title: Ori∆_SSDDCS_IME2_GI

description: Repli-Seq


title: Ori∆_SSDDCS_IME2_5

status: Public on Sep 23 2022

type: SRA

city: Cambridge

first: Philip

last: Zegerman

address: 80, Tennis Court Road

department: Department of Biochemistry

country: United Kingdom

institute: University of Cambridge

description: Repli-Seq

library_source: genomic

data_processing:
Library strategy: Repli-Seq
Sequence reads were mapped using Bowtie2 (version 2.3.4).
Unique reads mapping to a single genomic location were summed in 1 kb bins, using Samtools (version 1.7.2) and Bedtools (version 2.26.0).
Assembly: sacCer3
Supplementary files format and content: csv files containing number of unique reads in 1 kb bins normalised to G1 controls.

molecule: genomic DNA

organism: Saccharomyces cerevisiae

source_name: S-phase yeast cells - 5 min after G1 release

growth_protocol: Mid log phase yeast culture grown in standard media (YPRaff ) and arrested in G1. Released into synchronous S-phase in media with galactose (YPGal)

extract_protocol:
DNA was purified by phenol chloroform extraction.
Library preparation was carried out according to Illumina TruSeq Nano instructions.

treatment_protocol: Induction of SSDDCS overexpression by adding galactose to the media to the G1 arrested population.

tag: strain

value: sml1_delta SSDDCS ARS1008_delta ARS1009_delta

tag: genotype

value: W303a MATa ade2-1 ura3-1 his3-11, 15 trp1-1 leu2-3, 112 can1-100 rad5-535 leu2::Sld7-PGAL1-10-Cdc45::LEU2 his3::SLD3-A-PGAL1-10-Dbf4-A::HIS3 trp1::Sld2-PGAL1-10-Dpb11::TRP1 sml1_delta::HphNT ARS1008_delta ARS1009_delta

tag: strain background


title: RepTime_SSDDCS-60

description: Repli-Seq

data_processing:
Library strategy: Repli-Seq
Sequence reads were mapped using Bowtie2 (version 2.2.6).
Repli-Seq - Unique reads mapping to a single genomic location were summed in 1 kb bins, using Samtools (version 1.7.2) and Bedtools (version 2.26.0).
Assembly: sacCer3
Supplementary files format and content: Repli-Seq - csv files containing number of unique reads in 1 kb bins normalised to G1 controls.

source_name: S-phase yeast cells - 60 min after G1 release

value: sml1_delta SSDDCS

value: W303a MATa ade2-1 ura3-1 his3-11, 15 trp1-1 leu2-3, 112 can1-100 rad5-535 leu2::Sld7-PGAL1-10-Cdc45::LEU2 his3::SLD3-A-PGAL1-10-Dbf4-A::HIS3 trp1::Sld2-PGAL1-10-Dpb11::TRP1 sml1_delta::HphNT


title: RepTime_SSDDCS-45

description: Repli-Seq

source_name: S-phase yeast cells - 45 min after G1 release


title: RepTime_SSDDCS-50

description: Repli-Seq

source_name: S-phase yeast cells - 50 min after G1 release


title: RepTime_SSDDCS-35

description: Repli-Seq

source_name: S-phase yeast cells - 35 min after G1 release


title: RepTime_SSDDCS-40

description: Repli-Seq

source_name: S-phase yeast cells - 40 min after G1 release


title: RepTime_SSDDCS-30

description: Repli-Seq

source_name: S-phase yeast cells - 30 min after G1 release


title: RepTime_SSDDCS-25

description: Repli-Seq

source_name: S-phase yeast cells - 25 min after G1 release


title: Ori∆_SSDDCS_IME2_20

description: Repli-Seq

source_name: S-phase yeast cells - 20 min after G1 release


title: RepTime_SSDDCS-20

description: Repli-Seq


title: RepTime_SSDDCS-15

description: Repli-Seq

source_name: S-phase yeast cells - 15 min after G1 release


title: RepTime_SSDDCS-5

description: Repli-Seq


title: RepTime_SSDDCS-G1

description: Repli-Seq

source_name: Synchronised G1 phase yeast cells


title: Ori∆_SSDDCS_IME2_15

description: Repli-Seq


title: RepTime_sml1-25

description: Repli-Seq


title: RepTime_sml1-40

description: Repli-Seq


title: RepTime_sml1-45

description: Repli-Seq


title: RepTime_SSDDCS-10

description: Repli-Seq

source_name: S-phase yeast cells - 10min after G1 release


title: Ori∆_SSDDCS_IME2_10

description: Repli-Seq


title: RepTime_sml1-35

description: Repli-Seq


title: RepTime_sml1-30

description: Repli-Seq


title: RepTime_sml1-10

description: Repli-Seq


title: MNase-Seq_sml1-60

description: MNase-Seq

data_processing:
fastq files were mapped using Bowtie2 (version 2.2.6).
SAM files were converted to BAM files using samtools view (samtools version 0.1.19)
BAM files were sorted and indexes using samtools sort and samtools index, respectively (samtools version 0.1.19)
Identification of high confidence sub-nucleosomal peaks was performed following the analysis from Gutiérrez et al. (G&D 2019)
Assembly: sacCer3
Supplementary files format and content: txt file containing the location of each sub-nucleosomal peak and the corresponding signal in each sample

extract_protocol:
DNA was crosslinked, digested with MNase (5U for 20min at room temperature) and extracted using phenol-chloroform to isolate sub-nucleosomal sized fragments. Protocol adapted from Gutiérrez et al. (G&D 2019)
Library preparation was carried out according to Illumina TruSeq Nano instructions, with the following modifications: after end-repair and before A-tailing, samples were cleaned by performing a phenol-chloroform precipitation, in order to reduce the volume of the samples without using AMPure XP beads.  20% of the amount of adapters recommended by Illumina was used, to reduce adpater dimer formation. After ligation, samples were cleaned using 1.8x AMPure XP beads


title: RepTime_sml1-15

description: Repli-Seq


title: RepTime_sml1-20

description: Repli-Seq


title: RNA-Seq_SSDDCS-35 rep4

library_source: transcriptomic

data_processing:
Fastq files from Illumina sequencing were filtered for low quality reads (<Q20) and low quality bases were trimmed from the ends of the reads. Adaptors were removed using cutadapt.
filtered fastq data was mapped with TopHat version 2.0.6.
Read counts for each gene were extracted using genomic ranges and differential expression analysis was performed using DESeq2
Assembly: sacCer3
Supplementary files format and content: txt file containing normalised read counts for each gene across the whole genome

molecule: total RNA

extract_protocol:
RNA was purified by Ribopure (Invitrogen)
Library preparation was carried out according to Illumina TruSeq Nano instructions.


title: RepTime_sml1-60

description: Repli-Seq


title: RepTime_sml1-G1

description: Repli-Seq


title: RepTime_sml1-5

description: Repli-Seq


title: RNA-Seq_SSDDCS-60 rep4


title: RNA-Seq_SSDDCS-50 rep4


title: RNA-Seq_SSDDCS-45 rep4


title: RNA-Seq_SSDDCS-30 rep4


title: RNA-Seq_SSDDCS-20 rep4


title: RNA-Seq_SSDDCS-10 rep4


title: RNA-Seq_SSDDCS-25 rep4


title: RNA-Seq_SSDDCS-40 rep4


title: RNA-Seq_sml1-60 rep4


title: RNA-Seq_SSDDCS-15 rep4


title: RNA-Seq_sml1-20 rep4


title: RepTime_sml1-50

description: Repli-Seq


title: RNA-Seq_sml1-30 rep4


title: RNA-Seq_sml1-50 rep4


title: RNA-Seq_SSDDCS-G1 rep4


title: RNA-Seq_SSDDCS-5 rep4


title: RNA-Seq_sml1-40 rep4


title: RNA-Seq_sml1-10 rep4


title: RNA-Seq_sml1-45 rep4


title: RNA-Seq_sml1-25 rep4


title: RNA-Seq_sml1-15 rep4


title: RNA-Seq_sml1-5 rep4


title: RNA-Seq_sml1-35 rep4


title: RNA-Seq_SSDDCS-50 rep3


title: Ori∆_sml1_IME2_30

description: Repli-Seq

value: sml1_delta ARS1008_delta ARS1009_delta

value: W303a MATa ade2-1 ura3-1 his3-11, 15 trp1-1 leu2-3, 112 can1-100 rad5-535 sml1_delta::URA3 ARS1008_delta ARS1009_delta


title: RNA-Seq_sml1-G1 rep4


title: RNA-Seq_SSDDCS-40 rep3


title: RNA-Seq_SSDDCS-60 rep3


title: Ori∆_sml1_IME2_45

description: Repli-Seq


title: RNA-Seq_SSDDCS-35 rep3


title: RNA-Seq_SSDDCS-25 rep3


title: RNA-Seq_SSDDCS-45 rep3


title: RNA-Seq_SSDDCS-20 rep3


title: RNA-Seq_SSDDCS-5 rep3


title: RNA-Seq_SSDDCS-G1 rep3


title: RNA-Seq_SSDDCS-15 rep3


title: RNA-Seq_sml1-50 rep3


title: RNA-Seq_sml1-60 rep3


title: RNA-Seq_SSDDCS-30 rep3


title: RNA-Seq_sml1-40 rep3


title: RNA-Seq_sml1-35 rep3


title: RNA-Seq_sml1-45 rep3


title: RNA-Seq_sml1-30 rep3


title: RNA-Seq_sml1-10 rep3


title: RNA-Seq_sml1-20 rep3


title: RNA-Seq_sml1-15 rep3


title: RNA-Seq_sml1-5 rep3


title: RNA-Seq_SSDDCS-60 rep2


title: RNA-Seq_SSDDCS-45 rep2


title: RNA-Seq_SSDDCS-10 rep3


title: RNA-Seq_SSDDCS-40 rep2


title: RNA-Seq_SSDDCS-30 rep2


title: RNA-Seq_SSDDCS-20 rep2


title: RNA-Seq_SSDDCS-35 rep2


title: RNA-Seq_SSDDCS-G1 rep2


title: RNA-Seq_SSDDCS-10 rep2


title: RNA-Seq_SSDDCS-15 rep2


title: RNA-Seq_SSDDCS-5 rep2


title: RNA-Seq_sml1-25 rep3


title: RNA-Seq_sml1-50 rep2


title: RNA-Seq_sml1-45 rep2


title: RNA-Seq_sml1-40 rep2


title: RNA-Seq_sml1-G1 rep3


title: RNA-Seq_SSDDCS-50 rep2


title: RNA-Seq_sml1-15 rep2


title: RNA-Seq_sml1-35 rep2


title: RNA-Seq_SSDDCS-45 rep1


title: RNA-Seq_SSDDCS-60 rep1


title: RNA-Seq_sml1-20 rep2


title: RNA-Seq_sml1-5 rep2


title: RNA-Seq_SSDDCS-50 rep1


title: RNA-Seq_sml1-10 rep2


title: RNA-Seq_sml1-30 rep2


title: RNA-Seq_sml1-G1 rep2


title: RNA-Seq_SSDDCS-40 rep1


title: RNA-Seq_SSDDCS-5 rep1


title: RNA-Seq_SSDDCS-35 rep1


title: RNA-Seq_SSDDCS-20 rep1


title: RNA-Seq_SSDDCS-25 rep1


title: RNA-Seq_sml1-60 rep2


title: RNA-Seq_SSDDCS-15 rep1


title: RNA-Seq_SSDDCS-30 rep1


title: RNA-Seq_SSDDCS-10 rep1


title: RNA-Seq_sml1-30 rep1


title: RNA-Seq_sml1-35 rep1


title: RNA-Seq_sml1-60 rep1


title: RNA-Seq_sml1-25 rep1


title: RNA-Seq_SSDDCS-G1 rep1


title: RNA-Seq_sml1-20 rep1


title: RNA-Seq_sml1-40 rep1


title: RNA-Seq_sml1-10 rep1


title: RNA-Seq_sml1-50 rep1


title: RNA-Seq_sml1-15 rep1


title: RNA-Seq_sml1-45 rep1


title: MNase-Seq_SSDDCS-60 rep3

description:
MNase-Seq
SSDDCS_60.Fnor.smooth.wig

data_processing:
fastq files were mapped using Bowtie2 (version 2.2.6).
SAM files were converted to BAM files using samtools view (samtools version 0.1.19)
BAM files were sorted and indexes using samtools sort and samtools index, respectively (samtools version 0.1.19)
wig files were generated using danpos dpos command with following parameters -m 1 --mafrsz 170 --mifrsz 120 (DANPOS version 2.2.2)
Assembly: sacCer3
Supplementary files format and content: wig files containing nucleosome occupancy values at each base pair across the whole genome

extract_protocol:
DNA was crosslinked, digested with MNase (90U for 3 min at 37°C) and extracted using phenol-chloroform to isolate nucleosome sized fragments. Protocol adapted from Nocetti and Whitehouse (G&D 2016)
Library preparation was carried out according to Illumina TruSeq Nano instructions. The following XP bead clean-up steps were performed: 1 - AMPure XP beads were added (1.8x volume of DNA, DNA >100bp on beads, <100bp in supernatant) to each reaction to purify the mononucleosomal fragments after end-repair. Two subsequent steps of beads purification (1.4x volume of DNA) were performed after adapter ligation in order to remove adapter dimers. After PCR amplification, another step of beads purification (1.4x volume of DNA) was performed.


