title: VT_S110

status: Public on Feb 28 2019

type: SRA

city: Sydney

first: Dominik

last: Beck

state: NSW

address: Level 10, UTS Building 11 (CB11 81-113, Broadway, Ultimo)

department: School of Biomedical Engineering

country: Australia

institute: University of Technology Sydney

description:
MCD4899_A222
scRNA-seq VTP-50469-treated #110
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt

library_source: transcriptomic

data_processing:
Raw datasets were filtered to remove cells with small library size (<500k reads).
Paired-end scRNA-seq reads aligned to the human genome (hg19) using the software package STAR with standard parameters.
Cells with low alignment rate (<85%) were removed for downstream analysis.
Expression profiles in control and VTP-50469 cells was generated using featureCounts.
Differential expression analysis was performed on normalized read counts (transcripts per million bp) using the edgeQRF algorithm.
Genome_build: hg19
Supplementary_files_format_and_content: Summary of raw counts per gene per cell (.txt)

molecule: total RNA

organism: Homo sapiens

source_name: ALL xenograft cells

growth_protocol: ALL PDX cells were innoculated into NSG mice and expanded in vivo.

extract_protocol:
At day 28 post treatment initiation, ALL cells were harvested from mice bone marrow. Cell suspensions were stained with anti-human CD45-FITC antibodies; single cells were sorted into 96-well plates and then processed using the Smart-seq2 protocol.
Full length transcripts were reverse transcribed into cDNA using SuperScript II reverse transcriptase (ThermoFisher), followed by PCR amplification. Amplified cDNA from single cells was fragmented and indexed individually using Nextera XT DNA Sample Preparation Kit (Illumina). Single cell libraries were pooled and sequenced on an Illumina HiSeq2000 platform.

treatment_protocol: ALL enografted mice were treated with vehicle control or VTP-50469 for 4 weeks.

tag: treatment


title: VT_S112

description:
MCD4899_A224
scRNA-seq VTP-50469-treated #112
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S111

description:
MCD4899_A223
scRNA-seq VTP-50469-treated #111
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S109

description:
MCD4899_A221
scRNA-seq VTP-50469-treated #109
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S108

description:
MCD4899_A220
scRNA-seq VTP-50469-treated #108
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S106

description:
MCD4899_A218
scRNA-seq VTP-50469-treated #106
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S103

description:
MCD4899_A215
scRNA-seq VTP-50469-treated #103
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S104

description:
MCD4899_A216
scRNA-seq VTP-50469-treated #104
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S107

description:
MCD4899_A219
scRNA-seq VTP-50469-treated #107
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S105

description:
MCD4899_A217
scRNA-seq VTP-50469-treated #105
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S102

description:
MCD4899_A214
scRNA-seq VTP-50469-treated #102
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S101

description:
MCD4899_A213
scRNA-seq VTP-50469-treated #101
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S100

description:
MCD4899_A212
scRNA-seq VTP-50469-treated #100
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S99

description:
MCD4899_A211
scRNA-seq VTP-50469-treated #99
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S109

description:
MCD4899_A205
scRNA-seq VTP-50469-treated #93
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt

value: vehicle


title: C_S110

description:
MCD4899_A206
scRNA-seq VTP-50469-treated #94
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S111

description:
MCD4899_A207
scRNA-seq VTP-50469-treated #95
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S112

description:
MCD4899_A208
scRNA-seq VTP-50469-treated #96
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S108

description:
MCD4899_A204
scRNA-seq VTP-50469-treated #92
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S97

description:
MCD4899_A209
scRNA-seq VTP-50469-treated #97
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S98

description:
MCD4899_A210
scRNA-seq VTP-50469-treated #98
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S107

description:
MCD4899_A203
scRNA-seq VTP-50469-treated #91
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S106

description:
MCD4899_A202
scRNA-seq VTP-50469-treated #90
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S105

description:
MCD4899_A201
scRNA-seq VTP-50469-treated #89
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S102

description:
MCD4899_A198
scRNA-seq VTP-50469-treated #86
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S103

description:
MCD4899_A199
scRNA-seq VTP-50469-treated #87
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S104

description:
MCD4899_A200
scRNA-seq VTP-50469-treated #88
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S97

description:
MCD4899_A193
scRNA-seq VTP-50469-treated #81
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S98

description:
MCD4899_A194
scRNA-seq VTP-50469-treated #82
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S99

description:
MCD4899_A195
scRNA-seq VTP-50469-treated #83
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S100

description:
MCD4899_A196
scRNA-seq VTP-50469-treated #84
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S101

