title: Circulating tumor cells from patient 3

description: Gene expression at single-cell level form circulating tumor cells from newly-diagnosed patient

data_processing: After demultiplexing step, trimming, alignment and generation of matrix counts were done through the workflow developed by Becton Dickinson in SevenBridges, Precise Whole Transcriptome Assay Analysis Pipeline v2.0.

source_name: Circulating tumor cells

extract_protocol:
Cells were in-plate FACSorted and automatically lysed. Libraries were prepared according to Precise Assay instructions.
Libraries strategy are described in Precise Assay protocol.

tag: cell type

value: Circulating tumor cells


title: Circulating tumor cells from patient 2

status: Public on Feb 26 2019

type: SRA

city: New York

first: Juan-Jose

last: Garces

state: New York

address: 321 E 61st St

country: USA

institute: Memorial Sloan Kettering Cancer Center

library_source: transcriptomic

molecule: total RNA

organism: Homo sapiens

tag: disease state

value: Multiple Myeloma


title: Circulating clonal plasma cells (CTC) from patient 10

type: RNA

description: Gene expression data from circulating clonal plasma cells (ctc)

hyb_protocol: Hybridizations was performed according to protocols from Affymetrix in a GeneChip Hybridization Oven 645.

scan_protocol: Scanning were performed using the GeneChip Scanner 7G.

data_processing: The processing and normalization steps were performed using the Robust Multi-array Average (RMA) procedure from limma.

label: biotin

source_name: Circulating clonal plasma cells (CTC) newly-diagnosed patient

label_protocol: Labelling and washing were performed according to protocols from Affymetrix: 50 ng of total RNA were amplified and labeled using the WT Pico reagent kit (Affymetrix), washing was carried out in a GeneChip Fluidics Station 450.

extract_protocol: RNA was extracted using the AllPrep DNA/RNA micro kit (Qiagen) according to manufacturer's indications.

tag: tissue

value: Circulating clonal plasma cells (CTC)

tag: gender

value: female

tag: cell number

tag: disease stage

value: newly-diagnosed

tag: age


title: Bone marrow clonal plasma cells from patient 3

description: Gene expression at single-cell level form bone marrow clonal plasma cells from newly-diagnosed patient

source_name: Bone marrow clonal plasma cells

value: Bone marrow clonal plasma cells


title: Bone marrow clonal plasma cells from patient 11

description: Gene expression data from bone marrow clonal plasma cells

source_name: Bone marrow clonal plasma cells from relapse patient

value: relapse


title: Bone marrow clonal plasma cells from patient 2


title: Circulating clonal plasma cells (CTC) from patient 11

source_name: Circulating clonal plasma cells (CTC) relapse patient


title: Bone marrow clonal plasma cells from patient 1


title: Bone marrow clonal plasma cells from patient 04

source_name: Bone marrow clonal plasma cells from newly-diagnosed patient

value: male


title: Bone marrow clonal plasma cells from patient 05


title: Circulating clonal plasma cells (CTC) from patient 09


title: Circulating clonal plasma cells (CTC) from patient 05


title: Bone marrow clonal plasma cells from patient 10


title: Circulating clonal plasma cells (CTC) from patient 08


title: Circulating clonal plasma cells (CTC) from patient 04


title: Bone marrow clonal plasma cells from patient 01


title: Bone marrow clonal plasma cells from patient 03


title: Bone marrow clonal plasma cells from patient 09


title: Circulating clonal plasma cells (CTC) from patient 03


title: Circulating clonal plasma cells (CTC) from patient 02


title: Circulating clonal plasma cells (CTC) from patient 01


title: Circulating tumor cells from patient 51

description: Gene expression data from circulating tumor cells

hyb_protocol: Biotinylated cDNA was hybridized to GeneChip Human Gene 1.0 ST Array (AppliedBiosystems) overnight at 45 °C and stained on Genechip Fluidics Station 450 (AppliedBiosystems) using GeneChip™ Hybridization, Wash, and Stain Kit (AppliedBiosystems) according to manufacturer's instructions.

scan_protocol: Affymetrix GeneChip® Scanner 3000 7G was used with scanning and image acquisition protocol F450_007. The image data was analyzed in AGCC 3.2.0.1515 software. Feature extracture cell generation algorithm version was 1.0.0.1515

source_name: Circulating tumor cells from newly-diagnosed patient

label_protocol: RNA extracted from bone marrow and peripheral blood samples was amplified using the Ovation Pico WTA System V2 (NuGEN) according to manufacturer's instructions. Briefly, first strand cDNA was synthesized using chimeric primer mix, followed by second strand cDNA synthesis, SPIA amplification and purification of amplified cDNA. Samples were fragmented and labeled using Encore Biotin Module (NuGEN) according to manufacturer's instruction.

extract_protocol: RNA was extracted using the AllPrep DNA/RNA micro kit (Qiagen) according to manufacturer's indications. In those cases with a low concentration, SpeedVac was used.


title: Bone marrow clonal plasma cells from patient 48


title: Bone marrow clonal plasma cells from patient 08


title: Circulating tumor cells from patient 47

source_name: Circulating tumor cells from relapse patient


title: Circulating tumor cells from patient 1


title: Bone marrow clonal plasma cells from patient 47


title: Bone marrow clonal plasma cells from patient 51


title: Circulating tumor cells from patient 90


title: Circulating tumor cells from patient 48


title: Circulating tumor cells from patient 68


title: Bone marrow clonal plasma cells from patient 68


title: Bone marrow clonal plasma cells from patient 92


title: Circulating tumor cells from patient 89


title: Bone marrow clonal plasma cells from patient 89


title: Bone marrow clonal plasma cells from patient 90


title: Bone marrow clonal plasma cells from patient 66


title: Circulating tumor cells from patient 62


title: Circulating tumor cells from patient 92


title: DMSO SG2M-2

status: Public on Feb 20 2019

city: Boston

first: Sundari

last: Chetty

state: MA

address: 185 Cambridge Street

department: Harvard Medical School

institute: Massachusetts General Hospital, Harvard University

description: 24 Hour treatment with 2% DMSO.

data_processing:
RNA-seq reads were trimmed from 150 to 76 bases with the trimmomatic software (v. 0.36) using default parameters.
Trimmed FASTQ files were aligned to the GRCH38 reference genome with the STAR algorithm (v. 2.5.3a). Parameters used in the alignment were kept at default values with the exception of the following parameters:
--outFilterScoreMinOverLread 0
--outFilterMatchNminOverLread 0
--outFilterMatchNmin 0
--outFilterMismatchNmax 2
Gene and transcript expression was then quantified with RSEM (v1.1.17) using default parameters.
Genome_build: GRCh38
Supplementary_files_format_and_content: FPKM, TPM, and Expected Count files for genes and isoforms are tab-delimited text files that contain the aggregate output of the RSEM algorithm.

source_name: H9 iPSCs

growth_protocol: All hPSC cultures were maintained at 37°C, 5% CO2 and expanded on Matrigel (BD Biosciences) coated plates in mTeSR (STEMCELL Technologies) with 10 μM ROCK inhibitor Y-27632 (Abcam). The previously characterized human embryonic stem cell line H9 FUCCI was used in this study. The H9 FUCCI hPSC line was constructed by Singh et al., 2013 by introducing fluorescent reporters into expression vectors under the control of the constitutive CAG promoter and linked to neomycin or puromycin selectable markers through an internal ribosome entry site. G418 sulfate and the puromycin were used to maintain the H9-FUCCI hPSCs.

extract_protocol: For bulk-population RNA-seq, RNA was extracted from cell cycle sorted control and DMSO-treated hPSCs.  Integrity of extracted RNA was assayed by on-chip electrophoresis (Agilent Bioanalyzer) and only samples with a high RNA integrity (RIN) value were used for RNA-seq.  Purified total RNA was reverse-transcribed into cDNA using the Ovation RNA-seq System V2 (NuGEN) and cDNA was sheared using the Covaris S2 system (duty cycle 10%, intensity 5, cycle/burst 100, total time 5 min). Sheared cDNA was cleaned up using Agencourt AMPure XP beads (Beckman Coulter) and ligated to adaptors (Illumina) Sequencing libraries were constructed using the NEBNext Ultra DNA Library Prep Kit (New England Biolabs) using barcoded adaptors to enable multiplexing of libraries on the same sequencing lane. For each RNA-seq library, the effectiveness of adaptor ligation and effective library concentration was determined by Bioanalyzer before loading them in multiplexed fashion onto an Illumina HiSeq4000 (Stanford Functional Genomics Facility) to obtain ~150 bp paired-end reads.

treatment_protocol: For all RNA-seq experiments, H9 FUCCI hPSCs were plated onto plates coated with growth factor–reduced Matrigel (BD Biosciences) in mTeSR with 10 μM ROCK inhibitor Y-27632 (Abcam) at 1 million cells per well of a 6-well plate. After 24h, cells were cultured in mTeSR with or with 2% DMSO for another 24h. After a 24h DMSO treatment, cells were collected and prepared for fluorescence-activated cell sorting by flow cytometry for subsequent RNA-seq analyses. For differentiation experiments, after a 24h treatment with DMSO in mTESR, the medium was replaced with one of the following media at the start of each differentiation, with media replacement every day in all protocols.

value: H9 FUCCI hPSC

tag: treatment

value: DMSO

tag: cell cycle phase


title: NSC-1

status: Public on Feb 25 2019

first: Lorenz

last: Studer

state: NY

department: Developmental Biology

data_processing:
Basecalls performed using CASAVA version 1.4
RNASeq - FASTQ files were aligned using the STAR aligner to hg38.  BAM files were then processed into a counts table using HTSeq and imported into DESeq2 for further analysis
Genome_build: hg38
Supplementary_files_format_and_content: tab delimited files.  RNA seq - fold change compared to rosette cells

molecule: polyA RNA

source_name: hPSC-derived dorsal forebrain progentiors

growth_protocol: NSCs maintained in 10ng/ml of EGF, 10ng/ml of FGF; Astrocytes maintained in 10ng/ml LIF and 10ng/ml HB-EGF

extract_protocol:
RNA was isolated from cell pellets resuspended in TRIzol and purified using the RNEasy spin column
RNA libraries were prepared for sequencing using standard Illumina protocols

tag: growth condition

value: N2 media containing EGF/FGF2

tag: psc line

tag: prefix


title: NSC-3


title: NSC-2


title: Circulating tumor cells from patient 66


title: DMSO lateG1-2

description: 24 Hour treatment with 2% DMSO.

value: Late G1


title: DMSO SG2M-1

description: 24 Hour treatment with 2% DMSO.


title: Bone marrow clonal plasma cells from patient 54


title: Bone marrow clonal plasma cells from patient 62


title: Control lateG1-2

description: Control

value: Control


title: Control SG2M-1

description: Control


title: Circulating tumor cells from patient 54


title: Control SG2M-2

description: Control


title: DMSO earlyG1-1

description: 24 Hour treatment with 2% DMSO.

value: Early G1


title: Control lateG1-1

description: Control


title: DMSO lateG1-1

description: 24 Hour treatment with 2% DMSO.


title: DMSO earlyG1-2

description: 24 Hour treatment with 2% DMSO.


