diTAD_HiC_TPO_mg.domains.bed


title: Promoter Capture Hi-C serum-starved Rep 1


title: Subcellular RNA-seq Nucleoplasm STAT5 knockdown Rep 2

description:
processed data file:
NUCL_KD_merged_str1.bigWig
NUCL_KD_merged_str2.bigWig


title: Nuc_3_H9

city: Lund

first: Markus

last: Ringner

address: Sölvegatan 35

country: Sweden

institute: Lund University

data_processing:
Reads were mapped to the mm10 mouse genome using STAR v 2.4.1c with default settings
Reads per gene was computed using rpkmforgenes.py with default settings and NCBI RefSeq annotation with addition of the mCherry cDNA sequence
Genome_build: mm10
Supplementary_files_format_and_content: one .csv file containing mapped reads per gene for 98 libraries

source_name: Adult mouse midbrain

extract_protocol:
FACS
smart-seq2

value: FACS sorted nuclei

tag: neuronal subtype

value: adult midbrain dopamine neurons


title: Subcellular RNA-seq Cytoplasm Scrambled Rep 2

description:
processed data file:
CYTO_SCR_merged_str1.bigWig
CYTO_SCR_merged_str2.bigWig


title: Nuc_3_H8


title: Subcellular RNA-seq Cytoplasm STAT5 knockdown Rep 2


title: Subcellular RNA-seq Nucleoplasm STAT5 knockdown Rep 1


title: Subcellular RNA-seq Nucleoplasm Scrambled Rep 1

description:
processed data file:
NUCL_SCR_merged_str1.bigWig
NUCL_SCR_merged_str2.bigWig


title: Subcellular RNA-seq Chromatin STAT5 knockdown Rep 1

description:
processed data file:
CHRO_KD_merged_str1.bigWig
CHRO_KD_merged_str2.bigWig


title: Subcellular RNA-seq Chromatin STAT5 knockdown Rep 2


title: Nuc_3_H4


title: Resistant RNASeq rep3

source_name: Mouse BCC tumor

value: Ptch+/-, K14 Cre-ER, p53 fl/fl mice

value: Resistant


title: Subcellular RNA-seq Nucleoplasm Scrambled Rep 2


title: Subcellular RNA-seq Cytoplasm Scrambled Rep 1


title: Nuc_3_H5


title: Subcellular RNA-seq Chromatin Scrambled Rep 2

description:
processed data file:
CHRO_SCR_merged_str1.bigWig
CHRO_SCR_merged_str2.bigWig


title: Nuc_3_H3


title: Pkd1f_P7_WT2_miRNA-seq

status: Public on Jan 26 2018

city: Seoul

first: Jong Hoon

last: Park

address: Cheongpa-ro 47-gil 100, Yongsan-gu,

country: South Korea

institute: Sookmyung Women's university

data_processing:
Basecalls performed using CASAVA v1.8.2
the low-quality-reads were filtered by NGSQCToolkit_v2.3.3
The reads were aligned to the miRBase v21 & mm10 genome using miRdeep2
Read counts of representative genes and their isoforms were measured by HTSeq
Genome_build: mm10

extract_protocol:
Kidneys were removed and total RNA was harvested using Trizol reagent from each indicated time points of two ADPKD mice models.
RNA libraries were prepared for sequencing using standard Illumina protocols.

tag: strain background

tag: genotype/variation

value: Post natal day 7

value: kidney


title: Nuc_3_H2

tag: cell type


title: Nuc_3_H1


title: Nuc_3_H11


title: Hi-C TPO 30 minutes Rep 2


title: Nuc_3_H10


title: Nuc_3_G9


title: Subcellular RNA-seq Chromatin Scrambled Rep 1


title: Nuc_3_G7


title: Nuc_3_H7


title: Nuc_3_G5


title: Nuc_3_G4


title: Subcellular RNA-seq Cytoplasm serum-starved Rep 2

description:
processed data file:
CYTO_SS_merged_str1.bigWig
CYTO_SS_merged_str2.bigWig


title: Nuc_3_G3


title: Nuc_3_G11


title: Nuc_3_G1


title: Nuc_3_G2


title: Subcellular RNA-seq Nucleoplasm TPO 30 minutes Rep 2

description:
processed data file:
NUCL_TPO_merged_str1.bigWig
NUCL_TPO_merged_str2.bigWig


title: Subcellular RNA-seq Nucleoplasm TPO 30 minutes Rep 1


title: Nuc_3_F6


title: Subcellular RNA-seq Nucleoplasm serum-starved Rep 2

description:
processed data file:
NUCL_SS_merged_str1.bigWig
NUCL_SS_merged_str2.bigWig


title: Subcellular RNA-seq Nucleoplasm serum-starved Rep 1


title: Subcellular RNA-seq Cytoplasm TPO 30 minutes Rep 2

description:
processed data file:
CYTO_TPO_merged_str1.bigWig
CYTO_TPO_merged_str2.bigWig


title: Nuc_3_F7


title: Nuc_3_F8


title: Nuc_3_F5


title: Nuc_3_F3


title: Subcellular RNA-seq Chromatin serum-starved Rep 2

description:
processed data file:
CHRO_SS_merged_str1.bigWig
CHRO_SS_merged_str2.bigWig


title: Subcellular RNA-seq Chromatin TPO 30 minutes Rep 2

description:
processed data file:
CHRO_TPO_merged_str1.bigWig
CHRO_TPO_merged_str2.bigWig


title: Nuc_3_F11


title: Subcellular RNA-seq Cytoplasm TPO 30 minutes Rep 1


title: Nuc_3_F2


title: Nuc_3_F12


title: Nuc_3_F1


title: Nuc_3_E9


title: Subcellular RNA-seq Cytoplasm serum-starved Rep 1


title: Nuc_3_F10


title: Nuc_3_E8


title: Nuc_3_F9


title: Nuc_3_E6


title: Nuc_3_E7


title: IgG control ChIPSeq TPO 30 minutes

description:
processed data file:
IgG_TPO_1.ucsc.bigWig

data_processing:
ChIP-seq reads were aligned to the mouse reference genome (GRCm38 primary assembly, release 82) using Bowtie2 v2.2.3 (Langmead and Salzberg 2012), allowing no mismatch within a seed of 22 nt.
SAM files were converted to BAM format, sorted, de-duplicated and indexed with Sambamba v0.6.5 (Tarasov et al. 2015).
Reads mapping to unplaced scaffolds or to the mitochondrial genome were discarded.
Downstream analyses were performed using Homer v4.9 (Heinz et al. 2010). Uniquely aligned reads were used to generate Homer tag directories, and peak calling was performed using the findPeaks function. For H3K27ac ChIP-seq profiles, peaks were called against matched IgG control profiles in histone mode with a 1 kb window size and a 2.5 kb distance cutoff.
High-confidence peak regions were defined as the peak intersection between biological replicates (computed with BEDTools v2.17.0, minimum overlap of 1 bp).
genome build: mm10
processed data files format and content: Normalized (RPM) bigWig tracks were generated using the Homer v4.9 makeMultiWigHub.pl script.