description:
MCD4899_A197
scRNA-seq VTP-50469-treated #85
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S96

description:
MCD4899_A192
scRNA-seq VTP-50469-treated #80
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S93

description:
MCD4899_A189
scRNA-seq VTP-50469-treated #77
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S90

description:
MCD4899_A186
scRNA-seq VTP-50469-treated #74
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S91

description:
MCD4899_A187
scRNA-seq VTP-50469-treated #75
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S94

description:
MCD4899_A190
scRNA-seq VTP-50469-treated #78
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S92

description:
MCD4899_A188
scRNA-seq VTP-50469-treated #76
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S89

description:
MCD4899_A185
scRNA-seq VTP-50469-treated #73
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S95

description:
MCD4899_A191
scRNA-seq VTP-50469-treated #79
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: mMSC-Bone (BMSC-Osteo)

type: RNA

city: Odense

first: Moustapha

last: Kassem

address: JB Winsløwsvej 25.1

department: Department of Clinical Research

country: Denmark

institute: University of Southern Denmark

description:
M167-03
Isolation of spontaneous immortalized clonal cell lines as described in PMID: 21211534.

hyb_protocol: Hybridized to the Affymetrix GeneChip® MG430A 2.0 Array (Affymetrix, USA).

scan_protocol: Arrays were scanned using GeneArray scanner (Affymetrix, USA).

data_processing: Raw data was preprocessed at probe level using the Bioconductor package Oligo using the RMA (Robust Multi-array Average) method.

label: biotin

organism: Mus musculus

source_name: Spontaneously immortalized clonal bone-marrow mesenchymal stem cells

label_protocol: cDNA was synthesized from 8 μg RNA using SuperScript Choice System (Invitrogen, USA) according to the manufacturer's instructions. Biotinylation was performed using BioArray High Yield RNA transcript labelling kit (Enzo® kit, Enzo Diagnostics, USA), according to the protocol provided by the manufacturer.

growth_protocol: DMEM + 12% FBS 1% P/S.

extract_protocol: Total RNA was isolated with RNeasy Kit (QIAGEN Nordic, UK).

tag: strain/background

tag: genotype/variation

value: Beta-actin-GFP+ ApoE(-/-)

tag: cell type

value: mMSC-Bone


title: VT_S88

description:
MCD4899_A184
scRNA-seq VTP-50469-treated #72
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S84

description:
MCD4899_A180
scRNA-seq VTP-50469-treated #68
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S85

description:
MCD4899_A181
scRNA-seq VTP-50469-treated #69
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S86

description:
MCD4899_A182
scRNA-seq VTP-50469-treated #70
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: mMSC-Adipo (BMSC-Adipo)

description:
M167-02
Isolation of spontaneous immortalized clonal cell lines as described in PMID: 21211534.

value: mMSC-Adipo


title: VT_S78

description:
MCD4899_A174
scRNA-seq VTP-50469-treated #62
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S87

description:
MCD4899_A183
scRNA-seq VTP-50469-treated #71
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S81

description:
MCD4899_A177
scRNA-seq VTP-50469-treated #65
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S82

description:
MCD4899_A178
scRNA-seq VTP-50469-treated #66
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S79

description:
MCD4899_A175
scRNA-seq VTP-50469-treated #63
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S83

description:
MCD4899_A179
scRNA-seq VTP-50469-treated #67
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S80

description:
MCD4899_A176
scRNA-seq VTP-50469-treated #64
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S76

description:
MCD4899_A172
scRNA-seq VTP-50469-treated #60
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S77

description:
MCD4899_A173
scRNA-seq VTP-50469-treated #61
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S71

description:
MCD4899_A167
scRNA-seq VTP-50469-treated #55
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S72

description:
MCD4899_A168
scRNA-seq VTP-50469-treated #56
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S70

description:
MCD4899_A166
scRNA-seq VTP-50469-treated #54
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S74

description:
MCD4899_A170
scRNA-seq VTP-50469-treated #58
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S69

description:
MCD4899_A165
scRNA-seq VTP-50469-treated #53
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S67

description:
MCD4899_A163
scRNA-seq VTP-50469-treated #51
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S75

description:
MCD4899_A171
scRNA-seq VTP-50469-treated #59
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S66

description:
MCD4899_A162
scRNA-seq VTP-50469-treated #50
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S65

description:
MCD4899_A161
scRNA-seq VTP-50469-treated #49
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S64

description:
MCD4899_A160
scRNA-seq VTP-50469-treated #48
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S62

description:
MCD4899_A158
scRNA-seq VTP-50469-treated #46
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S63

