title: 24hr_Cre_plus1_Un-1

city: Galveston

last: Fang

state: Texas

address: 301 University Blvd

institute: utmb

description: uninfected Atg5 flox/flox Lyz-Cre BMMs 24 h p.i.

data_processing:
Base calling was using Illumina's bcl2fastq2-v2.20 converter
A genome index was built with STAR (v2.5.3a) using the mouse GRCm38 genome .
Reads were aligned to the mouse reference using the STAR software version 2.5.3a with the parameters recommended for the ENCODE consortium.
Read counts per gene and estimated RPKM were produced with GFOLD software version 1.1.4
Genome_build: mm10
Supplementary_files_format_and_content: Tab delimited tables of raw read counts and RPKM per gene

source_name: Bone marrow macrophage

extract_protocol:
Total cellular RNA was extracted using  Trizol Reagent (Life Technologies, CA) according to the manufacturer’s recommendations.
Poly-A plus RNA was enriched from  ~1 ug of total RNA and used to generate libraries using the Illumina TruSeq v2 RNA Kit.

value: Bone marrow macrophage

value: Atg5 deficient

tag: infection status

value: Uninfected


title: 24hr_Cre_minus3_Ra-1

status: Public on Apr 06 2021

type: SRA

first: Rong

department: Pathology

country: USA

description: Ra infected Atg5 flox/flox BMMs 24 h  p.i.

library_source: transcriptomic

molecule: total RNA

organism: Mus musculus

tag: cell type

tag: genotype

value: Atg5 competent

value: Rickettsia australis infected

tag: strain


title: sh_ctrl_3

type: RNA

city: Nanjing

last: Yin

address: Baiziting 42

department: Thoracic Surgery

country: China

institute: Nanjing Medical University Affiliated Jiangsu Cancer Hospital

hyb_protocol: Following fragmentation, 10 mg of cRNA were hybridized for 16 hr at 45°C on GeneChip® PrimeView™ Human Gene Expression Array. GeneChips were washed and stained in the Affymetrix Fluidics Station 450.

scan_protocol: GeneChips were scanned using Affymetrix GeneChip® Scanner 3000.

data_processing: Normalized probeset data were generated with Robust Multi-array Averaging (RMA) method.

label: biotin

organism: Homo sapiens

label_protocol: Biotin-labeled cRNA were prepared using MessageAmp™ Premier RNA Amplification Kit (Ambion) and the protocol of the kit manufacturer from 100 ng total RNA.

growth_protocol: A549 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Sigma, USA) supplemented with 10% FBS and 5% CO2 at 37°C.

extract_protocol: Trizol extraction of total RNA was performed according to the manufacturer's instructions.

treatment_protocol: we downregulated the expression of LINC00525 utilizing small interference RNA (siRNA) and used scrumble as negative control.

tag: cell line

value: non-small cell lungs carcinoma cell line

tag: genotype/variation

value: negative control


title: GapCARMN_Basal-C_3

city: Edinburgh

first: Julie

last: Rodor

address: 47 Little France Crescent

department: Centre for Cardiovascular Science

country: United Kingdom

institute: UNIVERSITY OF EDINBURGH

data_processing:
Fastq files obtained from GENEWIZ
Gene quantification (normalised expression value as FPKM) was obtained using RSEM (options: -bowtie2 -forward-prob 0 -paired-end), based on GENCODE annotation version 26 (primary assembly).
Genome_build: GRCh38
Supplementary_files_format_and_content: csv file with FPKM

source_name: CARMN depletion in basal-C HCASMC

growth_protocol: Primary human coronary arterial smooth muscle cells (HCASMC) were maintained in Smooth Muscle Cell Growth Medium 2 (PromoCell) with supplements at 37°C in a humidified atmosphere containing 5% CO2. All cells were used between passages 3-5.

extract_protocol:
miRNeasy mini kit (Qiagen) with on-column DNAse digestion
polyA enriched stranded libraries were prepared and sequenced on Illumina HiSeq by GENEWIZ, Inc.

treatment_protocol:
[Gapmer treatment]  HCASMCs were transfected with Gap143HG or GapCTR (40nM) 24h after plating (plating of 2X10^5 cells for MBCD experiments, of 1.8X10^5 for migration and 1X10^5 for proliferation) using Lipofectamine RNAimax reagent for 5 hours. Additional treatments were performed 24h after transfection.
[MBCD treatment]  HCASMCs were treated with methyl--cyclodextrin (MBCD) loading 10µg/mL in 0.2% Bovine Serum Albumin (BSA) medium for 72 hours or in 0.2% BSA medium for the untreated cells.
[migration treatment]  HCASMCs were starved in 0.2% Foetal Bovine Serum (FBS) medium for 48 hours then scratched with a sterile pipet (~500μm-wide wounds) and replaced with fresh medium for 10 hours.
[proliferation treatment]  HCASMCs were starved in 0.2% Foetal Bovine Serum (FBS) medium for 48 hours. Cells were then treated with Platelet Derived Growth Factor (PDGF)-BB (R&D System) 20ng/mL, or untreated with 0.2% FBS fresh medium for 48 hours.

tag: gapmer

value: GapCARMN

tag: condition

value: Control MBCD

value: Human Coronary Arterial Smooth Muscle Cells (HCASMCs)

tag: replicate


title: Control1_RNA

city: Boston

first: Brian

last: Kalish

state: MA

address: 220 Longwood Ave

department: Neurobiology

institute: Boston Children's Hospital

description:
Control_1
MasterNormalizedCountsTable_COVID_010821.tsv

data_processing:
Sequencing data were aligned to the genome and processed using the 10X Genomics Cell Ranger software, version 4.0.0. All cells were combined into a single dataset. Doublets were removed using Scrublet version 0.2.1, and the remaining cells were re-clustered. Mitochondrial genes were filtered from the dataset. Cells with fewer than 250 or more than 2500 unique genes were excluded. Cells were then clustered using the Seurat R package (Version 3.2.3). Specifically, the SCT functionality of Seurat was used to identify cell types that did not depend upon unique aspects of individual samples. Clustering resolution was set to 0.8, and the first 15 principal components were used. The data were log normalized and scaled to 10,000 transcripts per cell. The expression of known marker genes was used to assign each cluster to one of the main cell types. The Seurat FindMarkers function was used to identify genetic markers of cellular subtypes.
Genome_build: hg38
Supplementary_files_format_and_content: counts, filtered_contig_annotations

source_name: Umbilical cord mononuclear cells

extract_protocol:
CBMC aliquots were thawed in a 37˚C water bath and resuspended in RPMI-1640 with 10% FBS (Thermo Fisher). Samples were centrifuged at 350 x g for 7 minutes at 4˚C. Cells were resuspended in 100 microliters of 1X PBS with 2.5% FBS and 2 mM EDTA. Dead cells and red blood cells were depleted using the EasySep Dead Cell Depletion Kit and EasySep RBC Depletion Reagent (Stem Cell), according to manufacturer instructions. Cells were resuspended in RPMI/10% FBS and counted. Cells were loaded on to the 10X Chromium controller at a targeted recovery density of 10,000 cells per sample.
Samples were processed and sequencing libraries were created using the 10X the Chromium Next GEM single-cell V(D)J Reagent Kit v1.1 with human TCR V(D)J Enrichment following manufacturer instructions.
scRNA-seq

value: Umbilical cord mononuclear cells

value: control

tag: developmental stage

value: Term pregnancy


title: Single-cell_Arcuate organoid cells_40d_C3

city: Philadelphia

first: Hongjun

last: Song

address: 415 Curie Boulevard

institute: University of Pennsylvania

data_processing:
ARCO raw bulk sequencing data were demultiplexed with bcl2fastq2 v2.17.1.14 (Illumina) with adapter trimming using Trimmomatic v0.32 software (Bolger et al., 2014). Alignment was performed using STAR v2.5.2a (Dobin et al., 2013). GENCODE human genome release 28 (GRCh38.p12) was used for alignment. Single cell raw sequencing data was demultiplexed with bcl2fastq2 (Illumina) with adaptor trimming turned off. Additional processing was performed using drop-seq tools v.2.1.0 (Saunders et al., 2018) with GRCh38 as the reference genome, and GENCODE v.28 GTF as the annotation file.
Genome_build: GRCh38
Supplementary_files_format_and_content: raw counts, each file is a raw counts table for cells obtained from one Dropseq run (single-cell) or one sample (bulk).

source_name: Organoid cells

growth_protocol: Organoids were cultured using modified versions of protocols as previously described (Qian et al., 2019).

extract_protocol:
ARCOs were dissociated using a papain-based neural tissue dissociation kit (Miltenyi Biotech). Frozen hypothalamus pieces were dissociated using Nuclei EZ lysis buffer (Sigma) as previously described (Habib et al., 2017).
Single-cell libraries were constructed as per the Dropseq protocol (Macosko et al., 2015). Bulk RNAseq libraries were constructed using a modified version of the SMARTseq protocol as previously described (Weng et al., 2017).
Single-cell and Bulk RNASeq libraries

treatment_protocol: Organoids were not treated with specific reagents prior to experiments.

tag: tissue

value: Organoid


title: 24hr_Cre_minus3_Un

description: uninfected Atg5 flox/flox BMMs 24 h  p.i.


title: 24hr_Cre_plus1_Ra-3

description: Ra infected Atg5 flox/flox Lyz-Cre BMMs 24 h p.i.


title: sh_LINC00525_3

value: LINC00527 downregulated


title: Case3_RNA

description:
Case_3
MasterNormalizedCountsTable_COVID_010821.tsv

value: case


title: GapCARMN_Basal-C_1


title: Case2_RNA

description:
Case_2
MasterNormalizedCountsTable_COVID_010821.tsv


title: Case1_RNA

description:
Case_1
MasterNormalizedCountsTable_COVID_010821.tsv


title: Spheres_12

city: Rijswijk

first: Raissa

last: Timmerman

address: Lange Kleiweg 161

department: Alternatives

country: Netherlands

institute: Biomedical Primate Research Centre

data_processing:
Clustering and sequencing: Illumina NextSeq 500 was used for sample 1-40, Illumina NextSeq 6000 was used for sample 41-52
Reads were trimmed for adapter sequences using Trimmomatic v0.30
Presumed adapter sequences were removed from the read when the bases matched a sequence in the adapter sequence set (TruSeq adapters) with 2 or less mismatches and an alignment score of at least 12
The Macaca mulatta genomic reference (Macaca_mulatta.Mmul_8.01.dna.toplevel.fa) was used for alignment of the reads for each sample.
The reads were mapped to the reference sequence using a short read aligner based on Burrows-Wheeler Transform (Tophat v2.0.14) with default settings.
SAMtools v1.3 package was used to sort and index the BAM files. Based on the mapped locations in the alignment file the frequency of how often a read was mapped on a transcript was determined with HTSeq v0.6.1p1.
Genome_build: Macaca_mulatta.Mmul_8.01.dna.toplevel.fa
Supplementary_files_format_and_content: .count files containing raw gene counts for each sample
Supplementary_files_format_and_content: .tsv files containing FPKM values for each sample

organism: Macaca mulatta

source_name: Primary cultured microglia

growth_protocol: Primary microglia were cultured for 8 days in serum and exposed to M-CSF or IL-34, with or without TGF-b (sample1-16). Primary microglia were cultured for 8 days in serum+M-CSF or 4 days in serum followed by a serum-free washout of 4, 11 and 18 days, or cultured in serum-free medium for 22 days (sample21-40). Primary microglia were cultured for 8 days in serum+M-CSF, or cultured in serum for 4 days followed by a serum-free washout for 11 days, or co-cultured with oligodendrocytes and progenitor cells in serum for 4 days followed by a serum-free washout for 11 days (sample 41-52).

extract_protocol:
Total cellular RNA was isolated using the RNeasy minikit (Qiagen, Cat#74104) according to manufacturer’s protocol
For sample 1-40 the NEBNext Ultra Directional RNA Library Prep Kit for Illumina (New England Biolabs, Cat#E7420L) was used to prepare and process the samples, For sample 41-52 The NEBNext Low Input RNA Library Prep Kit (New England Biolabs, Cat#E6420L) for Illumina was used to process the samples

value: Primary cultured microglia

tag: brain area

value: Subcortical white matter

tag: donor nr

tag: age

tag: gender

value: Female


title: sh_ctrl_1


title: MG1655

city: Montreal

first: Jean-Benoit

last: Lalanne

state: QC

address: 5155 Chem. de la Rampe

department: Biochemistry and Molecular Medicine

country: Canada

institute: University of Montreal

description: ribosome protected mRNA

data_processing:
Base calls performed using Casava version 1.7.
Trimmed reads were aligned to NC_000913.2 using Bowtie v1.0.1 with options -v 1 -k 2 -m 2. To deal with non-template addition during reverse transcription, reads with a mismatch at their 5' end had their 5' end re-assigned to the immediate next downstream position. The footprint reads with size between 15 to 42 nucleotides in length were mapped to the genome using the center-weighted approach; for each footprint read, the center residues that are at least 10 nucleotides away from either ends were given a weight of one over the length of the fragment (minus the 10 nucleotides on both ends) to generate the wig file.
genome build: NC_000913.2
Base calls performed using Casava version 1.7.
Supplementary_files_format_and_content: wiggle file with two columns: first column containing chromosome positions and second column containing the number of reads mapped to the position (see publication for details).