extract_protocol:
Chromatin immunoprecipitation (ChIP) assays on serum-starved and TPO stimulated HPC7 cells were performed as previously described (Wilson et al. 2010). Briefly, 10^8 cells were cross-linked in 1% formaldehyde for 10 min at room temperature. Cells were lysed in 10mM Tris pH 8.0, 10mM NaCl and 0.2% NP40 containing inhibitors (1g/mL leupeptin, 10mM NaBu and 50g/mL PMSF) for 10 min on ice and were collected by centrifugation at 2500 rpm for 5 min at 4ºC. The nuclei pellet was frozen until further use. Frozen nuclei were resuspended in 50mM Tris pH 8.0, 10mM EDTA, 1% SDS supplemented with inhibitors (1g/mL leupeptin, 10mM NaBu and 50g/mL PMSF) and sonicated in an equal volume of IP dilution buffer (20mM Tris pH 8.0, 2mM EDTA, 150mM NaCl, 1% Triton X-100, 0.01% SDS and protease inhibitors) in ice-water (Bioruptor, Diagenode) for 5 cycles (30s on, 30s off). Chromatin was pre-cleared with non-specific rabbit IgG (2 μg/μl, Sigma) for 1 hour and 100μL of protein G Dynabeads (Invitrogen) for 2 hours. The beads/IgG were removed by magnetic separation. Chromatin was immunoprecipitated at 4ºC overnight using antibodies against H3K27ac (Abcam, 4729), Rad21 (Abcam, 992), CTCF (Millipore, 07-729), STAT1 (Cell Signaling, 9172) or a control rabbit IgG (Invitrogen, 9172) and 100μl of protein G Dynabeads (Invitrogen) were added for additional 2 hours. Immunocomplexes were washed and eluted twice from the beads with 150μL elution buffer (100mM NaHCO3, 1% SDS). Cross-linking was reversed overnight with 0.3M NaCl and 2μL of RNase (10mg/ml) at 65ºC, and samples were further treated with Proteinase K (20mg/ml) for 2 hours at 42ºC. The ChIP DNA was purified using a PCR purification kit (Qiagen).
Libraries were prepared according to Illumina's instructions accompanying the DNA and RNA Sample Kit. The purified DNA was captured on an Illumina flow cell for cluster generation. Libraries were sequenced on a HiSeq4000 sequencer.

tag: chip antibody

value: rabbit IgG (Invitrogen, 9172)


title: Subcellular RNA-seq Chromatin serum-starved Rep 1


title: Nuc_3_F4

tag: strain


title: Nuc_3_E5


title: Nuc_3_E10


title: Nuc_3_E12


title: Nuc_3_E4


title: Nuc_3_E2


title: STAT1 (uSTAT1) ChIPSeq serum-starved

description:
processed data file:
Stat1_SR_1.ucsc.bigWig

value: STAT1 (Cell Signaling, 9172)


title: STAT1 (pSTAT1) ChIPSeq TPO 30 minutes

description:
processed data file:
Stat1_TPO_0.ucsc.bigWig


title: Subcellular RNA-seq Chromatin TPO 30 minutes Rep 1


title: Rad21 ChIPSeq TPO 30 minutes Rep 2

description:
processed data file:
Rad21_TPO_3.ucsc.bigWig
Rad21_TPO_merged.bigWig

value: Rad21 (Abcam, 992)


title: Nuc_3_E3


title: Nuc_3_G8


title: Nuc_3_D4


title: Nuc_3_D6


title: Nuc_3_D10


title: Nuc_3_C7


title: Rad21 ChIPSeq TPO 30 minutes Rep 1

description:
processed data file:
Rad21_TPO_2.ucsc.bigWig
Rad21_TPO_merged.bigWig


title: Nuc_3_C6


title: Rad21 ChIPSeq serum-starved Rep 2

description:
processed data file:
Rad21_SS_3.ucsc.bigWig
Rad21_SS_merged.bigWig


title: Rad21 ChIPSeq serum-starved Rep 1

description:
processed data file:
Rad21_SS_2.ucsc.bigWig
Rad21_SS_merged.bigWig


title: Nuc_3_C5


title: CTCF ChIPSeq TPO 30 minutes Rep 2

description:
processed data file:
CTCF_TPO_3.ucsc.bigWig
CTCF_TPO_merged.bigWig

value: CTCF (Millipore, 07-729)


title: Nuc_3_C3


title: Nuc_3_C12


title: Nuc_3_C10


title: Nuc_3_B3


title: Resistant ExomeSeq rep4


title: IgG control ChIPSeq serum-starved

description:
processed data file:
IgG_SS_1.ucsc.bigWig


title: Nuc_3_A7


title: CTCF ChIPSeq TPO 30 minutes Rep 1

description:
processed data file:
CTCF_TPO_2.ucsc.bigWig
CTCF_TPO_merged.bigWig


title: CTCF ChIPSeq serum-starved Rep 2

description:
processed data file:
CTCF_SS_3.ucsc.bigWig
CTCF_SS_merged.bigWig


title: Nuc_3_B1


title: Nuc_3_A1


title: H3K27ac ChIPSeq TPO 30 minutes Rep 2

description:
processed data file:
H3K27ac_TPO_3.ucsc.bigWig
H3K27ac_TPO_merged.bigWig


title: CTCF ChIPSeq serum-starved Rep 1

description:
processed data file:
CTCF_SS_2.ucsc.bigWig
CTCF_SS_merged.bigWig


title: Nuc_3_D8


title: Nuc_3_A12


title: Nuc_3_A8


title: Nuc_3_A11


title: Nuc_3_A10


title: Nuc_2_H9


title: Nuc_2_G9


title: Nuc_3_C4


title: H3K27ac ChIPSeq TPO 30 minutes Rep 1

description:
processed data file:
H3K27ac_TPO_2.ucsc.bigWig
H3K27ac_TPO_merged.bigWig


title: Nuc_2_G7


title: Nuc_2_H8


title: H3K27ac ChIPSeq serum-starved Rep 2

description:
processed data file:
H3K27ac_SS_3.ucsc.bigWig
H3K27ac_SS_merged.bigWig


title: Nuc_2_F7


title: Nuc_2_F8


title: ASZ001_CCG3

value: resistant BCC cells

tag: treated with


title: ASZ001_CCG1


title: Nuc_2_E7


title: H3K27ac ChIPSeq serum-starved Rep 1

description:
processed data file:
H3K27ac_SS_2.ucsc.bigWig
H3K27ac_SS_merged.bigWig


title: Nuc_2_E9


title: Nuc_2_D7


title: Nuc_2_C9


title: Nuc_2_D9


title: Nuc_2_C7


title: ASZ001_Control3

value: vehicle control


title: ASZ001_Control1


title: Nuc_2_A9


title: Nuc_2_A8


title: ASZ001_Vismo3

value: Vismodegib (Vismo)