description:
MCD4899_A159
scRNA-seq VTP-50469-treated #47
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S61

description:
MCD4899_A157
scRNA-seq VTP-50469-treated #45
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S73

description:
MCD4899_A169
scRNA-seq VTP-50469-treated #57
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S68

description:
MCD4899_A164
scRNA-seq VTP-50469-treated #52
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S59

description:
MCD4899_A155
scRNA-seq VTP-50469-treated #43
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S58

description:
MCD4899_A154
scRNA-seq VTP-50469-treated #42
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S60

description:
MCD4899_A156
scRNA-seq VTP-50469-treated #44
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S53

description:
MCD4899_A149
scRNA-seq VTP-50469-treated #37
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S55

description:
MCD4899_A151
scRNA-seq VTP-50469-treated #39
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S56

description:
MCD4899_A152
scRNA-seq VTP-50469-treated #40
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S57

description:
MCD4899_A153
scRNA-seq VTP-50469-treated #41
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S54

description:
MCD4899_A150
scRNA-seq VTP-50469-treated #38
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S52

description:
MCD4899_A148
scRNA-seq VTP-50469-treated #36
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S46

description:
MCD4899_A142
scRNA-seq VTP-50469-treated #30
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S47

description:
MCD4899_A143
scRNA-seq VTP-50469-treated #31
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S51

description:
MCD4899_A147
scRNA-seq VTP-50469-treated #35
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S49

description:
MCD4899_A145
scRNA-seq VTP-50469-treated #33
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S43

description:
MCD4899_A139
scRNA-seq VTP-50469-treated #27
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S50

description:
MCD4899_A146
scRNA-seq VTP-50469-treated #34
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S45

description:
MCD4899_A141
scRNA-seq VTP-50469-treated #29
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S44

description:
MCD4899_A140
scRNA-seq VTP-50469-treated #28
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S48

description:
MCD4899_A144
scRNA-seq VTP-50469-treated #32
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S41

description:
MCD4899_A137
scRNA-seq VTP-50469-treated #25
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S40

description:
MCD4899_A136
scRNA-seq VTP-50469-treated #24
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S39

description:
MCD4899_A135
scRNA-seq VTP-50469-treated #23
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S42

description:
MCD4899_A138
scRNA-seq VTP-50469-treated #26
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S37

description:
MCD4899_A133
scRNA-seq VTP-50469-treated #21
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S36

description:
MCD4899_A132
scRNA-seq VTP-50469-treated #20
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S38

description:
MCD4899_A134
scRNA-seq VTP-50469-treated #22
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S34

description:
MCD4899_A130
scRNA-seq VTP-50469-treated #18
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S35

description:
MCD4899_A131
scRNA-seq VTP-50469-treated #19
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S29

description:
MCD4899_A125
scRNA-seq VTP-50469-treated #13
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S32

description:
MCD4899_A128
scRNA-seq VTP-50469-treated #16
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S30

description:
MCD4899_A126
scRNA-seq VTP-50469-treated #14
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S33

description:
MCD4899_A129
scRNA-seq VTP-50469-treated #17
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S31

description:
MCD4899_A127
scRNA-seq VTP-50469-treated #15
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S26

description:
MCD4899_A122
scRNA-seq VTP-50469-treated #10
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S25

description:
MCD4899_A121
scRNA-seq VTP-50469-treated #9
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S22

description:
MCD4899_A118
scRNA-seq VTP-50469-treated #6
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S21

description:
MCD4899_A117
scRNA-seq VTP-50469-treated #5
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S23

description:
MCD4899_A119
scRNA-seq VTP-50469-treated #7
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S20

description:
MCD4899_A116
scRNA-seq VTP-50469-treated #4
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S24

description:
MCD4899_A120
scRNA-seq VTP-50469-treated #8
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S17

description:
MCD4899_A113
scRNA-seq VTP-50469-treated #1
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S18

description:
MCD4899_A114
scRNA-seq VTP-50469-treated #2
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S16

description:
MCD4899_A112
scRNA-seq Control #112
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S15

description:
MCD4899_A111
scRNA-seq Control #111
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S96

description:
MCD4899_A96
scRNA-seq Control #96
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S1

description:
MCD4899_A97
scRNA-seq Control #97
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S12

description:
MCD4899_A108
scRNA-seq Control #108
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S5

description:
MCD4899_A101
scRNA-seq Control #101
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S9

description:
MCD4899_A105
scRNA-seq Control #105
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S6

description:
MCD4899_A102
scRNA-seq Control #102
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S13

description:
MCD4899_A109
scRNA-seq Control #109
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S3