title: MNase-Seq_SSDDCS-50 rep3

description:
MNase-Seq
SSDDCS_50.Fnor.smooth.wig


title: RNA-Seq_sml1-5 rep1


title: MNase-Seq_SSDDCS-35 rep3

description:
MNase-Seq
SSDDCS_35.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-30 rep3

description:
MNase-Seq
SSDDCS_30.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-40 rep3

description:
MNase-Seq
SSDDCS_40.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-25 rep3

description:
MNase-Seq
SSDDCS_25.Fnor.smooth.wig


title: RNA-Seq_sml1-G1 rep1


title: MNase-Seq_SSDDCS-5 rep3

description:
MNase-Seq
SSDDCS_5.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-45 rep3

description:
MNase-Seq
SSDDCS_45.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-20 rep3

description:
MNase-Seq
SSDDCS_20.Fnor.smooth.wig


title: MNase-Seq_sml1-60 rep3

description:
MNase-Seq
sml1D_60.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-15 rep3

description:
MNase-Seq
SSDDCS_15.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-G1 rep3

description:
MNase-Seq
SSDDCS_0.Fnor.smooth.wig


title: MNase-Seq_sml1-50 rep3

description:
MNase-Seq
sml1D_50.Fnor.smooth.wig


title: MNase-Seq_sml1-45 rep3

description:
MNase-Seq
sml1D_45.Fnor.smooth.wig


title: MNase-Seq_sml1-40 rep3

description:
MNase-Seq
sml1D_40.Fnor.smooth.wig


title: MNase-Seq_sml1-35 rep3

description:
MNase-Seq
sml1D_35.Fnor.smooth.wig


title: MNase-Seq_sml1-25 rep3

description:
MNase-Seq
sml1D_25.Fnor.smooth.wig


title: MNase-Seq_sml1-30 rep3

description:
MNase-Seq
sml1D_30.Fnor.smooth.wig


title: MNase-Seq_sml1-15 rep3

description:
MNase-Seq
sml1D_15.Fnor.smooth.wig


title: MNase-Seq_sml1-20 rep3

description:
MNase-Seq
sml1D_20.Fnor.smooth.wig


title: MNase-Seq_sml1-5 rep3

description:
MNase-Seq
sml1D_5.Fnor.smooth.wig


title: MNase-Seq_sml1-G1 rep3

description:
MNase-Seq
sml1D_0.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-10 rep3

description:
MNase-Seq
SSDDCS_10.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-60 rep2

description:
MNase-Seq
SSDDCS_60.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-50 rep2

description:
MNase-Seq
SSDDCS_50.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-45 rep2

description:
MNase-Seq
SSDDCS_45.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-40 rep2

description:
MNase-Seq
SSDDCS_40.Fnor.smooth.wig


title: MNase-Seq_sml1-10 rep3

description:
MNase-Seq
sml1D_10.Fnor.smooth.wig


title: MNase-Seq_sml1-35 rep2

description:
MNase-Seq
sml1D_35.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-20 rep2

description:
MNase-Seq
SSDDCS_20.Fnor.smooth.wig


title: MNase-Seq_sml1-15 rep2

description:
MNase-Seq
sml1D_15.Fnor.smooth.wig


title: MNase-Seq_sml1-45 rep2

description:
MNase-Seq
sml1D_45.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-15 rep2

description:
MNase-Seq
SSDDCS_15.Fnor.smooth.wig


title: MNase-Seq_sml1-20 rep2

description:
MNase-Seq
sml1D_20.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-35 rep2

description:
MNase-Seq
SSDDCS_35.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-10 rep2

description:
MNase-Seq
SSDDCS_10.Fnor.smooth.wig


title: MNase-Seq_sml1-30 rep2

description:
MNase-Seq
sml1D_30.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-5 rep2

description:
MNase-Seq
SSDDCS_5.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-30 rep2

description:
MNase-Seq
SSDDCS_30.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-G1 rep2

description:
MNase-Seq
SSDDCS_0.Fnor.smooth.wig


title: MNase-Seq_sml1-60 rep2

description:
MNase-Seq
sml1D_60.Fnor.smooth.wig


title: MNase-Seq_sml1-10 rep2

description:
MNase-Seq
sml1D_10.Fnor.smooth.wig


title: MNase-Seq_sml1-40 rep2

description:
MNase-Seq
sml1D_40.Fnor.smooth.wig


title: MNase-Seq_sml1-G1 rep2

description:
MNase-Seq
sml1D_0.Fnor.smooth.wig


title: MNase-Seq_sml1-5 rep2

description:
MNase-Seq
sml1D_5.Fnor.smooth.wig


title: MNase-Seq_sml1-50 rep2

description:
MNase-Seq
sml1D_50.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-5 rep1

description:
MNase-Seq
SSDDCS_5.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-G1 rep1

description:
MNase-Seq
SSDDCS_0.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-15 rep1

description:
MNase-Seq
SSDDCS_15.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-10 rep1

description:
MNase-Seq
SSDDCS_10.Fnor.smooth.wig


title: N2_REP_2

city: Koln

first: Jorge

last: Boucas

address: Joseph-Stelzmann-Str. 9b

department: Bioinformatics

country: Germany

institute: Max Planck Institute for Biology of Ageing

data_processing:
Quality check: FastQC 0.11.7
Alignment: Hisat version 2.1.0
Guided assembly: StringTie 1.3.4d; -p 18 -G ORIGINAL_GTF -f 0.99
Merging assemblies: Cuffmerge 2.2.1; --min-isoform-fraction 1.0
Quantification: Cuffquant 2.2.1
Differential analysis: Cuffdiff 2.2.1; --dispersion-method per-condition
Supplementary files format and content: tab-separated values or excel table of gene expression
Assembly: WBcel235_RELEASE_91

organism: Caenorhabditis elegans

source_name: Wildtype

growth_protocol: grouth under 20°C until day 1 adult

extract_protocol:
Isolation of total RNA was performed using Trizol. Age-synchronized worms were collected and washed twice with M9 buffer. Finally, 1 ml of Trizol was added and worms were frozen at -80°C for at least 1h. Subsequently worms were thawed at 37°C. Disruption of worms and RNA release was performed using silica beads and using the Precellys 24- Dual cell homogenizer (Peq-Lab) twice for 20 s at 5000 rpm. Samples were incubated for 15 min at room temperature. Subsequently, 100 µl 1-bromo-3-chloropropane was added to the samples and centrifuged for 15 min at 8000 rcf at 4°C to separate aqueous and organic phase. The aqueous phase was used to isolate total RNA with the RNeasy mini kit (Qiagen) following manufacturer instructions.
RNA libraries were prepared using the RNAeasy mini kit (Qiagen)

value: C. elegans wild type,DR subclone of CB original (Tc pattern I)

tag: age

value: Day 1 adult

tag: tissue

value: whole worm extract


title: MNase-Seq_SSDDCS-30 rep1

description:
MNase-Seq
SSDDCS_30.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-50 rep1

description:
MNase-Seq
SSDDCS_50.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-25 rep1

description:
MNase-Seq
SSDDCS_25.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-20 rep1

description:
MNase-Seq
SSDDCS_20.Fnor.smooth.wig


title: MNase-Seq_SSDDCS-40 rep1

description:
MNase-Seq
SSDDCS_40.Fnor.smooth.wig


title: N2_REP_3


title: N2_REP_1


title: MNase-Seq_SSDDCS-35 rep1

description:
MNase-Seq
SSDDCS_35.Fnor.smooth.wig


title: CPL1xrde1_REP_2


title: MNase-Seq_SSDDCS-60 rep1

description:
MNase-Seq
SSDDCS_60.Fnor.smooth.wig


title: CPL1xrde1_REP_3


title: MNase-Seq_SSDDCS-45 rep1

description:
MNase-Seq
SSDDCS_45.Fnor.smooth.wig


title: CPL1x_REP_3


title: CPL1x_REP_1


title: CPL1x_REP_2


title: CPL1wt_REP_3


title: CPL1xrde1_REP_1


title: CPL1wt_REP_2


title: MNase-Seq_sml1-5 rep1

description:
MNase-Seq
sml1D_5.Fnor.smooth.wig


title: MNase-Seq_sml1-25 rep1

description:
MNase-Seq
sml1D_25.Fnor.smooth.wig


title: MNase-Seq_sml1-35 rep1

description:
MNase-Seq
sml1D_35.Fnor.smooth.wig


title: MNase-Seq_sml1-20 rep1

description:
MNase-Seq
sml1D_20.Fnor.smooth.wig


title: MNase-Seq_sml1-60 rep1

description:
MNase-Seq
sml1D_60.Fnor.smooth.wig


title: MNase-Seq_sml1-45 rep1

description:
MNase-Seq
sml1D_45.Fnor.smooth.wig


title: MNase-Seq_sml1-30 rep1

description:
MNase-Seq
sml1D_30.Fnor.smooth.wig


title: MNase-Seq_sml1-10 rep1

description:
MNase-Seq
sml1D_10.Fnor.smooth.wig


title: Baseline, CD44+ Lung Single Cell

city: Singapore

first: Wharton

middle: O Y

last: Chan

state: Singapore

address: 8 College Road

department: Emerging Infectious Disease

country: Singapore

institute: Duke-NUS Medical School

description: 10X Genomics v3

library_source: transcriptomic single cell

data_processing:
The demultiplexing, barcoded processing, gene counting and aggregation were made using the Salmon Alevin 0.14.1
Assembly: Custom
Supplementary files format and content: Tab-separated values files and matrix files

molecule: polyA RNA

organism: Eonycteris spelaea

source_name: Lung

extract_protocol:
Bat lung single cell suspensions obtained from bats, washed and sorted with CD44+ and Live Dead Markers. Infectd bat lung samples only taken from PR3VM qPCR positive bats
10X Genomics 3’ v3

value: Lung

tag: cell type

value: Immune

tag: treatment

value: Untreated


title: PR3VM Infected, CD44+ Lung Single Cell

description: 10X Genomics v3

value: PR3VM Infected


title: MNase-Seq_sml1-15 rep1

description:
MNase-Seq
sml1D_15.Fnor.smooth.wig


title: MNase-Seq_sml1-40 rep1

description:
MNase-Seq
sml1D_40.Fnor.smooth.wig


title: MNase-Seq_sml1-50 rep1

description:
MNase-Seq
sml1D_50.Fnor.smooth.wig


title: MNase-Seq_sml1-G1 rep1

description:
MNase-Seq
sml1D_0.Fnor.smooth.wig


title: CPL1wt_REP_1


title: Blood Neutrophils, Animal 1

data_processing:
Alignment, gene counting and aggregation were made done the Salmon 0.14.1
Assembly: Custom
Supplementary files format and content: Tab-separated values files

source_name: Blood

extract_protocol:
RNEasy Micro (Qiagen)
SMART-Seq v4 Ultra Low Input RNA (Takara Bio)

value: Blood


title: Lung CD11b-, Animal 3


title: Blood Neutrophils, Animal 3


title: Lung CD11b-, Animal 1


title: Blood Neutrophils, Animal 2


title: Lung Neutrophils, Animal 1


title: Blood Monocytes, Animal 3


title: Lung Alevolar Macrophages, Animal 2


title: Lung Neutrophils, Animal 2


title: Blood Monocytes, Animal 2


title: Lung Neutrophils, Animal 3


title: Lung Alevolar Macrophages, Animal 1


title: Lung Alevolar Macrophages, Animal 3


title: Lung CD11b-, Animal 2


title: Blood Monocytes, Animal 1


title: Vector RNA-rep2

city: Tianjin

first: Zexing

last: Li

address: No.92 Weijin road

department: School of Life Sciences

country: China

institute: Tianjin University

description: PAR-CLIP with High Capacity Streptavidin Resin and anti-Flag beads successively

data_processing:
The STAR reference genome of human 45S rRNA is built with parameters: STAR --runThreadN 16 --runMode genomeGenerate --genomeDir hsa_45S --genomeFastaFiles hsa_rRNA_45S.fa
RNA-seq reads were aligned to the pre-built human 45S ribosomal RNA using STAR version 2.7.8a with the following parameters: STAR --runMode alignReads --runThreadN 8 --readFilesCommand zcat --quantMode TranscriptomeSAM GeneCounts --sjdbGTFfile [hsa_rRNA_45S.gtf] --genomeDir [dir_of_hsa_rRNA_45S] --readFilesIn [read1.fq.gz] [read2.fq.gz] --twopassMode Basic --outSAMtype BAM
The aligned BAM files was processed using featureCounts v2.0.1 with 10nt/100nt/200nt-binned gtf annotation file to get binned read statistics. The parameter is shown below: featureCounts -T 8 -p -B -C -f -t exon -g gene_id -a [binned_gtf.gtf] -o [featureCounts.binned.txt] [Aligned.sortedBySamtools.out.bam]
Assembly: The human 45S rRNA sequence is extracted from NCBI (GenBank:U13369.1), and the GTF annotation is generated from GFF3 file used gffread(v0.12.1). GTF file is then processed for a 10nt/100nt/200nt-binned read statistics.
Supplementary files format and content: TXT, FASTA and GTF

organism: Homo sapiens

source_name: RNA isolated by PAR-CLIP from HEK293T cells stably expressing SFB empty vector

growth_protocol: HEK293T cells stably expressing SFB empty vector or SFB tagged TopBP1 were incubated with DMEM in 37℃, 5% CO2 incubator.

extract_protocol:
The cells were placed in a tray filled with ice to keep cells cold and cross-linked with 0.4 J/cm2 total energy of 365 nm UV. Subsequently, the cells were lysed in NETN300 (50 mM Tris-HCl, pH 7.5, 300 mM NaCl, 2 mM EDTA, 1% NP40 ) in the presence of DTT, protease inhibitor cocktail (Roche), phosphatase inhibitor cocktail (Roche), RNase inhibitor and VRC, diluted with NETN0. The cell lysates were incubated with High Capacity Streptavidin Resin for 6 h at 4 °C. The beads were washed three times for 1 h with NETN300. After eluting two times for 1 h with biotin at 4 °C, the elution was incubated with anti-Flag beads (Sigma) for 6 h at 4 °C. The beads were washed three times for 1 h with NETN300. Then, the protein-associated RNA was isolated from the beads by using proteinase K digestion and precipitated with phenol-chloroform. After ethanol-precipitated, the RNA was resuspended with nuclease free water.
Library preparation and sequencing were finished by LC-Bio Technology CO., Ltd., Hangzhou, China. The protocol is as follows: 1) RNA fragment and reverse transcription; 2) End repair, add A and adaptor ligation; 3) PCR; 4)Single strand separation and cyclization; 5) DNA nanoball synthesis; 7) Sequencing.

treatment_protocol: The cells were treated with 6SG (Sigma) (final concentration, 0.1 mM) for 14 h.