title: ASZ001_Control2


title: Nuc_2_B9


title: Nuc_2_C8


title: Nuc_2_G8


title: Nuc_2_A7


title: Nuc_1_H5


title: ASZ001_Vismo2


title: Nuc_1_G5


title: Nuc_2_F9


title: Nuc_1_F6


title: Nuc_1_F5


title: Nuc_1_H6


title: ASZ001_CCG2


title: Nuc_1_E6


title: White389V

city: Los Angeles

first: Zachary

middle: Daniel

last: Burkett

address: 610 Charles E Young Drive East Room 1045

department: Integrative Biology & Physiology

institute: University of California, Los Angeles

data_processing:
Basecalling was done using Sanger/Illumina 1.9 software.
Reads were aligned to taeGut1 whole genome using STAR with the following parameters: --outSAMtype BAM SortedByCoordinate --outSAMattributes MD NH NM --outFilterMultimapNmax 1 --outFilterMismatchNmax 2
TPM (transcripts per million) for each sample were quantified by first counting all of the reads for each sample that mapped to any exon of a gene. Counts in each exon for each gene were then summed so as to produce a single gene-level count within each sample. TPM was calculated for each gene by calculating the number of reads counted for that gene by the gene's length, then dividing this value by the ratio of counts to gene length for all genes. This value is then multiplied by 1 million to generate TPM.
TPM values were log2 transformed and genes with zero variance across samples were removed. We used an iterative process of removing gene expression data from single samples whose expression was greater than 2.5 SD of that gene’s expression across all samples, repeating until no samples remained with expression greater than 2.5 SD away from the gene’s average expression across all samples. Finally, we calculated the intrasample correlation (ISC) and used a hard cutoff of 2 SD away from the group ISC for removal of samples from the study. No sample in any group (Area X or VSP was greater than 2 SD from the group ISC. Finally, data were quantile normalized as the last step before input to WGCNA.
Genome_build: taeGut1
Supplementary_files_format_and_content: .tsv files represent gene symbols and exon counts summed to the gene level for each sample

organism: Taeniopygia guttata

source_name: Brain

extract_protocol:
Total RNA was extracted from Area X or ventral striatopallidum tissue micropunches using Qiagen Rnease Micro Kits following the manufacturers protocol. We used QIAzol as the lysis reagent. We also performed an additional wash each in RW1 and RPE buffers beyond the manufacturers protocol. Total RNA extracts were provided to the UCLA Neurogenomics Core. PolyA transcripts were selected for as part of the library construction protocol.
Librarires were prepared using the Illumina TruSeq Poly-A prep kit using the manufacturer's protocol.

tag: brain region

value: Ventral striatopallidum

tag: virus group

tag: barcode

value: CTTGTA


title: KO-biological replicate 2-technical replicate 2

description: FcɛRI+ c-Kit+, purity >97.5%

data_processing:
Illumina Casava1.6 software used for basecalling.
Reads mapping and methylcytosine calling were analyzed by BS-RNA using converted mm10 and lambda genome as references.
BS-RNA: An efficient mapping and annotation tool for RNA bisulfite sequencing data. Computational biology and chemistry, doi:10.1016/j.compbiolchem.2016.09.003 (2016).
Splice sites information was added using refGene.gtf file during read mapping.
Cs with methylation rate above 0.1 and at least two reads supporting methylation in both technical and biological replicates of one group and below the non-conversion rate of each of the replicates of the other group (WT1-rep1:0.0015, WT1-rep2:0.0015, KO1-rep1:0.0017, KO1-rep2:0.0016, WT2-rep1:0.0012, WT2-rep2:0.0013, KO2-rep1:0.0012, KO2-rep2:0.0012,) were chosen as group-specific Cs.
Genome_build: GRCm38/mm10

extract_protocol:
Total RNAs were isolated by Trizol reagent. Poly-A tailed RNAs were enriched by GenElute™ mRNA Miniprep Kit (MRN70-1KT, Sigma). The mRNAs were treated with TURBO DNase, purified by a RNeasy MinElute Cleanup Kit (QIAGEN). Treated mRNAs with 0.5% unmethylated RNA controls from lambda genome were bisulfite converted using the EZ RNA Methylation kit (R5001, Zymo Research). Briefly, 500 ng mRNA was converted using three cycles of 10-min denaturation at 70℃ followed by 45min at 54℃. RNA separation from bisulfite solution, desulfonation, and purification were performed following the standard protocol of the kit.
Strand-specific libraries were constructed following the “TruSeq stranded mRNA sample preparation guide” from illumina for 150PE sequencing using Hiseq3000.

value: bone marrow derived mast cell


title: KO-biological replicate 2-technical replicate 1

description: FcɛRI+ c-Kit+, purity >97.5%


title: Nuc_1_E4


title: White388X

value: Area X

value: CAGATC


title: WT-biological replicate 2-technical replicate 2

description: FcɛRI+ c-Kit+, purity >97.5%

value: wild type


title: White385X

value: ATGTCA


title: White388V

value: GGCTAC


title: Nuc_1_D6

value: FACS sorted nuclei

value: adult midbrain dopamine neurons


title: Nuc_1_E5


title: Nuc_1_D5


title: KO-biological replicate 1-technical replicate 2

description: FcɛRI+ c-Kit+, purity >97.5%


title: WT-biological replicate 2-technical replicate 1

description: FcɛRI+ c-Kit+, purity >97.5%


title: Nuc_1_D4


title: White384X

value: ACTTGA


title: ASZ001_Vismo1


title: KO-biological replicate 1-technical replicate 1

description: FcɛRI+ c-Kit+, purity >97.5%


title: White389X

value: ACAGTG


title: Nuc_1_G6


title: Nuc_1_C6


title: WT-biological replicate 1-technical replicate 2

description: FcɛRI+ c-Kit+, purity >97.5%


title: Nuc_1_C5


title: White383X

value: GTGGCC


title: White383V

value: AGTTCC


title: Nuc_1_C4


title: Nuc_1_B5


title: White381X

value: GFP

value: AGTCAA


title: Nuc_1_B4


title: White381V

value: GATCAG


title: Nuc_1_A6


title: White360X

value: CGATGT


title: Nuc_1_A4


title: White360V

value: Ventral striatopallidum

value: GTGAAA


title: White330X

value: Area X


title: White359V

value: GTCCGC


title: White359X

value: CGTACG


title: sox_1

data_processing:
Reads were mapped to the mm10 mouse genome using STAR v 2.4.1c with default settings
Reads per gene was computed using rpkmforgenes.py with default settings and NCBI RefSeq annotation with addition of the mCherry cDNA sequence
Genome_build: mm10
Supplementary_files_format_and_content: one .csv file containing mapped reads per gene for 10 libraries