title: Control earlyG1-2

description: Control


title: Control earlyG1-1

description: Control


title: S2 cell (ExpressFive medium) smallRNA-seq replicate 2

status: Public on Feb 11 2019

first: Hans-Hermann

last: Wessels

address: 101 Avenue of the Americas

institute: New York Genome Center

data_processing:
library strategy: smRNA-seq
Libraries were quality-filtered reads using the fastx-tool kit [ -q 10 -p 95 ] (http://hannonlab.cshl.edu/fastx_toolkit/), and adapter-trimmed using cutadapt v1.8 (Martin 2011) [ --overlap=3; -m 24; --discard-untrimmed ] (5’adapter GUUCAGAGUUCUACAGUCCGACGAUC-NNNN; 3’ adapter NNNNTGGAATTCTCGGGTGCCAAGG), discarding untrimmed reads. Reads were collapsed (duplicate removal) still including the 4 randomized nucleotides (UMIs) at both ends of the sequencing read, followed by UMI-removal. Reads were first aligned to rRNA sequences (dm6 ensemble v81) using bowtie [-p 4 -q (-X 1000) --fr –best ] (Langmead et al. 2009), before the unaligned reads were mapped to the genome (dm6 ensemble v81) with STAR v2.4.2a [--alignEndsType EndToEnd --runThreadN 4  --outFilterMultimapNmax 10 --outSAMattributes All --outFilterIntronMotifs RemoveNoncanonical  --outReadsUnmapped Fastx --alignSJoverhangMin 12  --outFilterMatchNmin 15  --outFilterMismatchNmax 1 --outFilterMismatchNoverLmax 0.05 --outFilterMultimapScoreRange 3 --alignIntronMax 20000 --seedMultimapNmax 200000 --seedPerReadNmax 30000 ] (Dobin et al. 2013). Multimapping reads were filtered by keeping only the best alignment of a read if the second best alignment had more than one mismatch more than the best alignment. BigWig files were generated using bam2wig.py [ -t 5000000 -d "++,--" ] from RSeQC (Wang et al. 2012), followed by Kent Utils wigToBigWig transformation.    Read annotation was done as described previously (Mukherjee et al. 2014; Ascano et al. 2012). Briefly, for annotation we used annotate.pl, and annotation rank file from PARpipe (https://github.com/ohlerlab/PARpipe) and assigned annotation labels based on information provided by the dm6 ensemble v81 GTF. Repeat element information was extracted from UCSC and considered during annotation. For human AGO2 PARCLIP data sets, Gencode v19 annotation was applied. Reads mapping to miRNA genes were intersected with mature miRNA coordinates retrieved from miRBase v21 after coordinate liftover from dm3 to dm6 (Kozomara and Griffiths-Jones 2014). Mature miRNA reads were quantified and normalized to reads per million (RPM) by dividing by the total number of miRNA-annotating reads and multiplication with 1*106. Normalized counts were fitted to experimentally determined cellular miRNA copy numbers per cell (cpc) to estimate cpc for all detected miRNAs.
Genome_build: dm6
Supplementary_files_format_and_content: *bigwig: normalized read coverage
Supplementary_files_format_and_content: *distribution.txt: PARalyzer distribution file for microMUMMIE (Majoros et al. 2013) miRNA target site prediction
Supplementary_files_format_and_content: *clusters or *groups.csv:  annotated PARalyzer cluster or groups table
Supplementary_files_format_and_content: *counts.csv: countTable
Supplementary_files_format_and_content: XtailResults_dsAGO1low_vs_dsGFP.csv: differential Expression Results
Supplementary_files_format_and_content: S2-cell_miRNA_quantification.csv: normalized count table; miRNA quantification

organism: Drosophila melanogaster

growth_protocol: Drosophila S2 cells were grown at 25 °C in ExpressFive SFM medium (Life Technologies #10486025) with 10 % heat-inactivated fetal bovine serum (FBS) (Life Technologies #16000044), 12 % L-Glutamine (Life Technologies #25030024) and 1 % Penicillin-Streptomycin (Life Technologies #15070063).

extract_protocol: SmallRNA libraries from samples were generated using NEXTflex Small RNA Library Prep kit v3 (Bioo Scientific #5132), starting with 2 µg of total RNA and SRQC/ERDN-spike-in mix (Locati et al. 2015) (a generous gift from Dr. Timo Breit’s lab at University of Amsterdam) using the following modifications: Following upon excessive 3’ adapter removal the samples were eluted in 12 µl H2O. 10.5 µl were transferred to a new tube and supplemented with 1 µl 2S rRNA antisense DNA-oligo (100 µM). 2S rRNA antisense DNA oligonucleotides were annealed (5 min 75 °C; 15 min 37 °C; 15 min 25 °C) to block short and abundant 2S rRNA during 5’adapter ligation (Wickersheim and Blumenstiel 2013). Using pilot-PCR the minimal cycle number for the preparative smallRNA library was determined to 16-18 cycles. The amplicons were gel-purified and size-selected prior to sequencing on NextSeq500.

treatment_protocol: small RNA sequencing

tag: cell line

value: Schneider2 (S2) cells

tag: spike-in

value: ERDN/SRQC spike-in

tag: strandedness

value: stranded  ++,-

tag: molecule subtype

value: 5'-phosphorylated small RNAs


title: S2 cell (ExpressFive medium) smallRNA-seq replicate 1


title: AGO1 PARCLIP replicate 1

description: AGO1 PARCLIP

data_processing:
library strategy: PAR-CLIP
CLIP libraries were quality-filtered reads using the fastx-tool kit [ -q 10 -p 95 ] (http://hannonlab.cshl.edu/fastx_toolkit/), and adapter-trimmed using cutadapt v1.8 (Martin 2011) [ --overlap=3; -m 24; --discard-untrimmed ] (5’adapter GUUCAGAGUUCUACAGUCCGACGAUC-NNNN; 3’ adapter NNNNTGGAATTCTCGGGTGCCAAGG), discarding untrimmed reads. Reads were collapsed (duplicate removal) still including the 4 randomized nucleotides (UMIs) at both ends of the sequencing read, followed by UMI-removal. Reads were first aligned to rRNA sequences (dm6 ensemble v81) using bowtie [-p 4 -q (-X 1000) --fr –best ] (Langmead et al. 2009), before the unaligned reads were mapped to the genome (dm6 ensemble v81) with STAR v2.4.2a [--alignEndsType EndToEnd --runThreadN 4  --outFilterMultimapNmax 10 --outSAMattributes All --outFilterIntronMotifs RemoveNoncanonical  --outReadsUnmapped Fastx --alignSJoverhangMin 12  --outFilterMatchNmin 15  --outFilterMismatchNmax 1 --outFilterMismatchNoverLmax 0.05 --outFilterMultimapScoreRange 3 --alignIntronMax 20000 --seedMultimapNmax 200000 --seedPerReadNmax 30000 ] (Dobin et al. 2013). Multimapping reads were filtered by keeping only the best alignment of a read if the second best alignment had more than one mismatch more than the best alignment.   In addition, sequencing reads were processed with PARpipe (https://github.com/ohlerlab/PARpipe). For this, reads were pre-processed as described above. Reads were aligned to the genome (dm6 ensemble v81) using bowtie v.1.1.2 [ -v 1 -m 10 --best –strata ]. PARalyzer v1.5 were run using default setting, but removing [ =COLLAPSED ] option.    BigWig files were generated for both, STAR and Bowtie alignments using bam2wig.py [ -t 5000000 -d "++,--" ] from RSeQC (Wang et al. 2012), followed by Kent Utils wigToBigWig transformation [ -clip ].    Group and cluster annotation was done as described previously (Mukherjee et al. 2014; Ascano et al. 2012). Briefly, for annotation we used annotate.pl, and annotation rank file from PARpipe (https://github.com/ohlerlab/PARpipe) and assigned annotation labels based on information provided by the dm6 ensemble v81 GTF. Repeat element information was extracted from UCSC and considered during annotation.
Genome_build: dm6
Supplementary_files_format_and_content: *bigwig: normalized read coverage
Supplementary_files_format_and_content: *distribution.txt: PARalyzer distribution file for microMUMMIE (Majoros et al. 2013) miRNA target site prediction
Supplementary_files_format_and_content: *clusters or *groups.csv:  annotated PARalyzer cluster or groups table
Supplementary_files_format_and_content: *counts.csv: countTable
Supplementary_files_format_and_content: XtailResults_dsAGO1low_vs_dsGFP.csv: differential Expression Results
Supplementary_files_format_and_content: S2-cell_miRNA_quantification.csv: normalized count table; miRNA quantification

molecule: other

source_name: AGO1 PARCLIP

extract_protocol: For library preparation, cells were thawed on ice and quickly lysed in NP40 lysis buffer (50 mM Tris–HCl pH 7.4, 100 mM NaCl, 1 % Igepal CA-630 (NP40), 0.1 % SDS, 0.5 % sodium deoxycholate, Complete Protease Inhibitor to a final concentration of 2x, RNAsin 40 U/ml lysis buffer; 1 ml lysis buffer per approximately 0.3*109 cells, 1.2*109 to 1.3*109 cells in total per sample). After treatment with RNaseI (1:333 v/v or 300 U/ml lysate for HITSCLIP and 1:400 v/v or 250 U/ml lysate for PARCLIP samples, because of slight concentration differences between lysate; TurboDNase (4 U/ml) for 3 min at 37 °C and 1100 rpm, immunoprecipitation (IP) was carried out with polyclonal AGO1 (Abcam #ab5070, 20 µg per sample) coated magnetic protein A dynabeads (Life Technologies #10002D)(100 µl) on spin-cleared cell extracts for 2 hrs at 4 ºC.After IP, CLIP samples were stringently washed three times with high salt buffer (50 mM Tris–HCl, pH 7.4, 0.666 M NaCl, 1mM EDTA, 1% Igepal CA-630 (NP40), 0.1% SDS, 0.5% sodium deoxycholate), followed by PNK-buffer washes. Samples were radioactively 5’end-labelled with γ-32P-ATP including a subsequent addition of 1 µl high-molar ATP to the reaction for efficient 5’end phosphorylation. The crosslinked protein-RNA complexes were resolved on a 4-12 % Bis-Tris-Polyacrylamid gel. The SDS-PAGE gel was transferred to a nitrocellulose membrane and the protein-RNA complexes migrating at an expected molecular weight were excised. RNA was isolated by Proteinase K treatment and phenol-chloroform extraction, ligated to 3’ adapter and 5’ adapter, reverse transcribed using Superscript III (Life Technologies #18080044), PCR-amplified (PCR cycles: HITSCLIP 20 cycles; PARCLIP 19 cycles), and gel-purified. Note, after the 3’Adpater ligation step, each AGO1 sample was split into approximately 19-24 nt (miRNA fraction) and 24-35nt (target fraction) long fragments, cloned, amplified and sequenced separately. The amplicons were sequenced single-end as a multiplexed pool on HiSeq2000 (Illumina). Sequencing Reads of 19-24 nt and 24-35nt fractions were combined.

treatment_protocol: AGO1 HITSCLIP and PARCLIP experiments were performed in biological replicates, originally described in (Hafner et al. 2010) with the following changes. Buffers were used from (Huppertz et al. 2014). For AGO1 PARCLIP, culturing medium was supplemented with 400 µM 4-Thiouridine (4-SU) (SIGMA #T4509) 17 hours overnight before harvest. Semi-adherent cells were scraped and washed in ice-cold PBS prior to 254 nm or 365 nm UV-irradiation (400 mJ/cm2), respectively. Liquid cell pellets were snap frozen in liquid nitrogen and stored at -80°C until further usage.

tag: photoactivatable ribonucleoside analog and crosslinking wavelength

value: 365nm 400mJ/sqcm Spectrolinker

tag: antibody

value: polyclonal anti-AGO1 (ab5070 Abcam)

value: AGO1-bound RNA fragments


title: AGO1 PARCLIP replicate 2

description: AGO1 PARCLIP

data_processing:
library strategy: PAR-CLIP
CLIP libraries were quality-filtered reads using the fastx-tool kit [ -q 10 -p 95 ] (http://hannonlab.cshl.edu/fastx_toolkit/), and adapter-trimmed using cutadapt v1.8 (Martin 2011) [ --overlap=3; -m 24; --discard-untrimmed ] (5’adapter GUUCAGAGUUCUACAGUCCGACGAUC-NNNN; 3’ adapter NNNNTGGAATTCTCGGGTGCCAAGG), discarding untrimmed reads. Reads were collapsed (duplicate removal) still including the 4 randomized nucleotides (UMIs) at both ends of the sequencing read, followed by UMI-removal. Reads were first aligned to rRNA sequences (dm6 ensemble v81) using bowtie [-p 4 -q (-X 1000) --fr –best ] (Langmead et al. 2009), before the unaligned reads were mapped to the genome (dm6 ensemble v81) with STAR v2.4.2a [--alignEndsType EndToEnd --runThreadN 4  --outFilterMultimapNmax 10 --outSAMattributes All --outFilterIntronMotifs RemoveNoncanonical  --outReadsUnmapped Fastx --alignSJoverhangMin 12  --outFilterMatchNmin 15  --outFilterMismatchNmax 1 --outFilterMismatchNoverLmax 0.05 --outFilterMultimapScoreRange 3 --alignIntronMax 20000 --seedMultimapNmax 200000 --seedPerReadNmax 30000 ] (Dobin et al. 2013). Multimapping reads were filtered by keeping only the best alignment of a read if the second best alignment had more than one mismatch more than the best alignment.   In addition, sequencing reads were processed with PARpipe (https://github.com/ohlerlab/PARpipe). For this, reads were pre-processed as described above. Reads were aligned to the genome (dm6 ensemble v81) using bowtie v.1.1.2 [ -v 1 -m 10 --best –strata ]. PARalyzer v1.5 were run using default setting, but removing [ =COLLAPSED ] option.
BigWig files were generated for both, STAR and Bowtie alignments using bam2wig.py [ -t 5000000 -d "++,--" ] from RSeQC (Wang et al. 2012), followed by Kent Utils wigToBigWig transformation [ -clip ].
Group and cluster annotation was done as described previously (Mukherjee et al. 2014; Ascano et al. 2012). Briefly, for annotation we used annotate.pl, and annotation rank file from PARpipe (https://github.com/ohlerlab/PARpipe) and assigned annotation labels based on information provided by the dm6 ensemble v81 GTF. Repeat element information was extracted from UCSC and considered during annotation.
Genome_build: dm6
Supplementary_files_format_and_content: *bigwig: normalized read coverage
Supplementary_files_format_and_content: *distribution.txt: PARalyzer distribution file for microMUMMIE (Majoros et al. 2013) miRNA target site prediction
Supplementary_files_format_and_content: *clusters or *groups.csv:  annotated PARalyzer cluster or groups table
Supplementary_files_format_and_content: *counts.csv: countTable
Supplementary_files_format_and_content: XtailResults_dsAGO1low_vs_dsGFP.csv: differential Expression Results
Supplementary_files_format_and_content: S2-cell_miRNA_quantification.csv: normalized count table; miRNA quantification