value: Embryonic kidney

tag: cell line


title: Vector RNA-rep1

value: HEK293T cells stably expressing SFB empty vector


title: TopBP1 RNA-rep2

source_name: RNA isolated by PAR-CLIP from HEK293T cells stably expressing SFB tagged TopBP1


title: TopBP1 RNA-rep3

value: HEK293T cells stably expressing SFB tagged TopBP1 plasmid


title: TopBP1 RNA-rep1

value: HEK293T cells stably expressing SFB tagged TopBP1


title: RCMB32

city: Memphis

first: Kyle

middle: S

last: Smith

state: TN

address: 262 Danny Thomas Pl

country: USA

institute: St. Jude Children's Research Hospital

data_processing:
Basecalls were performed using bcl2fastq for Novaseq output.
reads were trimmed of adaptor sequences
reads were aligned to the UCSC hg38 genome using Bowtie. Only uniquely mapping reads with at most two mismatches were retained.
Peaks were called uing MACS v2.1.0 with the significance cut-off q-value <=0.01
Assembly: hg38
Supplementary files format and content: narrowPeak

source_name: Medulloblastoma

extract_protocol:
Sorted cells were spun at 400 g for 5 min at  4°C. Cell pellets were resuspended in 50 ul of cold lysis buffer (10 mM Tris-HCl, pH 7.5, 10 mM NaCl, 3mM MgCl2, 0.1% NP-40, 0.1% Tween-20 and 0.01% Digitonin) and incubated on ice for 3 min. 1 mL of wash buffer (10 mM Tris-HCl pH 7.5, 10 mM NaCl, 3mM MgCl2, and 0.1% Tween-20) was added to the suspension and was then spun at 500 g for 10 min at 4°C. The nuclei were then resuspended in the transposition reaction mix, using the Illumina Tagment DNA Enzyme and Buffer Kit (Illumina, 20034197) (25 ul of 2X TD buffer, 5 ul of TDE1 tagment DNA enzyme, 16.5 ul of 1X PBS, 0.1% Tween-20, 0.01% Digitonin and 5ul of nuclease free water). The suspension was incubated for 30 min at 37°C on thermomixer at 1,000 rpm.
DNA was then isolated using the Qiagen MinElute kit (Qiagen, 28204).

value: Medulloblastoma


title: Med113FH


title: RCMB24


title: RCMB20


title: RCMB18


title: RCMB28


title: Med1712FH


title: RCMB45


title: RCMB52


title: RCMB51


title: Med-411FH


title: Med-2312FH


title: Med-211FH


title: Icb1572


title: Med-1512FH


title: DMB006


title: Med-114FH


title: Icb1487


title: Icb1299


title: PR_95 (Cancer Detection)

city: Houston

first: Zejuan

state: Texas

address: 6565 Fannin St, MC227

department: Department of Pathology and Genomic Medicine

institute: Houston Methodist Hospital

data_processing:
Basecalls were converted using bcl2fastq2.
Sequencing quality of raw reads were evaluated using FastQC, and trimmed adaptors and low quality reads (Phred quality score < 15) using Trimmomatic
High quality reads were mapped to hg38  genome using bowtie2 with end-to-end mode
Duplicate reads were removed using samtools.
The reads were counted for overlap with 5 kb non-overlap bins using featureCounts.
Assembly: hg38
Supplementary files format and content: txt file for all sample with raw reads count summarised to genome-wide 5 kb non-overlap bins.

source_name: Plasma

extract_protocol:
Plasma was separated from whole blood by centrifuge 1350 g for 10 minutes at 4 °C. Plasma cfDNA was extracted using QIAamp Circulating Nucleic Acid Kit (QIAGEN, Germantown, MD) following manufacturer’s instructions.
Briefly, cfDNA was ligated with barcode adaptors after end repair and A-tailing. Then cfDNA was purified and was incubated with 50 mM HEPES buffer (pH 8.0), 25 mM MgCl2, 100 μM N3-UDP-azide-glucose and 1 μM T4 Phage β-gulcosyltransferase at 37 °C for 1 hour. After purification, cfDNA was incubated with 1 μL DBCO-PEG4-DBCO (a click chemistry tool, 4.5 mM stock in DMSO) at 37 °C for 2 hours. Next, the cfDNA was purified and incubated with 5 μL pre-washed C1 Streptavidin beads (Thermo Fisher Scientific, Waltham, MA) for 15 min. The beads were subsequently washed for 5 minutes eight times. The captured DNA fragments were amplified with 15-18 cycles of PCR. The PCR products were purified with AMPure XP beads according to the manufacturer’s instructions. The concentration of DNA library was measured using a Qubit Fluorometer with dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA) and Bioanalyzer 2100 with Agilent High Sensitivity Kit (Agilent Technologies, Santa Clara, CA).
nano-hmC-Seal

tag: diagnosis

value: Prostate

tag: training/validation group in bladder diagnostic model

tag: training/validation group in breast diagnostic model

tag: training/validation group in colorectal diagnostic model

tag: training/validation group in kidney diagnostic model

tag: training/validation group in lung diagnostic model

tag: training/validation group in prostate diagnostic model

value: Validation

tag: training/validation group in pan-cancer diagnostic model

value: Training


title: PR_93 (Cancer Detection)


title: PR_91 (Cancer Detection)


title: PR_97 (Cancer Detection)


title: PR_89 (Cancer Detection)


title: PR_50 (Cancer Detection)


title: PR_51 (Cancer Detection)


title: PR_47 (Cancer Detection)


title: PR_68 (Cancer Detection)


title: PR_84 (Cancer Detection)


title: PR_77 (Cancer Detection)


title: PR_64 (Cancer Detection)


title: PR_80 (Cancer Detection)


title: PR_52 (Cancer Detection)


title: PR_8 (Cancer Detection)


title: PR_87 (Cancer Detection)


title: PR_79 (Cancer Detection)


title: PR_65 (Cancer Detection)


title: PR_67 (Cancer Detection)


title: PR_86 (Cancer Detection)


title: PR_62 (Cancer Detection)


title: PR_5 (Cancer Detection)


title: PR_83 (Cancer Detection)


title: PR_46 (Cancer Detection)


title: PR_78 (Cancer Detection)


title: PR_58 (Cancer Detection)


title: PR_6 (Cancer Detection)


title: PR_63 (Cancer Detection)


title: PR_76 (Cancer Detection)


title: PR_49 (Cancer Detection)


title: PR_40 (Cancer Detection)


title: PR_41 (Cancer Detection)


title: PR_38 (Cancer Detection)


title: PR_31 (Cancer Detection)


title: PR_35 (Cancer Detection)


title: PR_32 (Cancer Detection)


title: PR_27 (Cancer Detection)


title: PR_29 (Cancer Detection)


title: PR_30 (Cancer Detection)


title: PR_232 (Cancer Detection)


title: PR_220 (Cancer Detection)


title: PR_227 (Cancer Detection)


title: PR_225 (Cancer Detection)


title: PR_25 (Cancer Detection)


title: PR_26 (Cancer Detection)


title: PR_229 (Cancer Detection)


title: PR_33 (Cancer Detection)


title: PR_216 (Cancer Detection)


title: PR_223 (Cancer Detection)


title: PR_215 (Cancer Detection)


title: PR_221 (Cancer Detection)


title: PR_22 (Cancer Detection)


title: PR_218 (Cancer Detection)


title: PR_39 (Cancer Detection)


title: PR_230 (Cancer Detection)


title: PR_36 (Cancer Detection)


title: PR_219 (Cancer Detection)


title: PR_210 (Cancer Detection)


title: PR_24 (Cancer Detection)


title: PR_34 (Cancer Detection)


title: PR_214 (Cancer Detection)


title: PR_21 (Cancer Detection)


title: PR_206 (Cancer Detection)


title: PR_213 (Cancer Detection)


title: PR_203 (Cancer Detection)


title: PR_20 (Cancer Detection)


title: PR_202 (Cancer Detection)


title: PR_196 (Cancer Detection)


title: PR_2 (Cancer Detection)


title: PR_190 (Cancer Detection)


title: PR_192 (Cancer Detection)


title: PR_189 (Cancer Detection)


title: PR_178 (Cancer Detection)


title: PR_197 (Cancer Detection)


title: PR_171 (Cancer Detection)


title: PR_180 (Cancer Detection)


title: PR_172 (Cancer Detection)


title: PR_194 (Cancer Detection)


title: PR_188 (Cancer Detection)


title: PR_185 (Cancer Detection)


title: PR_167 (Cancer Detection)


title: PR_17 (Cancer Detection)


title: PR_169 (Cancer Detection)


title: PR_16 (Cancer Detection)


title: PR_165 (Cancer Detection)


title: PR_204 (Cancer Detection)


title: PR_161 (Cancer Detection)


title: PR_159 (Cancer Detection)


title: PR_162 (Cancer Detection)


title: PR_166 (Cancer Detection)


title: PR_18 (Cancer Detection)


title: PR_191 (Cancer Detection)


title: PR_154 (Cancer Detection)


title: PR_152 (Cancer Detection)


title: PR_142 (Cancer Detection)


title: PR_148 (Cancer Detection)


title: PR_147 (Cancer Detection)


title: PR_135 (Cancer Detection)


title: PR_143 (Cancer Detection)


title: PR_137 (Cancer Detection)


title: PR_150 (Cancer Detection)


title: PR_141 (Cancer Detection)


title: PR_136 (Cancer Detection)


title: PR_134 (Cancer Detection)


title: PR_139 (Cancer Detection)


title: PR_130 (Cancer Detection)


title: PR_128 (Cancer Detection)


title: PR_113 (Cancer Detection)


title: PR_116 (Cancer Detection)


title: PR_12 (Cancer Detection)


title: PR_122 (Cancer Detection)


title: PR_151 (Cancer Detection)


title: PR_112 (Cancer Detection)


title: PR_107 (Cancer Detection)


title: PR_110 (Cancer Detection)


title: PR_120 (Cancer Detection)


title: PR_11 (Cancer Detection)


title: PR_104 (Cancer Detection)


title: PR_111 (Cancer Detection)


title: PR_103 (Cancer Detection)


title: PR_106 (Cancer Detection)


title: PR_101 (Cancer Detection)


title: Z86 (Cancer Detection)


title: Z87 (Cancer Detection)


title: Z91 (Cancer Detection)


title: Z99 (Cancer Detection)


title: Z95 (Cancer Detection)


title: Z110 (Cancer Detection)


title: Z98 (Cancer Detection)


title: Z96 (Cancer Detection)


title: Z85 (Cancer Detection)


title: Z89 (Cancer Detection)


title: Z94 (Cancer Detection)


title: Z92 (Cancer Detection)


title: Z84 (Cancer Detection)


title: Z82 (Cancer Detection)


title: Z97 (Cancer Detection)


title: Z106 (Cancer Detection)


title: Z77 (Cancer Detection)


title: Z80 (Cancer Detection)


title: Z75 (Cancer Detection)


title: Z81 (Cancer Detection)


title: Z76 (Cancer Detection)


title: Z83 (Cancer Detection)


title: Z67 (Cancer Detection)


title: Z90 (Cancer Detection)


title: Z88 (Cancer Detection)


title: PR_119 (Cancer Detection)


title: Z64 (Cancer Detection)


title: Z61 (Cancer Detection)


title: PR_105 (Cancer Detection)


title: Z62 (Cancer Detection)


title: Z38 (Cancer Detection)


title: Z26 (Cancer Detection)


title: Z46 (Cancer Detection)


title: Z39 (Cancer Detection)


title: Z12 (Cancer Detection)


title: Z42 (Cancer Detection)


title: Z59 (Cancer Detection)


title: Z5 (Cancer Detection)


title: Z66 (Cancer Detection)


title: Z65 (Cancer Detection)


title: Z32 (Cancer Detection)


title: Z22 (Cancer Detection)


title: Z30 (Cancer Detection)


title: Z36 (Cancer Detection)


title: Z19 (Cancer Detection)


title: Z29 (Cancer Detection)


title: Z117 (Cancer Detection)


title: Z24 (Cancer Detection)


title: Z41 (Cancer Detection)


title: Z116 (Cancer Detection)


title: Z17 (Cancer Detection)


title: Z25 (Cancer Detection)


title: Z28 (Cancer Detection)


title: Z112 (Cancer Detection)


title: Z103 (Cancer Detection)


title: Z100 (Cancer Detection)


title: KD_61 (Cancer Detection)

value: Kidney


title: KD_8 (Cancer Detection)


title: KD_7 (Cancer Detection)


title: KD_63 (Cancer Detection)


title: KD_9 (Cancer Detection)


title: Z102 (Cancer Detection)


title: KD_58 (Cancer Detection)


title: KD_62 (Cancer Detection)


title: KD_57 (Cancer Detection)


title: KD_56 (Cancer Detection)


title: KD_54 (Cancer Detection)


title: KD_49 (Cancer Detection)


title: KD_51 (Cancer Detection)


title: KD_45 (Cancer Detection)


title: KD_46 (Cancer Detection)


title: KD_47 (Cancer Detection)


title: KD_59 (Cancer Detection)


title: KD_55 (Cancer Detection)


title: KD_42 (Cancer Detection)


title: KD_44 (Cancer Detection)


title: KD_41 (Cancer Detection)


title: KD_40 (Cancer Detection)


title: KD_4 (Cancer Detection)


title: Z63 (Cancer Detection)


title: KD_36 (Cancer Detection)


title: KD_33 (Cancer Detection)


title: KD_39 (Cancer Detection)


title: KD_38 (Cancer Detection)


title: KD_35 (Cancer Detection)


title: KD_32 (Cancer Detection)


title: Z54 (Cancer Detection)


title: KD_30 (Cancer Detection)


title: KD_3 (Cancer Detection)


title: KD_34 (Cancer Detection)


title: KD_27 (Cancer Detection)


title: KD_52 (Cancer Detection)


title: KD_6 (Cancer Detection)


title: KD_26 (Cancer Detection)


title: KD_23 (Cancer Detection)


title: KD_25 (Cancer Detection)


title: KD_19 (Cancer Detection)


title: KD_21 (Cancer Detection)


title: KD_2 (Cancer Detection)


title: KD_20 (Cancer Detection)


title: KD_24 (Cancer Detection)


title: KD_17 (Cancer Detection)


title: KD_28 (Cancer Detection)


title: KD_14 (Cancer Detection)


title: KD_18 (Cancer Detection)


title: KD_13 (Cancer Detection)


title: KD_12 (Cancer Detection)


title: KD_16 (Cancer Detection)


title: KD_15 (Cancer Detection)


title: CC_99 (Cancer Detection)

value: Control


title: CC_96 (Cancer Detection)


title: CC_95 (Cancer Detection)


title: CC_93 (Cancer Detection)


title: CC_92 (Cancer Detection)


title: KD_22 (Cancer Detection)


title: CC_91 (Cancer Detection)