source_name: Developing mouse brain, E11.5

value: NPC

tag: biological replicate

tag: batch

value: I


title: sox_3


title: sox_2


title: White384V

value: TTAGGC


title: White385V

value: TAGCTT


title: Red173X

value: GAGTGG


title: ser_2

source_name: Adult mouse brainstem, raphe nuclei

value: SER


title: White330V

value: GTGGCC


title: Red173V

value: GTTTCG


title: ser_1


title: ser_3


title: WT-biological replicate 1-technical replicate 1

description: FcɛRI+ c-Kit+, purity >97.5%

source_name: bone marrow derived mast cell

value: bone marrow derived mast cell

tag: genotype

value: wild type


title: Red172X

value: GFP

value: CTTGTA


title: Red172V

value: Ventral striatopallidum


title: dat_3

value: mDA


title: dat_2


title: Red171V

value: ATCACG


title: dat_1


title: dat_4


title: Red152V

value: TGACCA


title: LightBlue559X

value: Area X


title: sox_k4_6

data_processing:
Reads were mapped to the mm10 mouse genome using Bowtie2 v2.2.6 with default settings
Duplicate reads were marked using Picard tools v1.92
Biological replicates were pooled within each batch separately using samtools v1.3
Coverage of pooled samples were generated with deepTools using the bamCoverage function. Counts were transformed to RPKMs, reads were extended to 170bp, binsize was set to 10bp, and duplicates were ignored.
Genome_build: mm10
Supplementary_files_format_and_content: bigWig files of coverage.

extract_protocol:
Three different neuronal cell types were extracted: midbrain dopamine producing neurons (mDA) from DATcreERT2-Rpl10a-mCherry mice, raphe nuclei serotonergic neurons (SER) from SERTcre-Rpl10a-mCherry mice, and neural progenitor cells (NPC) from embryos from a timed-pregnant (embryonic day 11.5 (E11.5)) mouse. Tissue was thawed and dissociated using a 1 mL dounce homogenizer (Wheaton) in ice-cold lysis buffer (0.32M sucrose, 5mM CaCl2, 3 mM MgAc, 0.1 mM Na2EDTA, 10 mM Tris-HCl, pH 8.0, 1 mM DTT, 1x complete proteinase inhibitor, EDTA-free (Roche)). The homogenate was layered over a sucrose cushion (1.8M sucrose, 3mM MgAc, 10 mM Tris-HCl, pH 8.0, 1 mM DTT) before centrifugation for 2.2h at 30.000g in a Beckman J-25 centrifuge using a J13.1 rotor. The pelleted nuclei were re-suspended in a nuclear storage buffer (15% sucrose, 10 mM Tris_HCl, pH 7.2, 70 mM KCl, 2 mM MgCl2) supplemented with RNAse inhibitor (RNAse out, Invitrogen) and proteinase inhibitor (Complete, Roche). Re-suspended nuclei were passed through a 30 μm cup filcon (BD Biosciences, 340625) into a BSA-coated tube for sorting. Nuclei from E11.5 mouse embryos were incubated with an Alexa 647-labelled antibody against SOX2 (Santa Cruz, sc-17320) using Alexa Fluor 647 Antibody Labeling Kit, (Molecular Probes, P26005) overnight prior to sorting. FACS was performed using a FACSAria cell sorter and the FACSDiva software (BD Biosciences). The nuclei were identified by forward and side scatter gating and a 633 nm laser with a 660/20 or 616/23 filter. A 100 μm nozzle, sheath pressure of 20–25 psi, and an acquisition rate of up to 1000 events per second were used. Cell-type specific nuclei were collected in batches of 1000, supplemented with a nuclear buffer (10 mM Tris (pH 8.0), 100 mM NaCl, 2 mM MgCl2, 0.3M Sucrose, 0.25% IGEPAL CA-630) to a total volume of 10 uL and subsequently snap frozen on dry ice for later use. ChIP and library generation for ChIP-seq was performed using the previously described ULI-NChIP protocol [20] with minor modifications. Briefly, one batch of 1000 nuclei per animal and antibody was thawed on ice and fragmented for 10 min using MNase at 21 °C and diluted in NChIP immunoprecipitation buffer (20 mM Tris-HCl pH 8.0, 2 mM EDTA, 15 mM NaCl, 0.1% Triton X-100, 1x EDTA-free protease inhibitor cocktail and 1 mM phenylmethanesulfonyl fluoride (PMSF, Sigma, P7626)). Chromatin was pre-cleared with 5 μl of a 1:1 mixture of Protein A and Protein G Dynabeads (Life Technologies, 10006D and 10007D) and IPed over night at 4 °C with 0.25 of anti-H3K9me3 (Active Motif, 39161), anti-H3K27me3 (Cell Signaling, 9733) or anti-H3K4me3 (Cell Signaling, 9751). Beads were washed twice with 400 μl of a buffer containing 20 mM Tris-HCl, pH 8.0, 0.1% SDS, 1% Triton X-100, 0.1% deoxycholate, 2 mM EDTA and 150 mM NaCl and twice with a buffer containing 20 mM Tris-HCl (pH 8.0), 0.1% SDS, 1% Triton X-100, 0.1% deoxycholate, 2 mM EDTA and 500 mM NaCl. Protein–DNA complexes were eluted in 30 μl of a ChIP elution buffer containing 100 mM NaHCO3 and 1% SDS for 2 h at 68 °C. The eluted chromatin was purified by phenol chloroform, precipitated with EtOH and re-suspended in 10 mM Tris-HCl pH 8.0.
To generate libraries for sequencing, ChIPed DNA was end-repaired in a mixture of dNTP (NEB, N0447L), T4 DNA polymerase (NEB, M0203L), Klenow DNA polymerase (NEB, M0210L) and T4 PNK (NEB, M0201L) in T4 DNA ligase buffer (NEB, B0202S) for 30 min at room temperature. The product was purified with 1.8x Agencourt AMPure XP beads (Beckman Coulter, A63881) and then poly-A tailed in dATP (NEB, N0440S) and Klenow (3’-5’ exo-) (NEB, M0212L) in NEB buffer 2 (NEB, B7002S) at 37 °C for 30 min followed by another 1.8x AMPure bead purification step. The product was ligated to NEBnext adaptors for Illumina (NEB, E7337A) with Quick DNA ligase (NEB, M2200L) in 2x Quick DNA ligation buffer at room temperature for 1-2h, trimmed with USER enzyme (NEB, E7338A) at 37 °C for 15 min and subsequently purified with 0.8x AMPure beads. The adapter-ligated ChIP-DNA was then PCR-amplified using 2x Phusion HF Master mix (NEB, M0531L) and NEBNext Multiplex Oligos for Illumina, Index Primers Set 1 (NEB, E7335L). The final libraries were cleaned up using 0.8x AMPure beads, quantified on a Qubit 3.0 Fluorometer (ThermoFisher) and quality checked on a 2100 Bioanalyzer system (Agilent) using High Sensitivity DNA kit (Agilent, 5067-4626).