description:
MCD4899_A99
scRNA-seq Control #99
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S8

description:
MCD4899_A104
scRNA-seq Control #104
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S14

description:
MCD4899_A110
scRNA-seq Control #110
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S28

description:
MCD4899_A124
scRNA-seq VTP-50469-treated #12
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S10

description:
MCD4899_A106
scRNA-seq Control #106
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S4

description:
MCD4899_A100
scRNA-seq Control #100
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S7

description:
MCD4899_A103
scRNA-seq Control #103
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S27

description:
MCD4899_A123
scRNA-seq VTP-50469-treated #11
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S11

description:
MCD4899_A107
scRNA-seq Control #107
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S2

description:
MCD4899_A98
scRNA-seq Control #98
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: VT_S19

description:
MCD4899_A115
scRNA-seq VTP-50469-treated #3
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S94

description:
MCD4899_A94
scRNA-seq Control #94
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S95

description:
MCD4899_A95
scRNA-seq Control #95
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S93

description:
MCD4899_A93
scRNA-seq Control #93
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S91

description:
MCD4899_A91
scRNA-seq Control #91
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S87

description:
MCD4899_A87
scRNA-seq Control #87
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S86

description:
MCD4899_A86
scRNA-seq Control #86
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S81

description:
MCD4899_A81
scRNA-seq Control #81
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S89

description:
MCD4899_A89
scRNA-seq Control #89
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S73

description:
MCD4899_A73
scRNA-seq Control #73
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S72

description:
MCD4899_A72
scRNA-seq Control #72
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S75

description:
MCD4899_A75
scRNA-seq Control #75
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S78

description:
MCD4899_A78
scRNA-seq Control #78
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S83

description:
MCD4899_A83
scRNA-seq Control #83
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S82

description:
MCD4899_A82
scRNA-seq Control #82
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S70

description:
MCD4899_A70
scRNA-seq Control #70
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S71

description:
MCD4899_A71
scRNA-seq Control #71
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S84

description:
MCD4899_A84
scRNA-seq Control #84
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S90

description:
MCD4899_A90
scRNA-seq Control #90
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S79

description:
MCD4899_A79
scRNA-seq Control #79
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S77

description:
MCD4899_A77
scRNA-seq Control #77
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S85

description:
MCD4899_A85
scRNA-seq Control #85
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S92

description:
MCD4899_A92
scRNA-seq Control #92
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S69

description:
MCD4899_A69
scRNA-seq Control #69
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S68

description:
MCD4899_A68
scRNA-seq Control #68
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S67

description:
MCD4899_A67
scRNA-seq Control #67
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S88

description:
MCD4899_A88
scRNA-seq Control #88
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S76

description:
MCD4899_A76
scRNA-seq Control #76
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S62

description:
MCD4899_A62
scRNA-seq Control #62
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S63

description:
MCD4899_A63
scRNA-seq Control #63
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S55

description:
MCD4899_A55
scRNA-seq Control #55
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S57

description:
MCD4899_A57
scRNA-seq Control #57
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S52

description:
MCD4899_A52
scRNA-seq Control #52
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S51

description:
MCD4899_A51
scRNA-seq Control #51
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S46

description:
MCD4899_A46
scRNA-seq Control #46
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S44

description:
MCD4899_A44
scRNA-seq Control #44
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S60

description:
MCD4899_A60
scRNA-seq Control #60
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S58

description:
MCD4899_A58
scRNA-seq Control #58
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S49

description:
MCD4899_A49
scRNA-seq Control #49
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S65

description:
MCD4899_A65
scRNA-seq Control #65
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S61

description:
MCD4899_A61
scRNA-seq Control #61
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S56

description:
MCD4899_A56
scRNA-seq Control #56
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S64

description:
MCD4899_A64
scRNA-seq Control #64
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S53

description:
MCD4899_A53
scRNA-seq Control #53
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S47

description:
MCD4899_A47
scRNA-seq Control #47
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S50

description:
MCD4899_A50
scRNA-seq Control #50
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S48

description:
MCD4899_A48
scRNA-seq Control #48
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S54

description:
MCD4899_A54
scRNA-seq Control #54
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S45

description:
MCD4899_A45
scRNA-seq Control #45
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S59

description:
MCD4899_A59
scRNA-seq Control #59
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S74

description:
MCD4899_A74
scRNA-seq Control #74
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S80

description:
MCD4899_A80
scRNA-seq Control #80
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S36

description:
MCD4899_A36
scRNA-seq Control #36
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S37

description:
MCD4899_A37
scRNA-seq Control #37
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S33