title: CC_90 (Cancer Detection)


title: KD_1 (Cancer Detection)


title: KD_11 (Cancer Detection)


title: KD_10 (Cancer Detection)


title: CC_98 (Cancer Detection)


title: CC_9 (Cancer Detection)


title: CC_97 (Cancer Detection)


title: CC_88 (Cancer Detection)


title: CC_89 (Cancer Detection)


title: CC_84 (Cancer Detection)


title: CC_86 (Cancer Detection)


title: CC_79 (Cancer Detection)


title: CC_8 (Cancer Detection)


title: CC_80 (Cancer Detection)


title: CC_81 (Cancer Detection)


title: CC_85 (Cancer Detection)


title: CC_77 (Cancer Detection)


title: CC_75 (Cancer Detection)


title: CC_72 (Cancer Detection)


title: CC_78 (Cancer Detection)


title: CC_74 (Cancer Detection)


title: CC_71 (Cancer Detection)


title: PR_153 (Cancer Detection)


title: CC_7 (Cancer Detection)


title: CC_69 (Cancer Detection)


title: CC_70 (Cancer Detection)


title: CC_82 (Cancer Detection)


title: CC_67 (Cancer Detection)


title: CC_66 (Cancer Detection)


title: CC_76 (Cancer Detection)


title: CC_63 (Cancer Detection)


title: CC_62 (Cancer Detection)


title: CC_61 (Cancer Detection)


title: CC_73 (Cancer Detection)


title: CC_60 (Cancer Detection)


title: CC_65 (Cancer Detection)


title: CC_58 (Cancer Detection)


title: CC_59 (Cancer Detection)


title: CC_57 (Cancer Detection)


title: CC_64 (Cancer Detection)


title: CC_55 (Cancer Detection)


title: CC_50 (Cancer Detection)


title: CC_54 (Cancer Detection)


title: CC_49 (Cancer Detection)


title: CC_48 (Cancer Detection)


title: CC_47 (Cancer Detection)


title: CC_51 (Cancer Detection)


title: CC_46 (Cancer Detection)


title: CC_44 (Cancer Detection)


title: CC_40 (Cancer Detection)


title: CC_6 (Cancer Detection)


title: CC_41 (Cancer Detection)


title: CC_43 (Cancer Detection)


title: CC_45 (Cancer Detection)


title: CC_52 (Cancer Detection)


title: CC_53 (Cancer Detection)


title: CC_36 (Cancer Detection)


title: CC_34 (Cancer Detection)


title: CC_35 (Cancer Detection)


title: CC_38 (Cancer Detection)


title: CC_4 (Cancer Detection)


title: CC_39 (Cancer Detection)


title: CC_32 (Cancer Detection)


title: CC_33 (Cancer Detection)


title: CC_30 (Cancer Detection)


title: CC_31 (Cancer Detection)


title: CC_3 (Cancer Detection)


title: CC_29 (Cancer Detection)


title: CC_28 (Cancer Detection)


title: CC_26 (Cancer Detection)


title: CC_27 (Cancer Detection)


title: CC_25 (Cancer Detection)


title: CC_68 (Cancer Detection)


title: CC_22 (Cancer Detection)


title: CC_23 (Cancer Detection)


title: CC_205 (Cancer Detection)


title: CC_200 (Cancer Detection)


title: CC_202 (Cancer Detection)


title: CC_201 (Cancer Detection)


title: CC_2 (Cancer Detection)


title: CC_206 (Cancer Detection)


title: CC_24 (Cancer Detection)


title: CC_196 (Cancer Detection)


title: CC_203 (Cancer Detection)


title: CC_204 (Cancer Detection)


title: CC_199 (Cancer Detection)


title: CC_197 (Cancer Detection)


title: CC_195 (Cancer Detection)


title: CC_198 (Cancer Detection)


title: CC_194 (Cancer Detection)


title: CC_192 (Cancer Detection)


title: CC_193 (Cancer Detection)


title: CC_191 (Cancer Detection)


title: CC_189 (Cancer Detection)


title: CC_19 (Cancer Detection)


title: CC_187 (Cancer Detection)


title: CC_186 (Cancer Detection)


title: CC_42 (Cancer Detection)


title: CC_184 (Cancer Detection)


title: CC_183 (Cancer Detection)


title: CC_182 (Cancer Detection)


title: CC_188 (Cancer Detection)


title: CC_181 (Cancer Detection)


title: CC_180 (Cancer Detection)


title: CC_175 (Cancer Detection)


title: CC_18 (Cancer Detection)


title: CC_173 (Cancer Detection)


title: CC_168 (Cancer Detection)


title: CC_164 (Cancer Detection)


title: CC_169 (Cancer Detection)


title: CC_179 (Cancer Detection)


title: CC_176 (Cancer Detection)


title: CC_160 (Cancer Detection)


title: CC_162 (Cancer Detection)


title: CC_170 (Cancer Detection)


title: CC_165 (Cancer Detection)


title: CC_171 (Cancer Detection)


title: CC_185 (Cancer Detection)


title: CC_167 (Cancer Detection)


title: CC_157 (Cancer Detection)


title: CC_155 (Cancer Detection)


title: CC_151 (Cancer Detection)


title: CC_152 (Cancer Detection)


title: CC_154 (Cancer Detection)


title: CC_172 (Cancer Detection)


title: CC_156 (Cancer Detection)


title: CC_161 (Cancer Detection)


title: CC_150 (Cancer Detection)


title: CC_163 (Cancer Detection)


title: CC_158 (Cancer Detection)


title: CC_15 (Cancer Detection)


title: CC_153 (Cancer Detection)


title: CC_149 (Cancer Detection)


title: CC_148 (Cancer Detection)


title: CC_147 (Cancer Detection)


title: CC_146 (Cancer Detection)


title: CC_144 (Cancer Detection)


title: CC_141 (Cancer Detection)


title: CC_143 (Cancer Detection)


title: CC_145 (Cancer Detection)


title: CC_139 (Cancer Detection)


title: CC_140 (Cancer Detection)


title: CC_136 (Cancer Detection)


title: CC_14 (Cancer Detection)


title: CC_134 (Cancer Detection)


title: CC_137 (Cancer Detection)


title: CC_133 (Cancer Detection)


title: CC_13 (Cancer Detection)


title: CC_135 (Cancer Detection)


title: CC_129 (Cancer Detection)


title: CC_125 (Cancer Detection)


title: CC_130 (Cancer Detection)


title: CC_127 (Cancer Detection)


title: CC_126 (Cancer Detection)


title: CC_128 (Cancer Detection)


title: CC_122 (Cancer Detection)


title: CC_123 (Cancer Detection)


title: CC_121 (Cancer Detection)


title: CC_120 (Cancer Detection)


title: CC_132 (Cancer Detection)


title: CC_142 (Cancer Detection)


title: CC_119 (Cancer Detection)


title: CC_116 (Cancer Detection)


title: CC_115 (Cancer Detection)


title: CC_112 (Cancer Detection)


title: CC_110 (Cancer Detection)


title: CC_111 (Cancer Detection)


title: CC_11 (Cancer Detection)


title: CC_109 (Cancer Detection)


title: CC_108 (Cancer Detection)


title: CC_107 (Cancer Detection)


title: CC_105 (Cancer Detection)


title: CC_106 (Cancer Detection)


title: CC_104 (Cancer Detection)


title: CC_1 (Cancer Detection)


title: CC_102 (Cancer Detection)


title: CR_67 (Cancer Detection)

value: Colorectal


title: CC_103 (Cancer Detection)


title: CR_70 (Cancer Detection)


title: CR_68 (Cancer Detection)


title: CR_65 (Cancer Detection)


title: CC_118 (Cancer Detection)


title: CR_7 (Cancer Detection)


title: CR_71 (Cancer Detection)


title: CR_63 (Cancer Detection)


title: CR_69 (Cancer Detection)


title: CR_64 (Cancer Detection)


title: CR_66 (Cancer Detection)


title: CR_60 (Cancer Detection)


title: CR_58 (Cancer Detection)


title: CR_62 (Cancer Detection)


title: CC_10 (Cancer Detection)


title: CR_57 (Cancer Detection)


title: CR_56 (Cancer Detection)


title: CR_50 (Cancer Detection)


title: CR_52 (Cancer Detection)


title: CR_5 (Cancer Detection)


title: CR_55 (Cancer Detection)


title: CR_46 (Cancer Detection)


title: CR_49 (Cancer Detection)


title: CR_43 (Cancer Detection)


title: CR_45 (Cancer Detection)


title: CR_42 (Cancer Detection)


title: CR_47 (Cancer Detection)


title: CR_41 (Cancer Detection)


title: CR_31 (Cancer Detection)


title: CR_27 (Cancer Detection)


title: CR_4 (Cancer Detection)


title: CR_26 (Cancer Detection)


title: CR_30 (Cancer Detection)


title: CR_37 (Cancer Detection)


title: CR_25 (Cancer Detection)


title: CR_23 (Cancer Detection)


title: CR_2 (Cancer Detection)


title: CR_51 (Cancer Detection)


title: CR_21 (Cancer Detection)


title: CR_17 (Cancer Detection)


title: CR_16 (Cancer Detection)


title: CR_19 (Cancer Detection)


title: CR_10 (Cancer Detection)


title: CR_1 (Cancer Detection)


title: CR_14 (Cancer Detection)


title: CR_40 (Cancer Detection)

value: Breast


title: BR_78 (Cancer Detection)


title: BR_76 (Cancer Detection)


title: CR_20 (Cancer Detection)


title: BR_70 (Cancer Detection)


title: BR_72 (Cancer Detection)


title: BR_74 (Cancer Detection)


title: BR_71 (Cancer Detection)


title: BR_7 (Cancer Detection)


title: BR_68 (Cancer Detection)


title: BR_65 (Cancer Detection)


title: BR_6 (Cancer Detection)


title: BR_64 (Cancer Detection)


title: BR_62 (Cancer Detection)


title: BR_60 (Cancer Detection)


title: BR_66 (Cancer Detection)


title: BR_63 (Cancer Detection)


title: BR_58 (Cancer Detection)


title: BR_55 (Cancer Detection)


title: BR_56 (Cancer Detection)


title: BR_75 (Cancer Detection)


title: BR_52 (Cancer Detection)


title: BR_54 (Cancer Detection)


title: BR_53 (Cancer Detection)


title: BR_69 (Cancer Detection)


title: BR_59 (Cancer Detection)


title: BR_51 (Cancer Detection)


title: CR_15 (Cancer Detection)


title: BR_45 (Cancer Detection)


title: BR_49 (Cancer Detection)


title: BR_48 (Cancer Detection)


title: BR_39 (Cancer Detection)


title: BR_44 (Cancer Detection)


title: BR_47 (Cancer Detection)


title: BR_50 (Cancer Detection)


title: BR_46 (Cancer Detection)


title: BR_5 (Cancer Detection)


title: BR_4 (Cancer Detection)


title: BR_43 (Cancer Detection)


title: BR_36 (Cancer Detection)


title: BR_29 (Cancer Detection)


title: BR_30 (Cancer Detection)


title: BR_23 (Cancer Detection)


title: BR_27 (Cancer Detection)


title: BR_25 (Cancer Detection)


title: BR_24 (Cancer Detection)


title: BR_33 (Cancer Detection)


title: BR_32 (Cancer Detection)


title: BR_26 (Cancer Detection)


title: BR_35 (Cancer Detection)


title: BR_31 (Cancer Detection)


title: BR_3 (Cancer Detection)


title: BD_8 (Cancer Detection)

value: Bladder


title: BD_9 (Cancer Detection)


title: BR_17 (Cancer Detection)


title: BR_18 (Cancer Detection)


title: CR_3 (Cancer Detection)


title: BR_1 (Cancer Detection)


title: BR_21 (Cancer Detection)


title: BR_19 (Cancer Detection)


title: BR_11 (Cancer Detection)


title: BR_2 (Cancer Detection)


title: BR_10 (Cancer Detection)


title: BR_15 (Cancer Detection)


title: BR_14 (Cancer Detection)


title: KD_50 (Cancer Detection)


title: BR_13 (Cancer Detection)


title: BR_12 (Cancer Detection)


title: BD_6 (Cancer Detection)


title: BD_43 (Cancer Detection)


title: BD_46 (Cancer Detection)


title: BD_42 (Cancer Detection)


title: BD_5 (Cancer Detection)


title: BD_47 (Cancer Detection)


title: BD_45 (Cancer Detection)


title: BD_38 (Cancer Detection)


title: BD_34 (Cancer Detection)


title: BD_40 (Cancer Detection)


title: BD_36 (Cancer Detection)


title: BD_35 (Cancer Detection)


title: BD_39 (Cancer Detection)


title: BD_41 (Cancer Detection)


title: BD_28 (Cancer Detection)


title: BD_3 (Cancer Detection)


title: BD_26 (Cancer Detection)


title: BD_32 (Cancer Detection)


title: BD_30 (Cancer Detection)


title: BD_24 (Cancer Detection)


title: BD_22 (Cancer Detection)


title: BD_23 (Cancer Detection)


title: BD_20 (Cancer Detection)


title: BD_25 (Cancer Detection)


title: BD_2 (Cancer Detection)


title: BD_11 (Cancer Detection)


title: BD_15 (Cancer Detection)


title: BD_16 (Cancer Detection)


title: BD_1 (Cancer Detection)


title: BD_13 (Cancer Detection)


title: BD_10 (Cancer Detection)


title: BD_29 (Cancer Detection)


title: BD_21 (Cancer Detection)


title: BD_17 (Cancer Detection)


title: BD_18 (Cancer Detection)


title: BD_19 (Cancer Detection)


title: XT0121_Mtb_Lysate

city: La Jolla

first: Cecilia

middle: Sofie

last: Lindestam

state: CA

address: 9420 Athena Circle

department: Sette

institute: La Jolla Institute for Immunology

data_processing:
Reads passing Illumina filters were further filtered for those mapping to tRNA, rRNA, adapters, and spike-in controls using Bowtie v1.0.0 (-S --fr)
Filtered reads were against the GRCh38 reference genome and Gencode v27 annotations using STAR v2.6.1 (default options)
Reads mapping to the reference genome are further filtered to remove low-complexity reads with PRINSEQ v0.20.3 (-lc_method "dust" -lc_threshold 4)
Sequencing read coverage per gene was counted using HTSeq-count v0.7.1 (-m union -s yes -t exon -i gene_id) and features with 0 counts across all samples were removed
Assembly: GRCh38.gencode.v27B
Supplementary files format and content: read count matrix with rows as genes and columns as cells

source_name: blood - PBMC

growth_protocol: PBMC were thawed and added at a density of 1 million cells per well to a round-bottom 96-well plate.