title: AGO1 HITSCLIP replicate 2

description: AGO1 HITSCLIP

data_processing:
library strategy: HITS-CLIP
CLIP libraries were quality-filtered reads using the fastx-tool kit [ -q 10 -p 95 ] (http://hannonlab.cshl.edu/fastx_toolkit/), and adapter-trimmed using cutadapt v1.8 (Martin 2011) [ --overlap=3; -m 24; --discard-untrimmed ] (5’adapter GUUCAGAGUUCUACAGUCCGACGAUC-NNNN; 3’ adapter NNNNTGGAATTCTCGGGTGCCAAGG), discarding untrimmed reads. Reads were collapsed (duplicate removal) still including the 4 randomized nucleotides (UMIs) at both ends of the sequencing read, followed by UMI-removal. Reads were first aligned to rRNA sequences (dm6 ensemble v81) using bowtie [-p 4 -q (-X 1000) --fr –best ] (Langmead et al. 2009), before the unaligned reads were mapped to the genome (dm6 ensemble v81) with STAR v2.4.2a [--alignEndsType EndToEnd --runThreadN 4  --outFilterMultimapNmax 10 --outSAMattributes All --outFilterIntronMotifs RemoveNoncanonical  --outReadsUnmapped Fastx --alignSJoverhangMin 12  --outFilterMatchNmin 15  --outFilterMismatchNmax 1 --outFilterMismatchNoverLmax 0.05 --outFilterMultimapScoreRange 3 --alignIntronMax 20000 --seedMultimapNmax 200000 --seedPerReadNmax 30000 ] (Dobin et al. 2013). Multimapping reads were filtered by keeping only the best alignment of a read if the second best alignment had more than one mismatch more than the best alignment. BigWig files were generated using bam2wig.py [ -t 5000000 -d "++,--" ] from RSeQC (Wang et al. 2012), followed by Kent Utils wigToBigWig transformation [ -clip ].
Genome_build: dm6
Supplementary_files_format_and_content: *bigwig: normalized read coverage
Supplementary_files_format_and_content: *distribution.txt: PARalyzer distribution file for microMUMMIE (Majoros et al. 2013) miRNA target site prediction
Supplementary_files_format_and_content: *clusters or *groups.csv:  annotated PARalyzer cluster or groups table
Supplementary_files_format_and_content: *counts.csv: countTable
Supplementary_files_format_and_content: XtailResults_dsAGO1low_vs_dsGFP.csv: differential Expression Results
Supplementary_files_format_and_content: S2-cell_miRNA_quantification.csv: normalized count table; miRNA quantification

source_name: AGO1 HITSCLIP

extract_protocol: For library preparation, cells were thawed on ice and quickly lysed in NP40 lysis buffer (50 mM Tris–HCl pH 7.4, 100 mM NaCl, 1 % Igepal CA-630 (NP40), 0.1 % SDS, 0.5 % sodium deoxycholate, Complete Protease Inhibitor to a final concentration of 2x, RNAsin 40 U/ml lysis buffer; 1 ml lysis buffer per approximately 0.3*109 cells, 1.2*109 to 1.3*109 cells in total per sample). After treatment with RNaseI (1:333 v/v or 300 U/ml lysate for HITSCLIP and 1:400 v/v or 250 U/ml lysate for PARCLIP samples, because of slight concentration differences between lysate; TurboDNase (4 U/ml) for 3 min at 37 °C and 1100 rpm, immunoprecipitation (IP) was carried out with polyclonal AGO1 (Abcam #ab5070, 20 µg per sample) coated magnetic protein A dynabeads (Life Technologies #10002D)(100 µl) on spin-cleared cell extracts for 2 hrs at 4 ºC.After IP, CLIP samples were stringently washed three times with high salt buffer (50 mM Tris–HCl, pH 7.4, 0.666 M NaCl, 1mM EDTA, 1% Igepal CA-630 (NP40), 0.1% SDS, 0.5% sodium deoxycholate), followed by PNK-buffer washes. Samples were radioactively 5’end-labelled with γ-32P-ATP including a subsequent addition of 1 µl high-molar ATP to the reaction for efficient 5’end phosphorylation. The crosslinked protein-RNA complexes were resolved on a 4-12 % Bis-Tris-Polyacrylamid gel. The SDS-PAGE gel was transferred to a nitrocellulose membrane and the protein-RNA complexes migrating at an expected molecular weight were excised. RNA was isolated by Proteinase K treatment and phenol-chloroform extraction, ligated to 3’ adapter and 5’ adapter, reverse transcribed using Superscript III (Life Technologies #18080044), PCR-amplified (PCR cycles: HITSCLIP 20 cycles; PARCLIP 19 cycles), and gel-purified. Note, after the 3’Adpater ligation step, each AGO1 sample was split into approximately 19-24 nt (miRNA fraction) and 24-35 nt (target fraction) long fragments, cloned, amplified and sequenced separately. The amplicons were sequenced single-end as a multiplexed pool on HiSeq2000 (Illumina). Sequencing Reads of 19-24 nt and 24-35 nt fractions were combined.

value: 254nm 400mJ/sqcm Spectrolinker


title: AGO1 HITSCLIP replicate 1

description: AGO1 HITSCLIP


title: S2 cell (Schneider medium) smallRNA-seq replicate 2

source_name: S2 cell (Schneider medium) smallRNA-seq

growth_protocol: Drosophila S2 cells (Life Technologies, #R69007) were grown at 25 °C cultured in Schneider’s Cell medium w/ L-Glutamine (SIGMA #S0146) with 10 % heat-inactivated fetal bovine serum (FBS) (Life Technologies #16000044) and 1 % Penicillin-Streptomycin (Life Technologies #15070063).


title: S2 cell stranded rRNA-depleted RNA-seq AGO1-dsRNA-treated replicate 3

data_processing:
RNA sequencing reads were 3’ end quality-filtered and quality-trimmed using the fastx toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) [ quality filter:  -q 25 -p 30; quality trimmer: -t 25 -l 30 ], as well as 5’end trimmed [ trimmer: -f 13 ]. rRNA (dm6 ensemble v81) and common viral sequences (Webster et al. 2016) were filtered out using bowtie v1.1.2 bowtie [ -q (-X 1000) --fr –best ] (Langmead et al. 2009). The remaining reads were aligned to the dm6 genome using ensembl v81 annotation and ERCC spike-in information using STAR v2.4.2a [ --chimSegmentMin 30 --chimJunctionOverhangMin 30 --outFilterMultimapNmax 20 --outFilterMismatchNmax 999 --outFilterMismatchNoverLmax 0.04 --outFilterType BySJout --alignIntronMin 20 --outSAMattributes All --outFilterMatchNmin 20 --alignIntronMax 100000 --alignMatesGapMax 1000000  --alignSJDBoverhangMin 3 --outFilterIntronMotifs RemoveNoncanonicalUnannotated  --alignSJoverhangMin 20 ] (Dobin et al 2013).   BigWig files were generated for both, STAR and Bowtie alignments using bam2wig.py [ -t 5000000 -d "++,--" ] from RSeQC (Wang et al. 2012), followed by Kent Utils wigToBigWig transformation [ -clip ].  Raw counts on the ensembl v81 dm6 gene model were calculated for RNA-seq using HTSeq [ -m union ] (Anders et al. 2015) across mature transcripts, while ribosomal footprinting counts were generated on coding regions only using R Bioconductor packages (GenomicFeatures, GenomicAlignments [ summarizeOverlaps with mode=”IntersectionStrict” ]) (Lawrence et al. 2013). Xtail (Xiao et al. 2016) was used to return log2 fold changes for translational efficiency, RNA expression levels and changes in ribosomal footprinting [ bins = 10000, ci = .95 ]. For differential expression analysis, only genes with > 1 count per million sequenced reads in all treatments and replicates in RNA-seq data as well as in ribosomal footprinting data were considered.
Genome_build: dm6
Supplementary_files_format_and_content: *bigwig: normalized read coverage
Supplementary_files_format_and_content: *distribution.txt: PARalyzer distribution file for microMUMMIE (Majoros et al. 2013) miRNA target site prediction
Supplementary_files_format_and_content: *clusters or *groups.csv:  annotated PARalyzer cluster or groups table
Supplementary_files_format_and_content: *counts.csv: countTable
Supplementary_files_format_and_content: XtailResults_dsAGO1low_vs_dsGFP.csv: differential Expression Results
Supplementary_files_format_and_content: S2-cell_miRNA_quantification.csv: normalized count table; miRNA quantification

extract_protocol: To 2.5 µg total RNA input we added ERCC spike-ins (External RNA Controls Consortium 2005) (AGO1 knockdown Experiment: mock and dsGFP: ERCC Mix-1; dsAGO1 low and high: ERCC Mix-2 (Life technologies #4456740 #4456739)), prior to rRNA depletion using RiboZero (Illumina #MRZG12324). 100ng rRNA-depleted RNA served as an input for RNA-seq libraries generated using the NEXTflex Rapid Directional qRNA-Seq kit (Bioo Scientific #5130) following manufacturer’s descriptions. AGO1 RNA-seq libraries were sequenced single-end with 75nt cycles as a multiplexed pool on NextSeq500.

treatment_protocol: dsRNA-mediated knockdown was carried out as described before (Clemens et al. 2000; Worby et al. 2001). Specifically, in vitro transcription DNA templates were amplified from plasmid DNA using primers including a T7 site. For in vitro transcription the MegaScript T7 kit (Life technologies # AM1334) was used following the manufacturer’s description. S2 cells were treated with dsRNA (dsGFP 5µg/ml (in 2ml), dsAGO1 1.25µg/ml (low) or 5µg/ml (high)) or without (mock) for 72h. We included a lower dsRNA concentration for AGO1, as it yielded similar knockdown efficiency after 72 hours as compared to commonly used higher dsRNA amounts. Experiments were carried out in biological triplicates on different days, while collecting matched samples for total RNA, ribosomal footprinting and protein from a single replicate in parallel.

tag: dsrna/rnai

value: dsRNA AGO1 (5µg/ml)

value: ERCC spike-in mix 2

value: stranded +-,-+

value: rRNA-depleted total RNA


title: S2 cell stranded rRNA-depleted RNA-seq AGO1-dsRNA-treated low concentration replicate 3

data_processing:
RNA sequencing reads were 3’ end quality-filtered and quality-trimmed using the fastx toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) [ quality filter:  -q 25 -p 30; quality trimmer: -t 25 -l 30 ], as well as 5’end trimmed [ trimmer: -f 13 ]. rRNA (dm6 ensemble v81) and common viral sequences (Webster et al. 2016) were filtered out using bowtie v1.1.2 bowtie [ -q (-X 1000) --fr –best ] (Langmead et al. 2009). The remaining reads were aligned to the dm6 genome using ensembl v81 annotation and ERCC spike-in information using STAR v2.4.2a [ --chimSegmentMin 30 --chimJunctionOverhangMin 30 --outFilterMultimapNmax 20 --outFilterMismatchNmax 999 --outFilterMismatchNoverLmax 0.04 --outFilterType BySJout --alignIntronMin 20 --outSAMattributes All --outFilterMatchNmin 20 --alignIntronMax 100000 --alignMatesGapMax 1000000  --alignSJDBoverhangMin 3 --outFilterIntronMotifs RemoveNoncanonicalUnannotated  --alignSJoverhangMin 20 ] (Dobin et al 2013).   BigWig files were generated for both, STAR and Bowtie alignments using bam2wig.py [ -t 5000000 -d "++,--" ] from RSeQC (Wang et al. 2012), followed by Kent Utils wigToBigWig transformation [ -clip ].  Raw counts on the ensembl v81 dm6 gene model were calculated for RNA-seq using HTSeq [ -m union ] (Anders et al. 2015) across mature transcripts, while ribosomal footprinting counts were generated on coding regions only using R Bioconductor packages (GenomicFeatures, GenomicAlignments [ summarizeOverlaps with mode=”IntersectionStrict” ]) (Lawrence et al. 2013). Xtail (Xiao et al. 2016) was used to return log2 fold changes for translational efficiency, RNA expression levels and changes in ribosomal footprinting [ bins = 10000, ci = .95 ]. For differential expression analysis, only genes with > 1 count per million sequenced reads in all treatments and replicates in RNA-seq data as well as in ribosomal footprinting data were considered. The differential expression data represents the contrast between low dsAGO1 treatment at 1.25 µg/ml and dsGFP control treatment.
Genome_build: dm6
Supplementary_files_format_and_content: *bigwig: normalized read coverage
Supplementary_files_format_and_content: *distribution.txt: PARalyzer distribution file for microMUMMIE (Majoros et al. 2013) miRNA target site prediction
Supplementary_files_format_and_content: *clusters or *groups.csv:  annotated PARalyzer cluster or groups table
Supplementary_files_format_and_content: *counts.csv: countTable
Supplementary_files_format_and_content: XtailResults_dsAGO1low_vs_dsGFP.csv: differential Expression Results
Supplementary_files_format_and_content: S2-cell_miRNA_quantification.csv: normalized count table; miRNA quantification