organism: Escherichia coli str. K-12 substr. MG1655

source_name: Bacteria

growth_protocol: Overnight cultures were diluted to OD600=0.0003 in 250 mL fresh media. The culture was kept in 2.8 L flask at 37C with aeration (220 rpm) until OD600 reached 0.3.

extract_protocol:
Extraction was performed as described in detail previously (Li et al., Cell 2014). 250 mL of cell culture was rapidly filtered at 37C by passing through a nitrocellulose filter. Cell pellets was were rapidly collected using a pre-warmed metal table crumber, flash frozen in liquid nitrogen, and combined with frozen droplets of lysis buffer. Cells and lysis buffer were pulverized in 10 mL canisters (Retsch) pre-chilled in liquid nitrogen using Qiagen TissueLyser II. Pulverized lysate was thawed on ice and clarified by centrifugation at 4C.
Ribosome protected mRNA fragments were size selected via gel purification, dephosphorylated at the 3’ end and ligated to 5' adenylated DNA oligo. After reverse transcription, the single stranded DNA was circularized, and PCR amplified.

tag: growth medium

value: MOPS complete (Teknova) with full supplement (Neidhart et al., 1974)


title: Single-cell_Arcuate organoid cells_20d_Min-2


title: Single-cell_Arcuate organoid cells_40d_C6-5


title: Single-cell_Arcuate organoid cells_20d_Maj-2


title: Single-cell_Arcuate organoid cells_20d_C6-5


title: Spheres_11


title: RORaDN-1

city: West Lafayette

first: Qing

last: Deng

state: Indiana

address: 915 W. State Street

department: Department of Biological Sciences

institute: Purdue University

data_processing:
Base calling and quality scoreing were performed by Real-Time Analysis (RTA) v2 in Illumina Novaseq 6000.
The bcl2fastq2 Conversion software were used to convert base call (bcl) files to FASTQ files, and trim adapter sequence at the same time.
The reads were mapped to the zebrafish genome using STAR (v2.5) RNA-seq aligner with the following parameter “--outSAMmapqUnique 60”.
Uniquely mapped sequencing reads were assigned to genes using featureCounts (from subread v1.5.1) with the following parameters: for Samples 1-5:  “-s 1 –Q 10”, for Samples 6-11, "-p -Q 10".
The data was filtered using read count per million (CPM) > 0.5 in more than 3 of the samples, normalized using TMM (trimmed mean of M values) method and subjected to differential expression analysis using edgeR (v3.20.8).
Genome_build: GRCz11
Supplementary_files_format_and_content: Raw counts and differential analysis

organism: Danio rerio

source_name: neutrophils

growth_protocol: zebrafish were grow in circulating system

extract_protocol:
tissue specific isolation: cells were labeled with fluorescent reporters and isolated from adult zebrafish whole kidney marrow using FACS. mRNA was extracted using Qiagen RNeasy Mini Kit  and total RNA was extracted using Invirtogen mirVANA kit for miRNA sequencing.
miRNA sequencing library were constructed using Illumina TruSeq Small RNA Preparation and 145-160bp band are excised for sequencing.  mRNA sequencing library were constructed withSMART-Seq v4 Ultra Low Input RNA Kit for Sequencing (Takara Clontech Laboratories, Inc.).

treatment_protocol: Transgenic lines expressing RORa-dominant negative or mcherry were used.

value: neutrophils

value: RORa-dominant negative


title: Single-cell_Arcuate organoid cells_20d_Min-1


title: RORaDN-2


title: SM4-SFM11_11


title: Single-cell_Arcuate organoid cells_20d_C3_run2


title: RORaDN-3


title: Influenza Pool - Rep 1

city: Kansas City

first: Konner

last: Winkley

state: MO

address: 2401 Gillham Road

department: Genomic Medicine Center

institute: Children's Mercy Research Institute

data_processing:
Sequenced reads were processed using the cellranger (v 3.1.0) indivually for each Repture.
Standard QC filtering and normalization/scaling were performed using Seurat and SCTransform using Reptures as a batch variable.
Pooled datasets were integrated using the standard Seurat integration workflow
Genome_build: GRCh38
Supplementary_files_format_and_content: hdf5 feature matrices contain gene counts for every barcode that was called a cell by the cellranger-count pipeline

molecule: polyA RNA

source_name: mid-turbinate swab

extract_protocol:
Each sample was diluted with cold PBS + 2% FBS up to a total volume of 5 mL and passed through a 40-µm nylon mesh cell strainer that had been prewetted with 2 mL of PBS + 2% FBS. The strainer was then rinsed with 7 mL of cold PBS + 2% FBS. The sample was transferred to a 15-mL conical tube and centrifuged at 300 x g at 4°C for 8 minutes. The supernatant was carefully removed without disturbing the cell pellet. The cell pellet was resuspended in 200 µL of cold PBS + 2% FBS, and the cell count and viability were assessed using 0.4% Trypan Blue and a Countess II automated cell counter. The cell suspension was transferred to a 1.5-mL tube and centrifuged at 300 x g at 4°C for 8 minutes, and the supernatant was carefully removed without disturbing the cell pellet. The cell pellet was resuspended in 1 mL of cold Recovery Cell Culture Freezing Medium, and the cell suspension was transferred to a cryogenic storage vial. Samples with less than 30% viability were excluded from analysis. All other samples were pooled by age. For each sample to be thawed, 10 mL of Thawing Medium consisting of DMEM/F-12 supplemented with 10% FBS and 100 units/mL of penicillin and 100 µg/mL of streptomycin was prewarmed in a 37°C bead bath. Each cryogenic storage vial containing a sample to be thawed was placed in the 37°C bead bath. No more than 5 samples were thawed at a time. When only a small ice crystal remained in the sample, both the cryogenic storage vial and the 15-mL conical tube containing the Thawing Medium were aseptically transferred to the biosafety cabinet. 1 mL of Thawing Medium was slowly added, dropwise, to the sample. The diluted sample was then mixed gently by pipetting and further diluted in the remaining 9 mL of Thawing Medium. The thawed and diluted cells were left at room temperature while the remaining samples were similarly thawed. When all samples in the batch were thawed, the samples were centrifuged at 300 x g for 8 min. The supernatant was carefully removed without disturbing the cell pellets. The cell pellets were each resuspended in 0.5 mL of Thawing Medium, and the cell suspensions were placed on ice. The cell suspensions were then combined and pooled together in age-defined groups. Each resulting pool was passed through a prewetted 40-µm nylon mesh cell strainer, and the cell strainers were rinsed with 5 mL of cold Thawing Medium. The pooled cell suspensions were centrifuged at 300 x g for 8 min at 4°C, and the supernatant was carefully aspirated without disturbing the cell pellets. The cell pellets were resuspended in 100 µL of cold Thawing Medium, and cell count and viability were assessed using 0.4% Trypan Blue and a Countess II automated cell counter. For each group, 4 wells of a Chromium Chip B were loaded with 20,000 cells each, and scRNA-seq was performed using the Chromium Single Cell 3’ Library & Gel Bead Kit v3.
Library was prepared according to the standard protocols of the 10x Genomics Chromium Single Cell 3’ Library & Gel Bead Kit v25

tag: disease

value: Influenza B

value: Nasal Mucosa


title: mCherry-3


title: Control Pool 6 - Rep 2

value: Control


title: Single-cell_Arcuate organoid cells_20d_Maj-1


title: SM M-CSF_11


title: GapCARMN_Basal-C_2

value: GapCARMN

value: Control MBCD


title: Single-cell_Arcuate organoid cells_20d_C3_run1


title: mCherry-2


title: mCherry-1


title: SM4-SFM11_12


title: Control Pool 6 - Rep 1


title: Spheres_10


title: Human hypothalamus cells_patient4368_run1

data_processing:
Raw sequencing data was demultiplexed with bcl2fastq2 (Illumina) with adaptor trimming turned off. Additional processing was performed using drop-seq tools v.2.1.0 (Saunders et al., 2018) with GRCh38 as the reference genome, and GENCODE v.28 GTF as the annotation file.
Genome_build: GRCh38
Supplementary_files_format_and_content: raw counts, each file is a raw counts table for cells obtained from one Dropseq run.

source_name: Human hypothalamus cells

growth_protocol: tissue obtained from NIH NeuroBioBank

extract_protocol:
Nuclei were extracted using Nuclei EZ lysis buffer kit (Sigma) and used for downstream experiments as per the Dropseq protocol (Macosko et al., 2015).
Libraries were constructed as per the Dropseq protocol (Macosko et al., 2015)

treatment_protocol: tissue obtained from NIH NeuroBioBank

value: Human brain


title: Control Pool 5 - Rep 4


title: Human hypothalamus cells_patient4389_run1


title: Influenza Pool - Rep 2


title: FAST5

city: Houston

first: Kyle

middle: Ryan

last: Navel

address: 2575 W. Bellfort Ave. Ste 270

department: Sales

institute: LC Sciences

description:
small RNA (15-50 nt)
Sample F5

data_processing:
Single end sequencing reads were cleaned with quality filter, adapter cutter, and length filter, using ACGT101_miR_v4.2g from LC Sciences.
The cleaned reads were mapped to miRBase (miRBase v21) using Bowtie v1.1.1.
The un-mapped reads were mapped to multiple databases including mRNA (http://www.genoscope.cns.fr/trout-ggb/data/Oncorhynchus mykiss mrna.fa.gz build 05/19/2014), Rfam11, and genomic DNA (http://www.genoscope.cns.fr/trout-ggb/data/Oncorhynchus mykiss chr.fa.gz build 04/28/2014), using Bowtie v1.1.1.
Novel miRNA candidates were identified from genome mapped reads based on the propensity of hairpin formation in corresponding genome positions, using ACGT101_miR_v4.2g from LC Sciences.
Data were normalized by dividing sequence counts of individual samples by corresponding normalization factors which are the median values of the ratios between specific sample counts and geometric mean counts of all samples.
Genome_build: http://www.genoscope.cns.fr/trout-ggb/data/Oncorhynchus mykiss chr.fa.gz build 04/28/2014

organism: Oncorhynchus mykiss

source_name: Liver

extract_protocol:
Livers were removed, flash frozen in liquid nitrogen, and RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit (Cat#FC-122-1001) 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: Walbaum

value: Liver

value: Juvenille

value: wild type


title: Human hypothalamus cells_patient4396_run1


title: FAST4

description:
small RNA (15-50 nt)
Sample F4


title: Control Pool 5 - Rep 3


title: Control Pool 5 - Rep 2


title: Human hypothalamus cells_patient5900_run1


title: FAST3

description:
small RNA (15-50 nt)
Sample F3


title: SM4-SFM11_9


title: SM M-CSF_9


title: SM4-SFM11_10


title: Human hypothalamus cells_patient4438_run1


title: Control Pool 5 - Rep 1


title: Control Pool 4 - Rep 4


title: CON6

description:
small RNA (15-50 nt)
Sample C6


title: CON4

description:
small RNA (15-50 nt)
Sample C4


title: E. coli  uL22 K90D_RPF_rep_3

city: Huntsville

first: Luis

middle: Rogelio

last: Cruz

state: Alabama

address: 301 Sparkman Drive

department: Biological Sciences

institute: University of Alabama in HUntsville

data_processing:
Library strategy: Ribo-seq
Performed by TB-SEQ, Inc. An Illumina NovaSeq6000 sequencer was used for basecalling
Performed by TB-SEQ, Inc. Sequence reads were trimmed of adaptor sequences, and checked for quality. Data was then sent to Luis Cruz-Vera.
Collapsed FASTAs of the trimmed fastq files were generated for the ribo-seq data and uploaded to RiboToolkit as indicated in (Liu, Shvarts, Sliz, and Gregory, 2020)
A gene expression count matrix was of the RNA-seq data was generated and uploaded to RiboToolkit.
Generation: The RNA-seq data was aligned using Bowtie (1.2.3) default settings and samtools (1.10) to convert format, and the featureCounts utilitiy from the subread package, following the RiboToolkit protocol.
The RiboToolkit webtool generated numerous normalized (RPKM), quantitative, and statistical outputs.
Genome_build: ASM584v2 (GCF_000005845.2)
Supplementary_files_format_and_content: Abundance measurments: Text Matrix of RPKM values for each sample

organism: Escherichia coli K-12

growth_protocol: E. coli cells were subcultured from an overnight culture and grown in 100 mL LB media (37˚C with shaking) to an approximate OD600 absorbance of .6.

extract_protocol:
Performed by TB-SEQ, Inc. using the ARTseq™ and Epicentre's Ribo-Zero™ kits as indicated in (Freeberg, Kuersten, and Syed, 2013)
Performed by TB-SEQ, Inc. using standard Illumina protocols for RNA and RIBO-seq

treatment_protocol: All samples were then treated with 100 µg/mL chloramphenicol and allowed to grow for three mintues. Cells were removed, cooled on ice, and harvested by centrifugation.