extract_protocol:
PBMCs were thawed and added at a density of 1x10e6 cells per well to a round-bottom 96-well plate. They were stimulated for 24 hrs at 37C with MTB300, Mav lysate, Mtb lysate, anti-CD28 (1g/ml, CD28.2; eBioscience) together with anti-CD3 (1g/ml pre-coated overnight at 4C, UCHT1; BioLegend), as well as left unstimulated as a negative control. After 24 hrs, cells were resuspended in PBS with 10% (v/v) FBS and incubated at 4C for 10 min. Cells were then stained with fixable viability dye eFluor506 (eBioscience) and an antibody mixture containing anti-human CD3-AF700 (UCHT1; ThermoFisher), CD4-APCeFluor780 (RPA-T4; eBioscience), CD8-V500 (RPA-T8; BD Horizon), CCR7-PerCPCy5.5 (G043H7, Biolegend) CD14-V500 (M5E2; BD Bioscience), CD19-V500 (HIB19; BD Bioscience), CD25-FITC (M-A251; BD Bioscience), CD45RA-eFluor450 (HI100, eBiosciences), CD137-APC (4B4-1; BioLegend), OX40-PECy7 (Ber-ACT35; BioLegend), and PD-L1-PE (29E.2A3; BioLegend) for 20 min at room temperature. Cells were washed and resuspended in PBS before being transferred into a 5 ml polypropylene FACS tube (BD Bioscience). PBMCs were sorted, based on forward and side scatter excluding debris and doublets (Figure S1D), on a FACSAria into TRIzol LS (Thermo Fisher). Acquisition files were analyzed using FlowJo. Total RNA was extracted from ~100,000 cells in TRIzol LS using the miRNeasy Micro Kit (Qiagen) on a QIAcube (Qiagen). Total RNA was amplified according to Smart Seq protocol (31). cDNA was purified using AMPure XP beads. cDNA was used to prepare a standard barcoded sequencing library (Illumina). Samples were sequenced using an Illumina HiSeq2500 to obtain 50-bp single end reads. Samples that failed to be sequenced due to limited sample availability or failed the quality control were eliminated from further sequencing and analysis. The full protocol can be found at protocols.io (http://dx.doi.org/10.17504/protocols.io.bxr6pm9e).
Sequencing was performed according to a paired-end strategy using the NovaSeq sequencer (Illumina). Samples that failed quality control standards were eliminated from further analysis21.

treatment_protocol: PBMCs were stimulated for 24hrs at 37 degree C with MTB300, Mtb or Mav whole cell lysate, as well as left unstimulated as a negative control. After 24hrs cells were stained for sorting.

tag: disease group

value: IGRA-HC

tag: stimulation

value: Mtb lysate

value: PBMC


title: BD_14 (Cancer Detection)


title: UN-SCC679_pLENTI6_OE_DSTYKwt_R1

city: Pamplona

first: Luis

last: Montuenga

state: Navarra

address: Avda. Pío XII, 55

country: Spain

institute: CIMA

data_processing:
Sequenced reads were trimmed for adaptor sequence, then mapped to mm10 genome with Kallisto
Calculate raw counts with Sleuth
Assembly: mm10
Supplementary files format and content: tab-delimited text files include raw counts from Sleuth

organism: Mus musculus

source_name: Lung Squamous carcinoma (LUSC)

extract_protocol:
samples were prepared with the Illumina TruSeq Stranded mRNA kit following the manufacturer’s indications and sequenced as single-end and reverse paired-end (75 bp) runs on the Nextseq sequencer
Truseq mRNA (Illumina)
full length strand specific mRNA sequencing

tag: strain

value: A/J

tag: phenotype

value: DSTYK overexpression


title: UN-SCC679_pLENTI6_OE_DSTYKwt_R2


title: UN-SCC679_pLENTI6_empty_R1


title: UN-SCC679_pLENTI6_empty_R2


title: UN-SCC679_pLENTI6_empty_R3


title: UN-SCC679_TPLKO_sh3_dox_R3

value: doxycycline

value: DSTYK downregulation


title: XT0121_aCD3_CD28


title: UN-SCC679_TPLKO_empty_dox_R2


title: UN-SCC679_TPLKO_sh3_dox_R1


title: UN-SCC679_TPLKO_empty_dox_R3


title: XT0121_MTB300


title: XT0121_Negative

value: Unstimulated


title: XT0121_MAC_Lysate

value: Mav lysate


title: LLC_TPLKO_sh3_dox_R3

source_name: Lung Adenocarcinoma (LUAD)


title: UN-SCC679_TPLKO_empty_dox_R1


title: Ribo-seq cpc-3 knockout DD44 rep2

city: College Station

first: Deborah

last: Bell-Pedersen

address: 3258 TAMU

department: Biology

institute: Texas A&M University

data_processing:
Library strategy: Ribo-seq
For WT, Dfrq, and Dcpc-3 samples, basecalling was performed using the Real Time Analysis (RTA) workflow ver. 2.7.7. with default parameters. For cpc-3c samples, basecalling was performed using Illumina BaseSpace app using the FASTQ generation application ver. 1.0.0 and bcl2fastq.
Ribosome profiling (ribo-seq) reads were trimmed using Cutadapt (Martin, 2011) to remove the 3′ adapter of the reads (adapter sequence – CTGTAGGCACCATCAAT).
Adapter-trimmed ribo-seq reads were size-filtered to the 28-34 nt range using Trimmomatic (Bolger et al., 2014).
Ribo-seq reads were aligned to the N. crassa genome FungiDB Release 38 using STAR (Dobin et al., 2013).
To calculate the ribo-seq read coverage over the 5’ UTR, 3’ UTR, or CDS regions, the geneBody_coverage2.py program from the RSeQC package (Wnag et al., 2012) was used.
Ribosome-protected footprint (RPF) features such as read length distribution and sub-codon phasing were determined using NGS toolkit Plastid (Dunn et al, 2016).
The RPF originating only from the coding sequences (CDS) were extracted and quantified using HTSeq-count (Anders et al., 2015). The counted reads were normalized using the median of ratios method employed in the DESeq2 analysis workflow (Love et al., 2014).
The normalized RPF time series was analyzed using the following Extended Circadian Harmonic Oscillation (ECHO, De Los Santos et al., 2019) settings: two unpaired replicates, smoothing function enabled, unexpressed genes excluded from the analysis at the default 70% threshold. Genes were considered rhythmic if they had Benjamini-Hochberg p-values < 0.05, and oscillation types harmonic, damped or forced.
Genome_build: N. crassa genome FungiDB Release 38
Supplementary_files_format_and_content: Output from normalized HTSeq-count done on the reads originating from the CDS and saved as plain text files containing ribosome-occupancy reads.

organism: Neurospora crassa

source_name: mycelia

growth_protocol: Mycelial disks in Vogel’s minimal media containing 2% glucose (pH 6.0) were synchronized to the same time of day by a shift from 30°C constant light (LL) to 25°C constant dark (DD). The cultures were grown in LL for a minimum of 4 h and transferred to DD on day 1 (for collection at DD 36, 40, 44, 48, 52), day 2 (for collection at DD 12, 16, 20, 24, 28, 32), day 3 (for collection at DD8), and harvested either at 9:00 a.m. (DD 12, 16, 20, 36, 40, 44) or 5:00 p.m. (DD 8, 24, 28, 32, 48, 52) on day 3.

extract_protocol:
Tissue was harvested in the dark, pressed in between paper towels to remove excess media, and immediately frozen in liquid N2. Ribosomes were pelleted by sucrose-density centrifugation and ribosome-protected mRNAs were isolated from the pelleted cell extracts using the Qiagen miRNeasy Mini Kit (Cat. # 217004).
All subsequent steps up to step 46 in the published ribosome profiling protocol (Ingolia et al., 2012) were followed as described. An rRNA depletion step was not performed, but instead after heat-inactivation of CircLigase, 2.0 μL of GlycoBlueTM, 6.0 μL 5 M NaCl, 74 μL of DEPC water, and 150 μL of isopropanol were added to each tube and precipitation was carried out overnight at -80°C. The DNA was pelleted by centrifugation for 30 min at 20,000 g at 4°C. The pellets were washed with 70% ethanol and allowed to air-dry for 10 min. The pellet was then resuspended in 5.0 μL of 10 mM Tris (pH 8.0), and cDNA synthesis using SuperScript™ III Reverse Transcriptase, followed by barcode addition by PCR amplification using Phusion Hot Start High-Fidelity DNA Polymerase were performed as described (101). Sequencing libraries were quantified and checked for quality on a 2100 Bioanalyzer using a DNA high sensitivity chip per the manufacturer’s instructions.

tag: genotype

value: cpc-3 knockout

tag: time point

tag: replicate

value: mycelia

tag: molecule subtype

value: ribosome-protected mRNA


title: UN-SCC679_pLENTI6_OE_DSTYKwt_R3

tag: treatment


title: UN-SCC679_TPLKO_sh3_dox_R2


title: Ribo-seq cpc-3 knockout DD48 rep2

tag: tissue


title: LLC_TPLKO_sh3_dox_R2


title: XT0109_Negative

tag: cell type


title: XT0109_MTB300


title: XT0109_Mtb_Lysate


title: Ribo-seq cpc-3 knockout DD40 rep2


title: LLC_TPLKO_sh3_dox_R1


title: LLC_TPLKO_empty_dox_R3


title: XT0109_MAC_Lysate


title: NOD_1

first: Helen

last: Makarenkova

address: 10550 N Torrey Pines Rd, MB-218

department: Molecular Medicine

institute: The Scripps Research Institute

data_processing:
All Visium libraries were sequenced simultaneously on the Illumina NextSeq2000 platform, at a sequencing depth of approximately 49M read-pairs for BALB/c_1, 52M for BALB/c_2, 85M for NOD.H-2b_1 and 100M for NOD.H-2b_2.
To gain more insight, BALB/c_2 and NOD.H-2b_1 were sequenced deeper to finally reach 380M and 288M read-pairs respectively.
Fastq files were processed with spaceranger-1.3.1 pipeline.
Assembly: mm10-2020-A
Supplementary files format and content: For each sample: tables with barcodes and features; matrix of filtered counts; json file; image of section.
Library strategy: Spatial Transcriptomics

source_name: Lacrimal Gland

growth_protocol: Mice were fed ad libitum and kept under a 12-hour light/12-hour dark cycle

extract_protocol:
10x Protocol CG000239 Rev C
Spatial 3' v1

value: Lacrimal Gland

tag: age

value: 4 months

tag: Sex

value: male


title: XT0109_aCD3_CD28


title: LLC_TPLKO_empty_dox_R1


title: BALB/c_1

value: BALB/cJ


title: TW0051_Negative

value: MACDZ


title: BALB/c_2


title: TW0051_MTB300


title: TW0051_Mtb_Lysate


title: NOD_2


title: Ribo-seq cpc-3 knockout DD28 rep2


title: Ribo-seq cpc-3 knockout DD24 rep2


title: Ribo-seq cpc-3 knockout DD36 rep2


title: LLC_TPLKO_empty_dox_R2


title: Ribo-seq cpc-3 knockout DD32 rep2


title: Sample 73 Patient 43

type: RNA

city: Foster City

first: Natahlie

last: Scholler

address: 324 Likeside Drive FC324-1

department: Oncology Research Department

institute: Gilead

hyb_protocol: After hybridization, samples are transferred to an nCounter instrument which removes excess probes. Purified target-probe complexes are bound, immobilized and aligned on the  imaging surface of the nCounter cartridge

scan_protocol: Sample cartridges are scanned by an automated fluorescence microscope. Barcodes are counted for each target molecule and the data are exported as a simple CSV file

data_processing: Raw data qualification and normalisation using internal Negative control, positive control and house keeping gene set.

label: Nanostring

source_name: DLBCL tumor

label_protocol: RNA hybridization with nCounter codeset chemistry (Capture probe & Reporter probe). The Reporter Probe carries the fluorescent barcode and the Capture Probe contains a biotin moiety that immobilizes the hybridized complex for data collection. Use of PanCancerImmune panel from nanostring completed with a 6 gene custom panel (CD69, IHH, PF4, PROM1, REN & TSLP)

extract_protocol: RNA extracted from 5 sections/ 4µm thickness - FFPE blocks. Region of interest identifed by pathologist expert based on H&E images.

tag: visit

value: PROGFCB

tag: molecular subgroup

value: ABC

tag: bestresponse

value: CR

tag: baseline tumour burden (mm2)

tag: worst grade of ne


title: Sample 74 Patient 12

value: BASELINE

value: GCB


title: Ribo-seq cpc-3 knockout DD20 rep2


title: Sample 69 Patient 08


title: Ribo-seq cpc-3 knockout DD16 rep2


title: Sample 72 Patient 22


title: TW0051_MAC_Lysate

tag: disease group

tag: stimulation

value: PBMC


title: normal kidney cortex tissue circRNA 3

city: Fuzhou

first: Nanwen

last: Zhang

address: Fuzhou

institute: Fujian Medical University

description: circRNA expression data from normal kidney cortex tissue

hyb_protocol: The labeled cRNAs were hybridized onto the Arraystar Human circRNA Arrays (8x15K, Arraystar), and incubated for 17 hours at 65°C in an Agilent Hybridization Oven.

scan_protocol: After washing, slides were scanned with the Agilent Scanner G2505C.

data_processing: Data was extracted using Agilent Feature Extraction software.A series of data processing including quantile normalization were performed using the R software package. The circRNAs that at least 7 out of 14 samples have flags in P or M (defined by GeneSpring software) were retained for further differential analyses. Differentially expressed circRNAs with statistical significance between the two groups were identified through Volcano Plot filtering. The circRNA/microRNA interaction was predicted with Arraystar's home-made miRNA target prediction software. All the differentially expressed circRNAs were annotated  in detail with the circRNA/miRNA interaction information. The circRNA/microRNA interaction was predicted with Arraystar's home-made miRNA target prediction software. All the differentially expressed circRNAs were annotated  in detail with the circRNA/miRNA interaction information.