value: NPC

value: H3K4me3 (Cell Signaling, 9751)

value: II


title: LightBlue558X

value: GCCAAT


title: sox_in_6


title: sox_in_5


title: LightBlue558V

value: GATCAG


title: sox_k4_5


title: LightBlue556X

value: CCGTCC


title: LightBlue555X

value: ACAGTG


title: LightBlue555V

value: GAGTGG


title: sox_k27_5

value: H3K27me3 (Cell Signaling, 9733)


title: sox_k9_5

value: H3K9me3 (Active Motif, 39161)


title: sox_in_4


title: LightBlue554X


title: sox_k4_4


title: sox_k9_4


title: LightBlue554V

value: CGTACG


title: DarkBlue688V

value: GFP


title: Red152X

value: ATGTCA


title: ser_in_6

value: SER


title: DarkBlue687X

value: ACTTGA


title: ser_k9_6


title: ser_k27_6


title: ser_in_5


title: LightBlue559V

value: Ventral striatopallidum

value: CGATGT


title: sox_k27_6

value: NPC


title: ser_in_4


title: ser_k9_5


title: ser_k9_4


title: ser_k27_4


title: ser_3_k4

value: H3K4me3 (Cell Signaling, 9751)

value: I


title: ser_3_k9


title: ser_3_in


title: ser_3_k27


title: DarkBlue688X

value: Area X

value: GGCTAC


title: ser_2_in


title: ser_2_k9


title: ser_1_k9


title: ser_1_k4


title: dat_3_in

value: mDA


title: ser_1_k27


title: m10, rep c

city: Uppsala

first: Johan

last: Fogelqvist

address: PO- Box 7080

department: Plant Biology

institute: Swdish Universitu of Agriculture

description: samples from the meeting point between 3928A and H7

data_processing:
Filtering: trimmomatic v0.32 ILLUMINACLIP:$ADAPTOR:2:30:10 HEADCROP:13 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36
Mapping: tophat v2.0.9 -i 20 -I 5000 --min-segment-intron 20 --max-segment-intron 5000 --min-coverage-intron 20 --max-coverage-intron 5000 --read-realign-edit-dist 0 --microexon-search --library-type fr-firststrand
Novel transcript detection: Cufflinks v2.2.1 --min-intron-length 20 --max-intron-length 5000 --library-type fr-firststrand
Read counting: Htseq v0.6.1
Read count normalization: DESeq2 1.8.1, regularized log transformation rld()
Genome_build: Phytophthora infestans T30-4, Acc. Nr NZ_AATU00000000.1  (assembly ASM14294v1)
Supplementary_files_format_and_content: new.rld.geo.2.tsv, tab separated values. Regularized log transformed count values for each sample

organism: Phytophthora infestans

source_name: Mycelia

extract_protocol:
RNA plant mini kit (Qiagen)
TruSeq stranded mRNA HT library preparation kit including polyA selection

tag: isolate

value: mix

tag: origin

value: UK

tag: mating type


title: ser_1_in


title: m9, rep a

description: samples from the meeting point between P1-5 and 1.2 isolates

value: Sweden


title: m10, rep b


title: m9, rep c


title: DarkBlue687V

value: CAGATC


title: m9, rep b


title: dat_3_k9

value: H3K9me3 (Active Motif, 39161)


title: m8, rep c

description: samples from the meeting point between 1.2 and 1.7 isolates


title: dat_3_k4


title: m10, rep a


title: m8, rep b


title: m8, rep a


title: m5, rep c

description: samples from the meeting point between P1-5 and 1.7 isolates


title: dat_3_k27

value: H3K27me3 (Cell Signaling, 9733)


title: m5, rep b


title: m4, rep c

description: samples from the meeting point between 800291.2 and IpO-0 isolates

value: Netherlands


title: m5, rep a


title: ser_2_k4

value: SER


title: dat_2_in


title: ser_2_k27


title: dat_2_k4


title: dat_2_k27


title: m4, rep b


title: m4, rep a


title: m3, rep c

description: samples from the meeting point between1.2  and  2.2 isolates


title: dat_1_k9


title: dat_1_k4


title: s8, rep a

description: mycelia, parental strain


title: m3, rep a


title: s8, rep b

description: mycelia, parental strain


title: m3, rep b


title: s7, rep b

description: mycelia, parental strain


title: sox_3_in


title: s7, rep a

description: mycelia, parental strain


title: s6, rep b

description: mycelia, parental strain


title: sox_3_k4


title: dat_1_k27


title: sox_3_k27


title: sox_2_k9


title: sox_2_in


title: s6, rep a

description: mycelia, parental strain


title: dat_2_k9


title: sox_2_k27


title: dat_1_in


title: s4, rep b

description: mycelia, parental strain

value: Hedemora (Sweden)


title: s5, rep a

description: mycelia, parental strain


title: sox_1_k27


title: s4, rep a

description: mycelia, parental strain


title: s3, rep a

description: mycelia, parental strain

value: Skåne (Sweden)


title: s3, rep b

description: mycelia, parental strain


title: sox_1_k4


title: sox_1_k9


title: s2, rep b

description: mycelia, parental strain

value: Halland (Sweden)


title: s2, rep a

description: mycelia, parental strain


title: sox_1_in

value: NPC


title: s1, rep c

description: mycelia, parental strain

value: Ultuna (Sweden)


title: s1, rep b

description: mycelia, parental strain


title: GRO-seq from ovaries of 1.9 transgenic strain

status: Public on Jan 24 2018

city: Moscow

first: Sergei

last: Ryazansky

address: Kurchatov sq., 2

department: Department of Molecular Genetics of Cell

country: Russia

institute: Institute of Molecular Genetics

data_processing:
Library strategy: GRO-seq
Reads were clipped 3'-adapter (5'-TGGAATTCTCGGGTGCCAAGG), and reads smaller than 18 nt were discarded. Reads were mapped to the dm3/R5 genomic assembly or transgene sequence by bowtie2 and deduplexed. Only single-mapped reads were used for the construction the genomic profiles.
Genome_build: dm3
Supplementary_files_format_and_content: GRO-seq: Genomic profiles of RPM-normalized single-mapped reads.

molecule: nuclear RNA

organism: Drosophila melanogaster

source_name: ovaries

growth_protocol: Flies were grown according to standard protocols.

extract_protocol: GRO-seq: Nuclei were purified from homogenized ovaries, and were used for run-on reacton with Br-UTP in vitro (Rozkov et al. 2013). De novo synthesized RNA was subjected to library preparation for RNA-seq.