description:
MCD4899_A33
scRNA-seq Control #33
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S34

description:
MCD4899_A34
scRNA-seq Control #34
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S32

description:
MCD4899_A32
scRNA-seq Control #32
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S28

description:
MCD4899_A28
scRNA-seq Control #28
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S31

description:
MCD4899_A31
scRNA-seq Control #31
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S30

description:
MCD4899_A30
scRNA-seq Control #30
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S29

description:
MCD4899_A29
scRNA-seq Control #29
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S24

description:
MCD4899_A24
scRNA-seq Control #24
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S39

description:
MCD4899_A39
scRNA-seq Control #39
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S38

description:
MCD4899_A38
scRNA-seq Control #38
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S35

description:
MCD4899_A35
scRNA-seq Control #35
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S66

description:
MCD4899_A66
scRNA-seq Control #66
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S19

description:
MCD4899_A19
scRNA-seq Control #19
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S22

description:
MCD4899_A22
scRNA-seq Control #22
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S23

description:
MCD4899_A23
scRNA-seq Control #23
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S20

description:
MCD4899_A20
scRNA-seq Control #20
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S18

description:
MCD4899_A18
scRNA-seq Control #18
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S12

description:
MCD4899_A12
scRNA-seq Control #12
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S14

description:
MCD4899_A14
scRNA-seq Control #14
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S21

description:
MCD4899_A21
scRNA-seq Control #21
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S13

description:
MCD4899_A13
scRNA-seq Control #13
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S9

description:
MCD4899_A09
scRNA-seq Control #9
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S7

description:
MCD4899_A07
scRNA-seq Control #7
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S6

description:
MCD4899_A06
scRNA-seq Control #6
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S15

description:
MCD4899_A15
scRNA-seq Control #15
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S8

description:
MCD4899_A08
scRNA-seq Control #8
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S5

description:
MCD4899_A05
scRNA-seq Control #5
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S16

description:
MCD4899_A16
scRNA-seq Control #16
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S4

description:
MCD4899_A04
scRNA-seq Control #4
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S11

description:
MCD4899_A11
scRNA-seq Control #11
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S40

description:
MCD4899_A40
scRNA-seq Control #40
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S41

description:
MCD4899_A41
scRNA-seq Control #41
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S42

description:
MCD4899_A42
scRNA-seq Control #42
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S17

description:
MCD4899_A17
scRNA-seq Control #17
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S43

description:
MCD4899_A43
scRNA-seq Control #43
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S3

description:
MCD4899_A03
scRNA-seq Control #3
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S25

description:
MCD4899_A25
scRNA-seq Control #25
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S2

description:
MCD4899_A02
scRNA-seq Control #2
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S27

description:
MCD4899_A27
scRNA-seq Control #27
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S26

description:
MCD4899_A26
scRNA-seq Control #26
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S1

description:
MCD4899_A01
scRNA-seq Control #1
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: C_S10

description:
MCD4899_A10
scRNA-seq Control #10
SmartSeq2_PDX_VTP50469_Control_featurecount_rawcounts.txt


title: ChIP_PolIItotal_siPCF11_R1

city: Oxford

first: Kinga

last: Kamieniarz-Gdula

address: South Parks Road

department: Sir William Dunn School of Pathology

country: United Kingdom

institute: University of Oxford

library_source: genomic

data_processing:
ChIP-seq reads were mapped with bowtie2 with hg19 genome index.
Macs2 was used to detect significant enrichments – broad peaks, q-value < 0.01.
For visualisation genome-wide coverage within 50 bp was calculated normalised for sequencing depth (rpm). Reads were calibarted additionally for D.melanogaster content. BigWig files were generated to visualize the data in the UCSC browser.
Genome_build: hg19
Supplementary_files_format_and_content: Bed: Table with three columns: chr, start, end; contains peak coordinates
Supplementary_files_format_and_content: bigWig: created form bedGraph file (table: chr, start, end, value) with the bedGraphToBigWig utility available form UCSC; scores represent sequencing-depth normalized read density or log2(IP/input)

molecule: genomic DNA

source_name: HeLa cells

growth_protocol: HeLa cells were maintained in DMEM with 10% fetal bovine serum.

extract_protocol:
ChIP was performed as described in accompanying paper. Antibodies used are indicated for each sample.
Sample 1-12: genomic libraries were prepared using NEBNext ChIP-Seq master-mix kit. Sample 13-25: Diagenode MicroPlex library preparation kit v2 (C05010012) was used to prepare libraries for sequencing, following manufacturer’s instructions.