value: rlpV AAA90GAC

tag: analysis method

value: Ribosome profiling

tag: cell collection

value: usage of chloramphenicol

tag: molecule subtype

value: protected mRNA


title: Human hypothalamus cells_patient4458_run14


title: Control Pool 4 - Rep 3


title: SM M-CSF_10

value: Primary cultured microglia

value: Subcortical white matter

value: Female


title: CON3

description:
small RNA (15-50 nt)
Sample C3

tag: strain

tag: genotype


title: Human hypothalamus cells_patient4458_run12


title: Human hypothalamus cells_patient4458_run13


title: E. coli  uL22 K90D_RPF_rep_1


title: E. coli  uL22 K90D_RPF_rep_2


title: refed highCHO 50

description:
small RNA (15-50 nt)
Sample 50

value: adult


title: Control Pool 4 - Rep 2

value: Nasal Mucosa


title: Control Pool 4 - Rep 1

value: Control


title: Human hypothalamus cells_patient4458_run11


title: Spheres_9


title: Control Pool 3 - Rep 2


title: E. coli uL22 WT_RPF_rep_3

value: rplV wild type


title: Human hypothalamus cells_patient4458_run10


title: E. coli  uL22 WT_RPF_rep_2


title: Human hypothalamus cells_patient4458_run9


title: Control Pool 3 - Rep 1


title: E. coli  uL22 WT_RPF_rep_1


title: Control Pool 2 - Rep 4


title: refed highCHO 38

description:
small RNA (15-50 nt)
Sample 38


title: Human hypothalamus cells_patient4458_run8


title: E. coli  uL22 K90D_RNA_rep_3

data_processing:
Performed by TB-SEQ, Inc. An Illumina NovaSeq6000 sequencer was used for basecalling
Performed by TB-SEQ, Inc. Sequence reads were trimmed of adaptor sequences, and checked for quality. Data was then sent to Luis Cruz-Vera.
Collapsed FASTAs of the trimmed fastq files were generated for the ribo-seq data and uploaded to RiboToolkit as indicated in (Liu, Shvarts, Sliz, and Gregory, 2020)
A gene expression count matrix was of the RNA-seq data was generated and uploaded to RiboToolkit.
Generation: The RNA-seq data was aligned using Bowtie (1.2.3) default settings and samtools (1.10) to convert format, and the featureCounts utilitiy from the subread package, following the RiboToolkit protocol.
The RiboToolkit webtool generated numerous normalized (RPKM), quantitative, and statistical outputs.
Genome_build: ASM584v2 (GCF_000005845.2)
Supplementary_files_format_and_content: Abundance measurments: Text Matrix of RPKM values for each sample

value: RNA-seq

value: mRNA


title: refed noCHO 31

description:
small RNA (15-50 nt)
Sample 31

value: Walbaum


title: Human hypothalamus cells_patient4458_run7

value: Human brain


title: refed noCHO 33

description:
small RNA (15-50 nt)
Sample 33

value: Liver


title: Control Pool 2 - Rep 3


title: Control Pool 2 - Rep 2


title: Human hypothalamus cells_patient4458_run6


title: E. coli  uL22 K90D_RNA_rep_1


title: refed noCHO 21

description:
small RNA (15-50 nt)
Sample 21

value: wild type


title: Human hypothalamus cells_patient4458_run5


title: E. coli uL22 WT_RNA_rep_3


title: Control Pool 2 - Rep 1


title: refed highCHO 49

description:
small RNA (15-50 nt)
Sample 49


title: refed noCHO 20

description:
small RNA (15-50 nt)
Sample 20


title: refed highCHO 45

description:
small RNA (15-50 nt)
Sample 45


title: Human hypothalamus cells_patient4458_run4


title: Control Pool 1 - Rep 4


title: E. coli  uL22 WT_RNA_rep_2


title: Human hypothalamus cells_patient4458_run3


title: Control Pool 1 - Rep 3


title: E. coli  uL22 WT_RNA_rep_1


title: fasted 13

description:
small RNA (15-50 nt)
Sample 13


title: fasted 10

description:
small RNA (15-50 nt)
Sample 10


title: E. coli  uL22 K90D_RNA_rep_2

value: rlpV AAA90GAC


title: Control Pool 1 - Rep 2


title: Human hypothalamus cells_patient4458_run2


title: fasted 8

description:
small RNA (15-50 nt)
Sample 8


title: NQ1390

description: ribosome protected mRNA

data_processing:
Base calls performed using Casava version 1.7.
Trimmed reads were aligned to CP011495.1 and CP011496.1  using Bowtie v1.0.1 with options -v 1 -k 2 -m 2. To deal with non-template addition during reverse transcription, reads with a mismatch at their 5' end had their 5' end re-assigned to the immediate next downstream position. The footprint reads with size between 15 to 42 nucleotides in length were mapped to the genome using the center-weighted approach; for each footprint read, the center residues that are at least 10 nucleotides away from either ends were given a weight of one over the length of the fragment (minus the 10 nucleotides on both ends) to generate the wig file.
genome build: CP011495.1
genome build: CP011496.1
Base calls performed using Casava version 1.7.
Supplementary_files_format_and_content: wiggle file with two columns: first column containing chromosome positions and second column containing the number of reads mapped to the position (see publication for details).

organism: Escherichia coli

growth_protocol: Pre-cultures were diluted to OD600=0.015 in 250 mL fresh media. The culture was kept in 2.8 L flask at 37C with aeration (220 rpm) until OD600 reached 0.3.

extract_protocol:
Extraction was performed as described in detail previously (Li et al., Cell 2014). 250 mL of cell culture was rapidly filtered at 37C by passing through a nitrocellulose filter in two sequential batches of 125 mL (to avoid filter clogging). Cell pellets was were rapidly collected using a pre-warmed metal table crumber, flash frozen in liquid nitrogen, and combined with frozen droplets of lysis buffer. Cell pellets from the two 125 mL batches were combined. Cells and lysis buffer were pulverized in 10 mL canisters (Retsch) pre-chilled in liquid nitrogen using Qiagen TissueLyser II. Pulverized lysate was thawed on ice and clarified by centrifugation at 4C.
To multiplex cDNA library preparation with other experiments, ribosome protected mRNA fragments were pooled 1:1 with B. subtilis ribosome protected fragments (demultiplexing is possible & unambiguous downstream by read alignment). Ribosome protected mRNA fragments were size selected via gel purification, dephosphorylated at the 3’ end and ligated to 5' adenylated DNA oligo. After reverse transcription, the single stranded DNA was circularized, and PCR amplified.

value: M9 + 0.5% glucose + 40 μM 3-MBA


title: Control Pool 1 - Rep 1


title: NCM3722

description: ribosome protected mRNA

growth_protocol: Pre-cultures were diluted to OD600=0.003 in 250 mL fresh media. The culture was kept in 2.8 L flask at 37C with aeration (220 rpm) until OD600 reached 0.3.

extract_protocol:
Extraction was performed as described in detail previously (Li et al., Cell 2014). 250 mL of cell culture was rapidly filtered at 37C by passing through a nitrocellulose filter. Cell pellets was were rapidly collected using a pre-warmed metal table crumber, flash frozen in liquid nitrogen, and combined with frozen droplets of lysis buffer. Cells and lysis buffer were pulverized in 10 mL canisters (Retsch) pre-chilled in liquid nitrogen using Qiagen TissueLyser II. Pulverized lysate was thawed on ice and clarified by centrifugation at 4C.
To multiplex cDNA library preparation with other experiments, ribosome protected mRNA fragments were pooled 1:1 with B. subtilis ribosome protected fragments (demultiplexing is possible & unambiguous downstream by read alignment). Ribosome protected mRNA fragments were size selected via gel purification, dephosphorylated at the 3’ end and ligated to 5' adenylated DNA oligo. After reverse transcription, the single stranded DNA was circularized, and PCR amplified.

value: M9 + 0.5% glucose


title: fasted 7

description:
small RNA (15-50 nt)
Sample 7


title: Human hypothalamus cells_patient4458_run1


title: TMMp Smart-seq2 Pool 2 Cell 336

status: Public on Apr 05 2021

city: Stanford

first: Dominique

last: Bergmann

state: CA

address: 371 Jane Stanford Way

institute: Stanford University

description: P9-F4-Camila-TMM7-GGACTCCT-TCGCATAA_S369_R1_001

data_processing:
Data processing steps and methods used are included in the manuscript. Otherwise, note that the raw data for the "ATML1p 10X Genomics pool 1" sample were re-generated using bamtofastq (https://support.10xgenomics.com/docs/bamtofastq).
Genome_build: TAIR10
Supplementary_files_format_and_content: Processed files include compiled TPM values and FACS outputs for Smart-seq2 scRNA-seq datasets. Cell Ranger outputs are included for the 10X Genomics datasets.

organism: Arabidopsis thaliana

source_name: aerial tissue

extract_protocol: Smart-seq2 chemistry, miniaturized Nextera XT protocol

value: 10 days post-germination

tag: transgene

tag: pool

value: pool 2


title: TMMp Smart-seq2 Pool 2 Cell 335

description: P8-F3-Camila-TMM7-CGGAGCCT-TCGCATAA_S368_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 334

description: P7-F2-Camila-TMM7-TCCTGAGC-TCGCATAA_S367_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 332

description: P4-E9-Camila-TMM7-GGAGCTAC-TCGCATAA_S364_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 333

description: P6-F1-Camila-TMM7-GCGTAGTA-TCGCATAA_S366_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 331

description: P3-E8-Camila-TMM7-CGTACTAG-TCGCATAA_S363_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 330

description: P2-E6-Camila-TMM7-ACTCGCTA-TCGCATAA_S362_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 329

description: P24-G8-Camila-TMM7-TCGACGTC-TCGCATAA_S384_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 328

description: P23-G7-Camila-TMM7-ATCTCAGG-TCGCATAA_S383_R1_001


title: SM M-CSF_12

value: Primary cultured microglia

value: Subcortical white matter


title: TMMp Smart-seq2 Pool 2 Cell 327

description: P22-G6-Camila-TMM7-TGCAGCTA-TCGCATAA_S382_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 325

description: P19-G3-Camila-TMM7-GTAGAGGA-TCGCATAA_S379_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 326

description: P1-E5-Camila-TMM7-TAAGGCGA-TCGCATAA_S361_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 322

description: P13-F8-Camila-TMM7-CTCTCTAC-TCGCATAA_S373_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 324

description: P18-G2-Camila-TMM7-CCTAAGAC-TCGCATAA_S378_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 323

description: P14-F9-Camila-TMM7-TAGCGCTC-TCGCATAA_S374_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 320

description: P11-F6-Camila-TMM7-TAGGCATG-TCGCATAA_S371_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 316

description: O7-C10-Camila-TMM7-TCCTGAGC-CGGAGAGA_S343_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 319

description: P10-F5-Camila-TMM7-TACGCTGC-TCGCATAA_S370_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 317

description: O8-C11-Camila-TMM7-CGGAGCCT-CGGAGAGA_S344_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 321

description: P12-F7-Camila-TMM7-ATGCGCAG-TCGCATAA_S372_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 312

description: O2-C5-Camila-TMM7-ACTCGCTA-CGGAGAGA_S338_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 313

description: O3-C6-Camila-TMM7-CGTACTAG-CGGAGAGA_S339_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 314

description: O5-C8-Camila-TMM7-AGGCAGAA-CGGAGAGA_S341_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 309

description: O21-E1-Camila-TMM7-GCTCATGA-CGGAGAGA_S357_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 315

description: O6-C9-Camila-TMM7-GCGTAGTA-CGGAGAGA_S342_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 311

description: O24-E4-Camila-TMM7-TCGACGTC-CGGAGAGA_S360_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 306

description: O19-D11-Camila-TMM7-GTAGAGGA-CGGAGAGA_S355_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 308

description: O20-D12-Camila-TMM7-CGATCAGT-CGGAGAGA_S356_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 305

description: O18-D10-Camila-TMM7-CCTAAGAC-CGGAGAGA_S354_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 310

description: O23-E3-Camila-TMM7-ATCTCAGG-CGGAGAGA_S359_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 307

description: O1-C4-Camila-TMM7-TAAGGCGA-CGGAGAGA_S337_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 303

description: O16-D7-Camila-TMM7-ACTGAGCG-CGGAGAGA_S352_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 302

description: O15-D6-Camila-TMM7-CGAGGCTG-CGGAGAGA_S351_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 301

description: O14-D5-Camila-TMM7-TAGCGCTC-CGGAGAGA_S350_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 300

description: O13-D4-Camila-TMM7-CTCTCTAC-CGGAGAGA_S349_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 318

description: O9-C12-Camila-TMM7-GGACTCCT-CGGAGAGA_S345_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 297

description: O10-D1-Camila-TMM7-TACGCTGC-CGGAGAGA_S346_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 298

description: O11-D2-Camila-TMM7-TAGGCATG-CGGAGAGA_S347_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 296

description: N9-A12-Camila-TMM7-GGACTCCT-TAAGGCTC_S321_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 287

description: N22-C1-Camila-TMM7-TGCAGCTA-TAAGGCTC_S334_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 288

description: N23-C2-Camila-TMM7-ATCTCAGG-TAAGGCTC_S335_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 291

description: N4-A7-Camila-TMM7-GGAGCTAC-TAAGGCTC_S316_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 285

description: N1-A4-Camila-TMM7-TAAGGCGA-TAAGGCTC_S313_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 289

description: N24-C3-Camila-TMM7-TCGACGTC-TAAGGCTC_S336_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 304

description: O17-D9-Camila-TMM7-AAGAGGCA-CGGAGAGA_S353_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 282

description: N17-B8-Camila-TMM7-AAGAGGCA-TAAGGCTC_S329_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 294

description: N7-A10-Camila-TMM7-TCCTGAGC-TAAGGCTC_S319_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 290

description: N3-A6-Camila-TMM7-CGTACTAG-TAAGGCTC_S315_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 278