source_name: normal kidney cortex tissue

label_protocol: Total RNA from each sample was amplified and transcribed into fluorescent cRNA utilizing ramdom primer according to Arraystar Super RNA Labeling protocol (Arraystar, Inc.)

growth_protocol: HCC and matched non-tumor hepar tissues

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

treatment_protocol: Tissues from HCC surgury and promptly frozen in liquid nitrogen.

value: normal kidney cortex tissue

tag: gender


title: kidney cortex tissue of DKI mouse circRNA1

description: circRNA expression data from kidney cortex tissue of DKI

source_name: kidney cortex tissue of DKI mouse

value: kidney cortex tissue of DKI


title: normal kidney cortex tissue circRNA 2


title: TW0051_aCD3_CD28


title: Ribo-seq cpc-3 knockout DD12 rep2

tag: genotype


title: kidney cortex tissue of DKI mouse circRNA2


title: TW0048_MTB300


title: TW0048_Negative

value: Unstimulated


title: Ribo-seq cpc-3 knockout DD48 rep1

value: cpc-3 knockout

tag: time point


title: Sample 71 Patient 09


title: Sample 68 Patient 61


title: Sample 64 Patient 15


title: Sample 70 Patient 32

value: PR


title: normal kidney cortex tissue circRNA 1


title: Sample 67 Patient 11


title: kidney cortex tissue of DKI mouse circRNA3

description: circRNA expression data kidney cortex tissue of DKI


title: TW0048_Mtb_Lysate

value: Mtb lysate


title: TW0048_MAC_Lysate

value: Mav lysate


title: Ribo-seq cpc-3 knockout DD36 rep1

tag: replicate

value: mycelia

value: ribosome-protected mRNA


title: Sample 66 Patient 10


title: Ribo-seq cpc-3 knockout DD44 rep1

tag: molecule subtype


title: Sample 65 Patient 49


title: Ribo-seq cpc-3 knockout DD40 rep1


title: Sample 63 Patient 12


title: Ribo-seq cpc-3 knockout DD32 rep1


title: Sample 62 Patient 48


title: Ribo-seq cpc-3 knockout DD28 rep1


title: Sample 61 Patient 56


title: Ribo-seq cpc-3 knockout DD24 rep1


title: Sample 60 Patient 64


title: Sample 59 Patient 46


title: Ribo-seq cpc-3 knockout DD20 rep1


title: TW0042_Negative


title: TW0048_aCD3_CD28


title: TW0042_Mtb_Lysate


title: TW0042_MTB300


title: Sample 53 Patient 63

value: SCREENING

value: PD


title: TW0042_aCD3_CD28

value: MACDZ


title: Sample 58 Patient 05

value: UNCLASSIFIED


title: Sample 55 Patient 38

value: CR


title: Sample 50 Patient 17


title: Sample 51 Patient 24


title: Sample 52 Patient 06

value: BASELINE


title: Sample 56 Patient 34

value: GCB


title: Sample 57 Patient 34


title: Ribo-seq cpc-3 knockout DD12 rep1


title: TW0036_Negative

value: PBMC


title: Ribo-seq frq knockout DD48 rep2

value: frq knockout


title: Sample 49 Patient 55

value: ABC

value: SD


title: Sample 47 Patient 56


title: Ribo-seq cpc-3 knockout DD16 rep1

value: cpc-3 knockout


title: TW0036_MTB300


title: Sample 54 Patient 01


title: Sample 46 Patient 51

value: PR


title: TW0042_MAC_Lysate


title: TW0036_Mtb_Lysate

value: Mtb lysate


title: Ribo-seq frq knockout DD44 rep2


title: Ribo-seq frq knockout DD40 rep2


title: Sample 44 Patient 53


title: TW0036_MAC_Lysate

value: Mav lysate


title: Sample 48 Patient 59


title: Sample 43 Patient 16


title: Ribo-seq frq knockout DD36 rep2

value: mycelia


title: Sample 42 Patient 31


title: Ribo-seq frq knockout DD28 rep2

value: ribosome-protected mRNA


title: Ribo-seq frq knockout DD32 rep2


title: TW0035_Negative

value: Unstimulated


title: TW0036_aCD3_CD28


title: TW0035_Mtb_Lysate


title: Sample 41 Patient 20

value: SCREENING


title: TW0035_MAC_Lysate

value: MACDZ


title: Sample 36 Patient 39


title: Sample 39 Patient 46


title: Sample 35 Patient 36


title: Ribo-seq frq knockout DD24 rep2


title: Sample 40 Patient 65


title: Sample 38 Patient 54


title: Sample 34 Patient 13

value: PD


title: Sample 45 Patient 44

value: CR


title: TW0028_Negative


title: Ribo-seq frq knockout DD16 rep2


title: Sample 37 Patient 49

value: BASELINE


title: Ribo-seq frq knockout DD20 rep2


title: TW0028_MTB300


title: Sample 32 Patient 03

value: GCB


title: Ribo-seq frq knockout DD12 rep2


title: Sample 30 Patient 57


title: Sample 31 Patient 58


title: TW0028_Mtb_Lysate

value: PBMC


title: Sample 33 Patient 04


title: Ribo-seq frq knockout DD48 rep1

value: frq knockout


title: TW0028_MAC_Lysate


title: TW0028_aCD3_CD28


title: Ribo-seq frq knockout DD44 rep1


title: Sample 28 Patient 61


title: Sample 27 Patient 41


title: Ribo-seq frq knockout DD40 rep1


title: TW0026_Negative


title: Sample 25 Patient 18


title: Sample 26 Patient 14


title: Sample 24 Patient 34


title: Sample 21 Patient 01


title: Ribo-seq frq knockout DD36 rep1


title: Ribo-seq frq knockout DD32 rep1


title: TW0026_MTB300


title: TW0026_Mtb_Lysate


title: Sample 19 Patient 11

value: ABC


title: Sample 29 Patient 62


title: Sample 18 Patient 07

value: SD


title: Ribo-seq frq knockout DD28 rep1


title: Ribo-seq frq knockout DD24 rep1


title: TW0026_MAC_Lysate


title: Sample 20 Patient 60


title: Sample 16 Patient 47


title: Sample 15 Patient 45


title: Ribo-seq frq knockout DD20 rep1


title: Ribo-seq frq knockout DD16 rep1


title: Sample 13 Patient 37


title: Sample 14 Patient 40

value: ND


title: TW0025_MTB300


title: Sample 17 Patient 50


title: Sample 12 Patient 30

value: PR


title: Sample 09 Patient 33


title: Sample 11 Patient 52

value: UNCLASSIFIED


title: Sample 10 Patient 42


title: Ribo-seq frq knockout DD12 rep1


title: TW0026_aCD3_CD28


title: Ribo-seq wild-type DD48 rep2

value: wild-type


title: Ribo-seq wild-type DD44 rep2

value: mycelia


title: Sample 08 Patient 25


title: Sample 05 Patient 27


title: Sample 22 Patient 02


title: Sample 04 Patient 26


title: TW0025_aCD3_CD28


title: Ribo-seq wild-type DD40 rep2

value: ribosome-protected mRNA


title: Sample 06 Patient 28


title: TW0025_Negative

value: Unstimulated


title: Sample 03 Patient 23

value: SCREENING


title: Sample 02 Patient 29


title: TW0023_Negative

value: MACDZ


title: TW0025_Mtb_Lysate

value: Mtb lysate


title: TW0025_MAC_Lysate

value: Mav lysate


title: TW0023_Mtb_Lysate


title: TW0023_MTB300


title: Sample 07 Patient 21


title: Ribo-seq wild-type DD36 rep2


title: Ribo-seq wild-type DD32 rep2


title: Ribo-seq wild-type DD28 rep2


title: Ribo-seq wild-type DD24 rep2


title: HUVEC CRISPR ADM TTS

city: Frankfurt am Main

first: Timothy

last: Warwick

state: Hesse

address: Theodor-Stern-Kai 7

department: Institute for Cardiovascular Physiology

institute: Goethe Universität Frankfurt am Main

data_processing:
FastQC (0.11.9)
Picard rmdup (2.27.1)
Bowtie2 (2.4.5)
samtools (1.15.1)
bamCoverage (3.5.1)
Assembly: hg19
Supplementary files format and content: bigWig (normalised ATAC-seq coverage)

source_name: Primary cells

growth_protocol: Pooled human umbilical vein endothelial cells (HUVECs) were purchased from PromoCell (C-12203, Lot No. 405Z013, 408Z014, 416Z042, Heidelberg, Germany). HUVECs were cultured in a humidified atmosphere of 5% CO2 at 37 °C. Fibronectin-coated (356009, Corning Incorporated, USA) dishes were used to culture the cells. Endothelial growth medium (EGM), consisting of endothelial basal medium (EBM) supplemented with human recombinant epidermal growth factor (EGF), EndoCGS-Heparin (PeloBiotech, Germany), 8% fetal calf serum (FCS) (S0113, Biochrom, Germany), penicillin (50 U/mL) and streptomycin (50 µg/mL) (15140-122, Gibco/ Lifetechnologies, USA) was used.

extract_protocol:
100.000 HUVECs were used for ATAC library preparation using Tn5 Transposase from Nextera DNA Sample Preparation Kit (Illumina). Cell pellets were resuspended in 50 µL PBS and mixed with 25 µL TD-Buffer, 2.5 µL Tn5, 0.5 µL 10% NP-40 and 22 µL H2O. The mixture was incubated at 37 °C for 30 min followed by 30 min at 50 °C together with 500 mM EDTA pH 8.0 for optimal recovery of digested DNA fragments. 100 µL of 50 mM MgCl2 was added for neutralization. The DNA fragments were purified with the MinElute PCR Purification Kit (Qiagen).
Amplification of library together with indexing was performed as described above.

treatment_protocol: Guide RNAs (gRNA) were designed with the help of the web-interfaces of CRISPR design (http://crispr.mit.edu/). CRISPR activation (CRISPRa) was performed with a catalytically inactive Cas9 (dCas9), which is fused to the transcription activator VP64 (pHAGE EF1α dCas9-VP64), whereas CRISPRi was performed with a dCas9 fusion to the KRAB repressive domain. Both were used together with a sgRNA(MS2) vector containing the individual guide RNA (gRNA) to induce or repress HIF1α-AS1 gene expression. pHAGE EF1α dCas9-VP64 and pHAGE EF1α dCas9-KRAB were a gift from Rene Maehr and Scot Wolfe (Addgene plasmid # 50918, # 50919)60 and sgRNA(MS2) cloning backbone was a gift from Feng Zhang (Addgene plasmid # 61424)61. The following oligonucleotides were used for cloning of the guide RNAs into the sgRNA(MS2) vector: For CRISPRa of HIF1α-AS1 5’-CACCGGGGC CGGCCTCGGCGTTAAT-3’ and 5’-AAACATTAACGCCGAGGCCGGCCCC-3’, and for CRISPRi of HIF1α-AS1 5’-CACCGGTCTGGTGAGGATCGCATGA-3’ and 5’-AAACTCATGCGATCCTCACCAGACC-3’. After cloning, plasmids were purified and sequenced. The transfection of the plasmids in HUVEC was performed using the NEON electroporation system (Invitrogen). For lentiCRISPR, guide RNAs (gRNA) were selected using the publicly available CRISPOR algorithm (http://crispor.tefor.net/)(Haeussler et al. 2016). A dual gRNA approach consisting of gRNA-A and gRNA-B was used to facilitate the individual deletions. The gRNAs were cloned into lentiCRISPRv2 vector backbone with Esp3I (Thermo Fisher, FD0454) according to the standard protocol(Sanjana et al. 2014).

value: Primary cells

value: HUVEC

tag: treatment

value: gRNA against ADM TTS


title: HUVEC CRISPR NTC

tag: tissue

value: Non-targeting gRNA


title: HUVEC CRISPRi CTL

tag: cell type

value: CTL gRNA


title: HUVEC CRISPRi HIF1a-AS1

value: HIF1a gRNA


title: Sample 01 Patient 19

value: GCB


title: Sample 23 Patient 35

value: BASELINE

value: SD


title: TW0023_MAC_Lysate

value: PBMC


title: TW0023_aCD3_CD28


title: HUVEC CRISPR EPHA2 TTS

value: gRNA against EPHA2 TTS


title: HUVEC CRISPR HIF1a-AS1 TFR

value: gRNA against HIF1a-AS1 TFR


title: TW0020_MTB300


title: TW0020_Negative

value: Unstimulated


title: Ribo-seq wild-type DD20 rep2

value: wild-type

value: mycelia

value: ribosome-protected mRNA


title: Ribo-seq wild-type DD16 rep2


title: Ribo-seq wild-type DD12 rep2


title: HUVEC CRISPRa HIF1a-AS1


title: Ribo-seq wild-type DD48 rep1


title: HUVEC CRISPRa CTL


title: TW0020_MAC_Lysate

value: MACDZ

value: Mav lysate


title: TW0020_Mtb_Lysate

value: Mtb lysate


title: TW0020_aCD3_CD28


title: TW0017_Negative


title: Ribo-seq wild-type DD40 rep1


title: Ribo-seq wild-type DD36 rep1


title: Ribo-seq wild-type DD44 rep1


title: Ribo-seq wild-type DD32 rep1


title: TW0017_Mtb_Lysate


title: TW0017_MTB300


title: TW0017_aCD3_CD28


title: TW0017_MAC_Lysate


title: iSat, BPS, rep3

city: Calgary

first: Jennifer

last: Thompson

state: Alberta

address: 3330 Hospital Dr NW

department: Physiology and Pharmacology

country: Canada

institute: University of Calgary

data_processing:
Kallisto 0.42.4
Assembly: NCBI RefSeq
Supplementary files format and content: text file
Supplementary files format and content: Includes transcript counts

source_name: iSAT

extract_protocol:
RNA was harvested using Rneasy mini plus kit (Qiagen). 1000 ng of total RNA was used for the construction of sequencing libraries.
RNA libraries for RNA-seq were prepared using NEBNext Ultra II directional RNA library prep kit for Illumina

treatment_protocol: Cells were treated with DMSO as a vehicle control or BPS [2.5 uM] dissolved in DMSO