treatment_protocol: siRNAs from Dharmacon were transfected for 48 hrs according to RNAiMAX reagent protocol at the concentrations indicated.

tag: cell line

value: HeLa cells

tag: molecule subtype

value: total Pol II bound DNA

tag: genotype/treatment

value: PCF11 knock-down (5 nM)

tag: antibody

value: Rabbit polyclonal anti-Pol II total (N-20) Santa Cruz Cat# sc-899

tag: calibration


title: ChIP_PolIIS2ph_siLUC_R1

value: Pol II S2ph bound DNA

value: siLUC control for knock-down (5 nM)

value: Mouse monoclonal anti-Pol II CTD S2ph; MBL international Cat# MABI0602; RRID:AB_2747403


title: ChIP_PolIIS2ph_siPCF11_R1


title: FOXP1 ChIPseq in OCI-LY1 cells

city: New York

first: Ashley

middle: Stephen

last: Doane

state: NY

address: 1300 York Ave

country: USA

institute: Weill Cornell Medicine

data_processing:
Counts: Raw image data were converted into base calls via the Illumina pipeline CASAVA version 1.8 with default parameters. All 51-bp-long paired-end reads were mapped to the reference human genome sequence, hg38, using BWA aligner (Li H. and Durbin R. (2009) Bioinformatics, 25:1754-60) with the default parameters.
Peaks: Peak detection was performed with the ChIPseeqer program (Giannopoulou and Elemento, BMC Bioinformatics 2011, 12:277) and annotated to genes and/or promoters based on hg38 refseq genes downloaded from the UCSC Genome Browser
Genome_build: human hg38 build
Supplementary_files_format_and_content: bigwig, bigbed, narrowPeak, and bed formats
Genome Build:
LY1_FOXP1_fc.bw: hg38
LY1_FOXP1_peaks.bb: hg38
LY1_FOXP1_peaks.narrowPeak: hg38

source_name: OCI-LY1 cells

growth_protocol: cultured cells, IMDM+10%FBS

extract_protocol:
Chromatin was sheared to 200-400bp, incubated with H3K27ac antibody (abcam 4729) overnight at 4C on a rocker, antibody chromatin complex was captured by ProteinA agarose beads, washed with low salt, high salt, LiCl, and TE buffer, eluted and then de-crosslinked at 65C with 0.3M NaCl final for at least 6 hr.  Final chormatin was purified using Qiagen PCR purification columns.
Libraries were prepared using the Illumina Truseq ChIPseq Library preparation Kit following the manufacture’s instructions (Illumina, CA).

tag: tissue

value: OCI-LY7 cells

tag: chip antibody

tag: chip antibody host, amount, catelog number and provider

value: ab4729, Rabbit, 2 ug


title: ChIP_input_forPolIIS2ph_siLUC_R1

value: input DNA


title: ChIP_PolIItotal_siLUC_R1


title: ChIP_input_forPolIItotal_siPCF11_R2


title: ChIP_input_forPolIItotal_siLUC_R1


title: ChIP_input_forPolIItotal_siPCF11_R1


title: ChIP_input_forPolIItotal_siLUC_R2


title: Normal-d4PGCLCs rep1

city: Wuhan

first: Zili

last: Li

state: Hubei

country: China

institute: HuaZhong Science and Technology University

data_processing:
Illumina Casava1.7 software was used for basecalling.
The raw reads were trimmed to remove the adapter sequences and filter the low-quality reads using Trim Galore
The software HTSeq was used to evaluate the quality of the sequencing data.
All the clean reads were mapped to the human genome (GRCh37/hg19) using STAR software.
Fragments Per Kilobase of transcript per Million fragments mapped (FPKM) were calculated from these mapped reads using the HTSeq and analyzed further in log2(FPKM+1).
Genome_build: GRCh37/hg19
Supplementary_files_format_and_content: tab-delimited text files include RPKM values for each Sample.

source_name: PGCLCs derived from healthy donor

growth_protocol: All iPSC lines and ESCs were maintained under a feeder-free condition in mTeSR1 medium (Stem Cell Technologies).For iMeLC induction, the iPSCs/ESCs were dissociated into single cells with a 1 to 1 mixture of TrypLE Select (Life Technologies) and 0.5 mM EDTA/PBS, and 2×105 cells were plated into per well of a human plasma fibronectin (Millipore)-coated 12-well plate in GMEM (Life Technologies) supplemented with 15% KSR, 0.1 mM NEAA, 2 mM L-glutamine, 1 mM sodium pyruvate, 0.1 mM 2-mercaptoethanol, 50 ng/ml Activin A (R&D Systems), 3 mM CHIR99021(Selleck) and 10 µM ROCK inhibitor. After 2 days of pre-induction, about 3×103 cells were plated into per well of the low-attachment 96-well plate (Corning) in GMEM supplemented with 15% KSR, 0.1 mM NEAA, 2 mM L-glutamine, 1 mM sodium pyruvate, 0.1 mM 2-mercaptoethanol, 200 ng/ml BMP4, 100 ng/ml SCF, 20 ng/ml human LIF, 50 ng/ml EGF and 10 µM ROCK inhibitor for PGCLC induction. Media were replaced every day.