description: N13-B4-Camila-TMM7-CTCTCTAC-TAAGGCTC_S325_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 280

description: N15-B6-Camila-TMM7-CGAGGCTG-TAAGGCTC_S327_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 286

description: N21-B12-Camila-TMM7-GCTCATGA-TAAGGCTC_S333_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 281

description: N16-B7-Camila-TMM7-ACTGAGCG-TAAGGCTC_S328_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 277

description: N12-B3-Camila-TMM7-ATGCGCAG-TAAGGCTC_S324_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 276

description: N11-B2-Camila-TMM7-TAGGCATG-TAAGGCTC_S323_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 284

description: N19-B10-Camila-TMM7-GTAGAGGA-TAAGGCTC_S331_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 274

description: M8-G5-Camila-TMM6-CGGAGCCT-ATTAGACG_S296_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 272

description: M6-G3-Camila-TMM6-GCGTAGTA-ATTAGACG_S294_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 295

description: N8-A11-Camila-TMM7-CGGAGCCT-TAAGGCTC_S320_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 299

description: O12-D3-Camila-TMM7-ATGCGCAG-CGGAGAGA_S348_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 273

description: M7-G4-Camila-TMM6-TCCTGAGC-ATTAGACG_S295_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 293

description: N6-A9-Camila-TMM7-GCGTAGTA-TAAGGCTC_S318_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 292

description: N5-A8-Camila-TMM7-AGGCAGAA-TAAGGCTC_S317_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 270

description: M3-F11-Camila-TMM6-CGTACTAG-ATTAGACG_S291_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 269

description: M2-F10-Camila-TMM6-ACTCGCTA-ATTAGACG_S290_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 271

description: M5-G2-Camila-TMM6-AGGCAGAA-ATTAGACG_S293_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 268

description: M24-A3-Camila-TMM7-TCGACGTC-ATTAGACG_S312_R1_001


title: Control Pool 6 - Rep 3


title: TMMp Smart-seq2 Pool 2 Cell 283

description: N18-B9-Camila-TMM7-CCTAAGAC-TAAGGCTC_S330_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 279

description: N14-B5-Camila-TMM7-TAGCGCTC-TAAGGCTC_S326_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 265

description: M20-H5-Camila-TMM6-CGATCAGT-ATTAGACG_S308_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 263

description: M19-H4-Camila-TMM6-GTAGAGGA-ATTAGACG_S307_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 266

description: M21-H6-Camila-TMM6-GCTCATGA-ATTAGACG_S309_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 264

description: M1-F9-Camila-TMM6-TAAGGCGA-ATTAGACG_S289_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 262

description: M18-H3-Camila-TMM6-CCTAAGAC-ATTAGACG_S306_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 261

description: M17-H2-Camila-TMM6-AAGAGGCA-ATTAGACG_S305_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 260

description: M16-H1-Camila-TMM6-ACTGAGCG-ATTAGACG_S304_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 275

description: M9-G6-Camila-TMM6-GGACTCCT-ATTAGACG_S297_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 259

description: M15-G12-Camila-TMM6-CGAGGCTG-ATTAGACG_S303_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 255

description: M11-G8-Camila-TMM6-TAGGCATG-ATTAGACG_S299_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 256

description: M12-G9-Camila-TMM6-ATGCGCAG-ATTAGACG_S300_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 258

description: M14-G11-Camila-TMM6-TAGCGCTC-ATTAGACG_S302_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 253

description: L8-E1-Camila-TMM6-CGGAGCCT-ATAGCCTT_S272_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 250

description: L5-D10-Camila-TMM6-AGGCAGAA-ATAGCCTT_S269_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 251

description: L6-D11-Camila-TMM6-GCGTAGTA-ATAGCCTT_S270_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 254

description: M10-G7-Camila-TMM6-TACGCTGC-ATTAGACG_S298_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 247

description: L23-F7-Camila-TMM6-ATCTCAGG-ATAGCCTT_S287_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 246

description: L22-F6-Camila-TMM6-TGCAGCTA-ATAGCCTT_S286_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 257

description: M13-G10-Camila-TMM6-CTCTCTAC-ATTAGACG_S301_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 249

description: L4-D9-Camila-TMM6-GGAGCTAC-ATAGCCTT_S268_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 248

description: L24-F8-Camila-TMM6-TCGACGTC-ATAGCCTT_S288_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 243

description: L1-D5-Camila-TMM6-TAAGGCGA-ATAGCCTT_S265_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 252

description: L7-D12-Camila-TMM6-TCCTGAGC-ATAGCCTT_S271_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 244

description: L20-F4-Camila-TMM6-CGATCAGT-ATAGCCTT_S284_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 267

description: M23-A2-Camila-TMM7-ATCTCAGG-ATTAGACG_S311_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 245

description: L21-F5-Camila-TMM6-GCTCATGA-ATAGCCTT_S285_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 240

description: L16-E11-Camila-TMM6-ACTGAGCG-ATAGCCTT_S280_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 238

description: L12-E5-Camila-TMM6-ATGCGCAG-ATAGCCTT_S276_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 236

description: L10-E3-Camila-TMM6-TACGCTGC-ATAGCCTT_S274_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 235

description: K9-B10-Camila-TMM6-GGACTCCT-AGGCTTAG_S249_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 239

description: L15-E10-Camila-TMM6-CGAGGCTG-ATAGCCTT_S279_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 237

description: L11-E4-Camila-TMM6-TAGGCATG-ATAGCCTT_S275_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 233

description: K7-B8-Camila-TMM6-TCCTGAGC-AGGCTTAG_S247_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 231

description: K5-B6-Camila-TMM6-AGGCAGAA-AGGCTTAG_S245_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 230

description: K4-B5-Camila-TMM6-GGAGCTAC-AGGCTTAG_S244_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 232

description: K6-B7-Camila-TMM6-GCGTAGTA-AGGCTTAG_S246_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 227

description: K24-D4-Camila-TMM6-TCGACGTC-AGGCTTAG_S264_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 226

description: K22-D2-Camila-TMM6-TGCAGCTA-AGGCTTAG_S262_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 228

description: K2-B3-Camila-TMM6-ACTCGCTA-AGGCTTAG_S242_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 224

description: K20-C12-Camila-TMM6-CGATCAGT-AGGCTTAG_S260_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 225

description: K21-D1-Camila-TMM6-GCTCATGA-AGGCTTAG_S261_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 222

description: K19-C11-Camila-TMM6-GTAGAGGA-AGGCTTAG_S259_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 220

description: K17-C7-Camila-TMM6-AAGAGGCA-AGGCTTAG_S257_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 218

description: K15-C5-Camila-TMM6-CGAGGCTG-AGGCTTAG_S255_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 219

description: K16-C6-Camila-TMM6-ACTGAGCG-AGGCTTAG_S256_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 242

description: L19-F3-Camila-TMM6-GTAGAGGA-ATAGCCTT_S283_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 221

description: K18-C10-Camila-TMM6-CCTAAGAC-AGGCTTAG_S258_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 241

description: L17-E12-Camila-TMM6-AAGAGGCA-ATAGCCTT_S281_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 216

description: K13-C3-Camila-TMM6-CTCTCTAC-AGGCTTAG_S253_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 217

description: K14-C4-Camila-TMM6-TAGCGCTC-AGGCTTAG_S254_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 214

description: K11-C1-Camila-TMM6-TAGGCATG-AGGCTTAG_S251_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 212

description: J9-G11-Camila-TMM5-GGACTCCT-CTTAATAG_S225_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 213

description: K10-B12-Camila-TMM6-TACGCTGC-AGGCTTAG_S250_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 215

description: K12-C2-Camila-TMM6-ATGCGCAG-AGGCTTAG_S252_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 234

description: K8-B9-Camila-TMM6-CGGAGCCT-AGGCTTAG_S248_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 209

description: J5-G6-Camila-TMM5-AGGCAGAA-CTTAATAG_S221_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 210

description: J6-G7-Camila-TMM5-GCGTAGTA-CTTAATAG_S222_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 211

description: J8-G9-Camila-TMM5-CGGAGCCT-CTTAATAG_S224_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 229

description: K3-B4-Camila-TMM6-CGTACTAG-AGGCTTAG_S243_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 204

description: J23-A12-Camila-TMM6-ATCTCAGG-CTTAATAG_S239_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 206

description: J2-G3-Camila-TMM5-ACTCGCTA-CTTAATAG_S218_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 207

description: J3-G4-Camila-TMM5-CGTACTAG-CTTAATAG_S219_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 223

description: K1-B2-Camila-TMM6-TAAGGCGA-AGGCTTAG_S241_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 208

description: J4-G5-Camila-TMM5-GGAGCTAC-CTTAATAG_S220_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 205

description: J24-B1-Camila-TMM6-TCGACGTC-CTTAATAG_S240_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 199

description: J19-A4-Camila-TMM6-GTAGAGGA-CTTAATAG_S235_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 202

description: J21-A6-Camila-TMM6-GCTCATGA-CTTAATAG_S237_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 201

description: J20-A5-Camila-TMM6-CGATCAGT-CTTAATAG_S236_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 197

description: J17-A1-Camila-TMM6-AAGAGGCA-CTTAATAG_S233_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 198

description: J18-A3-Camila-TMM6-CCTAAGAC-CTTAATAG_S234_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 191

description: I9-E5-Camila-TMM5-GGACTCCT-TACTCCTT_S201_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 192

description: J11-H1-Camila-TMM5-TAGGCATG-CTTAATAG_S227_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 193

description: J12-H2-Camila-TMM5-ATGCGCAG-CTTAATAG_S228_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 194

description: J13-H3-Camila-TMM5-CTCTCTAC-CTTAATAG_S229_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 195

description: J15-H5-Camila-TMM5-CGAGGCTG-CTTAATAG_S231_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 189

description: I7-E3-Camila-TMM5-TCCTGAGC-TACTCCTT_S199_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 190

description: I8-E4-Camila-TMM5-CGGAGCCT-TACTCCTT_S200_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 185

description: I22-F11-Camila-TMM5-TGCAGCTA-TACTCCTT_S214_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 184

description: I21-F8-Camila-TMM5-GCTCATGA-TACTCCTT_S213_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 183

description: I20-F6-Camila-TMM5-CGATCAGT-TACTCCTT_S212_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 186

description: I23-F12-Camila-TMM5-ATCTCAGG-TACTCCTT_S215_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 203

description: J22-A11-Camila-TMM6-TGCAGCTA-CTTAATAG_S238_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 182

description: I19-F5-Camila-TMM5-GTAGAGGA-TACTCCTT_S211_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 181

description: I18-F4-Camila-TMM5-CCTAAGAC-TACTCCTT_S210_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 188

description: I6-E2-Camila-TMM5-GCGTAGTA-TACTCCTT_S198_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 187

description: I4-D11-Camila-TMM5-GGAGCTAC-TACTCCTT_S196_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 175

description: I12-E9-Camila-TMM5-ATGCGCAG-TACTCCTT_S204_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 179

description: I16-F2-Camila-TMM5-ACTGAGCG-TACTCCTT_S208_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 176

description: I13-E10-Camila-TMM5-CTCTCTAC-TACTCCTT_S205_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 200

description: J1-G2-Camila-TMM5-TAAGGCGA-CTTAATAG_S217_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 178

description: I15-F1-Camila-TMM5-CGAGGCTG-TACTCCTT_S207_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 177

description: I14-E11-Camila-TMM5-TAGCGCTC-TACTCCTT_S206_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 172

description: H9-B12-Camila-TMM5-GGACTCCT-TCTTACGC_S177_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 180

description: I17-F3-Camila-TMM5-AAGAGGCA-TACTCCTT_S209_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 174

description: I11-E7-Camila-TMM5-TAGGCATG-TACTCCTT_S203_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 196

description: J16-H6-Camila-TMM5-ACTGAGCG-CTTAATAG_S232_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 173

description: I10-E6-Camila-TMM5-TACGCTGC-TACTCCTT_S202_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 170

description: H7-B10-Camila-TMM5-TCCTGAGC-TCTTACGC_S175_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 171

description: H8-B11-Camila-TMM5-CGGAGCCT-TCTTACGC_S176_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 169

description: H6-B9-Camila-TMM5-GCGTAGTA-TCTTACGC_S174_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 168

description: H5-B8-Camila-TMM5-AGGCAGAA-TCTTACGC_S173_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 166

description: H3-B6-Camila-TMM5-CGTACTAG-TCTTACGC_S171_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 165

description: H2-B5-Camila-TMM5-ACTCGCTA-TCTTACGC_S170_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 164

description: H24-D6-Camila-TMM5-TCGACGTC-TCTTACGC_S192_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 167

description: H4-B7-Camila-TMM5-GGAGCTAC-TCTTACGC_S172_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 163

description: H23-D5-Camila-TMM5-ATCTCAGG-TCTTACGC_S191_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 162

description: H22-D3-Camila-TMM5-TGCAGCTA-TCTTACGC_S190_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 161

description: H21-D2-Camila-TMM5-GCTCATGA-TCTTACGC_S189_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 160

description: H20-D1-Camila-TMM5-CGATCAGT-TCTTACGC_S188_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 159

description: H1-B4-Camila-TMM5-TAAGGCGA-TCTTACGC_S169_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 157

description: H18-C11-Camila-TMM5-CCTAAGAC-TCTTACGC_S186_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 155

description: H16-C7-Camila-TMM5-ACTGAGCG-TCTTACGC_S184_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 158