tag: tissue

value: ovaries

value: 1.9 transgenic


title: GRO-seq from ovaries of 3.6 transgenic strain

value: 3.6 transgenic


title: s5, rep b

description: mycelia, parental strain


title: H1 RRBS

city: chengdu

first: Yixin

last: Wang

state: sichuan

address: 211 huimin street

department: Institute of Animal Genetics and Breeding, College of Animal Science and Technology

institute: Sichuan Agricultural University

data_processing:
High quality data were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. At the same time, Q20, Q30 and GC content of the clean data were calculated.
High quality data were mapped to the mouse reference genome using Tophat version 2.0.11 and Bismark RRBS. When multiple reads from one sequencing library were mapped to same genomic location, these reads were regarded as potential clonal duplicates arising from PCR amplification biases and were identified to be a single read.
Defined region extending from − 2,200 to + 500 bp from the gene translation start site (TSS) as the promoter[24]. We calculated average cytosine methylation level in promoter region of three biological reduplicate which contained more than 3 cytosine and obtained abundant genes promoter methylation level.
Genome_build: Mus_musculus. GRCm38.81
Supplementary_files_format_and_content: files format:txt files;file content:Methylation level (methylated cytosines sequenced/total cytosines sequenced) for each cytosine site

source_name: Hemicastrated mouse

growth_protocol: Three hemicastrated mice and three normal mice were fed until body maturation.

extract_protocol:
DNA of each collected tissue was extracted using DNeasy Blood & Tissue Kit (Qiagen).
Genomic DNA was digested by restriction enzyme MspI. Subsequently, Ilumina paired-end protocol, consisted with end repair, A-tailing and methylated-adaptor ligation, was performed to construct libraries. 40-220bp fragment was selected and purified, then treatment with bisulfite by ZYMO EZ DNA Methylation-Gold kit. All bisulfite converted products were amplified by PCR for library preparation and only qualified library was sequenced with IlluminaGAII.

treatment_protocol: Three pretubertal mice were performed hemicastration to removed left testis. At the same time, three normal mice were operated sham surgery. Until body maturation, right testis were sampled rapidly, and immediately frozen in liquid nitrogen

value: KM

value: Testis


title: C2 RRBS

source_name: Normal mouse


title: H3 RRBS


title: C3 RRBS


title: C1 RRBS


title: Single-cell_RNA-seq_Subject6_TCM_Cell038

city: La Jolla

first: Pandurangan

last: Vijayanand

state: California

address: 9420 Athena Circle

department: Division of Vaccine Discovery

institute: La Jolla Institute for Allergy and Immunology

description: Whole Transcriptome Amplification (SmartSeq2 - Picelli et al.2014)

data_processing:
Single-cell RNA–seq data were mapped against the human hg19 reference genome using TopHat (v1.4.1., --library-type fr-secondstrand -C) and Gencode version 19 (GRCh37.p13) as gene model reference for alignment.
Sequencing read coverage per gene was counted using HTSeq-count (-m union -s yes -t exon -i gene_id, http://www huber.embl.de/users/anders/HTSeq/). Counts per gene are obtained by counting all the gene_id, http://www huber.embl.de/users/anders/HTSeq/). Counts per gene are obtained by counting all the transcripts mapping to a gene and is together referred to as transcript.
Genome_build: hg19
Supplementary_files_format_and_content: SC_raw_counts.txt contains raw counts of sequencing reads obtained with HTSeq-count

source_name: Subject6_DENV positive_PBMC_TCM

growth_protocol: CD4 memory cell types were isolated from PBMCs and directly sorted by Flow cytometry into a 96-well plate wells with lysis buffer with RNAase inhibitor (Takara) - Described in Picelli et al. Nat Protoc. 2014, PMID:24385147. No particular cell growth procedure was required.

extract_protocol:
Single cell RNAseq was performed as described in Picelli et al.Nat Protoc. 2014, PMID:24385147. We performed 23 cycles of amplification.
Single cell RNAseq was perfromed as described in Picelli et al.Nat Protoc. 2014, PMID:24385147. Barcoded Illumina sequencing libraries (Nextera XT library preparation kit, Illumina) were prepared as following manufacturer's protocol.
DNA sequencing - Libraries were sequenced on the Illumina HiSeq 2500  platform to obtain 50-bp single end reads (barcoded Illumina sequencing libraries (Nextera XT library preparation kit, Illumina)).

treatment_protocol: For DENV antigen specifc cells, PBMCs were stimulated with DENV peptide pool following the protocol described previously by Weiskopf et al., 2014 and IFNγ producing single cells were sorted for single-cell RNA-seq analysis