value: iSAT

value: male

value: preadipocyte

value: WT C57 BL6

value: BPS


title: iSat, DMSO, rep2

value: DMSO


title: iSat, DMSO, rep1


title: iSat, BPS, rep4


title: iSat, DMSO, rep4


title: iSat, BPS, rep1


title: iSat, BPS, rep2


title: iSat, DMSO, rep3


title: iSat, BPS, rep5


title: iSat, DMSO, rep5


title: 14idBALBCmo6rp2

data_processing:
Data was analyzed by ROSALIND® (https://rosalind.bio/), with a HyperScale architecture developed by ROSALIND, Inc. (San Diego, CA).
Reads were trimmed using cutadapt
Quality scores were assessed using FastQC
Reads were aligned to the Mus musculus genome build GRCm38 using STAR
Individual sample reads were quantified using HTseq
Reads were normalized via Relative Log Expression (RLE) using DESeq2 R library
Read Distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC
DEseq2 was also used to calculate fold changes and p-values and perform optional covariate correction. Clustering of genes for the final heatmap of differentially expressed genes was done using the PAM (Partitioning Around Medoids) method using the fpc R library
Assembly: Mus musculus genome build GRCm38
Supplementary files format and content: Raw Count matrix are provided in .txt file
Supplementary files format and content: Normalized Count matrix are provided in .txt file

extract_protocol:
Each lacrimal gland was transferred into 2mL bead beating tubes (cat# 19-628, OMNI, Kennesaw GA) containing 700 µL of Qiazol lysis reagent (#79306, Qiagen). The lacrimal gland was disrupted using Bead Ruptor 4 (cat# 25-010, Omni) in two cycles: first at speed 5, for 40 sec and then at speed 5, for 30 sec. Between the two cycles, the tubes were placed on ice for 2 mins. RNA was isolated with the miRNeasy Mini kit (#217084, Qiagen) including a DNAse treatment step, according to the manufacturer’s instructions.
The amount of total RNA was estimated by a Nanodrop ND-1000 spectrophotometer and checked for purity and integrity (RIN) in a Bioanalyzer-2100 device (Agilent Technologies, Inc., Santa Clara, CA). 800 ng of total RNA (RIN>8) from each sample was used to prepare RNAseq libraries using the NEBNext rRNA Depletion Kit (Human/Mouse/Rat) followed by the NEBNext Ultra II RNA Library Prep Kit for Illumina (9 cycles of PCR). Completed libraries had been sequenced (2 x 75 paired ends) using Illumina NextSeq500 to generate 50M reads for each sample.

value: Lacrimal Gland

tag: strain

value: BALB/cJ

tag: age

value: 6 months


title: 17idNODmo6rp2

tag: Sex


title: 18idNODmo6rp3


title: 15idBALBCmo6rp3


title: TW0014_Negative

value: PBMC


title: Ribo-seq wild-type DD24 rep1


title: Ribo-seq wild-type DD20 rep1


title: Ribo-seq wild-type DD16 rep1


title: Ribo-seq wild-type DD28 rep1


title: 07idBALBCmo4rp1

value: 4 months


title: 10idNODmo4rp1


title: 06idNODmo2rp3

value: 2 months


title: 11idNODmo4rp2


title: 09idBALBCmo4rp3


title: TW0014_MTB300


title: 12idNODmo4rp3


title: 08idBALBCmo4rp2


title: 16idNODmo6rp1


title: 13idBALBCmo6rp1


title: 03idBALBCmo2rp3


title: 04idNODmo2rp1


title: TW0014_Mtb_Lysate


title: TW0014_MAC_Lysate


title: Ribo-seq wild-type DD12 rep1

value: wild-type

value: mycelia


title: 02idBALBCmo2rp2


title: TW0014_aCD3_CD28

value: MACDZ


title: TW0012_Negative

value: Unstimulated


title: TW0012_MTB300


title: TW0012_Mtb_Lysate

value: Mtb lysate


title: TW0012_MAC_Lysate

value: Mav lysate


title: TW0012_aCD3_CD28


title: RNA-Seq cpc-3 knockout DD28 rep2

data_processing:
For WT, Dfrq, and Dcpc-3 samples, basecalling was performed using the Real Time Analysis (RTA) workflow ver. 2.7.7. with default parameters. For cpc-3c samples, basecalling was performed using Illumina BaseSpace app using the FASTQ generation application ver. 1.0.0 and bcl2fastq.
RNA-seq reads were aligned to the N. crassa genome FungiDB Release 38 using STAR (Dobin et al, 2013).
Mapped RNA-seq reads were normalized to fragments per kilobase of exon model per million mapped reads (FPKM) values using Cufflinks (Trapnell et al., 2010; 2012).
The RNA-seq time series was analyzed using the following Extended Circadian Harmonic Oscillation (ECHO, De Los Santos et al., 2019) settings: two unpaired replicates, smoothing function enabled, unexpressed genes excluded from the analysis at the default 70% threshold, and OE/RE cutoff of 1.25. Genes were considered rhythmic if they had Benjamini-Hochberg p-values < 0.05, and oscillation types harmonic, damped or forced.
Genome_build: N. crassa genome FungiDB Release 38
Supplementary_files_format_and_content: Output from Cufflinks in generic FPKM tracking format saved as plain text files containing the estimated gene-level expression values.

extract_protocol:
Tissue was harvested in the dark, pressed in between paper towels to remove excess media, and immediately frozen in liquid N2. polyA+ RNA was isolated from 100 μg of total RNA with NEB Oligo d(T)25 Magnetic Beads.
Purified mRNA (8 ng) was used as the input to generate RNA-seq libraries using the Lexogen SENSE Total RNA-seq Library Preparation Kit (Cat. #009).

value: cpc-3 knockout


title: RNA-Seq cpc-3 knockout DD20 rep2


title: RNA-Seq cpc-3 knockout DD44 rep2


title: RNA-Seq cpc-3 knockout DD36 rep2


title: RNA-Seq cpc-3 knockout DD48 rep2


title: RNA-Seq cpc-3 knockout DD24 rep2


title: RNA-Seq cpc-3 knockout DD32 rep2


title: RNA-Seq cpc-3 knockout DD40 rep2


title: 2022164_E12_F2

description:
10x Genomics
2022164_E12_F2_S6

data_processing:
The demultiplexing, barcoded processing, gene counting and aggregation were made using the Cell Ranger
Assembly: mm10
Supplementary files format and content: Tab-separated values files

source_name: Cerebellum

extract_protocol:
Single cells from developing mouse cerebellar tissue were processed using the microfluidics-based 10x Chromium protocol
Single cells were processed using the Chromium v3 and v.3.1 Single Cell 3′ Library and Gel Bead Kit according to the manufacturers’ protocol

value: Cerebellum


title: 2022162_E12_F1

description:
10x Genomics
2022162_E12_F1_S4


title: 01idBALBCmo2rp1

value: male


title: 05idNODmo2rp2


title: TW0010_Negative


title: RNA-Seq cpc-3 knockout DD16 rep2


title: ESC budesonide_rep2 [GM8]

city: Baronissi

first: Giovanna

middle: Maria

last: Ventola

state: Salerno

address: Via S. Allende, 43/L

country: Italy

data_processing:
Illumina bcl2fastq software used for basecalling.
Sequenced reads were trimmed for adaptor sequence, polyA and masked for low-complexity or low-quality sequence by using cutadapt software (version 2.5).
The sample was mapped on m27 - GrCm39 (latest version of GenCode) using the bioinformatics tool STAR (version 2.7.5c) [4], with the standard parameters for paired reads.
The quantification of transcripts expressed for each sequenced samples was performed using featureCount algorithm (version 2.0.
The Bioconductor package DESeq2 was used to normalize the data, using the median of ratio, to perform the differential expression analysis. The counts were divided by sample-specific size factors determined by median ratio of gene counts relative to geometric mean per gene.
Assembly: m27 - GrCm39
Supplementary files format and content: Matrix table with raw gene counts for every gene and every sample
Supplementary files format and content: tab-delimited text files include normalized values (median of ratio of gene counts) for each Sample.

source_name: 2D ESCs

extract_protocol:
Total RNA was extracted using the RNAeasy kit.
Lexogen’s QuantSeq kit provides a library preparation protocol designed to generate Illumina compatible libraries from polyadenylated RNA. The QuantSeq protocol generates only one fragment per transcript, resulting in extremely accurate gene expression values, and the sequences obtained are close to the 3’ end of the transcripts.

tag: esc line

value: Tretaed with Budesonide for 72 hrs


title: ESC proline_rep2 [GM5]

value: Tretaed with Proline for 72 hrs


title: ESC proline budesonide_rep1 [GM10]

value: Treated with Proline and Budesonide for 72hrs


title: ESC proline budesonide_rep3 [GM12]


title: ESC proline budesonide_rep2 [GM11]


title: ESC budesonide_rep3 [GM9]


title: ESC proline_rep3 [GM6]


title: TW0010_MTB300


title: ESC budesonide_rep1 [GM7]


title: ESC Control_rep3 [GM3]

value: Untreated


title: ESC proline_rep1 [GM4]


title: 3D gastruloids Budesonide_rep3 [B3]

data_processing:
Illumina bcl2fastq software used for basecalling.
Sequenced reads were trimmed for adaptor sequence and masked for low-complexity or low-quality sequence by using cutadapt software (version 2.5).
The sample was mapped on m27 - GrCm39 (latest version of GenCode) using the bioinformatics tool STAR (version 2.7.5c) [4], with the standard parameters for paired reads.
The quantification of transcripts expressed for each sequenced samples was performed using featureCount algorithm (version 2.0.
The Bioconductor package DESeq2 was used to normalize the data, using the median of ratio, to perform the differential expression analysis. The counts were divided by sample-specific size factors determined by median ratio of gene counts relative to geometric mean per gene.
Assembly: m27 - GrCm39
Supplementary files format and content: Matrix table with raw gene counts for every gene and every sample
Supplementary files format and content: tab-delimited text files include normalized values (median of ratio of gene counts) for each Sample.

source_name: 3D ESC aggregates

extract_protocol:
Total RNA was extracted using the RNAeasy kit.
RNA libraries were prepared for sequencing using standard Illumina protocols. The quality of RNA were evaluated by NanoDrop One spectrophotometer (NanoDrop) and by TapeStation 4200 (Agilent Technologies  ), respectively. Indexed libraries were prepared from 1 µg/ea purified RNA with TruSeq Stranded mRNA (Illumina) Li-brary Prep Kit according to the manufacturer’s instructions.

value: Treated with Budesonide for 80hrs


title: 2012822_P4_F2

description:
10x Genomics
2012822_P4_F2_S2


title: TW0010_MAC_Lysate

value: PBMC


title: 2012818_P0_F2

description:
10x Genomics
2012818_P0_F2_S4


title: 2012821_P4_M2

description:
10x Genomics
2012821_P4_M2_S7


title: 2022163_E12_M2

description:
10x Genomics
2022163_E12_M2_S5


title: 2012819_P4_M1

description:
10x Genomics
2012819_P4_M1_S5


title: 2022161_E12_M1

description:
10x Genomics
2022161_E12_M1_S3


title: ESC Control_rep1 [GM1]


title: RNA-Seq cpc-3 knockout DD40 rep1


title: RNA-Seq cpc-3 knockout DD48 rep1


title: ESC Control_rep2 [GM2]


title: 3D gastruloids Budesonide_rep2 [B2]


title: TW0010_aCD3_CD28


title: 2012816_P0_F1

description:
10x Genomics
2012816_P0_F1_S2


title: RNA-Seq cpc-3 knockout DD32 rep1


title: 2012817_P0_M2

description:
10x Genomics
2012817_P0_M2_S3


title: 3D gastruloids Control_rep3 [A3]


title: RNA-Seq cpc-3 knockout DD12 rep2


title: TW0010_Mtb_Lysate


title: 2012820_P4_F1

description:
10x Genomics
2012820_P4_F1_S6


title: RNA-Seq cpc-3 knockout DD28 rep1


title: 3D gastruloids Budesonide_rep1 [B1]


title: 3D gastruloids Control_rep2 [A2]


title: RNA-Seq cpc-3 knockout DD44 rep1


title: 2012815_P0_M1

description:
10x Genomics
2012815_P0_M1_S1


title: TW0001_Negative


title: 2008335_E17_F2

description:
10x Genomics
2008335_E17_F2_S10


title: RNA-Seq cpc-3 knockout DD36 rep1


title: 2008332_E17_M1

description:
10x Genomics
2008332_E17_M1_S7


title: 2008334_E17_M2

description:
10x Genomics
2008334_E17_M2_S9


title: TW0001_MTB300


title: TW0001_MAC_Lysate


title: RNA-Seq cpc-3 knockout DD12 rep1


title: RNA-Seq cpc-3 knockout DD24 rep1


title: TU0162_2_MTB300

value: IGRA-HC


title: 2008333_E17_F1

description:
10x Genomics
2008333_E17_F1_S8


title: RNA-Seq cpc-3 knockout DD16 rep1


title: TU0162_2_Negative


title: TW0001_aCD3_CD28


title: TW0001_Mtb_Lysate


title: TU0162_2_Mtb_Lysate


title: 2008330_E14_M2

description:
10x Genomics
2008330_E14_M2_S5


title: TU0162_2_MAC_Lysate


title: 2008331_E14_F2

description:
10x Genomics
2008331_E14_F2_S6


title: 2008329_E14_F1

description:
10x Genomics
2008329_E14_F1_S4


title: D283_6

data_processing:
Sequence reads were trimmed for adaptor sequence/low-quality sequence
Trimmed sequence reads were mapped to hg38 using STAR
Assembly: hg38
Supplementary files format and content: tab-delimited text files include count values for each Sample

extract_protocol:
After 7 days culture cells were collected
RNA libraries for RNA-seq were prepared following manufacturer's protocols.