description: H19-C12-Camila-TMM5-GTAGAGGA-TCTTACGC_S187_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 154

description: H15-C6-Camila-TMM5-CGAGGCTG-TCTTACGC_S183_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 152

description: H13-C4-Camila-TMM5-CTCTCTAC-TCTTACGC_S181_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 150

description: H11-C2-Camila-TMM5-TAGGCATG-TCTTACGC_S179_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 148

description: G9-G12-Camila-TMM4-GGACTCCT-TATGCAGT_S153_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 147

description: G8-G11-Camila-TMM4-CGGAGCCT-TATGCAGT_S152_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 145

description: G5-G8-Camila-TMM4-AGGCAGAA-TATGCAGT_S149_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 146

description: G7-G10-Camila-TMM4-TCCTGAGC-TATGCAGT_S151_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 156

description: H17-C10-Camila-TMM5-AAGAGGCA-TCTTACGC_S185_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 149

description: H10-C1-Camila-TMM5-TACGCTGC-TCTTACGC_S178_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 144

description: G4-G7-Camila-TMM4-GGAGCTAC-TATGCAGT_S148_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 141

description: G24-B3-Camila-TMM5-TCGACGTC-TATGCAGT_S168_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 153

description: H14-C5-Camila-TMM5-TAGCGCTC-TCTTACGC_S182_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 139

description: G22-B1-Camila-TMM5-TGCAGCTA-TATGCAGT_S166_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 143

description: G3-G6-Camila-TMM4-CGTACTAG-TATGCAGT_S147_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 140

description: G23-B2-Camila-TMM5-ATCTCAGG-TATGCAGT_S167_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 142

description: G2-G5-Camila-TMM4-ACTCGCTA-TATGCAGT_S146_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 138

description: G21-A7-Camila-TMM5-GCTCATGA-TATGCAGT_S165_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 136

description: G1-G4-Camila-TMM4-TAAGGCGA-TATGCAGT_S145_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 134

description: G18-A4-Camila-TMM5-CCTAAGAC-TATGCAGT_S162_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 135

description: G19-A5-Camila-TMM5-GTAGAGGA-TATGCAGT_S163_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 137

description: G20-A6-Camila-TMM5-CGATCAGT-TATGCAGT_S164_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 132

description: G15-A1-Camila-TMM5-CGAGGCTG-TATGCAGT_S159_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 133

description: G16-A2-Camila-TMM5-ACTGAGCG-TATGCAGT_S160_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 130

description: G13-H5-Camila-TMM4-CTCTCTAC-TATGCAGT_S157_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 128

description: G11-H3-Camila-TMM4-TAGGCATG-TATGCAGT_S155_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 151

description: H12-C3-Camila-TMM5-ATGCGCAG-TCTTACGC_S180_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 131

description: G14-H6-Camila-TMM4-TAGCGCTC-TATGCAGT_S158_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 129

description: G12-H4-Camila-TMM4-ATGCGCAG-TATGCAGT_S156_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 125

description: F8-E11-Camila-TMM4-CGGAGCCT-ACTCTAGG_S128_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 126

description: F9-E12-Camila-TMM4-GGACTCCT-ACTCTAGG_S129_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 123

description: F4-E7-Camila-TMM4-GGAGCTAC-ACTCTAGG_S124_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 124

description: F6-E9-Camila-TMM4-GCGTAGTA-ACTCTAGG_S126_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 121

description: F2-E5-Camila-TMM4-ACTCGCTA-ACTCTAGG_S122_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 116

description: F20-F11-Camila-TMM4-CGATCAGT-ACTCTAGG_S140_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 113

description: F18-F9-Camila-TMM4-CCTAAGAC-ACTCTAGG_S138_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 117

description: F21-F12-Camila-TMM4-GCTCATGA-ACTCTAGG_S141_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 112

description: F17-F8-Camila-TMM4-AAGAGGCA-ACTCTAGG_S137_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 114

description: F19-F10-Camila-TMM4-GTAGAGGA-ACTCTAGG_S139_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 109

description: F14-F5-Camila-TMM4-TAGCGCTC-ACTCTAGG_S134_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 107

description: F11-F2-Camila-TMM4-TAGGCATG-ACTCTAGG_S131_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 120

description: F24-G3-Camila-TMM4-TCGACGTC-ACTCTAGG_S144_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 115

description: F1-E4-Camila-TMM4-TAAGGCGA-ACTCTAGG_S121_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 110

description: F15-F6-Camila-TMM4-CGAGGCTG-ACTCTAGG_S135_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 118

description: F22-G1-Camila-TMM4-TGCAGCTA-ACTCTAGG_S142_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 127

description: G10-H2-Camila-TMM4-TACGCTGC-TATGCAGT_S154_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 106

description: F10-F1-Camila-TMM4-TACGCTGC-ACTCTAGG_S130_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 108

description: F12-F3-Camila-TMM4-ATGCGCAG-ACTCTAGG_S132_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 111

description: F16-F7-Camila-TMM4-ACTGAGCG-ACTCTAGG_S136_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 105

description: E9-C11-Camila-TMM4-GGACTCCT-CTCCTTAC_S105_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 100

description: E4-C6-Camila-TMM4-GGAGCTAC-CTCCTTAC_S100_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 99

description: E3-C5-Camila-TMM4-CGTACTAG-CTCCTTAC_S99_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 101

description: E5-C7-Camila-TMM4-AGGCAGAA-CTCCTTAC_S101_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 103

description: E7-C9-Camila-TMM4-TCCTGAGC-CTCCTTAC_S103_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 102

description: E6-C8-Camila-TMM4-GCGTAGTA-CTCCTTAC_S102_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 98

description: E24-E3-Camila-TMM4-TCGACGTC-CTCCTTAC_S120_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 97

description: E22-E1-Camila-TMM4-TGCAGCTA-CTCCTTAC_S118_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 119

description: F23-G2-Camila-TMM4-ATCTCAGG-ACTCTAGG_S143_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 122

description: F3-E6-Camila-TMM4-CGTACTAG-ACTCTAGG_S123_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 94

description: E1-C3-Camila-TMM4-TAAGGCGA-CTCCTTAC_S97_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 93

description: E19-D9-Camila-TMM4-GTAGAGGA-CTCCTTAC_S115_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 92

description: E18-D8-Camila-TMM4-CCTAAGAC-CTCCTTAC_S114_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 91

description: E17-D7-Camila-TMM4-AAGAGGCA-CTCCTTAC_S113_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 90

description: E16-D6-Camila-TMM4-ACTGAGCG-CTCCTTAC_S112_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 86

description: E12-D2-Camila-TMM4-ATGCGCAG-CTCCTTAC_S108_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 85

description: E11-D1-Camila-TMM4-TAGGCATG-CTCCTTAC_S107_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 89

description: E15-D5-Camila-TMM4-CGAGGCTG-CTCCTTAC_S111_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 84

description: E10-C12-Camila-TMM4-TACGCTGC-CTCCTTAC_S106_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 87

description: E13-D3-Camila-TMM4-CTCTCTAC-CTCCTTAC_S109_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 83

description: D8-A10-Camila-TMM4-CGGAGCCT-AGCTAGAA_S80_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 88

description: E14-D4-Camila-TMM4-TAGCGCTC-CTCCTTAC_S110_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 81

description: D6-A8-Camila-TMM4-GCGTAGTA-AGCTAGAA_S78_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 104

description: E8-C10-Camila-TMM4-CGGAGCCT-CTCCTTAC_S104_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 80

description: D5-A7-Camila-TMM4-AGGCAGAA-AGCTAGAA_S77_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 96

description: E21-D11-Camila-TMM4-GCTCATGA-CTCCTTAC_S117_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 77

description: D2-A4-Camila-TMM4-ACTCGCTA-AGCTAGAA_S74_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 76

description: D24-C2-Camila-TMM4-TCGACGTC-AGCTAGAA_S96_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 74

description: D21-B11-Camila-TMM4-GCTCATGA-AGCTAGAA_S93_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 75

description: D22-B12-Camila-TMM4-TGCAGCTA-AGCTAGAA_S94_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 79

description: D4-A6-Camila-TMM4-GGAGCTAC-AGCTAGAA_S76_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 71

description: D19-B9-Camila-TMM4-GTAGAGGA-AGCTAGAA_S91_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 70

description: D18-B8-Camila-TMM4-CCTAAGAC-AGCTAGAA_S90_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 95

description: E20-D10-Camila-TMM4-CGATCAGT-CTCCTTAC_S116_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 68

description: D15-B5-Camila-TMM4-CGAGGCTG-AGCTAGAA_S87_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 69

description: D16-B6-Camila-TMM4-ACTGAGCG-AGCTAGAA_S88_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 72

description: D1-A3-Camila-TMM4-TAAGGCGA-AGCTAGAA_S73_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 64

description: C9-G5-Camila-TMM3-GGACTCCT-AGAGGATA_S57_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 66

description: D11-B1-Camila-TMM4-TAGGCATG-AGCTAGAA_S83_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 82

description: D7-A9-Camila-TMM4-TCCTGAGC-AGCTAGAA_S79_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 67

description: D14-B4-Camila-TMM4-TAGCGCTC-AGCTAGAA_S86_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 62

description: C7-G3-Camila-TMM3-TCCTGAGC-AGAGGATA_S55_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 65

description: D10-A12-Camila-TMM4-TACGCTGC-AGCTAGAA_S82_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 73

description: D20-B10-Camila-TMM4-CGATCAGT-AGCTAGAA_S92_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 58

description: C3-F8-Camila-TMM3-CGTACTAG-AGAGGATA_S51_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 63

description: C8-G4-Camila-TMM3-CGGAGCCT-AGAGGATA_S56_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 56

description: C23-A1-Camila-TMM4-ATCTCAGG-AGAGGATA_S71_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 60

description: C5-G1-Camila-TMM3-AGGCAGAA-AGAGGATA_S53_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 51

description: C18-H2-Camila-TMM3-CCTAAGAC-AGAGGATA_S66_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 50

description: C17-H1-Camila-TMM3-AAGAGGCA-AGAGGATA_S65_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 47

description: C14-G10-Camila-TMM3-TAGCGCTC-AGAGGATA_S62_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 57

description: C2-F5-Camila-TMM3-ACTCGCTA-AGAGGATA_S50_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 59

description: C4-F10-Camila-TMM3-GGAGCTAC-AGAGGATA_S52_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 52

description: C19-H3-Camila-TMM3-GTAGAGGA-AGAGGATA_S67_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 61

description: C6-G2-Camila-TMM3-GCGTAGTA-AGAGGATA_S54_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 78

description: D3-A5-Camila-TMM4-CGTACTAG-AGCTAGAA_S75_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 54

description: C21-H5-Camila-TMM3-GCTCATGA-AGAGGATA_S69_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 45

description: C12-G8-Camila-TMM3-ATGCGCAG-AGAGGATA_S60_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 44

description: C11-G7-Camila-TMM3-TAGGCATG-AGAGGATA_S59_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 55

description: C22-H6-Camila-TMM3-TGCAGCTA-AGAGGATA_S70_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 42

description: B7-D4-Camila-TMM3-TCCTGAGC-CTAGTCGA_S31_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 40

description: B5-D1-Camila-TMM3-AGGCAGAA-CTAGTCGA_S29_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 43

description: B9-D6-Camila-TMM3-GGACTCCT-CTAGTCGA_S33_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 38

description: B3-C11-Camila-TMM3-CGTACTAG-CTAGTCGA_S27_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 49

description: C16-G12-Camila-TMM3-ACTGAGCG-AGAGGATA_S64_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 53

description: C20-H4-Camila-TMM3-CGATCAGT-AGAGGATA_S68_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 41

description: B6-D3-Camila-TMM3-GCGTAGTA-CTAGTCGA_S30_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 35

description: B23-F1-Camila-TMM3-ATCTCAGG-CTAGTCGA_S47_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 48

description: C15-G11-Camila-TMM3-CGAGGCTG-AGAGGATA_S63_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 31

description: B1-C7-Camila-TMM3-TAAGGCGA-CTAGTCGA_S25_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 33

description: B21-E9-Camila-TMM3-GCTCATGA-CTAGTCGA_S45_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 39

description: B4-C12-Camila-TMM3-GGAGCTAC-CTAGTCGA_S28_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 34

description: B22-E11-Camila-TMM3-TGCAGCTA-CTAGTCGA_S46_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 36

description: B24-F3-Camila-TMM3-TCGACGTC-CTAGTCGA_S48_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 37

description: B2-C10-Camila-TMM3-ACTCGCTA-CTAGTCGA_S26_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 28

description: B17-E4-Camila-TMM3-AAGAGGCA-CTAGTCGA_S41_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 32

description: B20-E7-Camila-TMM3-CGATCAGT-CTAGTCGA_S44_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 29

description: B18-E5-Camila-TMM3-CCTAAGAC-CTAGTCGA_S42_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 30

description: B19-E6-Camila-TMM3-GTAGAGGA-CTAGTCGA_S43_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 26

description: B14-E1-Camila-TMM3-TAGCGCTC-CTAGTCGA_S38_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 25

description: B13-D11-Camila-TMM3-CTCTCTAC-CTAGTCGA_S37_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 22

description: A9-B3-Camila-TMM3-GGACTCCT-ATAGAGAG_S9_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 18

description: A4-A5-Camila-TMM3-GGAGCTAC-ATAGAGAG_S4_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 46