value: dsRNA AGO1 (1.25µg/ml)


title: S2 cell stranded rRNA-depleted RNA-seq AGO1-dsRNA-treated low concentration replicate 2


title: S2 cell stranded rRNA-depleted RNA-seq AGO1-dsRNA-treated replicate 2


title: S2 cell stranded rRNA-depleted RNA-seq GFP-dsRNA-treated replicate 3

value: dsRNA GFP (5µg/ml)

value: ERCC spike-in mix 1


title: S2 cell stranded rRNA-depleted RNA-seq GFP-dsRNA-treated replicate 2


title: S2 cell stranded rRNA-depleted RNA-seq AGO1-dsRNA-treated low concentration replicate 1


title: S2 cell stranded rRNA-depleted RNA-seq GFP-dsRNA-treated replicate 1


title: S2 cell stranded rRNA-depleted RNA-seq mock-treated replicate 2


title: S2 cell stranded rRNA-depleted RNA-seq mock-treated replicate 3


title: S2 cell stranded rRNA-depleted RNA-seq mock-treated replicate 1


title: S2 cell ribosomal footprinting AGO1-dsRNA-treated low concentration replicate 1

data_processing:
library strategy: ribosomal footprinting
Ribosome profiling sequencing reads were trimmed using cutadapt v1.8 (Martin 2011) [ -m 18; --discard-untrimmed ] (5’adapter GUUCAGAGUUCUACAGUCCGACGAUC-NNNN; 3’ adapter NNNNTGGAATTCTCGGGTGCCAAGG). After collapsing the sequencing reads using the fastx toolkit (http://hannonlab.cshl.edu/fastx_toolkit/), we removed the four randomized nucleotides from both ends of the reads. Reads were first aligned to rRNA sequences (dm6 ensemble v81) using bowtie [-p 4 -q (-X 1000) --fr –best ] (Langmead et al. 2009), before the unaligned reads were mapped to the genome (dm6 ensemble v81) with STAR v2.4.2a [--alignEndsType EndToEnd --outFilterMismatchNmax 3  --chimScoreSeparation 10 --chimScoreMin 20 --chimSegmentMin 15 --outFilterIntronMotifs RemoveNoncanonicalUnannotated --alignSJoverhangMin 500 --outSAMtype BAM SortedByCoordinate --outSAMmultNmax 1 --outMultimapperOrder Random ] (Dobin et al. 2013). We confirmed expected periodicity of ribosome-protected fragments using RiboTaper (Calviello et al. 2016). BigWig files were generated using bam2wig.py [ -t 5000000 -d "++,--" ] from RSeQC (Wang et al. 2012), followed by Kent Utils wigToBigWig transformation [ -clip ].    Raw counts on the ensembl v81 dm6 gene model were calculated for RNA-seq using HTSeq [ -m union ] (Anders et al. 2015) across mature transcripts, while ribosomal footprinting counts were generated on coding regions only using R Bioconductor packages (GenomicFeatures, GenomicAlignments [ summarizeOverlaps with mode=”IntersectionStrict” ]) (Lawrence et al. 2013). Xtail (Xiao et al. 2016) was used to return log2 fold changes for translational efficiency, RNA expression levels and changes in ribosomal footprinting [ bins = 10000, ci = .95 ]. For differential expression analysis, only genes with > 1 count per million sequenced reads in all treatments and replicates in RNA-seq data as well as in ribosomal footprinting data were considered.  The differential expression data represents the contrast between low dsAGO1 treatment at 1.25 µg/ml and dsGFP control treatment.
Genome_build: dm6
Supplementary_files_format_and_content: *bigwig: normalized read coverage
Supplementary_files_format_and_content: *distribution.txt: PARalyzer distribution file for microMUMMIE (Majoros et al. 2013) miRNA target site prediction
Supplementary_files_format_and_content: *clusters or *groups.csv:  annotated PARalyzer cluster or groups table
Supplementary_files_format_and_content: *counts.csv: countTable
Supplementary_files_format_and_content: XtailResults_dsAGO1low_vs_dsGFP.csv: differential Expression Results
Supplementary_files_format_and_content: S2-cell_miRNA_quantification.csv: normalized count table; miRNA quantification

extract_protocol: Ribosomal footprinting was carried out as described in (Calviello et al. 2016), with minor changes. In brief, cells were rinsed from the plate and transferred to a 50 ml falcon tube, preloaded with Cycloheximide (SIGMA #C4859) for a final concentration of 0.1 mg/ml. Cells were spun down at 200g for 5 min at 4°C and washed once with cold PBS supplemented with 0.1 mg/ml CHX. After aspiration, the cell pellet was snap-frozen in liquid nitrogen. The subsequent lysis, footprinting and recovery of ribosome-protected fragments were carried out as described in (Calviello et al. 2016). Ribosomal footprinting libraries were then generated using the NEXTflex Small RNA Library Prep Kit v3 (Bioo Scientific #5132) using rRNA-depleted (RiboZero; Illumina #MRZG12324) and T4 PNK (NEB #M0201) treated RNA as input. By pilot-PCR the minimal PCR cycle number for library amplification was determined (AGO1 samples 14-15 cycles). Amplicons were gel-purified prior to sequencing a multiplexed pool on one flow cell (NextSeq500 high output mode, 75 cycles single-end.)

value: Ribosome-protected fragments


title: S2 cell ribosomal footprinting AGO1-dsRNA-treated replicate 3

data_processing:
library strategy: ribosomal footprinting
Ribosome profiling sequencing reads were trimmed using cutadapt v1.8 (Martin 2011) [ -m 18; --discard-untrimmed ] (5’adapter GUUCAGAGUUCUACAGUCCGACGAUC-NNNN; 3’ adapter NNNNTGGAATTCTCGGGTGCCAAGG). After collapsing the sequencing reads using the fastx toolkit (http://hannonlab.cshl.edu/fastx_toolkit/), we removed the four randomized nucleotides from both ends of the reads. Reads were first aligned to rRNA sequences (dm6 ensemble v81) using bowtie [-p 4 -q (-X 1000) --fr –best ] (Langmead et al. 2009), before the unaligned reads were mapped to the genome (dm6 ensemble v81) with STAR v2.4.2a [--alignEndsType EndToEnd --outFilterMismatchNmax 3  --chimScoreSeparation 10 --chimScoreMin 20 --chimSegmentMin 15 --outFilterIntronMotifs RemoveNoncanonicalUnannotated --alignSJoverhangMin 500 --outSAMtype BAM SortedByCoordinate --outSAMmultNmax 1 --outMultimapperOrder Random ] (Dobin et al. 2013). We confirmed expected periodicity of ribosome-protected fragments using RiboTaper (Calviello et al. 2016). BigWig files were generated using bam2wig.py [ -t 5000000 -d "++,--" ] from RSeQC (Wang et al. 2012), followed by Kent Utils wigToBigWig transformation [ -clip ].    Raw counts on the ensembl v81 dm6 gene model were calculated for RNA-seq using HTSeq [ -m union ] (Anders et al. 2015) across mature transcripts, while ribosomal footprinting counts were generated on coding regions only using R Bioconductor packages (GenomicFeatures, GenomicAlignments [ summarizeOverlaps with mode=”IntersectionStrict” ]) (Lawrence et al. 2013). Xtail (Xiao et al. 2016) was used to return log2 fold changes for translational efficiency, RNA expression levels and changes in ribosomal footprinting [ bins = 10000, ci = .95 ]. For differential expression analysis, only genes with > 1 count per million sequenced reads in all treatments and replicates in RNA-seq data as well as in ribosomal footprinting data were considered.
Genome_build: dm6
Supplementary_files_format_and_content: *bigwig: normalized read coverage
Supplementary_files_format_and_content: *distribution.txt: PARalyzer distribution file for microMUMMIE (Majoros et al. 2013) miRNA target site prediction
Supplementary_files_format_and_content: *clusters or *groups.csv:  annotated PARalyzer cluster or groups table
Supplementary_files_format_and_content: *counts.csv: countTable
Supplementary_files_format_and_content: XtailResults_dsAGO1low_vs_dsGFP.csv: differential Expression Results
Supplementary_files_format_and_content: S2-cell_miRNA_quantification.csv: normalized count table; miRNA quantification


title: S2 cell ribosomal footprinting AGO1-dsRNA-treated replicate 1


title: S2 cell ribosomal footprinting AGO1-dsRNA-treated low concentration replicate 3


title: S2 cell ribosomal footprinting GFP-dsRNA-treated replicate 2

description:
processed data file:
S2-cell_RiboFP_SummarizedOverlapsCounts
XtailResults_dsAGO1low_vs_dsGFP.csv


title: S2 cell ribosomal footprinting AGO1-dsRNA-treated low concentration replicate 2


title: S2 cell ribosomal footprinting AGO1-dsRNA-treated replicate 2


title: S2 cell ribosomal footprinting GFP-dsRNA-treated replicate 1

data_processing:
library strategy: ribosomal footprinting
Ribosome profiling sequencing reads were trimmed using cutadapt v1.8 (Martin 2011) [ -m 18; --discard-untrimmed ] (5’adapter GUUCAGAGUUCUACAGUCCGACGAUC-NNNN; 3’ adapter NNNNTGGAATTCTCGGGTGCCAAGG). After collapsing the sequencing reads using the fastx toolkit (http://hannonlab.cshl.edu/fastx_toolkit/), we removed the four randomized nucleotides from both ends of the reads. Reads were first aligned to rRNA sequences (dm6 ensemble v81) using bowtie [-p 4 -q (-X 1000) --fr –best ] (Langmead et al. 2009), before the unaligned reads were mapped to the genome (dm6 ensemble v81) with STAR v2.4.2a [--alignEndsType EndToEnd --outFilterMismatchNmax 3  --chimScoreSeparation 10 --chimScoreMin 20 --chimSegmentMin 15 --outFilterIntronMotifs RemoveNoncanonicalUnannotated --alignSJoverhangMin 500 --outSAMtype BAM SortedByCoordinate --outSAMmultNmax 1 --outMultimapperOrder Random ] (Dobin et al. 2013). We confirmed expected periodicity of ribosome-protected fragments using RiboTaper (Calviello et al. 2016). BigWig files were generated using bam2wig.py [ -t 5000000 -d "++,--" ] from RSeQC (Wang et al. 2012), followed by Kent Utils wigToBigWig transformation [ -clip ].    Raw counts on the ensembl v81 dm6 gene model were calculated for RNA-seq using HTSeq [ -m union ] (Anders et al. 2015) across mature transcripts, while ribosomal footprinting counts were generated on coding regions only using R Bioconductor packages (GenomicFeatures, GenomicAlignments [ summarizeOverlaps with mode=”IntersectionStrict” ]) (Lawrence et al. 2013). Xtail (Xiao et al. 2016) was used to return log2 fold changes for translational efficiency, RNA expression levels and changes in ribosomal footprinting [ bins = 10000, ci = .95 ]. For differential expression analysis, only genes with > 1 count per million sequenced reads in all treatments and replicates in RNA-seq data as well as in ribosomal footprinting data were considered. The differential expression data represents the contrast between low dsAGO1 treatment at 1.25 µg/ml and dsGFP control treatment.
Genome_build: dm6
Supplementary_files_format_and_content: *bigwig: normalized read coverage
Supplementary_files_format_and_content: *distribution.txt: PARalyzer distribution file for microMUMMIE (Majoros et al. 2013) miRNA target site prediction
Supplementary_files_format_and_content: *clusters or *groups.csv:  annotated PARalyzer cluster or groups table
Supplementary_files_format_and_content: *counts.csv: countTable
Supplementary_files_format_and_content: XtailResults_dsAGO1low_vs_dsGFP.csv: differential Expression Results
Supplementary_files_format_and_content: S2-cell_miRNA_quantification.csv: normalized count table; miRNA quantification


title: S2 cell (Schneider medium) smallRNA-seq replicate 1


title: S2 cell ribosomal footprinting mock-treated replicate 2


title: BF-EPA-4

status: Public on Dec 26 2018

city: Lubbock

first: Naima

last: Moustaid-Moussa

state: TX

address: 1301 Akron Street

department: Nutritional Sciences & Obesity Research Institute

institute: Texas Tech University

data_processing:
Illumina small RNA adapter sequences were trimmed from the reads, and reads of length below 10nt or ending in homopolymers of length 9 nt or above were discarded.
Total usable number of reads for each sample was calculated.
The reads were mapped to the miRBase reference using BLAST; the abundance of each expressed microRNA was quantified as a fraction of the usable reads, and expressed as parts per million.
Genome_build: mm10
Supplementary_files_format_and_content: TAB-delimited text format, containing quantile normalized microRNA abundance

organism: Mus musculus

source_name: Brown Adipose Tissue (BAT5: BF-EPA-4)

extract_protocol:
At the end of 11 week feeding period, the mice were fasted for 4 hours and then euthanized by CO2 inhalation. Epidedimal (VAT), inguinal (SAT) and interscapular (BAT) adipose tissues were harvested, and snap frozen immediately in liquid N2, followed by storage at -80°C. Frozen adipose tissue samples (SAT, BAT, and VAT- 3 samples per group) were homogenized in QIAzol® (QIAGEN, Redwood City, CA).  Total RNA was isolated according to manufacturer’s protocol using RNeasy Mini Kit.
Small RNA libraries were prepared using the Illumina small RNA protocol (Illumina, Inc., San Diego, CA)

treatment_protocol: C57BL/6J mice aged 5-6 week were fed on a high fat diet which consisted of 45%, 20% and 35% of energy from fat, protein and carbohydrate respectively for 11 weeks.