tag: subject id

tag: dengue infections history

value: DENV positive

value: PBMC

tag: cell subtype surface markers

tag: cell subtype

value: TCM


title: Single-cell_RNA-seq_Subject6_TCM_Cell037

tag: cell type


title: Single-cell_RNA-seq_Subject6_TCM_Cell024


title: Single-cell_RNA-seq_Subject6_TCM_Cell030


title: Single-cell_RNA-seq_Subject6_TCM_Cell032


title: Single-cell_RNA-seq_Subject6_TCM_Cell022


title: Single-cell_RNA-seq_Subject6_TCM_Cell023


title: Single-cell_RNA-seq_Subject6_TCM_Cell025


title: H2 RRBS

tag: strain


title: Single-cell_RNA-seq_Subject6_TCM_Cell033


title: Single-cell_RNA-seq_Subject6_TCM_Cell027


title: Single-cell_RNA-seq_Subject6_TCM_Cell034


title: Single-cell_RNA-seq_Subject6_TCM_Cell021


title: Single-cell_RNA-seq_Subject6_TCM_Cell039


title: Single-cell_RNA-seq_Subject6_TCM_Cell040


title: Single-cell_RNA-seq_Subject6_TCM_Cell029


title: Single-cell_RNA-seq_Subject6_TCM_Cell036


title: Single-cell_RNA-seq_Subject6_TCM_Cell041


title: Single-cell_RNA-seq_Subject6_TCM_Cell031


title: Single-cell_RNA-seq_Subject6_TCM_Cell028


title: Single-cell_RNA-seq_Subject6_TCM_Cell026


title: Single-cell_RNA-seq_Subject6_TCM_Cell035


title: Single-cell_RNA-seq_Subject6_TCM_Cell020


title: Single-cell_RNA-seq_Subject6_TCM_Cell018


title: Single-cell_RNA-seq_Subject6_TCM_Cell017


title: Single-cell_RNA-seq_Subject6_TCM_Cell016


title: Single-cell_RNA-seq_Subject6_TCM_Cell015


title: Single-cell_RNA-seq_Subject6_TCM_Cell014


title: Single-cell_RNA-seq_Subject6_TCM_Cell019


title: Single-cell_RNA-seq_Subject6_TCM_Cell013


title: Single-cell_RNA-seq_Subject6_TCM_Cell011


title: Single-cell_RNA-seq_Subject6_TCM_Cell012


title: Single-cell_RNA-seq_Subject6_TCM_Cell003


title: Single-cell_RNA-seq_Subject6_TCM_Cell009


title: Single-cell_RNA-seq_Subject6_TCM_Cell010


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell089

source_name: Subject6_DENV positive_PBMC_DENV antigen specific TEM

value: DENV antigen specific TEM


title: Single-cell_RNA-seq_Subject6_TCM_Cell004


title: Single-cell_RNA-seq_Subject6_TCM_Cell008


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell088


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell086


title: Single-cell_RNA-seq_Subject6_TCM_Cell006


title: Single-cell_RNA-seq_Subject6_TCM_Cell007


title: Single-cell_RNA-seq_Subject6_TCM_Cell001


title: Single-cell_RNA-seq_Subject6_TCM_Cell005


title: Single-cell_RNA-seq_Subject6_TCM_Cell002


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell078


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell087


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell080


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell077


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell085


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell084


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell076


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell074


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell073


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell072


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell082


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell079


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell081


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell083


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell075


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell070


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell071


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell066


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell068


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell067


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell069


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell058


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell059


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell064


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell052


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell054


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell065


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell053


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell061


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell060


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell048


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell051


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell056


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell047


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell049


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell046


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell042


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell044


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell043


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell045


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell063


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell062


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell055


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell041


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell057


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell040


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell037


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell038


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell036


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell035


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell039


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell031


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell030


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell026


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell029


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell025


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell032


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell034


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell022


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell027


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell023


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell033


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell024


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell019


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell021


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell017


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell020


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell016


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell018


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell015


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell014


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell012


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell013


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell028


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell011


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell008


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell009


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell050


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell006


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell004


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell073

source_name: Subject6_DENV positive_PBMC_DENV antigen specific TEMRA

value: DENV antigen specific TEMRA


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell002


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell003


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell072


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell070


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell068


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell071


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell069


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell064


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell066


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell062


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell065


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell067


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell063


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell010


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell060


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell061


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell059


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell057


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell058


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell007


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell056


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell054


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell053


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell005


title: Single-cell_RNA-seq_Subject6_DENV_TEM_Cell001


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell052


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell050


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell049


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell045


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell044


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell047


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell042


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell046


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell043


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell041


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell040


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell037


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell039


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell055


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell038


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell036


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell033


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell035


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell029


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell034


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell031


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell032


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell051


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell028


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell026


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell024


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell025


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell023


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell048


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell027


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell030


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell022


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell019


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell020


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell017


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell021


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell014


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell013


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell012


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell018


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell016


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell009


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell011


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell010


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell007


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell005


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell002


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell006


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell004


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell048

source_name: Subject5_DENV positive_PBMC_TEMRA

value: TEMRA


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell045


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell046


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell001


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell008


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell003


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell047


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell043


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell041


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell042


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell040


title: Single-cell_RNA-seq_Subject6_DENV_TEMRA_Cell015


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell039


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell036


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell035


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell037


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell029


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell033


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell028


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell031


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell034


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell030


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell024


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell014


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell044


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell020


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell023


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell013


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell026


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell012


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell019


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell025


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell022


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell018


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell027


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell021


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell015


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell011


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell038


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell010


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell008


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell007


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell009


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell004


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell005


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell003


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell002


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell006


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell032


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell017


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell016


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell045

source_name: Subject4_DENV negative_PBMC_TEMRA

value: DENV negative


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell046


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell044


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell048


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell047


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell022


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell021


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell020


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell023


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell025


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell026


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell041


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell042


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell039