description: C13-G9-Camila-TMM3-CTCTCTAC-AGAGGATA_S61_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 20

description: A6-A11-Camila-TMM3-GCGTAGTA-ATAGAGAG_S6_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 27

description: B16-E3-Camila-TMM3-ACTGAGCG-CTAGTCGA_S40_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 17

description: A3-A4-Camila-TMM3-CGTACTAG-ATAGAGAG_S3_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 16

description: A2-A3-Camila-TMM3-ACTCGCTA-ATAGAGAG_S2_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 21

description: A7-B1-Camila-TMM3-TCCTGAGC-ATAGAGAG_S7_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 24

description: B12-D10-Camila-TMM3-ATGCGCAG-CTAGTCGA_S36_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 23

description: B10-D7-Camila-TMM3-TACGCTGC-CTAGTCGA_S34_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 19

description: A5-A6-Camila-TMM3-AGGCAGAA-ATAGAGAG_S5_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 12

description: A21-C3-Camila-TMM3-GCTCATGA-ATAGAGAG_S21_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 15

description: A24-C6-Camila-TMM3-TCGACGTC-ATAGAGAG_S24_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 10

description: A1-A1-Camila-TMM3-TAAGGCGA-ATAGAGAG_S1_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 8

description: A18-B12-Camila-TMM3-CCTAAGAC-ATAGAGAG_S18_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 13

description: A22-C4-Camila-TMM3-TGCAGCTA-ATAGAGAG_S22_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 9

description: A19-C1-Camila-TMM3-GTAGAGGA-ATAGAGAG_S19_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 11

description: A20-C2-Camila-TMM3-CGATCAGT-ATAGAGAG_S20_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 14

description: A23-C5-Camila-TMM3-ATCTCAGG-ATAGAGAG_S23_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 7

description: A17-B11-Camila-TMM3-AAGAGGCA-ATAGAGAG_S17_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 4

description: A14-B8-Camila-TMM3-TAGCGCTC-ATAGAGAG_S14_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 2

description: A11-B5-Camila-TMM3-TAGGCATG-ATAGAGAG_S11_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 6