treatment_protocol: D283 cells were infected with GFP expressing lentiviruses encoding either non-targeting control gRNA or gRNA targeting OTX2 in triplicate

tag: cell line

value: medulloblastoma

value: OTX2 knockout


title: TU0162_2_aCD3_CD28


title: RNA-Seq frq knockout DD48 rep2

value: frq knockout

value: mycelia


title: TU0092_Negative

value: Unstimulated


title: RNA-Seq frq knockout DD40 rep2


title: D283_5


title: 2008328_E14_M1

description:
10x Genomics
2008328_E14_M1_S3


title: RNA-Seq frq knockout DD36 rep2


title: RNA-Seq frq knockout DD44 rep2


title: 1924264_3_cd1_p2_F

description:
10x Genomics
1924264_3_cd1_p2_F_S3


title: D283_2

value: control


title: RNA-Seq frq knockout DD28 rep2


title: D283_3


title: 1924265_4_cd1_p2_F

description:
10x Genomics
1924265_4_cd1_p2_F_S4


title: 1924263_2_cd1_p2_M

description:
10x Genomics
1924263_2_cd1_p2_M_S2


title: RNA-Seq frq knockout DD32 rep2


title: RNA-Seq frq knockout DD24 rep2


title: TU0092_MTB300


title: TU0092_MAC_Lysate

value: Mav lysate


title: TU0092_Mtb_Lysate

value: Mtb lysate


title: TU0092_aCD3_CD28


title: D283_1


title: RNA-Seq frq knockout DD20 rep2


title: RNA-Seq cpc-3 knockout DD20 rep1

value: cpc-3 knockout


title: TU0078_Negative

value: IGRA+HC


title: D283_4


title: RL-e13-UT-2

description: 10x Genomics

value: Cerebellum


title: ChIPExo-mDANeuron aPGBD1

city: Berlin

first: Amit

last: Pande

address: Robert-Rössle-Straße 10

institute: Max Delbruck Center for Medicine

data_processing:
Basecalls were performed using bcl2fastq v2.17
To map the sequencing reads, the FASTQ files were aligned to the human genome hg19 using Bowtie2. To filter out PCR duplicates, we used Samtools in the following steps: (i) filter out low-quality (<20) reads; (ii) sort reads by name (sort -n); (iii) fix read pairs (fixmate); (iv) sort reads by chromosomal position (sort); (v) mark duplicates (markdup); (vi) extract only read_1 from each non-duplicated pair (view -f 0x40). As the majority of TFs bind as dimers (either homo or hetero), it was necessary to determine the optimal trim length. Therefore, we took 3 times the radius of the PGBD1 and converted that size into number of bps. After rounding, this resulted in the optimal trim length in bp. BEDTools “genome coverage” function was employed to generate the read profiles for both strands separately using the determined optimal trim length. Subsequently, both strands were combined and only base positions where there are reads on both strands were reported as the PGBD1 binding profile. After this step, the replicates were normalized based on their average background read count and then combined using their average read count per base position. GEMv3.4 was used to detect narrow peaks using the following command:   --d Read_Distribution_ChIP-exo.txt --expt PGBD1.bam --ctrl PGBD1.bam --g hg19.chrom.sizes --f BAM --genome hg/ --k_min 6 --k_max 18 --outBED --outNP --smooth 3 --mrc 20 –q 3
Peaks were called uing MACS v2.1.0 with the significance cut-off q-value <=0.01
Supplementary files format and content: BED Files
Library strategy: ChIP-Exo

source_name: Neuron

extract_protocol: The libraries were quantified by using the KAPA library quantification kit for Illumina sequencing platforms (KAPA Biosystems, KK4824) and sequenced on HiSeq following the manufacturer’s protocol.

value: Neuron


title: OE 2 (over expression 2) Tamas

data_processing:
Raw reads filtering
Software tools such as the FASTX-Toolkit and Trimmomatic were used to discard low-quality reads, trim adaptor sequences, and eliminate poor-quality bases. Outliers with over 30 % disagreement were discarded.
Read alignment
Salmon was used to build index and align the reads using the following commands: salmon index -t transcripts.fa -i transcripts_index --decoys decoys.txt -k 31, ./bin/salmon quant -i transcripts_index/ -l IU -1 fastq -2 fastq --validateMappings –o output. Quantified data was checked for GC content and gene length biases using R package NOISeq to provide useful plots for quality control of count data.
Reproducibility
Assembly: Mean variance and PCA were computed between biological replicates using the tximport package in R using lengthscaledTPM (CPM cutoff >2 and sample cutoff 2 between the replicates) for the analyzed groups (NPC-KD, KD-Neuron). Batch effects were removed using the RUv package of Bioconductor. Subsequently, the samples were normalized using TMM (weighted trimmed mean of M-values) method for differential expression analysis. The differential expression analysis was conducted using the DESeq2 package. Dataset for the analysis of schizophrenia and control was obtained from GSE145656 and analyzed as above.

source_name: human

extract_protocol:
Total RNA was extracted from frozen brain samples by using the Direct-zol™ RNA Kit (Zymo Research) following the manufacturer’s instructions.
1µl of total RNA was loaded on Total RNA Pico Chip and measured for its integrity with Bionalyzer 2100 (Agillent Biotechnology). To concentrate the samples for the preparation of RNA seq libraries, the RNAs were precipitated at -80°C  overnight by solution of isopropanol, Na-Acetate and glycogen. 500 ng total RNA was used to prepare seq Libraries by Roche KAPA RNA HyperPrep with  Ryboerase Kit according to the manufacturer instructions. High throughput sequencing was run at Hiseq4000, with single indexed, paired end, 75bp mode.
To deplete PGBD1, we first used a CRISPR/Cas9-mediated KO approach, however, no stable, proliferative KO line could be generated in either hESCs or neuroprogenitor cells (NPC).
300,000 human neuroprogenitor cells (NPC) were transfected by different pmiRNA constructs together with pSB100 plasmid encoding SB100 transposase in ratio 10:1. 3 days posttransfection the pmiRNA contained cells were FACS sorted based on mCherry marker gene intensity. The sorted single cells were cultured for additional three weeks then cells were FACS sorted repeatedly. Then PGBD1 protein level was determined by Western blotting from individual stable cell lines lysate

treatment_protocol: We first let the NPCs to differentiate onto Matrigel coated plates for 8 days using a medium containing: Neurobasal: DMEM/F12 (1:1), N2 (1x), B27 (1x), purmorphamine (1 µM), vitamin C (200 µM), and FGF8 (100 ng/ml).

value: human

value: Neuroprogenitor cells

value: midbrain dopaminergic neurons (mDAN)

value: PGBD1 over expression

value: undifferentiated NPC


title: ChIPExo-Neuroprogenitor control

source_name: neuroprogenitor cells

value: neuroprogenitor cells

value: WT


title: ChIPExo-mDANeuron control


title: ChIPExo-Neuroprogenitor aPGBD1


title: RNA-Seq frq knockout DD16 rep2

value: frq knockout


title: TU0078_MTB300

value: PBMC


title: RNA-Seq frq knockout DD12 rep2

value: mycelia


title: 1924262_1_cd1_p2_M

description:
10x Genomics
1924262_1_cd1_p2_M_S1


title: RL-e13-UT-1

description: 10x Genomics


title: RL-e14-UT-2

description: 10x Genomics


title: TU0078_MAC_Lysate


title: TU0078_Mtb_Lysate


title: EomesCreER-e16

description: 10x Genomics


title: RNA-Seq frq knockout DD40 rep1


title: RL-e14-UT

description: 10x Genomics


title: TU0078_aCD3_CD28


title: TU0077_Negative

value: IGRA-HC

value: Unstimulated


title: OE 1 (over expression 1) Tamas


title: KD 5 (knock down 5) Tamas

value: PGBD1 Knock down


title: KD 6 (knock down 6) Tamas


title: RNA-Seq frq knockout DD36 rep1


title: Pax2+ renal progenitors-Panobinostat

city: Firenze

first: Roberto

last: Semeraro

address: Viale Pieraccini, 6

department: Medicina Sperimentale e Clinica

institute: Università degli studi di Firenze

data_processing:
Basecall performed with cellranger mkfastq v3.0.1.
Single cell feature counts performed with cellranger count v.3.0.1.
Genome_build: mm10
Supplementary_files_format_and_content: matrix.mtx (filtered count matrix), barcodes.tsv, and features.tsv files generated through cellranger count.

source_name: kidney

growth_protocol: Kidney cortex of healthy Pax2.rtTA;TetO.Cre; R26.mT/mG mouse (n=2) was minced into small fragments, incubated 30 min at 37°C with 1 mg/mL collagenase Type IV and then was gently pressed and sieved through a 70 μm mesh cell strainer. Strainer was repeatedly rinsed with cold D-PBS and the filtrate was collected, washed and resuspended in RPMI-1640 medium (Sigma-Aldrich) supplemented with 20% FBS (Hyclone, GE Healthcare Life Sciences) and 1% penicillin-streptomycin solution. Glomeruli were cultured at 37°C in a humidified atmosphere containing 95 % air 5 % CO2 and after 5 days glomerular cell outgrowths were trypsinized, cells resuspended in D-PBS and sorted for endogenous expression of GFP by using the FACSAria III BD (Becton Dickinson).

extract_protocol:
The single cell suspension with 98% viability was run on a 10x Chromium Single Cell instrument (10x Genomics, Pleasanton, CA) following Manufacturer’s instructions
3’ gene expression libraries were constructed following 10x Genomics's instructions
Libraries were sequenced on an Illumina NextSeq500 (Illumina Inc., San Diego, CA).

treatment_protocol: Following sorting, cells were plated in 6 well dishes at a density of 80,000 cell/well in RPMI supplemented with 20% FBS. Following 16 hours of starvation, cells were stimulated for 48 hours with 0.2 µM panobinostat, or DMSO (Sigma-Aldrich) in DMEM-F12 medium (D2906, Sigma-Aldrich) containing 10% FBS (Hyclone, GE Healthcare Life Sciences).

value: Pax2+ renal progenitors from mouse kidney

tag: developmental stage

value: 9 weeks

value: Pax2.rtTA;TetO.Cre; R26.mT/mG

value: Panobinostat


title: TU0077_MTB300

data_processing:
Reads passing Illumina filters were further filtered for those mapping to tRNA, rRNA, adapters, and spike-in controls using Bowtie v1.0.0 (-S --fr)
Filtered reads were against the GRCh38 reference genome and Gencode v27 annotations using STAR v2.6.1 (default options)
Reads mapping to the reference genome are further filtered to remove low-complexity reads with PRINSEQ v0.20.3 (-lc_method "dust" -lc_threshold 4)
Sequencing read coverage per gene was counted using HTSeq-count v0.7.1 (-m union -s yes -t exon -i gene_id) and features with 0 counts across all samples were removed
Assembly: GRCh38.gencode.v27B
Supplementary files format and content: read count matrix with rows as genes and columns as cells

source_name: blood - PBMC

extract_protocol:
PBMCs were thawed and added at a density of 1x10e6 cells per well to a round-bottom 96-well plate. They were stimulated for 24 hrs at 37C with MTB300, Mav lysate, Mtb lysate, anti-CD28 (1g/ml, CD28.2; eBioscience) together with anti-CD3 (1g/ml pre-coated overnight at 4C, UCHT1; BioLegend), as well as left unstimulated as a negative control. After 24 hrs, cells were resuspended in PBS with 10% (v/v) FBS and incubated at 4C for 10 min. Cells were then stained with fixable viability dye eFluor506 (eBioscience) and an antibody mixture containing anti-human CD3-AF700 (UCHT1; ThermoFisher), CD4-APCeFluor780 (RPA-T4; eBioscience), CD8-V500 (RPA-T8; BD Horizon), CCR7-PerCPCy5.5 (G043H7, Biolegend) CD14-V500 (M5E2; BD Bioscience), CD19-V500 (HIB19; BD Bioscience), CD25-FITC (M-A251; BD Bioscience), CD45RA-eFluor450 (HI100, eBiosciences), CD137-APC (4B4-1; BioLegend), OX40-PECy7 (Ber-ACT35; BioLegend), and PD-L1-PE (29E.2A3; BioLegend) for 20 min at room temperature. Cells were washed and resuspended in PBS before being transferred into a 5 ml polypropylene FACS tube (BD Bioscience). PBMCs were sorted, based on forward and side scatter excluding debris and doublets (Figure S1D), on a FACSAria into TRIzol LS (Thermo Fisher). Acquisition files were analyzed using FlowJo. Total RNA was extracted from ~100,000 cells in TRIzol LS using the miRNeasy Micro Kit (Qiagen) on a QIAcube (Qiagen). Total RNA was amplified according to Smart Seq protocol (31). cDNA was purified using AMPure XP beads. cDNA was used to prepare a standard barcoded sequencing library (Illumina). Samples were sequenced using an Illumina HiSeq2500 to obtain 50-bp single end reads. Samples that failed to be sequenced due to limited sample availability or failed the quality control were eliminated from further sequencing and analysis. The full protocol can be found at protocols.io (http://dx.doi.org/10.17504/protocols.io.bxr6pm9e).
Sequencing was performed according to a paired-end strategy using the NovaSeq sequencer (Illumina). Samples that failed quality control standards were eliminated from further analysis21.


title: control 3 Tamas

value: human


title: control 1 Tamas

value: Neuroprogenitor cells


title: TU0077_Mtb_Lysate

value: Mtb lysate


title: control 2 Tamas

value: midbrain dopaminergic neurons (mDAN)


title: Pax2+ renal progenitors-vehicle

value: vehicle


title: RNA-Seq frq knockout DD32 rep1


title: RNA-Seq frq knockout DD44 rep1


title: RNA-Seq frq knockout DD48 rep1


title: 2177376_TP-190a-NSUN-GTF_2F9_4

first: Stanislav

last: Zakharenko

state: Tennessee

address: 262 Danny Thomas Place

department: Developmental Neurobiology

data_processing:
Total stranded RNA sequencing data were processed by  the internal AutoMapper pipeline.
First the raw reads were firs trimmed (Trim-Galore version 0.60),  mapped to human genome assembly (GRCh38) (STAR v2.7)
and then the gene level values were quantified (RSEM v1.31) based on GENCODE annotation (v31) .
Genome_build: hg38
Supplementary_files_format_and_content: gene-level RSEM raw count, TPM, FPKM txt files

source_name: Cortical organoid

extract_protocol:
Total RNA was isolated by using mirVana microRNA Isolation Kit (Life Technologies, Carlsbad, CA)
Libraries were prepared from total RNA with the TruSeq Stranded Total RNA Library Prep Kit according to the manufacturer’s instructions (Illumina, PN 20020599)

value: Cortical organoid

value: 68 days

value: NSUN5-GTF2ird2 +/-


title: KD 4 (knock down 4) Tamas

value: undifferentiated NPC


title: RNA-Seq frq knockout DD24 rep1

value: frq knockout

value: mycelia


title: TU0077_MAC_Lysate

value: IGRA-HC

value: Mav lysate

value: PBMC


title: TU0077_aCD3_CD28