tag: strain

tag: tissue

value: Brown Adipose


title: BF-EPA-3

source_name: Brown Adipose Tissue (BAT4: BF-EPA-3)


title: BF-EPA-5

source_name: Brown Adipose Tissue (BAT6: BF-EPA-5)


title: scTrioSeq2Met_CRC13_PT2_556

status: Public on Nov 29 2018

city: Beijing

first: Yu

last: Hou

state: Beijing

address: No.5 Yiheyuan Street

department: School of life sciences

country: China

institute: Peking University

description: single cell DNA methylome seq

library_source: genomic

data_processing:
Illumina CASAVA version 1.8 were used to the basecalling.
The raw sequencing reads were trimmed to remove sequencing adapters, amplification primers, and low-quality bases in read ends.
For the data from scRNA-seq performed according to Tang protocol (Tang et al., 2009), trimmed clean reads were aligned to human reference genome (hg19) using STAR (v2.5.0) 2-pass mapping steps. For the data from scRNA-seq performed according to the multiplexed scRNA-seq method, the analyses were done as the previous paper (Dong et al., 2018).
For bisulfite sequencing data, trimmed clean reads were mapped to human reference genome (hg19) using Bismark (v0.7.6)
Genome_build: hg19
Supplementary_files_format_and_content: The FPKM.txt files include the FPKM values of RefSeq genes. The TPM.txt files include the TPM values of RefSeq genes. The SingleC files include the DNA methylation level of each singleC sites.

molecule: genomic DNA

source_name: CRC

growth_protocol: The HEp-2 (ATCC CCL-23) cell line were cultured in RPMI 1640 with 10% FBS, and digested with trypsin before single cell picking.

extract_protocol:
The single viable cells were individually picked into 200-uL tubes containing lysis buffer. For scTrio-seq2, we used magnetic beads (Invitrogen, cat. #65011) to separate the nucleus and RNA of one single cell. We added 0.2 ?L magnetic beads to each single-cell lysis buffer. Then the single cells were lysed and vortexed for 1 min to release RNA. The lysis products were then centrifuged at 1,000 × g for 5 min at 4°C, and placed on the magnetic rack for 5 min. The magnetic beads can aggregate on the surface of the nucleus to maintain the nucleus in the pellet, while the RNA was released in the supernatants. The supernatants containing RNA were transferred to a new tube for transcriptome sequencing. The remaining beads containing a single nucleus were re-suspended with lysis buffer of scBS-seq for DNA methylation sequencing
For single-cell whole-genome bisulfite sequencing, the scBS-seq libraries were constructed according to the published protocol as described in 2017 (Clark et al., 2017). For CRC01 and CRC02, the transcriptome sequencing libraries were constructed according to Tang protocol (Tang et al., 2009); for the remaining patients, the transcriptome sequencing libraries were constructed according to a multiplexed scRNA-seq method, in which the poly T primers were combined with barcodes and unique molecule identifiers (UMIs) (Dong et al., 2018).

treatment_protocol: Informed consent was obtained from all patients preoperatively. Multi-regional sampling tissues were collected. For each tumor, 3-6 regions, including both surface and center areas, were sampled. Except for the MP of CRC01 which was sampled after six cycles of chemotherapy, other tumors were all sampled before treatment. Patient-derived tumors and adjacent normal tissue were collected and processed immediately after surgical resection. The dissected tissues were then mechanically dissociated and enzymatically digested to single-cell suspension using collagenase type II (Invitrogen, cat. #17101015) and collagenase type IV (Invitrogen, cat. #17104019). For 5 patients (CRC11, CRC12, CRC13, CRC14, and CRC15), leukocytes were depleted by magnetic-activated cell sorting (MACS) (CD45 Microbeads, Miltenyi Biotec, cat. #130-045-801) or fluorescence-activated cell sorting (FACS) (BV421 Mouse Anti-Human CD45, BD Horizon, cat. #563879).

tag: disease

value: Carcinoma


title: SUB-EPA-3

source_name: Subcutaneous Adipose Tissue (SAT5: SUB-EPA-3)

value: Subcutaneous Adipose


title: scTrioSeq2Met_CRC13_LN2_363

description: single cell DNA methylome seq


title: scTrioSeq2Met_CRC13_LN2_364

description: single cell DNA methylome seq


title: scTrioSeq2Met_CRC11_PT2_479

description: single cell DNA methylome seq


title: scTrioSeq2Met_CRC11_PT3_770

description: single cell DNA methylome seq


title: S2 cell ribosomal footprinting GFP-dsRNA-treated replicate 3

value: Schneider2 (S2) cells

value: stranded  ++,-


title: scTrioSeq2Met_CRC11_PT3_769

description: single cell DNA methylome seq


title: scTrioSeq2Met_CRC13_LN3_476

description: single cell DNA methylome seq


title: Healty control T cell

status: Public on Jan 06 2019

city: Seoul

first: Kwang Hoon

last: Lee

address: 50-1, Yonsei-ro, Seodaemun-gu

department: Dermatology

country: South Korea

institute: Yonsei University College of Medicine

description:
SAMPLE 1
replicate 1

hyb_protocol: Following fragmentation, 0.75 ug of cRNA were hybridized to the Illumina Expression Beadchip according to the protocols provided by the manufacturer.

scan_protocol: Arrays were scanned using the Illumina Bead Array Reader Confocal Scanner

data_processing: Array data export processing and analysis was performed using Illumina GenomeStudio v2011.1 (Gene Expression Module v1.9.0)

source_name: human T cell

label_protocol: Biotinylated cRNA were prepared from 0.55 ug total RNA using the Illumina TotalPrep RNA Amplification Kit (Ambion, Austin,TX)

extract_protocol: Total T cells were isolated from PBMCs and crude RNA was extracted using RNeasy mini kit (QIAGEN, Germany) following the manufacturer's instruction.

tag: sample type

value: Male=6, Female=3/ Age :25.78  plus/minus  7.63

tag: gender

value: female


title: Atopic dermatitis T cell

description:
SAMPLE 2
replicate 1

value: Male=7, Female=9/ Age :20.38  plus/minus  6.89

value: male

tag: age


title: Subject_283: CALGB1063498-1_E-16

status: Public on Oct 16 2018

city: Chapel Hill

first: Charles

middle: M.

last: Perou

state: NC

address: 12-044 Lineberger Comprehensive Cancer Center CB# 7295

department: Professor of Genetics, and Pathology & Laboratory Medicine; Lineberger Comprehensive Cancer Center

institute: University of North Carolina at Chapel Hill

description: Fresh-frozen RNA was isolated with Qiagen RNAeasy kit. Illumina TruSeq RNA Sample Preparation Kit (RS-122-2001 or RS-122-2002)

data_processing:
Basecalls by Casava
Alignment by MapSplice2
Genome to transcriptome coordiantes by UBU (https://github.com/mozack/ubu/wiki)
Gene expression estimates by RSEM using Sept 2011 UCSC knownGene transcript definitions
More information on the pipeline can be found here: https://webshare.bioinf.unc.edu/public/mRNAseq_TCGA/
Genome_build: hg19
Supplementary_files_format_and_content: tab delimited matrix of gene level count estimates
Purity-filtered reads were aligned to the human reference GRCh38/hg38 genome using Spliced Transcripts Aligned to a Reference (STAR) version 2.4.2a
Transcript (GENCODE v22) abundance estimates were generated by Salmon version 0.6.030 in ‘-quant’ mode, based on the STAR alignments
Fixed upper quartile normalization
Genome_build: GRCh38/hg38
Supplementary_files_format_and_content: tab-delimited, normalized, transcript abundance estimates

source_name: Breast

value: breast cancer


title: Subject_282: CALGB1063282-1_E-12

extract_protocol: Fresh-frozen RNA was isolated with Qiagen RNAeasy kit. Illumina TruSeq RNA Sample Preparation Kit (RS-122-2001 or RS-122-2002)


title: Subject_284: CALGB1063501-1_E-14


title: S2 cell ribosomal footprinting mock-treated replicate 3


title: Subject_281: CALGB1063261-1_E-13


title: Subject_285: CALGB1063694-1_E-17


title: Atopic dermatitis patient 2

description:
SAMPLE 4
replicate 2

source_name: human skin

extract_protocol: Skin tissues were homogenized and crude RNA was extracted using RNeasy mini kit (QIAGEN, Germany) following the manufacturer's instruction.


title: Healty control 2

description:
SAMPLE 2
replicate 2

value: Male (n=4, 37 plus/minus 17.76)

value: 37 plus/minus 17.29


title: Subject_280: CALGB1063230-1_E-10


title: S2 cell ribosomal footprinting mock-treated replicate 1


title: SUB-EPA-8

source_name: Subcutaneous Adipose Tissue (SAT6: SUB-EPA-8)


title: Atopic dermatitis patient 1

description:
SAMPLE 3
replicate 1

value: Female (n=3, 35 plus/minus 13)

value: 35 plus/minus 35.29


title: Subject_278: CALGB1063131-1_E-11


title: Subject_277: C01_O-24-CALGB1063106-1


title: Subject_269: CALGB1062653-1_E-4


title: Subject_272: CALGB1062845-1_E-6


title: Subject_276: CALGB1063104-1_E-9


title: Subject_265: CALGB1062463-1-H33


title: Healty control 1

description:
SAMPLE 1
replicate 1

value: Female (n=2, 44.5 plus/minus 21.92)


title: Subject_259: CALGB1060785-1_22-0


title: Subject_261: CALGB1061013-1-H32


title: Subject_256: CALGB1060385-1_C-48


title: Subject_253: CALGB1060326-1_C-34


title: Subject_248: CALGB1059601-1_C-42


title: Subject_252: CALGB1060211-1_C-41


title: Subject_244: CALGB1059073-1_C-26


title: Subject_241: CALGB1058688-1_C-25


title: Subject_242: CALGB1058690-1_C-33


title: Subject_240: CALGB1058604-1_C-39


title: Subject_239: CALGB1058551-1_C-24


title: Subject_243: CALGB1058809-1_C-45


title: Subject_235: CALGB1058388-1_C-38


title: Subject_236: CALGB1058437-1_C-44


title: Subject_233: CALGB1058098-1_C-37


title: Subject_229: CALGB1057878-1_C-36


title: Subject_232: CALGB1058059-1_C-23


title: Bone marrow clonal plasma cells from patient 02

source_name: Bone marrow clonal plasma cells from newly-diagnosed patient

value: Bone marrow clonal plasma cells

tag: cell number

tag: disease stage

value: newly-diagnosed

tag: disease state

value: Multiple Myeloma


title: SUB-EPA-1

source_name: Subcutaneous Adipose Tissue (SAT4: SUB-EPA-1)