extract_protocol:
For hiPSCs/hESCs and pre-induced iMeLCs, total RNAs were extracted with DirectZol RNA mini-prep (Zymo research),and The cDNA libraries were prepared using the TruSeq Stranded Total RNA LT Sample Prep Kit.For EpCAM/INTEGRINα6 double positive PGCLCs sorted form day 4 embryoids, the full-length cDNA libraries were produced with SMART-Seq v4 Ultra Low Input RNA Kit (Clontech).
RNA libraries were prepared for sequencing using standard Illumina protocols

tag: patient diagnosis

value: healthy

tag: Stage

value: PGC-induced stage

value: primodial germ cell-like cells

tag: facs antibody

value: EpCAM and INTEGRINα6 (both eBioscience)


title: 1122-d4PGCLCs rep1

source_name: PGCLCs derived from idiopathic azoospermia patient

value: idiopathic azoospermia


title: ChIP_PolIItotal_siPCF11_R2


title: Normal-d4PGCLCs rep2


title: ChIP_input_forPolIIS2ph_siPCF11_R1


title: 1106-d4PGCLCs rep1


title: H1-d4PGCLCs rep2

source_name: PGCLCs derived from H1 cell line


title: 1106-d4PGCLCs rep2


title: Normal-iMeLCs rep2

source_name: iMeLCs derived from healthy donor

value: pre-induced stage

value: incipient mesoderm-like cells


title: ChIP_PolIItotal_siLUC_R2

value: HeLa cells


title: Normal-iMeLCs rep1


title: Input for FOXP1 in OCI-LY1 cells

data_processing:
Counts: Raw image data were converted into base calls via the Illumina pipeline CASAVA version 1.8 with default parameters. All 51-bp-long paired-end reads were mapped to the reference human genome sequence, hg38, using BWA aligner (Li H. and Durbin R. (2009) Bioinformatics, 25:1754-60) with the default parameters.
Peaks: Peak detection was performed with the ChIPseeqer program (Giannopoulou and Elemento, BMC Bioinformatics 2011, 12:277) and annotated to genes and/or promoters based on hg38 refseq genes downloaded from the UCSC Genome Browser
Genome_build: human hg38 build
Supplementary_files_format_and_content: bigwig, bigbed, narrowPeak, and bed formats


title: H1-d4PGCLCs rep1


title: 1106-iMeLCs rep1

source_name: iMeLCs derived from idiopathic azoospermia patient


title: H1-iMeLCs rep1

source_name: iMeLCs derived from H1 cell line


title: 1122-iMeLCs rep2


title: 1122-iMeLCs rep1


title: 1106-iMeLCs rep2


title: Normal-iPSCs rep2

source_name: iPSCs derived from healthy donor

value: pluripotent stage

value: induced pluripotent stem cells


title: H1-iMeLCs rep2


title: 1122-d4PGCLCs rep2


title: H1ESCs rep1

source_name: ESCs derived from H1 cell line

value: embryonic stem cells


title: 1106-iPSCs rep2

source_name: iPSCs derived from idiopathic azoospermia patient


title: 1122-iPSCs rep2


title: 1106-iPSCs rep1


title: H1ESCs rep2


title: tumor Treg LFPRLR

city: Cleveland

first: Kuan-Hui

middle: Ethan

last: Chen

state: MS

address: 1003 W Sunflower Rd

institute: Delta State University

description: processed data: tumor Treg gene_exp.diff

data_processing:
RNAseq reads from whole tumor samples were aligned to mm10 (UCSC) by topcoat 2.1.1.
cufflinks/cuffmerge/cuffdiff (version 2.0.2) were performed to assemble transcripts, to estimate their abundances, and to test for differential expression
For RNAseq reads from tumor Tregs samples, reads were aligned to mm10 (UCSC) by hisat2 (2.0.4)
stringtie (version 1.2.3) was performed to assemble transcripts and to estimate their abundances
The differential expression analysis was then examined using cuffdiff (2.0.2)
Genome_build: mm10
Supplementary_files_format_and_content: gtf

source_name: primary breast tumor

extract_protocol:
Total RNA was extracted using Trizol followed by rRNA depletion (NEB)
cDNA library was constructed as described in manufacturer's instruction (Kapa Biosystems)

treatment_protocol: mice were orthotopically injected with 4T1 breast cancer cells treated with other control splice modulating oligomer or LFPRLR splice modulating oligomer for 12 days. Whole tumor or sorted tumor Tregs were collected and the gene expression was analyzed by RNAseq between the control and LFPRLR treated mice. N=6 in each group.