description: A16-B10-Camila-TMM3-ACTGAGCG-ATAGAGAG_S16_R1_001


title: TMMp Smart-seq2 Pool 2 Cell 5

description: A15-B9-Camila-TMM3-CGAGGCTG-ATAGAGAG_S15_R1_001


title: TMMp Smart-seq2 Pool 1 Cell 141

value: pool 1


title: TMMp Smart-seq2 Pool 2 Cell 1

description: A10-B4-Camila-TMM3-TACGCTGC-ATAGAGAG_S10_R1_001


title: TMMp Smart-seq2 Pool 1 Cell 142


title: TMMp Smart-seq2 Pool 1 Cell 137


title: TMMp Smart-seq2 Pool 1 Cell 139


title: TMMp Smart-seq2 Pool 1 Cell 136


title: TMMp Smart-seq2 Pool 1 Cell 133


title: TMMp Smart-seq2 Pool 1 Cell 134


title: TMMp Smart-seq2 Pool 2 Cell 3

description: A13-B7-Camila-TMM3-CTCTCTAC-ATAGAGAG_S13_R1_001


title: TMMp Smart-seq2 Pool 1 Cell 135


title: TMMp Smart-seq2 Pool 1 Cell 138


title: TMMp Smart-seq2 Pool 1 Cell 127


title: TMMp Smart-seq2 Pool 1 Cell 131


title: TMMp Smart-seq2 Pool 1 Cell 130


title: TMMp Smart-seq2 Pool 1 Cell 128


title: TMMp Smart-seq2 Pool 1 Cell 129


title: TMMp Smart-seq2 Pool 1 Cell 122


title: TMMp Smart-seq2 Pool 1 Cell 126


title: TMMp Smart-seq2 Pool 1 Cell 123


title: TMMp Smart-seq2 Pool 1 Cell 121


title: TMMp Smart-seq2 Pool 1 Cell 124


title: TMMp Smart-seq2 Pool 1 Cell 125


title: TMMp Smart-seq2 Pool 1 Cell 140


title: TMMp Smart-seq2 Pool 1 Cell 119


title: TMMp Smart-seq2 Pool 1 Cell 120


title: TMMp Smart-seq2 Pool 1 Cell 117


title: TMMp Smart-seq2 Pool 1 Cell 113


title: TMMp Smart-seq2 Pool 1 Cell 115


title: TMMp Smart-seq2 Pool 1 Cell 109


title: TMMp Smart-seq2 Pool 1 Cell 114


title: TMMp Smart-seq2 Pool 1 Cell 111


title: TMMp Smart-seq2 Pool 1 Cell 108


title: TMMp Smart-seq2 Pool 1 Cell 112


title: TMMp Smart-seq2 Pool 1 Cell 132


title: TMMp Smart-seq2 Pool 1 Cell 105


title: TMMp Smart-seq2 Pool 1 Cell 106


title: TMMp Smart-seq2 Pool 1 Cell 103


title: TMMp Smart-seq2 Pool 1 Cell 104


title: TMMp Smart-seq2 Pool 1 Cell 102


title: TMMp Smart-seq2 Pool 1 Cell 100


title: TMMp Smart-seq2 Pool 1 Cell 99


title: TMMp Smart-seq2 Pool 1 Cell 98


title: TMMp Smart-seq2 Pool 1 Cell 118


title: TMMp Smart-seq2 Pool 1 Cell 116


title: TMMp Smart-seq2 Pool 1 Cell 91


title: TMMp Smart-seq2 Pool 1 Cell 90


title: TMMp Smart-seq2 Pool 1 Cell 95


title: TMMp Smart-seq2 Pool 1 Cell 96


title: TMMp Smart-seq2 Pool 1 Cell 92


title: TMMp Smart-seq2 Pool 1 Cell 89


title: TMMp Smart-seq2 Pool 1 Cell 110


title: TMMp Smart-seq2 Pool 1 Cell 107


title: TMMp Smart-seq2 Pool 1 Cell 86


title: TMMp Smart-seq2 Pool 1 Cell 88


title: TMMp Smart-seq2 Pool 1 Cell 87


title: TMMp Smart-seq2 Pool 1 Cell 85


title: TMMp Smart-seq2 Pool 1 Cell 83


title: TMMp Smart-seq2 Pool 1 Cell 84


title: TMMp Smart-seq2 Pool 1 Cell 82


title: TMMp Smart-seq2 Pool 1 Cell 77


title: TMMp Smart-seq2 Pool 1 Cell 81


title: TMMp Smart-seq2 Pool 1 Cell 78


title: TMMp Smart-seq2 Pool 1 Cell 76


title: TMMp Smart-seq2 Pool 1 Cell 93


title: TMMp Smart-seq2 Pool 1 Cell 94


title: TMMp Smart-seq2 Pool 1 Cell 73


title: TMMp Smart-seq2 Pool 1 Cell 74


title: TMMp Smart-seq2 Pool 1 Cell 75


title: TMMp Smart-seq2 Pool 1 Cell 101


title: TMMp Smart-seq2 Pool 1 Cell 72


title: TMMp Smart-seq2 Pool 1 Cell 71


title: TMMp Smart-seq2 Pool 1 Cell 67


title: TMMp Smart-seq2 Pool 1 Cell 66


title: TMMp Smart-seq2 Pool 1 Cell 68


title: TMMp Smart-seq2 Pool 1 Cell 65


title: TMMp Smart-seq2 Pool 1 Cell 69


title: TMMp Smart-seq2 Pool 1 Cell 64


title: TMMp Smart-seq2 Pool 1 Cell 61


title: TMMp Smart-seq2 Pool 1 Cell 60


title: TMMp Smart-seq2 Pool 1 Cell 79


title: TMMp Smart-seq2 Pool 1 Cell 63


title: TMMp Smart-seq2 Pool 1 Cell 62


title: TMMp Smart-seq2 Pool 1 Cell 58


title: TMMp Smart-seq2 Pool 1 Cell 80


title: TMMp Smart-seq2 Pool 1 Cell 57


title: TMMp Smart-seq2 Pool 1 Cell 59


title: TMMp Smart-seq2 Pool 1 Cell 54


title: TMMp Smart-seq2 Pool 1 Cell 55


title: TMMp Smart-seq2 Pool 1 Cell 53


title: TMMp Smart-seq2 Pool 1 Cell 50


title: TMMp Smart-seq2 Pool 1 Cell 52


title: TMMp Smart-seq2 Pool 1 Cell 49


title: TMMp Smart-seq2 Pool 1 Cell 51


title: TMMp Smart-seq2 Pool 1 Cell 47


title: TMMp Smart-seq2 Pool 1 Cell 48


title: TMMp Smart-seq2 Pool 1 Cell 46


title: TMMp Smart-seq2 Pool 1 Cell 70


title: TMMp Smart-seq2 Pool 1 Cell 43


title: TMMp Smart-seq2 Pool 1 Cell 45


title: TMMp Smart-seq2 Pool 1 Cell 42


title: TMMp Smart-seq2 Pool 1 Cell 44


title: TMMp Smart-seq2 Pool 1 Cell 39


title: TMMp Smart-seq2 Pool 1 Cell 40


title: TMMp Smart-seq2 Pool 1 Cell 36


title: TMMp Smart-seq2 Pool 1 Cell 38


title: TMMp Smart-seq2 Pool 1 Cell 35


title: TMMp Smart-seq2 Pool 1 Cell 31


title: TMMp Smart-seq2 Pool 1 Cell 34


title: TMMp Smart-seq2 Pool 1 Cell 56


title: TMMp Smart-seq2 Pool 1 Cell 33


title: TMMp Smart-seq2 Pool 1 Cell 29


title: TMMp Smart-seq2 Pool 1 Cell 37


title: TMMp Smart-seq2 Pool 1 Cell 32


title: TMMp Smart-seq2 Pool 1 Cell 30


title: TMMp Smart-seq2 Pool 1 Cell 28


title: TMMp Smart-seq2 Pool 1 Cell 26


title: TMMp Smart-seq2 Pool 1 Cell 24


title: TMMp Smart-seq2 Pool 1 Cell 25


title: TMMp Smart-seq2 Pool 1 Cell 27


title: TMMp Smart-seq2 Pool 1 Cell 23


title: TMMp Smart-seq2 Pool 1 Cell 22


title: TMMp Smart-seq2 Pool 1 Cell 19


title: TMMp Smart-seq2 Pool 1 Cell 41


title: TMMp Smart-seq2 Pool 1 Cell 18


title: TMMp Smart-seq2 Pool 1 Cell 16


title: TMMp Smart-seq2 Pool 1 Cell 15


title: TMMp Smart-seq2 Pool 1 Cell 14


title: TMMp Smart-seq2 Pool 1 Cell 13


title: TMMp Smart-seq2 Pool 1 Cell 12


title: TMMp Smart-seq2 Pool 1 Cell 10


title: TMMp Smart-seq2 Pool 1 Cell 6


title: TMMp Smart-seq2 Pool 1 Cell 8


title: TMMp Smart-seq2 Pool 1 Cell 7


title: TMMp Smart-seq2 Pool 1 Cell 9


title: TMMp Smart-seq2 Pool 1 Cell 5


title: TMMp Smart-seq2 Pool 1 Cell 11


title: TMMp Smart-seq2 Pool 1 Cell 2


title: TMMp Smart-seq2 Pool 1 Cell 97


title: TMMp Smart-seq2 Pool 1 Cell 21


title: ATML1p Smart-seq2 Pool 2 Cell 101


title: TMMp Smart-seq2 Pool 1 Cell 1


title: ATML1p Smart-seq2 Pool 2 Cell 99


title: TMMp Smart-seq2 Pool 1 Cell 20


title: ATML1p Smart-seq2 Pool 2 Cell 100


title: ATML1p Smart-seq2 Pool 2 Cell 94


title: ATML1p Smart-seq2 Pool 2 Cell 98


title: ATML1p Smart-seq2 Pool 2 Cell 97


title: ATML1p Smart-seq2 Pool 2 Cell 95


title: ATML1p Smart-seq2 Pool 2 Cell 92


title: TMMp Smart-seq2 Pool 1 Cell 17


title: ATML1p Smart-seq2 Pool 2 Cell 93


title: ATML1p Smart-seq2 Pool 2 Cell 90


title: ATML1p Smart-seq2 Pool 2 Cell 91


title: ATML1p Smart-seq2 Pool 2 Cell 89


title: ATML1p Smart-seq2 Pool 2 Cell 88


title: TMMp Smart-seq2 Pool 1 Cell 3


title: ATML1p Smart-seq2 Pool 2 Cell 86


title: TMMp Smart-seq2 Pool 1 Cell 4


title: ATML1p Smart-seq2 Pool 2 Cell 82


title: ATML1p Smart-seq2 Pool 2 Cell 81


title: ATML1p Smart-seq2 Pool 2 Cell 85


title: ATML1p Smart-seq2 Pool 2 Cell 76


title: ATML1p Smart-seq2 Pool 2 Cell 84


title: ATML1p Smart-seq2 Pool 2 Cell 83


title: ATML1p Smart-seq2 Pool 2 Cell 80


title: ATML1p Smart-seq2 Pool 2 Cell 79


title: ATML1p Smart-seq2 Pool 2 Cell 78


title: ATML1p Smart-seq2 Pool 2 Cell 75


title: ATML1p Smart-seq2 Pool 2 Cell 77


title: ATML1p Smart-seq2 Pool 2 Cell 74


title: ATML1p Smart-seq2 Pool 2 Cell 73


title: ATML1p Smart-seq2 Pool 2 Cell 70


title: ATML1p Smart-seq2 Pool 2 Cell 65


title: ATML1p Smart-seq2 Pool 2 Cell 66


title: ATML1p Smart-seq2 Pool 2 Cell 87


title: ATML1p Smart-seq2 Pool 2 Cell 71


title: ATML1p Smart-seq2 Pool 2 Cell 72


title: ATML1p Smart-seq2 Pool 2 Cell 68


title: ATML1p Smart-seq2 Pool 2 Cell 96


title: ATML1p Smart-seq2 Pool 2 Cell 67


title: ATML1p Smart-seq2 Pool 2 Cell 69


title: ATML1p Smart-seq2 Pool 2 Cell 63


title: ATML1p Smart-seq2 Pool 2 Cell 61


title: ATML1p Smart-seq2 Pool 2 Cell 60


title: ATML1p Smart-seq2 Pool 2 Cell 64


title: ATML1p Smart-seq2 Pool 2 Cell 59


title: ATML1p Smart-seq2 Pool 2 Cell 62


title: ATML1p Smart-seq2 Pool 2 Cell 56


title: ATML1p Smart-seq2 Pool 2 Cell 58


title: ATML1p Smart-seq2 Pool 2 Cell 57


title: ATML1p Smart-seq2 Pool 2 Cell 46


title: ATML1p Smart-seq2 Pool 2 Cell 55


title: ATML1p Smart-seq2 Pool 2 Cell 54


title: ATML1p Smart-seq2 Pool 2 Cell 40


title: ATML1p Smart-seq2 Pool 2 Cell 41


title: ATML1p Smart-seq2 Pool 2 Cell 39


title: ATML1p Smart-seq2 Pool 2 Cell 42


title: ATML1p Smart-seq2 Pool 2 Cell 37


title: ATML1p Smart-seq2 Pool 2 Cell 32


title: ATML1p Smart-seq2 Pool 2 Cell 53


title: ATML1p Smart-seq2 Pool 2 Cell 52


title: ATML1p Smart-seq2 Pool 2 Cell 51


title: ATML1p Smart-seq2 Pool 2 Cell 49


title: ATML1p Smart-seq2 Pool 2 Cell 38


title: ATML1p Smart-seq2 Pool 2 Cell 23


title: ATML1p Smart-seq2 Pool 2 Cell 47


title: ATML1p Smart-seq2 Pool 2 Cell 22


title: ATML1p Smart-seq2 Pool 2 Cell 45


title: ATML1p Smart-seq2 Pool 2 Cell 20


title: ATML1p Smart-seq2 Pool 2 Cell 50


title: ATML1p Smart-seq2 Pool 2 Cell 44


title: ATML1p Smart-seq2 Pool 2 Cell 43


title: ATML1p Smart-seq2 Pool 2 Cell 17


title: ATML1p Smart-seq2 Pool 2 Cell 36


title: ATML1p Smart-seq2 Pool 2 Cell 48


title: ATML1p Smart-seq2 Pool 2 Cell 35


title: ATML1p Smart-seq2 Pool 2 Cell 34


title: ATML1p Smart-seq2 Pool 2 Cell 33


title: ATML1p Smart-seq2 Pool 2 Cell 31


title: ATML1p Smart-seq2 Pool 2 Cell 29


title: ATML1p Smart-seq2 Pool 2 Cell 30


title: ATML1p Smart-seq2 Pool 2 Cell 28


title: ATML1p Smart-seq2 Pool 2 Cell 13


title: ATML1p Smart-seq2 Pool 2 Cell 12


title: ATML1p Smart-seq2 Pool 2 Cell 27


title: ATML1p Smart-seq2 Pool 2 Cell 26


title: ATML1p Smart-seq2 Pool 2 Cell 11


title: ATML1p Smart-seq2 Pool 2 Cell 21


title: ATML1p Smart-seq2 Pool 2 Cell 6


title: ATML1p Smart-seq2 Pool 2 Cell 8


title: ATML1p Smart-seq2 Pool 2 Cell 24


title: ATML1p Smart-seq2 Pool 2 Cell 3


title: ATML1p Smart-seq2 Pool 2 Cell 1


title: ATML1p Smart-seq2 Pool 2 Cell 19


title: ATML1p Smart-seq2 Pool 2 Cell 2


title: ATML1p Smart-seq2 Pool 1 Cell 42


title: ATML1p Smart-seq2 Pool 2 Cell 18


title: ATML1p Smart-seq2 Pool 2 Cell 25


title: ATML1p Smart-seq2 Pool 2 Cell 15


title: ATML1p Smart-seq2 Pool 2 Cell 10


title: ATML1p Smart-seq2 Pool 2 Cell 14


title: ATML1p Smart-seq2 Pool 2 Cell 4


title: ATML1p Smart-seq2 Pool 2 Cell 16


title: ATML1p Smart-seq2 Pool 1 Cell 36


title: ATML1p Smart-seq2 Pool 1 Cell 38


title: ATML1p Smart-seq2 Pool 1 Cell 41


title: ATML1p Smart-seq2 Pool 2 Cell 5


title: ATML1p Smart-seq2 Pool 2 Cell 9


title: Schizont stage duplex RNA-seq rep 2

city: Cambridge

first: Sabah

last: Kadri

state: Massachusetts

address: 7 Cambridge Center

department: Computational Biology

institute: Broad institute

description:
duplex RNA
753631-3_S3

data_processing:
The paired end reads from the duplex RNA-Seq data were first trimmed to remove any adapter sequences using fastp v0.20.0 (Chen et al. 2018).
Then, STAR v2.5.2 (Dobin et al. 2013) was used to align the trimmed data to the P. falciparum genome (v34) with the parameters --genomeSAindexNbases 11 and  --alignIntronMax 500.
A custom filter was then used to remove low mapping quality (mapping quality =0) and secondary or supplementary alignments.
featurecounts v2.0.0 (Liao et al. 2014) was used to perform gene counting using the latest gene annotations from (Chappell et al. 2020). Reads that mapped to mitochondrial, apicoplast transcripts, rRNAs and tRNAs were removed from this gene expression analysis.
Genome_build: P. falciparum genome (v34)
Supplementary_files_format_and_content: Matrix table with raw gene counts for every mRNA, ncRNA, snoRNA, snRNA gene and every sample

organism: Plasmodium falciparum

source_name: P. falciparum 3D7 cultured in human RBCs

growth_protocol: P. falciparum laboratory adapted parasite line 3D7 was cultured at 2% hematocrit in purified human erythrocytes/RBCs using standard culturing techniques (Trager and Jensen 1976; Duffy and Avery 2018) using RPMI 1640 medium (supplemented with 37.5 mM HEPES, 7 mM D-glucose, 6 mM NaOH, 25 µg of gentamicin sulfate/ml, 2 mM L-glutamine, and 10% Albumax II (Life Technologies) at a pH of 7.2 in a 3% carbon dioxide environment. The asexual blood stage parasite density was calculated routinely using thick blood films. Baseline RBC count was used to calculate the parasitaemia (parasites/µl).

extract_protocol:
P. falciparum parasites were isolated after saponin lysis and washed with incomplete medium (without Albumax). The RNA from each stage were isolated by the Trizol (Thermofisher) method followed by Turbo DNAse (Thermofisher) treatment. Total RNA was then subjected to two rounds of rRNA depletion with custom biotinylated oligos designed against P.falciparum’s rRNAs (Suppl. Table 1). Briefly, 500 pmol of biotinylated capture probes were used in a binding buffer containing 10 mM Tris-Cl pH 7.4, 3 mM MgCl2, 300 mM NaCl and 0.5% Igepal-CA 630. The hybridization reaction was prepared on ice in the presence of 50 ug of total RNA in each tube and 2ul of RNase inhibitor. The oligo probe and total RNA mixture was heated at 70 °C for 5 minutes followed by gradually reducing the heat until the oligonucleotides have reached room temperature. Finally, rRNA bound oligos were separated by Streptavidin conjugated magnetic Dynabeads M-280 (Invitrogen) followed by ethanol precipitation of rRNA depleted total RNAs. Next, RNA samples were treated with a single-strand specific ribonuclease, RNase One (Promega) (5 units per ug of RNA), according to the manufacturer’s instructions. After nuclease treatment, each RNA sample was fragmented with Fragmentation Reagents (Ambion) and then end corrected with T4PNK (Thermofisher). The RNA went through phenol-chloroform extraction and ethanol precipitation before using them for RNA-seq library preparation.
P. falciparum duplex RNA sequencing libraries were prepared from the three developmental stages: ring, trophozoite and schizont (see results section for details). Two independent sets of libraries were prepared (2 replicates for each developmental stage). The sequencing library was constructed using the Small RNA Sequencing Kit v3 (Illumina, San Diego, CA) as per manufacturer’s instructions.
Library quality was checked by Bioanalyzer and then pair-end sequencing was done on two different Illumina high-throughput sequencing platforms, HiSeq2500 for RNA-seq-1 (Otogenetics Corp., Atlanta, GA) and NextSeq500 for RNA-seq-2 (ACGT Inc, Germantown, MD) (Table-1).

treatment_protocol: P.falciparum Infected Red Blood Cells (iRBCs) were treated with a pre-warm aliquot of 5% D-sorbitol in RPMI at 37°C to attain synchronized parasite cultures in order to isolate homogeneous stages of parasites. Multiple sorbitol treatments were administered to achieve >90% synchrony of parasites. Parasites were collected from the three different developmental stages (ring, trophozoite and schizont) after stringent synchronizations.

tag: Stage

value: Schizont

tag: collection

value: 30-42 hours post-invasion of parasite in human RBCs


title: ATML1p Smart-seq2 Pool 1 Cell 34

tag: developmental stage


title: ATML1p Smart-seq2 Pool 1 Cell 39


title: ATML1p Smart-seq2 Pool 1 Cell 35


title: ATML1p Smart-seq2 Pool 1 Cell 37


title: Ring stage duplex RNA-seq rep 2

description:
duplex RNA
753631-4_S4

value: Ring

value: 2-12 hours post-invasion of parasite in human RBCs


title: ATML1p Smart-seq2 Pool 2 Cell 7


title: Trophozoite stage duplex RNA-seq rep 2

description:
duplex RNA
753631-2_S2

value: Trophozoite

value: 18-26 hours post-invasion of parasite in human RBCs


title: ATML1p Smart-seq2 Pool 1 Cell 32


title: ATML1p Smart-seq2 Pool 1 Cell 33


title: ATML1p Smart-seq2 Pool 1 Cell 27


title: CD4Lo_ATAC-seq replicate2

first: Michael

last: Slifker

state: PA

address: 333 Cottman Ave.

institute: Fox Chase Cancer Center

library_source: genomic

data_processing:
Data was aligned to Mm10 genome using BoWTie2
Samples were filtered for regions blacklisted by the ENCODE project57  and deduplicated using Picard tools (http://broadinstitute.github.io/picard).
Alignment coordinates were converted to BED format using BEDTools v.2.1758 and peak calling was performed using MACS255 with default parameters
To visualize the relative occupancy of ThPOK along with ATAC-Seq peaks and the relative relationship to genes we binned the chromosomes into consequtive 10Kb regions and plotted the read distribution of ATAC-Seq and ThPOK ChIP-Seq data.
Genome_build: Mm10
Supplementary_files_format_and_content: bigwig

molecule: genomic DNA

source_name: OB11/OB11_Flow sorted Naïve T cells

growth_protocol: Directly out of the mouse

extract_protocol:
5 × 10^4 flow sorted naïve CD4 cells from WT or CD4lo cells from OB11 mice  were pelleted at 500 g for 5 min at 4 °C, and whole cell pellets resuspended in 50μL ATAC-RSB buffer (10 mM Tris-HCl pH 7.4 10 mM NaCl; 3 mM MgCl2 , 0.1% Igepal CA-630), 0.1% Tween 20 (Sigma),  and 0.01% Digitonin, and kept on ice for 3 min. 1ml of cold ATAC-RSB + 0.1% Tween20 was added, and samples centrifuged 500g for 10 min at 4°c (fixed angle rotor) to obtain nuclear pellet.
Transposase reaction of open chromatin was achieved by resuspending free nuclei in tagmentation mix (22.5 μL Tagment DNA Buffer; 2.5 μL Tagment DNA enzyme; 25μL H2O) (Illumina, FC-121- 1030) and incubating at 37 °C for 30 min. Purification of DNA was performed with Diapure kit (Diagenode), according to the manufacturer’s protocol. Barcoding and amplification was performed using Nextera Index Kit (Illumina, FC-121-1011), as previously described56. Amplified ATAC-seq libraries were purified using Gene-Read Size Selection Kit (Qiagen, 180514) according to the manufacturer’s protocol. Quality and quantity of final ATAC-seq libraries were assessed with the High Sensitivity DNA kit (Agilent, 5067-4626) run on an Agilent 2100 Bioanalyzer. ATAC-seq libraries were sequenced using Illumina 125-bp paired-ends sequencing on a HiSeq2500 platform with, generating between 38 and 43 million reads per condition per biological replicate.