title: Subject_231: CALGB1058056-1_C-31


title: Subject_227: CALGB1057808-1_C-22


title: Subject_228: CALGB1057813-1_C-30


title: Subject_225: CALGB1057637-1_C-43


title: Subject_226: CALGB1057727-1-H26


title: Subject_223: CALGB1057521-1_C-29


title: Subject_221: CALGB1057336-1_C-14


title: Subject_220: CALGB1057195-1_C-8


title: Subject_254: CALGB1060329-1_C-28


title: Subject_251: CALGB1060209-1_C-47


title: Subject_219: CALGB1052991-1_C-13


title: Subject_218: CALGB1052929-1_C-7


title: Subject_246: CALGB1059296-1_C-46


title: Subject_217: CALGB1052758-1_C-12


title: Subject_215: CALGB1052692-1_C-6


title: Subject_245: CALGB1059206-1_C-40


title: Subject_216: CALGB1052754-1_C-20


title: Subject_208: CALGB1051982-1_C-11


title: Subject_211: CALGB1052385-1_C-5


title: Subject_207: CALGB1051873-1_C-4


title: Subject_206: CALGB1051824-1_C-17


title: Subject_210: CALGB1052278-1_B-45


title: Subject_203: CALGB1051653-1_B-43


title: Subject_204: CALGB1051766-1_C-10


title: Subject_202: CALGB1051619-1_C-16


title: Subject_224: CALGB1057552-1_C-21


title: Subject_201: CALGB1051521-1_B-34


title: Subject_198: CALGB1051372-1_B-48


title: Subject_199: CALGB1051449-1_C-9


title: Subject_195: C01_O-16-CALGB1051156-1


title: Subject_197: CALGB1051360-1_B-42


title: Subject_194: CALGB1051002-1_B-40


title: Subject_196: CALGB1051192-1_C-2


title: Subject_191: CALGB1050991-1_B-47


title: Subject_192: CALGB1050997-1_B-46


title: Subject_193: C01_O-15-CALGB1051000-1


title: Subject_190: CALGB1050989-1_B-41


title: Subject_186: CALGB1050379-1_B-37


title: Subject_189: CALGB1050909-1_C-15


title: Subject_212: CALGB1052415-1_C-19


title: Subject_184: CALGB1050366-1_B-39


title: Subject_188: CALGB1050850-1_B-38


title: Subject_181: C01_O-7-CALGB1049470


title: Subject_182: CALGB1049868-1_B-36


title: Subject_200: CALGB1051453-1_C-3


title: Subject_177: CALGB1049264_H13


title: Subject_209: CALGB1052117-1_C-18


title: Subject_179: CALGB1049383-A-17


title: Subject_176: C01_O-22-CALGB1049217


title: Subject_180: C01_O-23-CALGB1049384


title: Subject_173: CALGB1049045_H12


title: Subject_175: CALGB1049077_E-3


title: Subject_170: CALGB1048895_E-1


title: Subject_174: CALGB1049046_E-2


title: Subject_164: CALGB1048420_H11


title: Subject_169: C01_O-6-CALGB1048864


title: Subject_163: CALGB1048334-A-15


title: Subject_165: C01_O-5-CALGB1048421


title: Subject_153: CALGB1047437-A-13


title: Subject_149: CALGB1047254-A-11


title: Subject_152: CALGB1047393-A-12


title: Subject_154: CALGB1047504_H9


title: Subject_185: CALGB1050377-1_B-35


title: Subject_145: CALGB1047113-A-10


title: Subject_142: C01_O-20-CALGB1046918


title: Subject_141: CALGB1046844_H8


title: Subject_140: CALGB1046795-A-9


title: Subject_125: CALGB1043058_H6


title: Subject_132: CALGB1046192-A-7


title: Subject_124: CALGB1043043-A-8


title: Subject_138: C01_O-21-CALGB1046613


title: Subject_120: C01_O-18-CALGB1042953


title: Subject_118: CALGB1042870-A-6


title: Subject_130: CALGB1046129_H7


title: Subject_129: CALGB1045999-H35


title: Subject_110: CALGB1042509_B-28


title: Subject_113: CALGB1042593-A-5


title: Subject_112: CALGB1042569_B-20


title: Subject_105: CALGB1042047_B-26


title: Subject_106: CALGB1042098_B-21


title: Subject_108: CALGB1042338_B-27


title: Subject_102: CALGB1041910_B-25


title: Subject_100: CALGB1041839_B-22


title: Subject_101: CALGB1041842-H22


title: Subject_162: CALGB1048249_H10


title: Subject_107: CALGB1042192_B-24


title: Subject_161: CALGB1048221-A-16


title: Subject_99: CALGB1041668_B-19


title: Subject_98: CALGB1041661-A-4


title: Subject_95: CALGB1041144_B-15


title: Subject_89: CALGB1040870-A-37


title: Subject_88: CALGB1040810_B-14


title: Subject_97: C01_O-19-CALGB1041648


title: Subject_87: C01_O-13-CALGB1040775


title: Subject_84: C01_O-17-CALGB1040529


title: Subject_86: C01_O-12-CALGB1040558


title: Subject_83: CALGB1040391_B-13


title: Subject_77: CALGB1039804_B-8


title: Subject_79: CALGB1040047-A-36


title: Subject_78: CALGB1039846_B-12


title: Subject_104: CALGB1041963_B-23


title: Subject_81: CALGB1040234_B-9


title: Subject_76: CALGB1039533_B-17


title: Subject_72: CALGB1039170-A-34


title: Subject_69: CALGB1037914-A-33


title: Subject_64: CALGB1037611-A-32


title: Subject_68: C01_O-3-CALGB1037901


title: Subject_67: C01_O-2-CALGB1037698


title: Subject_65: CALGB1037677_B-4


title: Subject_66: CALGB1037691_H4


title: Subject_62: CALGB1037473_B-6


title: Subject_71: C01_O-14-CALGB1038009


title: Subject_61: CALGB1037445-A-46


title: Subject_60: CALGB1037426_H3


title: Subject_63: CALGB1037568_B-2


title: Subject_54: CALGB1036899-A-2


title: Subject_57: CALGB1037097-A-31


title: Subject_58: CALGB1037302_B-7


title: Subject_50: C01_O-11-CALGB1036753


title: Subject_49: CALGB1036727_H2


title: Subject_48: CALGB1036612-A-45


title: Subject_82: CALGB1040254_B-16


title: Subject_52: CALGB1036852-A-48


title: Subject_47: C01_O-1-CALGB1036611


title: Subject_73: CALGB1039361_B-10


title: Subject_43: CALGB1036442-A-47


title: Subject_42: CALGB1036352_B-33


title: Subject_44: CALGB1036454-A-1


title: Subject_46: CALGB1036514_B-5


title: Subject_41: CALGB1036076_H1


title: Subject_40: C01_O-10-CALGB1036044


title: Subject_39: CALGB1035984-H24


title: Subject_35: CALGB1035421_B-1


title: Subject_34: CALGB1035333_B-31


title: Subject_59: CALGB1037324-A-35


title: Subject_36: CALGB1035450_B-32


title: Subject_33: CALGB1035199-A-19


title: Subject_32: CALGB1035107_B-29


title: Subject_37: CALGB1035572_B-3


title: Subject_28: CALGB1033100_B-30


title: Subject_27: CALGB1032976-A-30


title: Subject_25: CALGB1032742-A-42


title: Subject_29: CALGB1033193-A-40


title: Subject_31: C01_O-9-CALGB1034966


title: Subject_21: CALGB1031762-A-38


title: Subject_20: CALGB1031745-A-29


title: Subject_24: CALGB1032704-A-41


title: Subject_18: CALGB1031675-A-27


title: Subject_22: CALGB1031764-A-28


title: Subject_75: CALGB1039518-A-3


title: Subject_15: CALGB1031394-A-26


title: Subject_12: CALGB1030089-A-23


title: Subject_10: CALGB1028807-A-21


title: Subject_8: CALGB1027810_20-0


title: Subject_11: CALGB1028824-A-22


title: Subject_13: CALGB1030929-A-24


title: Subject_7: CALGB1027796-A-20


title: Subject_5: CALGB1026666-A-44


title: Subject_6: CALGB1026816_H19


title: Subject_3: CALGB1024630-A-18


title: Subject_2: CALGB1024392-A-43


title: Subject_1: CALGB1024171_H18


title: Subject_38: CALGB1035983-H25


title: Subject_14: CALGB1031001-A-25


title: Subject_26: C01_O-8-CALGB1032878


title: Subject_274: CALGB1062896-1_E-7


title: Subject_271: CALGB1062843-1_D-8


title: Subject_266: CALGB1062618-1_D-10


title: Subject_264: CALGB1062371-1_D-6


title: Subject_4: CALGB1026204_21-0


title: Subject_263: CALGB1061079-1_D-12


title: Subject_260: CALGB1060796-1_D-9


title: Subject_267: CALGB1062626-1_D-13


title: Subject_257: CALGB1060391-1_D-5


title: Subject_258: CALGB1060503-1_15-0


title: Subject_249: CALGB1059907-1_D-3


title: Subject_255: CALGB1060368-1_19-0


title: Subject_247: CALGB1059346-1_D-2


title: Subject_237: CALGB1058441-1_D-1


title: Subject_230: CALGB1057961-1_H31


title: Subject_234: CALGB1058367_D-15


title: Subject_222: CALGB1057518-1_H30


title: Subject_214: CALGB1052653-1_H29


title: Subject_178: CALGB1049382-D-43


title: Subject_171: CALGB1049013-D-47


title: Subject_268: CALGB1062649-1_D-7


title: Subject_187: CALGB1050439-1_H27


title: Subject_166: CALGB1048459-D-46


title: Subject_270: CALGB1062697-1_5-0


title: Subject_183: CALGB1050213-1_D-4


title: Subject_205: CALGB1051821-1_B-44


title: Subject_151: CALGB1047390-D-28


title: Subject_159: CALGB1048030-D-45


title: Subject_155: CALGB1047585-D-32


title: Subject_148: CALGB1047237-D-39


title: Subject_158: CALGB1047687-D-44


title: Subject_150: CALGB1047342-D-31


title: Subject_157: CALGB1047682-D-42


title: Subject_147: CALGB1047148_H34


title: Subject_144: CALGB1046986-D-30


title: Subject_143: CALGB1046926-D-36


title: Subject_146: CALGB1047147-D-27


title: Subject_139: CALGB1046642-D-34


title: Subject_137: CALGB1046543-D-38


title: Subject_135: CALGB1046407-D-41


title: Subject_136: CALGB1046515-D-26


title: Subject_134: CALGB1046358-D-35


title: Subject_128: CALGB1043099_D-19


title: Subject_127: CALGB1043063_D-22


title: Subject_133: CALGB1046318-D-40


title: Subject_156: CALGB1047662-D-37


title: Subject_123: CALGB1042990_17-0


title: Subject_122: CALGB1042979_D-16


title: Subject_121: CALGB1042976_H5


title: Subject_115: CALGB1042675_H36


title: Subject_111: CALGB1042546_D-20


title: Subject_96: CALGB1041330_H21


title: Subject_116: CALGB1042697_D-21


title: Subject_85: CALGB1040531_7-0


title: Subject_117: CALGB1042793_18-0


title: Subject_119: CALGB1042947_D-18


title: Subject_109: CALGB1042372_D-17


title: Subject_93: CALGB1041127_16-0


title: Subject_91: CALGB1040986_13-0


title: Subject_70: CALGB1038004_D-14


title: Subject_80: CALGB1040203_4-0


title: Subject_74: CALGB1039489_H15


title: Subject_53: CALGB1036874_2-0


title: Subject_55: CALGB1036967_10-0


title: Subject_17: CALGB1031646_9-0


title: Subject_23: CALGB1032452_8-0


title: C2C12 H3K27ac at 1k cells replicate 2

status: Public on Oct 09 2018

city: Fukuoka

first: Yasuyuki

last: Ohkawa

address: 3-1-1 Maidashi

country: Japan

institute: Medical Institute of Bioregulation

description:
countMatrix4k_C2C12_cellnumbers.csv.gz
countMatrix10k_C2C12_cellnumbers.csv.gz

data_processing:
The raw sequence reads were aligned to the mouse and human reference genomes (GRCm38 and GRCh38) using HISAT2 (version 2.0.4), and the uniquely mapped reads were retained.
The bigWig files were generated using bamCoverage command of deepTools (ver. 2.4.2) with the option: --smoothLength x --binSize y, where x and y are specified in the file names.
The csv countmatrix files were generated with multiBamSummary with the option bins -bs 4000 (or 10000).
ChILT peaks were generated with Homer findPeaks with the options -style histone -style histone -gsize 3e9 -size 4000 -regionRes 1 -minDist 1 -fdr 0.2.
ChIP peaks were generated with MACS2 (ver. 2.1.1) with the options -g hs -q 0.01 --nomodel --shift 0 --extsize 200 --keep-dup all.
Genome_build: GRCm38/mm10 and GRCh38/hg38
Supplementary_files_format_and_content: bigWig files contain signal data for visualization via tools such as the Integrative Genomics Viewer, csv files include comma-delimited matrices, the bed file contains the peak data for C2C12, MCF-7 and MDA-MB-231 data.

source_name: Immortal cell line

extract_protocol:
Immunostaining half of the protocol includes cell dispensing, fixation, permeabilization, blocking, reaction to primary anti-body and the ChILT probe. The reactive probe are integrated into a proximal genomic region to where the primary antibody is bound upon Tn5 activation. The integrated ChILT DNA is used for the linear amplification of genomic sequences and the transcribed RNAs are utilized for library preparation for deep-sequencing.
Immunostaining half of the protocol includes cell dispensing, fixation, permeabilization, blocking, reaction to primary anti-body and the ChILT probe. The reactive probe are integrated into a proximal genomic region to where the primary antibody is bound upon Tn5 activation. The integrated ChILT DNA is used for the linear amplification of genomic sequences and the transcribed RNAs are utilized for library preparation for deep-sequencing.
Fixed cells were lysed and histone-DNA complexes were isolated with antibodies.
Libraries were prepared on the Illumina HiSeq1500 and 2500 platforms.