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell030


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell043


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell028


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell036


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell040


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell027


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell034


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell037


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell033


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell032


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell029


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell038


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell031


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell035


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell024


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell017


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell019


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell018


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell015


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell016


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell014


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell013


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell008


title: Single-cell_RNA-seq_Subject3_TEM_Cell044

source_name: Subject3_DENV positive_PBMC_TEM

value: TEM


title: Single-cell_RNA-seq_Subject3_TEM_Cell046


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell011


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell012


title: Single-cell_RNA-seq_Subject3_TEM_Cell047


title: Single-cell_RNA-seq_Subject3_TEM_Cell045


title: Single-cell_RNA-seq_Subject3_TEM_Cell042


title: Single-cell_RNA-seq_Subject3_TEM_Cell041


title: Single-cell_RNA-seq_Subject3_TEM_Cell039


title: Single-cell_RNA-seq_Subject3_TEM_Cell040


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell004


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell005


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell006


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell007


title: Single-cell_RNA-seq_Subject3_TEM_Cell048


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell001


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell002


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell009


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell003


title: Single-cell_RNA-seq_Subject3_TEM_Cell038


title: Single-cell_RNA-seq_Subject3_TEM_Cell043


title: Single-cell_RNA-seq_Subject3_TEM_Cell036


title: Single-cell_RNA-seq_Subject3_TEM_Cell035


title: Single-cell_RNA-seq_Subject3_TEM_Cell034


title: Single-cell_RNA-seq_Subject3_TEM_Cell037


title: Single-cell_RNA-seq_Subject4_TEMRA_Cell010


title: Single-cell_RNA-seq_Subject3_TEM_Cell033


title: Single-cell_RNA-seq_Subject3_TEM_Cell032


title: Single-cell_RNA-seq_Subject3_TEM_Cell027


title: Single-cell_RNA-seq_Subject3_TEM_Cell028


title: Single-cell_RNA-seq_Subject3_TEM_Cell026


title: Single-cell_RNA-seq_Subject3_TEM_Cell030


title: Single-cell_RNA-seq_Subject3_TEM_Cell021


title: Single-cell_RNA-seq_Subject3_TEM_Cell022


title: Single-cell_RNA-seq_Subject3_TEM_Cell029


title: Single-cell_RNA-seq_Subject3_TEM_Cell020


title: Single-cell_RNA-seq_Subject3_TEM_Cell023


title: Single-cell_RNA-seq_Subject3_TEM_Cell024


title: Single-cell_RNA-seq_Subject3_TEM_Cell016


title: Single-cell_RNA-seq_Subject3_TEM_Cell015


title: Single-cell_RNA-seq_Subject3_TEM_Cell018


title: Single-cell_RNA-seq_Subject3_TEM_Cell025


title: Single-cell_RNA-seq_Subject3_TEM_Cell014


title: Single-cell_RNA-seq_Subject3_TEM_Cell013


title: Single-cell_RNA-seq_Subject3_TEM_Cell012


title: Single-cell_RNA-seq_Subject3_TEM_Cell017


title: Single-cell_RNA-seq_Subject3_TEM_Cell031


title: Single-cell_RNA-seq_Subject3_TEM_Cell010


title: Single-cell_RNA-seq_Subject3_TEM_Cell011


title: Single-cell_RNA-seq_Subject3_TEM_Cell009


title: Single-cell_RNA-seq_Subject3_TEM_Cell008


title: Single-cell_RNA-seq_Subject5_TEMRA_Cell001


title: Single-cell_RNA-seq_Subject3_TEM_Cell005


title: Single-cell_RNA-seq_Subject3_TEM_Cell003


title: Single-cell_RNA-seq_Subject3_TEM_Cell004


title: Single-cell_RNA-seq_Subject3_TEM_Cell007


title: Single-cell_RNA-seq_Subject3_TEM_Cell002


title: Single-cell_RNA-seq_Subject3_TEM_Cell006


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell047

source_name: Subject3_DENV positive_PBMC_TEMRA


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell046


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell048


title: Single-cell_RNA-seq_Subject3_TEM_Cell001


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell044


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell042


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell043


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell041


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell039


title: Single-cell_RNA-seq_Subject3_TEM_Cell019


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell038


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell034


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell035


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell036


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell037


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell040


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell032


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell033


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell029


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell028


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell030


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell031


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell027


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell026


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell045


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell023


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell022


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell021


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell020


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell025


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell015


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell018


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell014


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell024


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell013


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell012


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell019


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell010


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell008


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell006


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell004


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell005


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell003


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell009


title: Single-cell_RNA-seq_Subject3_TCM_Cell046

source_name: Subject3_DENV positive_PBMC_TCM


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell002


title: Single-cell_RNA-seq_Subject3_TCM_Cell045


title: Single-cell_RNA-seq_Subject3_TCM_Cell044


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell017


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell016


title: Single-cell_RNA-seq_Subject3_TCM_Cell043


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell011


title: Single-cell_RNA-seq_Subject3_TCM_Cell041


title: Single-cell_RNA-seq_Subject3_TCM_Cell039


title: Single-cell_RNA-seq_Subject3_TCM_Cell042


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell007


title: Single-cell_RNA-seq_Subject3_TCM_Cell040


title: Single-cell_RNA-seq_Subject3_TCM_Cell033


title: Single-cell_RNA-seq_Subject3_TCM_Cell038


title: Single-cell_RNA-seq_Subject3_TCM_Cell035


title: Single-cell_RNA-seq_Subject3_TCM_Cell034


title: Single-cell_RNA-seq_Subject3_TCM_Cell029


title: Single-cell_RNA-seq_Subject3_TCM_Cell037


title: Single-cell_RNA-seq_Subject3_TCM_Cell036


title: Single-cell_RNA-seq_Subject3_TCM_Cell031


title: Single-cell_RNA-seq_Subject3_TCM_Cell032


title: Single-cell_RNA-seq_Subject3_TEMRA_Cell001


title: Single-cell_RNA-seq_Subject3_TCM_Cell028


title: Single-cell_RNA-seq_Subject3_TCM_Cell027


title: Single-cell_RNA-seq_Subject3_TCM_Cell024


title: Single-cell_RNA-seq_Subject3_TCM_Cell026


title: Single-cell_RNA-seq_Subject3_TCM_Cell047


title: Single-cell_RNA-seq_Subject3_TCM_Cell025


title: Single-cell_RNA-seq_Subject3_TCM_Cell023


title: Single-cell_RNA-seq_Subject3_TCM_Cell020


title: Single-cell_RNA-seq_Subject3_TCM_Cell019


title: Single-cell_RNA-seq_Subject3_TCM_Cell022


title: Single-cell_RNA-seq_Subject3_TCM_Cell021


title: Single-cell_RNA-seq_Subject3_TCM_Cell018


title: Single-cell_RNA-seq_Subject3_TCM_Cell017


title: Single-cell_RNA-seq_Subject3_TCM_Cell030


title: Single-cell_RNA-seq_Subject3_TCM_Cell015


title: Single-cell_RNA-seq_Subject3_TCM_Cell016


title: Single-cell_RNA-seq_Subject3_TCM_Cell014


title: Single-cell_RNA-seq_Subject3_TCM_Cell013


title: Single-cell_RNA-seq_Subject3_TCM_Cell007


title: Single-cell_RNA-seq_Subject3_TCM_Cell004


title: Single-cell_RNA-seq_Subject3_TCM_Cell008


title: Single-cell_RNA-seq_Subject3_TCM_Cell012


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell095

source_name: Subject2_DENV negative_PBMC_TEMRA


title: Single-cell_RNA-seq_Subject3_TCM_Cell011


title: Single-cell_RNA-seq_Subject3_TCM_Cell003


title: Single-cell_RNA-seq_Subject3_TCM_Cell009


title: Single-cell_RNA-seq_Subject3_TCM_Cell002


title: Single-cell_RNA-seq_Subject3_TCM_Cell005


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell092


title: Single-cell_RNA-seq_Subject3_TCM_Cell006


title: Single-cell_RNA-seq_Subject3_TCM_Cell001


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell093


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell090


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell089


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell088


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell087


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell083


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell094


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell091


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell086


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell082


title: Single-cell_RNA-seq_Subject3_TCM_Cell010


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell081


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell080


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell079


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell084


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell077


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell075


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell074


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell076


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell073


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell085


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell070


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell069


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell065


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell064


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell068


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell061


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell066


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell062


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell060


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell058


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell057


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell056


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell078


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell059


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell055


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell053


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell050


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell051


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell054


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell072


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell052


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell071


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell048


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell063


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell067


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell044


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell049


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell045


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell047


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell043


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell040


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell039


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell041


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell038


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell037


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell035


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell036


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell034


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell033


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell030


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell029


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell028


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell032


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell031


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell025


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell024


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell027


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell042


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell026


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell046


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell018


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell022


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell023


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell021


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell017


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell015


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell019


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell020


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell013


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell012


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell014


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell008


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell011


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell009


title: Single-cell_RNA-seq_Subject1_TEMRA_Cell095

source_name: Subject1_DENV negative_PBMC_TEMRA


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell007


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell005


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell006


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell004


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell010


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell003


title: Single-cell_RNA-seq_Subject1_TEMRA_Cell093


title: Single-cell_RNA-seq_Subject1_TEMRA_Cell092


title: Single-cell_RNA-seq_Subject1_TEMRA_Cell094


title: Single-cell_RNA-seq_Subject1_TEMRA_Cell089


title: Single-cell_RNA-seq_Subject1_TEMRA_Cell091


title: Single-cell_RNA-seq_Subject2_TEMRA_Cell016


title: Single-cell_RNA-seq_Subject1_TEMRA_Cell086


title: Single-cell_RNA-seq_Subject1_TEMRA_Cell085


title: Single-cell_RNA-seq_Subject1_TEMRA_Cell088


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