treatment_protocol: Naïve T cell subsets isolated directly from spleen by fluorescence-activated cell sorting (FACS)

tag: genotype/variation

value: 6 weeks

tag: tissue

value: Spleen

tag: cell type

value: Flow sorted Naive T cells


title: WTCD4_ATAC-seq replicate2

source_name: WT_Flow sorted Naïve T cells

value: WT

tag: age


title: ATML1p Smart-seq2 Pool 1 Cell 31


title: ATML1p Smart-seq2 Pool 1 Cell 25


title: WTCD4_ATAC-seq replicate1


title: Sample9_WTCD4_L1

data_processing:
Data was aligned to Mm10 genome using Tophat2.
Gene counts were estimated using HTSeq algorithm.
Genome_build: Mus musculus Mm10
Supplementary_files_format_and_content: Counts file

extract_protocol:
Naïve T cells were sorted directly in Trizol LS
The Takara pico RNA protocol was used.  In short, 10ng total RNA was used as input. After fragmentation at 94°C for 4min, SMART technology was used to make the directional cDNA library. The ribosomal cDNA was then depleted and P5, P7 Illumina adptors were added to the library via PCR.
ribosomal cDNA depletion, directional cDNA library.

treatment_protocol: Naïve T cells isolateddirectly from spleen by Flowcytometry based sorting


title: ATML1p Smart-seq2 Pool 1 Cell 30


title: Sample6_CD8_2


title: ATML1p Smart-seq2 Pool 1 Cell 29


title: ATML1p Smart-seq2 Pool 1 Cell 40


title: ATML1p Smart-seq2 Pool 1 Cell 28


title: ATML1p Smart-seq2 Pool 1 Cell 20


title: Sample10_WTCD4_L2


title: Sample8_DP_2


title: CD4Lo_ATAC-seq replicate1


title: ATML1p Smart-seq2 Pool 1 Cell 24


title: ATML1p Smart-seq2 Pool 1 Cell 23


title: Sample5_CD8_1


title: ATML1p Smart-seq2 Pool 1 Cell 22


title: ATML1p Smart-seq2 Pool 1 Cell 26


title: Sample4_CD4Lo_L2


title: ATML1p Smart-seq2 Pool 1 Cell 17


title: ATML1p Smart-seq2 Pool 1 Cell 16


title: ATML1p Smart-seq2 Pool 1 Cell 15


title: Liver_Neutrophil_Treated_IL10_rep2

city: Barcelona

first: Pere

last: Santamaria

address: C/Rosselló 149-153

country: Spain

institute: IDIBAPS

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-IL10 Ab
IL10-2_34179
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx

data_processing:
Contaminant rRNA filtering with Bowtie2 (2.2.5)
Alignment with STAR 2.5.3a to the GENCODE  release 16 of the Mus musculus genome (mm10 assembly)
Raw count of reads per gene was obtained with Partek “Quantify to annotation model (Partek E/M)” tool
Counts were normalized by CPM
Differential expression analysis was performed using DESeq2
Genome_build: mm10
Supplementary_files_format_and_content: Excel sheets (xlsx) containint differential expression analysis

extract_protocol:
RNA purification using Rneasy Mini kit (QIAGEN)
Libraries were prepared using the TruSeq Stranded mRNA Sample Prep Kit v2 according to the  manufacturer's protocol (Illumina, San Diego, CA)
Bulk RNAseq

treatment_protocol: Treated sample come from NOD.c3c4 mice treated intravenously with 10 doses of PDC166-coated NPs. Some samples come from mice treated additionally with blocking antibodies against several cytokines.

value: Liver

value: Neutrophils

tag: treatment

value: PDC166-NP + blocking anti-IL10 Ab


title: ATML1p Smart-seq2 Pool 1 Cell 21

value: 10 days post-germination


title: Sample3_CD4Lo_L1


title: ATML1p Smart-seq2 Pool 1 Cell 14


title: Liver_Neutrophil_Treated_TGFb_rep2

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-TGFb Ab
TGFb3_34174
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx

value: PDC166-NP + blocking anti-TGFb Ab


title: ATML1p Smart-seq2 Pool 1 Cell 13


title: ATML1p Smart-seq2 Pool 1 Cell 12


title: Liver_Neutrophil_Treated_TGFb_rep1

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-TGFb Ab
TGFb2_34173
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx


title: ATML1p Smart-seq2 Pool 1 Cell 11


title: Sample2_CD4HI_2


title: ATML1p Smart-seq2 Pool 1 Cell 19


title: Sample1_CD4HI_1


title: Liver_Neutrophil_Treated_IL21R_rep1

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-IL-21R Ab
IL21R1_34169
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx

value: PDC166-NP + blocking anti-IL21R Ab


title: ATML1p Smart-seq2 Pool 1 Cell 18

value: pool 1


title: ATML1p Smart-seq2 Pool 1 Cell 10


title: Liver_Neutrophil_Treated_GCF_rep3

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-GCF Ab
GCF3_34186
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx

value: PDC166-NP + blocking anti-GCF Ab


title: Liver_Neutrophil_Treated_IL10_rep1

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-IL10 Ab
IL10-1_34178
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx


title: ATML1p Smart-seq2 Pool 1 Cell 6


title: Sample7_DP_1


title: Liver_Neutrophil_Treated_IL35_rep2

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-IL35 Ab
IL35-3_34177
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx

value: PDC166-NP + blocking anti-IL35 Ab


title: Liver_Neutrophil_Treated_Isotype_rep3

description:
Liver_NeutrLiver Neutrophils from Treated mice with Isotype control Ab
ISO3_34168
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx

value: PDC166-NP + Isotype control Ab


title: Blood_Neutrophil_Treated_rep3

description:
Blood Neutrophils from Treated mice
Neu_B3_26681
Partek_DEseq2_Blood_Treated_Untreated.xlsx
Partek_DEseq2_Treated_Liver_Blood_Treated.xlsx
Raw_counts_Liver_Blood.xlsx

source_name: Blood

value: Blood


title: ATML1p Smart-seq2 Pool 1 Cell 5


title: ATML1p Smart-seq2 Pool 1 Cell 2


title: Blood_Neutrophil_Treated_rep2

description:
Blood Neutrophils from Treated mice
Neu_B2_26680
Partek_DEseq2_Blood_Treated_Untreated.xlsx
Partek_DEseq2_Treated_Liver_Blood_Treated.xlsx
Raw_counts_Liver_Blood.xlsx


title: Liver_Neutrophil_Treated_IL35_rep1

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-IL35 Ab
IL35-1_34175
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx


title: ATML1p Smart-seq2 Pool 1 Cell 1


title: Liver_Neutrophil_Treated_IL21R_rep2

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-IL-21R Ab
IL21R2_34170
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx


title: ATML1p Smart-seq2 Pool 1 Cell 9


title: ATML1p Smart-seq2 Pool 1 Cell 8


title: Liver_Neutrophil_Treated_GCF_rep2

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-GCF Ab
GCF2_34185
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx


title: Liver_Neutrophil_Treated_GCF_rep1

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-GCF Ab
GCF1_34184
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx


title: ATML1p Smart-seq2 Pool 1 Cell 7


title: ChIPseq-TM5-DoxNeg-input

city: Research Triangle Park

first: Anton

middle: M

last: Jetten

state: NC

address: 111 TW Alexander Drive

department: DIR

institute: NIEHS, NIH

data_processing:
ChIP-seq reads were filtered to retain only those with mean base quality scores at least 20, followed by adapter trimming via Cutadapt v1.12 (parameters -a AGATCGGAAGAG -O 5 -q 0).  Reads less than 30bp after removal of adapter were ignored.  Filtered and trimmed reads were aligned against the hg19 reference genome (excluding haplotype chromosomes) via Bowtie v1.2 with parameter '-m 1'.  Mapped reads were deduplicated via MarkDuplicates.jar (Picard tools v1.110).
For visualization purposes, uniquely-mapped non-duplicate ChIP-seq reads were extended to estimated average fragment length of 150bp.  Alignments were converted to bedGraph format via genomeCoverageBed (Bedtools v2.24.0) then depth normalized to 25M fragments, followed by conversion to bigWig format via UCSC utility bedGraphToBigWig.
ChIP-seq peaks were called by HOMER v4.10.3 at FDR e-5 (all other parameters default) for both the DOX- and DOX+ samples.  Peaks for the DOX+ sample were filtered to retain only those meeting the following criteria: (a) did not overlap any peak in the DOX- sample, (b) at least 8x fold change over input signal [equivalent to HOMER parameter F8], and (c) at least 8x fold change over local signal [equivalent to HOMER parameter L8].  Finally, the filtered peaks were re-sized as 200mers centered on the called peak midpoint.
RNA-seq reads were filtered to retain only those with mean base quality scores at least 20, then aligned via STAR v2.5 with parameters "--outSAMattrIHstart 0 --outFilterType BySJout --alignSJoverhangMin 8 --limitBAMsortRAM 55000000000 --outSAMstrandField intronMotif --outFilterIntronMotifs RemoveNoncanonical".
RNA-seq counts per gene were determined via featureCounts (Subread v1.5.0-p1) with parameters "-s0 -Sfr" for Gencode V28 gene models.
For visualization purposes, RNA-seq depth tracks were generated by STAR v2.5 with parameters "--outWigType bedGraph --outWigStrand Unstranded --outWigNorm None", followed by depth normalization using size factors as reported by DESeq2 then conversion to bigWig format via UCSC utility bedGraphToBigWig.  Each of the 3 RNA-seq datasets were depth-normalized independently.
Genome_build: hg19
Supplementary_files_format_and_content: normalized depth tracks (bigWig), ChIP-seq peaks (BED), and RNA-seq counts-per-gene matrix per dataset (text files)

source_name: TM5 cell line

growth_protocol: Cell lines were grown in culture media with cell specific growth factors

extract_protocol:
RNA from cultured HTM and TM5 cells was isolated with aPureLink RNA mini kit  and ChIP-Seq analysis was performed using TM5 cells stably expressing doxycycline (Dox)-inducible GLIS1-HA.  (Illumina).
HTM RNA Sequencing reads were obtained using a NextSeq Sequencing System (Illumina) and TM5 ChIP Sequencing reading was performed with a NextSeq 500 system

treatment_protocol: Cells were infected with Glis1 shRNA and control lentivirus for 48 hours, then RNA was extracted. Doxycyclin inducible stable cells were treated with or without doxycycline and fixed. Then the cells were used for ChIP.

value: Human trabacular meshwork cell line

tag: chip antibody


title: Liver_Neutrophil_Untreated_rep1

description:
Liver Neutrophils from Untreated mice
LN_U1_30025
Partek_DEseq2_Liver_Treated_Untreated.xlsx
Raw_counts_Liver_Blood.xlsx

value: Untreated


title: ATML1p Smart-seq2 Pool 1 Cell 3


title: ATML1p Smart-seq2 Pool 1 Cell 4


title: Liver_Neutrophil_Treated_IL21R_rep3

description:
Liver_NeutrLiver Neutrophils from Treated mice with anti-IL-21R Ab
IL21R3_34171
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx


title: Liver_Neutrophil_Treated_Isotype_rep2

description:
Liver_NeutrLiver Neutrophils from Treated mice with Isotype control Ab
ISO2_34167
Partek_DESeq2_Anti-cytok_Iso.xlsx
Raw_counts_Cytokines.xlsx


title: Blood_Neutrophil_Untreated_rep1

description:
Blood Neutrophils from Untreated mice
BN_U1_30022
Partek_DEseq2_Blood_Treated_Untreated.xlsx
Raw_counts_Liver_Blood.xlsx


title: PTEN shRNA + PAX2-2

city: Chicago

first: Joanna

last: Burdette

state: IL

address: 900 S. Ashland Ave

institute: University of Illinois at Chicago

data_processing:
Adapters were trimmed using cutadapt with default parameters
Trimmed reads were aligned to the mouse reference genome, mm10, using STAR and default parameters
Raw read counts per gene were tabulated using HTSeq count and default parameters
Normalization and Differential expression was performed using DESeq2
Genome_build: mm10
Supplementary_files_format_and_content: reads counts

source_name: Murine oviductal cells

growth_protocol: Cells were grown in MTEC media (Russo A, Oncogene, 2018)

extract_protocol:
RNAeasy Qiagen
RNA libraries were prepared for sequencing using standard Illumina protocols

tag: treament

value: PAX2 re-expression


title: PTEN shRNA + PAX2-1


title: PTEN shRNA + PAX2-3


title: PTEN shRNA-3

value: control


title: Liver_Neutrophil_Treated_rep1

description:
Liver Neutrophils from Treated mice
Neu_T2_26677
Partek_DEseq2_Liver_Treated_Untreated.xlsx
Partek_DEseq2_Treated_Liver_Blood_Treated.xlsx
Raw_counts_Liver_Blood.xlsx


title: ChIPseq-TM5-DoxPos-input


title: Liver_Neutrophil_Treated_rep2

description:
Liver Neutrophils from Treated mice
Neu_T3_26678
Partek_DEseq2_Liver_Treated_Untreated.xlsx
Partek_DEseq2_Treated_Liver_Blood_Treated.xlsx
Raw_counts_Liver_Blood.xlsx


title: ChIPseq-TM5-DoxPos-Glis1

value: HA antibody; Cell Signaling C29F4


title: PTEN shRNA-1


title: Liver_Neutrophil_Untreated_rep2

description:
Liver Neutrophils from Untreated mice
LN_U2_30026
Partek_DEseq2_Liver_Treated_Untreated.xlsx
Raw_counts_Liver_Blood.xlsx