tag: antibody target

tag: primary antibody

value: mouse monoclonal anti-H3K27ac (Kimura et al., Cell Struct Funct. 2008;33(1):61-73., CMA309)


title: C2C12 H3K27ac at 1k cells replicate 1

tag: cell line


title: C2C12 H3K4me3 at 10k cells replicate 1

value: mouse monoclonal anti-H3K4me3 (Kimura et al., Cell Struct Funct. 2008;33(1):61-73., CMA304)


title: C2C12 H3K4me3 at 10k cells replicate 2


title: ORX, sample6

status: Public on Sep 03 2018

city: Turku

first: Matti

last: Poutanen

address: Kiinamyllynkatu 10

department: Institute of Biomedicine

country: Finland

institute: University of Turku

description:
ORX_6
74_ORX_S11

data_processing:
The base calling was done using bcl2fastq (v1.8.4)
Read alignment was done using star (2.5.0c) to mm10 mouse genome reference available at UCSC.
Genewise read counts were calculated using subread (v1.5.1)
Genome_build: mm10
Supplementary_files_format_and_content: countdata.txt is a tab delimited text file and contains the

source_name: ORX_Adrenal gland

growth_protocol: Athymic Nude male mice at approximately 5-6 weeks of age were inoculated (VCaP cells) at the study week 0, allocated to study groups (Intact and ORX) at the study week 7 and sacrificed at stydy week 15.

extract_protocol:
Total RNA for RNA-seq were extracted from adrenal gland samples using TRIsure (Invitrogen, Carlsbad, CA, USA), and purified using RNeasy Mini Kit (Qiagen) according to manufacturer’s instructions. The amount of 300 ng of total RNA was used according to Illumina TruSeq® Stranded mRNA Sample Preparation Guide.
Library preparation was done according to Illumina TruSeq® RNA Sample Preparation v2 Guide

treatment_protocol: At the end of the experiment the mice were sacrificed, and adrenal glands were collected for RNA isolation. Tissue samples were stored at -80 oC after initial freezing in liquid nitrogen.

value: Hsd:Athymic Nude-Foxn1nu

tag: Sex

tag: treatment

value: Orchectomized

value: Adrenal gland


title: Subject_92: CALGB1041013_14-0


title: ORX, sample3

description:
ORX_3
48_ORX_S9


title: ORX, sample5

description:
ORX_5
70_ORX_S10


title: ORX, sample2

description:
ORX_2
31_ORX_S8


title: ORX, sample4

description:
ORX_4
5_ORX_S6


title: INTACT, sample5

description:
INTACT_5
84_INT_S5

source_name: INTACT_Adrenal gland

value: Intact


title: ORX, sample1

description:
ORX_1
11_ORX_S7


title: INTACT, sample4

description:
INTACT_4
69_INT_S4


title: INTACT, sample1

description:
INTACT_1
50_INT_S1


title: INTACT, sample2

description:
INTACT_2
51_INT_S2


title: INTACT, sample3

description:
INTACT_3
67_INT_S3


title: VIS-HF-20

status: Public on Feb 28 2018

source_name: Visceral Adipose Tissue (VAT3: VISHF20)

extract_protocol:
At the end of 11 week feeding period, the mice were fasted for 4 hours and then euthanized by CO2 inhalation. Epididymal (VAT), inguinal (SAT) and interscapular (BAT) adipose tissues were harvested, and snap frozen immediately in liquid N2, followed by storage at -80°C. Frozen adipose tissue samples (SAT, VAT and BAT- 3 samples per group) were homogenized in QIAzol® (QIAGEN, Redwood City, CA).  Total RNA was isolated according to manufacturer’s protocol using RNeasy Mini Kit.
Small RNA libraries were prepared using the Illumina small RNA protocol (Illumina, Inc., San Diego, CA)

value: Visceral Adipose Tissue


title: VIS-HF-14

source_name: Visceral Adipose Tissue (VAT1: VISHF14)


status: Public on Aug 01 2018

city: Vilnius

first: Rapolas

last: Zilionis

state: LT

address: Sauletekis av 7

department: Life Sciences Center

country: Lithuania

institute: Vilnius University

description: Uninjured trachea, mouse id 54, 1798 cells after all filtering steps

data_processing:
Raw fastq were prepared from Illumina bcl files in a format compatible with the inDrops.py pipeline. In brief, each NextSeq run results in 16 fastq files (4 lanes x 4 reads per lane). The read files have the following content, also as described in the documentation for inDrops.py (github/indrops):
*_R1_001.fastq.gz   : contains the 3' UTR cDNA read
*_R2_001.fastq.gz   : contains the first half of the cell barcode
*_R3_001.fastq.gz   : contains the library index for demultiplexing libraries
*_R4_001.fastq.gz   : contains the second half of the barcode and the UMI."
All  subsequent processing steps from fastq files to count matrixes were performed using a custom pipeline for inDrops data analysis (github.com/indrops), using the parameter values specified in the yaml files provided as supplementary material to the published paper (Plasschaert et al.). The following steps are provided as an overview on the internal workings of inDrops.py.
inDrops.py first filters reads based on information present in read files 2,3, and 4, for presence of i) two cell barcode sequences, ii) unique molecular identifier, iii) Illumina libray index. inDrops.py demultiplexes by Illumina library index (read 3).
For read 3, the library indices provided to the indrop.py custom pipeline are given in the yaml files. Libraries called ""not_included_in_study_*"" were not included in the study, but their indexes should be specified when using the indrop.py pipeline for demultiplexing."
inDrops.py then trims Read 1 using Trimmomatic (version 0.32; parameters: LEADING:28 SLIDINGWINDOW:4:20  MINLEN:30).
inDrops.py then matches cell barcodes (revealed by Read 2 and 4) against a list of the pre-determined barcodes. The reads were then split into barcode-specific files for mapping and UMI filtering.
inDrops.py then aligns the reads using Bowtie (version 1.1.1, see Supplementary Files 1-3 of the manuscript for details). The reference transcriptome was built using annotations from ENSEMBL (GRCm38 and GRCh37 for mouse and human, respectively). The reference index is assembled using inDrops.py as described in the manual.
inDrops.py performs UMI correction and returns gene expression counts of UMI-filtered transcripts per gene. Resulting raw count files are provided and called  *.counts.tsv.gz.
Subsequent steps are performed using the output of inDrops.py to generate the final normalized counts files provided here. Individual cell barcodes were filtered for: i) total counts > N; ii) mitochondrial gene content <M. For mouse samples N = 1500 and M = 20%. For human sample, M = 25%, and  N = 1500,1500, 2000, and 1500 for donors 1-3 and the lentiviral transduction experiment, respectively. In mouse data, cell with strong immune gene expression were also excluded from this study. Counts were normalized as described in methods, and combined into three normalized filtered matrices (genes x cells ): filtered_normalized_counts_mouse.tsv.gz, filtered_normalized_counts_human.tsv.gz, and filtered_normalized_counts_human_viral_transduction.tsv.gz)
genome build: hg19 and mm10
The *.counts.tsv.gz files include raw unique molecular identifier (UMI)-filtered counts for all cell barcodes (prior to filtering). The file filtered_normalized_counts_*.tsv.gz contains filtered normalized counts. The file meta_raw_counts_*.tsv.gz contains meta information for each cell prior to filtering, including raw count file name, cell barcode, and the  index of the cell in the filtered normalized count matrix ('nan' for cells that were filtered out). The file meta_filtered_counts_*.tsv.gz contains meta information about filtered counts, such as library id, mouse id, raw count file name, barcode, and x and y coordinates used for 2-dimensional data representations.

source_name: Trachea

growth_protocol: C57/BL6 mice from the Jackson Laboratory aged 6-8 wk were used for all studies. Animals were handled in accordance with Novartis Institutes for Biomedical Research Animal Care and Use Committee protocols and regulations. Mice were housed in a temperature- and humidity-controlled animal facility with ad libitum access to food and water and acclimated for at least 3 d before experimental manipulation.

extract_protocol:
For single-cell isolation for scRNA-seq, tracheas were dissected and opened longitudinally in Ham?s F12 (Life Technologies, 11765-054) plus 1% Pen-Strep on ice. Each trachea was individually placed in a 15 mL conical tube with 5 mL of 1.5 mg/mL Pronase (Roche, 10165921001) in Ham?s F12 plus 1% Pen-Strep and incubated for 18h at 4°C. 500 mL FBS was added to inactivate pronase and conical tubes were vigorously inverted to dislodge cells. Each trachea was transferred twice to a 15 mL conical tube containing Ham?s F12 plus 1% Pen-Strep plus 10% FBS and then inverted. Media from each of the three tubes was pooled and cells were pelleted at 400g for 10 min at 4°C. Cells were resuspended in 500 µL DNase (Sigma-Aldrich, DN25), incubated on ice for 5 min and then pelleted at 400g for 10 min at 4°C. Cells were then washed twice in Hams F12 1% Pen-Strep 10% FBS and then resuspended in PBS + 0.02% BSA.  Cells were diluted to 90,000 cells/mL in 15% Optiprep + 0.02% BSA in PBS for scRNA-seq.
Cells were encapsulated using the inDrops platform, into droplets on ice and lysed in the 3 nL microfluidic droplets using a final concentration of 0.4% w/v Igepal-CA630. Single cell lysates were subject to reverse transcription at 50°C without purification of RNA (as previously described in Klein et al., Cell 2015 and Zilionis et al., Nat. Protoc. 2017). Libraries were prepared as in Zilionis et al., Nat. Protoc. 2017, with modifications summarized in Supplementary Table 10 of this manuscript (Plasschaert et al).

treatment_protocol: Samples come from uninjured adult mice, and from mice following Polidocanol-induced injury. Polidocanol-induced injury was performed as previously described in the paper. Briefly, mice were anesthetized and delivered one dose of 15 μL 2% Polidacanol or PBS vehicle control by oralpharyngeal aspiration to induce injury. Tracheas were harvested at 1 day (1d), 2d, 3d and 7d following injury for scRNA-seq or for fixation and immunofluorescence.

value: 6-8 weeks

tag: cell type

value: upper airway epithelial cells


title: Sub-HF-19

source_name: Subcutaneous Adipose Tissue (SAT2: SUBHF19)

value: Subcutaneous Adipose Tissue


title: VIS-HF-17

source_name: Visceral Adipose Tissue (VAT2: VISHF17)


title: Subject_30: CALGB1034915_1-0

value: breast cancer


title: Sub-HF-20

source_name: Subcutaneous Adipose Tissue (SAT3: SUBHF20)


title: Subject_19: CALGB1031679_H14


title: BF-HF-15

source_name: Brown Adipose Tissue (BAT2: BFHF15)

value: Brown Adipose Tissue


title: Sub-HF-14

source_name: Subcutaneous Adipose Tissue (SAT1: SUBHF14)


title: BF-HF-12

source_name: Brown Adipose Tissue (BAT1: BFHF12)


title: BF-HF-20

source_name: Brown Adipose Tissue (BAT3: BFHF20)


title: miRNA_healthy_18

status: Public on Feb 22 2018

city: Munich

first: Bernd

middle: Henrik

last: Northoff

address: Marchioninistr. 15

department: Institute of Laboratory Medicine

country: Germany

institute: Ludwig-Maximilians-University Munich

description:
Sample#38
LH_78_GGCTAC_L005

data_processing:
Basecalling was performed using Casava software version1.8.2 (Illumina).
Bioinformatic analyses were performed according to Stokowy et al (Stokowy T, Eszlinger M, Swierniak M, Fujarewicz K, Jarzab B, Paschke R, Krohn K. Analysis options for high-throughput sequencing in miRNA expression profiling. BMC research notes. 2014;7:144).
Adapter trimming and quality filtering was done using Cutadapt software, quality conrol was done using FastQC program (Bioinformatics Group at the Babraham Institute, UK).
Mapping was performed using bowtie2 alignment to the sequence of the human genome (hg19) allowing one mismatch and alignments to multiple reference targets. miRNA sequences were identified by intersecting human genomic miRNA coordinates from miRbase 20 using BEDTools.
Differential expression of miRNAs was calculated using DESeq2 and EdgeR.
Genome_build: hg19
Supplementary_files_format_and_content: CSV (comma-separated values) file created by DESeq2 and EdgeR

extract_protocol:
RNA samples with length < 1000 nucleotides were isolated from two plasma aliquots of 200 µl using the miRCURY RNA Isolation Kit – Biofluids (Exiqon).
Reverse transcription was performed with the miScript Reverse Transcription kit (Qiagen) according to the manufacturer’s protocols in a 10 μL reaction. 500 ng of RNA prepared from the plasma samples was used in the small RNA protocol with the TruSeq™ Small RNA sample prepkit v2 (Illumina) following the instructions of the manufacturer. The barcoded libraries were size restricted between 140 and 165 base pairs for additional enrichment of miRNAs, purified and quantified using the Library Quantification Kit-Illumina/Universal (KAPA Biosystems, Woburn, MA, USA) according to the instructions of the manufacturer. Libraries were pooled at a concentration of 10 nM for cluster generation using an Illumina cBot. Sequencing of 50 base pairs was performed with an Illumina HighScan-SQ sequencer at the sequencing core facility of the IZKF Leipzig (Faculty of Medicine, University Leipzig) using version 3 chemistry and flowcell following the instructions of the manufacturer.

tag: subject group

value: healthy control subjects (HCs)

value: Platelet-poor-plasma

tag: molecule subtype

value: miRNA


title: miRNA_healthy_19

description:
Sample#39
LH_79_CTTGTA_L005


title: miRNA_healthy_17

description:
Sample#37
LH_77_TAGCTT_L004


title: miRNA_healthy_20

description:
Sample#40
LH_80_AGTCAA_L005


title: miRNA_healthy_16

description:
Sample#36
LH_76_GATCAG_L004


title: miRNA_healthy_10

description:
Sample#30
LH_70_TTAGGC_L003


title: miRNA_healthy_15

description:
Sample#35
LH_75_ACTTGA_L004


title: miRNA_healthy_12

description:
Sample#32
LH_72_ACAGTG_L003


title: miRNA_healthy_13

description:
Sample#33
LH_73_GCCAAT_L003


title: miRNA_healthy_06

description:
Sample#26
LH_66_CTTGTA_L003


title: miRNA_healthy_04

description:
Sample#24
LH_64_TAGCTT_L003


title: T8.TE.LCMV.d7.Sp#2_RNA-seq

status: Public on Jan 13 2018

first: Liang

last: Yang

address: 77 Avenue Louis Pasteur

department: Pathology

institute: Harvard Medical School

description: Splenic CD8+ Terminal Effector, LCMV d7

data_processing:
Reads were mapped to the mouse reference genome (mm10) with STAR 2.5.3
Non-uniquely mapped reads were discarded.
The uniquely mapped reads were assigned to genes using featureCounts with GENCODE annotation (vM12)
Genome_build: mm10
Supplementary_files_format_and_content: raw read counts

source_name: Spleen

growth_protocol: 6 week old C57BL/6J mice from the Jackson Laboratory (Bar Harbor, ME) housed in facility at least one week before harvesting cells

extract_protocol:
Extractions follow ImmGen standard operating protocol (available on https://www.immgen.org/). Briefly, organs are harvested at 9am on day of sort, perterbed as little as possible during preparation, and 1000 cells are double sorted into TCL buffer (Qiagen, Hilden, Germany) containing 1% BME.
Libraries are constructed according to the standard protocol for Smart-seq2 (Picelli et al, 2013) at the Broad Techology Labs in Cambridge, MA

tag: strain

value: 7 weeks old

tag: sorting markers

value: Spleen

value: ab T cells


title: miRNA_healthy_01

description:
Sample#21
LH_61_CAGATC_L002

tag: tissue


title: T8.TE.LCMV.d7.Sp#1_RNA-seq

description: Splenic CD8+ Terminal Effector, LCMV d7


title: miRNA_healthy_02

description:
Sample#22
LH_62_ACTTGA_L002


title: miRNA_healthy_08

description:
Sample#28
LH_68_ATCACG_L003


title: T.8.Nve.Sp#2_RNA-seq

description: Splenic Na<ef>ve CD8+ T cells

value: 6 weeks old


title: ARN-509 treated, sample14

status: Public on Oct 09 2017

data_processing:
The base calling was done using bcl2fastq (v1.8.4)
Read alignment was done using TopHat (v2.1.0) to hg19 human genome reference
The bam files were merged using picard tools (v1.77)
Genewise read counts were calculated using HTSeq (v0.6.1)
Genome_build: hg19
Supplementary_files_format_and_content: countdata.txt is a tab delimited text file and contains the genewise read counts across samples.

source_name: human (tissue xenograft grown in mouse)

growth_protocol: VCaP tumors were allowed to grow for 4-5 weeks in mice, until at least 60% of animals had reached serum PSA >5 µg/l, and the mean serum PSA value for the study cohort was approximately 15 µg/l. Mice with tumors were castrated and the treatment began when the serum PSA of all castrated mice had reached the pre-castration levels. The mice were allocated to the treatment arms based on the following 5 parameters: PSA concentration in the serum, the change in the serum PSA concentration, body weight of the mice, cage placement, and the week at which castration took place .

extract_protocol:
Total RNA for RNA-seq analyses was extracted from tumor samples using RNase Mini Kit (Qiagen) according to manufacturer’s instructions. RNA was DNA I -treated (Amplification Grade, Invitrogen), and then the RNA quality was determined using Fragment Analyzer™ (Advanced Analytical Technologies).
Library preparation was done according to Illumina TruSeq® RNA Sample Preparation v2 Guide

treatment_protocol: The vehicle (n=15) and the antiandrogens [20 mg/kg/day of enzalutamide (n=14) or ARN-509 (n=15)] were administered to the mice by gavage once a day.

value: prostate cancer xenograft (VCaP tumor)

value: VCaP

tag: passage

tag: host strain

value: HSD:Athymic Nude–Foxn1nu


title: miRNA_healthy_03

description:
Sample#23
LH_63_GATCAG_L003


title: ARN-509 treated, sample15


title: ARN-509 treated, sample13


title: T.8.Nve.Sp#1_RNA-seq

description: Splenic Na<ef>ve CD8+ T cells

tag: age


title: ARN-509 treated, sample11


title: ARN-509 treated, sample9


title: ARN-509 treated, sample10


title: miRNA_healthy_14

description:
Sample#34
LH_74_CAGATC_L004


title: miRNA_healthy_11

description:
Sample#31
LH_71_TGACCA_L003


title: ARN-509 treated, sample8


title: ARN-509 treated, sample7


title: miRNA_healthy_09

description:
Sample#29
LH_69_CGATGT_L003


title: ARN-509 treated, sample6


title: miRNA_healthy_07

description:
Sample#27
LH_67_AGTCAA_L003


title: miRNA_healthy_05

description:
Sample#25
LH_65_GGCTAC_L003


title: ARN-509 treated, sample5


title: ARN-509 treated, sample4


title: ARN-509 treated, sample3


title: ARN-509 treated, sample2


title: Enzalutamide treated, sample14


title: ARN-509 treated, sample1


title: ARN-509 treated, sample12


title: Enzalutamide treated, sample11


title: Enzalutamide treated, sample12


title: Enzalutamide treated, sample10


title: Enzalutamide treated, sample8


title: Enzalutamide treated, sample9


title: Enzalutamide treated, sample7


title: Enzalutamide treated, sample4


title: Enzalutamide treated, sample3


title: Enzalutamide treated, sample1


title: Enzalutamide treated, sample2


title: Vehicle treated, sample14


title: Vehicle treated, sample15


title: Vehicle treated, sample11


title: Vehicle treated, sample12


title: Vehicle treated, sample13


title: Vehicle treated, sample9


title: Vehicle treated, sample7


title: Vehicle treated, sample8


title: Vehicle treated, sample6


title: Vehicle treated, sample5


title: Enzalutamide treated, sample13


title: Vehicle treated, sample4


title: Vehicle treated, sample1


title: Vehicle treated, sample2


title: Vehicle treated, sample3


title: Enzalutamide treated, sample6


title: Tumor at diagnosis. Patient p57

status: Public on Aug 21 2017

city: Vienna

first: Fikret

last: Rifatbegovic

address: Zimmermannplatz 10

department: Tumor biology

country: Austria

institute: Children's Cancer Research Institute

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T57d

data_processing:
RNA-seq reads were mapped with GSNAP v2014-12- 28 [20147302] (“-- maxsearch=100 -- npaths=1 -- max-mismatches=1 --novelsplicing=0 “) to human reference genome build GRCh37. Known SNPs and splice sites were extracted from dbSNP build 138 and Ensembl GRCh37 build 75, respectively.
DESeq2-normalized fragments per million (FPM), log2 scale
Genome_build: hg19
Supplementary_files_format_and_content: DESeq expression matrix (log2)

source_name: Tumor

extract_protocol:
miRNeasy Micro Kit
30 ng of total RNA was used for cDNA synthesis following the NEBNext® UltraT RNA Library Prep Kit for Illumina® protocol (New England BioLabs). From the point when cDNA was synthesized, the library was completed in an automated way at the EMBL Genomics Core Facility (Heidelberg, Germany) following the manufacture's protocol.

value: Tumor cells from the primary tumor


title: Tumor at diagnosis. Patient p62

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T62d


title: Tumor at diagnosis. Patient p63

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T63d


title: Enzalutamide treated, sample5


title: Tumor at diagnosis. Patient p55

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T55d


title: Tumor at diagnosis. Patient p53

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T53d


title: Tumor at diagnosis. Patient p49

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T49d


title: Tumor at diagnosis. Patient p52

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T52d


title: Tumor at diagnosis. Patient p47

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T47d


title: Tumor at diagnosis. Patient p44

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T44d


title: Tumor at diagnosis. Patient p48

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T48d


title: Tumor at diagnosis. Patient p05

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T05d


title: Vehicle treated, sample10


title: Tumor at diagnosis. Patient p06

description:
mRNA profiles of a Ewings Sarcoma cellline (clone of A673 with inducible sh EWS-FLI1 knockdown) treated with control sponge
T06d


title: S2_scRNA_Trp_5_R2

status: Public on Jun 23 2017

city: Basel

first: Dirk

last: Schuebeler

address: Fabrikstrasse 24

country: Switzerland

institute: Friedrich Miescher Institute for Biomedical Research

description:
nascent RNA
short RNA sequencing

data_processing:
After alignment against drosophila and mouse genomes (see above for details). Reads were counted in a window around TSSs [-100:200] using the qCount function of QuasR (Gaidatzis et al., 2014). To reduce potential contamination with full-length transcripts we used the second read from the pair representing the 3’ of the transcript for counting. Pairs having insert length>180bp were excluded.  We used the mouse spike-ins to perform inter-sample normalization and enable quantification of the global effects expected to occur upon inhibition of transcription. Under the Assumption that read counts from the mouse should be constant across samples; reads counts from each fly sample were normalized by the sum of the reads detected at mouse promoters within the same sample. Abundance of scRNA at each time-point was calculated relative to the respective amount before inhibition. This relative level was calculated for each replicate separately to correct for batch variations.
Genome_build: Dm3
Supplementary_files_format_and_content: bigwig files

source_name: S2 - Schneider cells

growth_protocol: Schneider S2 cells were grown at 25 degrees in Schneider’s Drosophila medium (LifeTech:21720-001) supplemented with 10%FBS. OSC cells were grown at 25 degrees in Shields and Sang M3 Insect Medium (Sigma, S8398) supplemented with 1% insulin, 1% glutathione, 10% heat-inactivated FBS, and 10% Fly Extract.

extract_protocol: scRNA protocol was adapted fron Nechaev et al, Science 2010.For each condition 20 10^6 of Triptolide treated drosophila cells were spiked with 2 10^6 untreated mouse embryonic stem cells. scRNA protocol was adapted from (Nechaev et al., 2010). Cells were re-suspended in ice-cold lysis buffer (10mM Tris (pH=7.4), 10mM NaCl, 3mM Mgcl2, 0.1mM EDTA, 0.5% NP40), incubated 10min on ice, span down. Nuclei were washed with ice cold (10mM Tris (pH=7.4), 10mM NaCl, 3mM Mgcl2, 0.1mM EDTA) and nuclear pellets were dissolved in Trizol (Thermofisher). RNA was size selected (17-200bp) using a two-step column purification strategy (RNA clean and concentrator, Zymo-R1016). 10μg of purified RNA was successively treated by 5’ dephosphorylation  - 20U at 37°C for 30min (Epicentre - RP8092H); 5’ terminator exonuclease - 1U in Buffer A at 30°C for 60min (Epicentre - TER51020); cap-clip decapping enzyme – 5U  at 37°C for 90min. After each reaction, short RNA was column purified (RNA clean and concentrator, Zymo-R1016). The resulting RNA was used for library preparation using TruSeq small RNA library  (Illumina). Libraries were purified on 6% TBE gels (150-300bp – Novex - EC6265BOX). Size distribution of the libraries were controlled on Bioanalyser High sensitivity (Agilent 5067-4626). Two biologically independent inhibition time courses were performed. The samples were run on an Illumina NextSeq generating 38bp paired-end reads.

treatment_protocol: Transcription inhibition was performed as previously described (Henriques et al. 2013). Triptolide and/or flavopiridol were added at a final concentration of 10uM(BM to the culture media for the indicated time (0, 2.5, 5, 10, 20, 40 minutes). Heat shock was performed in S2 cells as previously described (Wirbelauer  et al. 2005). Temperature was quickly raised by mixing equivalent amount of cell containing medium at 25C(BC with pre-warmed medium (BC). The mix was incubated for 1 min at 37C(BC. After heat shock (HS), cells were let to recover for 5 min.Transcription inhibition (Trp, Fl) inhibition was performed 2.5 minutes prior heat-shock.

value: S2 (Schneider cells)

tag: sample type

value: short RNA sequencing

value: Triptolide inhibition


title: S2_scRNA_Trp_10_R2

description:
nascent RNA
short RNA sequencing


title: S2_scRNA_Trp_2_R2

description:
nascent RNA
short RNA sequencing


title: S2_scRNA_Trp_0_R2

description:
nascent RNA
short RNA sequencing


title: S2_scRNA_Trp_20_R2

description:
nascent RNA
short RNA sequencing


title: S2_scRNA_Trp_10_R1

description:
nascent RNA
short RNA sequencing


title: S2_scRNA_Trp_20_R1

description:
nascent RNA
short RNA sequencing


title: S2_scRNA_Trp_0_R1

description:
nascent RNA
short RNA sequencing