tag: strain

value: Balb/c

tag: injected with

value: 4T1 breast cancer cells treated with LFPRLR splice modulating oligomer

value: mammary

tag: tissue/cell subtype

value: sorted tumor Tregs


title: Normal-iPSCs rep1


title: tumor Tregs control

value: 4T1 breast cancer cells treated with other control splice modulating oligomer


title: HL60_DMSO_1_RNAseq

city: Hefei

first: Li

last: Wang

state: Anhui

address: Shushanhu road#350, building#2,Room 413

institute: High Magnetic Field Laboratory, Chinese Academy of Sciences (CHMFL)

data_processing:
Raw data (raw reads) of fastq format were firstly processed through Trimmomatic v0.36  to obtain clean data by removing reads containing adapter and other illumina-specific sequences, ploy-N and with low quality. All the downstream analyses were based on the clean data with high quality.
Index  of  the  reference  genome  was  built  using  Kallisto (version v0.43.1)   and paired-end clean reads were aligned to the reference genome using Kallisto (version v0.43.1) .
The transcript abundances are output by kallisto in transcripts per million(TPM) units(Bray NL, Pimentel H, Melsted P, Pachter L. et al., 2016)
Genome_build: GRCh37
Supplementary_files_format_and_content: tab-delimited text files include TPM values for each Sample

molecule: polyA RNA

source_name: peripheral blood

growth_protocol: cultured in RPMI 1640 media (Corning, USA) with 10% fetal bovine serum (FBS) and supplemented with 2% L-glutamine 1% penicillin/streptomycin

extract_protocol:
cells were collected and flash frozen on dry ice, RNA was harvested using Trizol reagent. Illumina TruSeq RNA Sample Prep Kit (Cat#FC-122-1001) was used with 1 ug of total RNA for the construction of sequencing libraries.The sequencing is done by Novogene Corporation (Beijing, China)
RNA libraries were prepared for sequencing using standard Illumina protocols

treatment_protocol: 1*106 HL60 cells were seeded in 6-well plate and treated with 10nM JSH009 for 10hours

value: AML cells

tag: treatment

value: DMSO

value: peripheral blood

tag: genotype

value: myc +


title: HL60_JSH-009_1_RNAseq

tag: cell type


title: HL60_JSH-009_2_RNAseq

tag: cell line


title: HL60_DMSO_2_RNAseq


title: whole tumor LFPRLR

description: processed data: whole tumor gene_exp.diff

value: whole tumor


title: HUMAN_SPERM_ATACseq_Rep1

city: Atlanta

first: Yoonhee

last: Jung

state: Georgia

address: 615 Michael Street

department: Human Genetics

institute: Emory university

data_processing:
All libraries were sequenced using an Illumina Hiseq v4 sequencer and 50 bp paired-end format. Paired reads were aligned to mouse reference genome mm9, human reference genome hg19 and monkey reference genome rheMac8 using Bowtie2.  ATAC-seq reads were aligned using default parameters except -X 2000 -m 1. Duplicates were removed using Samtools.  To adjust for fragment size, we aligned all reads as + strands offset by +4 bp and – strands offset by -5 bp (Buenrostro et al. 2013). To separate nucleosome-free (THSS)  and  mono-nucleosome (Nucleosome) fragments, we chose reads 50-115 bp 180-247 bp in length, respectively. Mono-nucleosome reads were analyzed using DANPOS2 (Chen et al. 2013). MACS was used for peak calling with nucleosome-free reads (Liu 2014). For generating wiggle files, we combined each replicates.
Genome_build: mm9, hg19, rheMac8

source_name: Sperm

extract_protocol:
After sperm cells were counted, the nuclei from 100,000 sperm were isolated with Lysis Buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3mM MgCl2) containing 0.1% NP40, 0.1% Tween-20, and 0.01% Digitonin. Purified sperm nuclei pellet was then resuspended in the transposase reaction mix containing 0.05% digitonin and incubated for 30 min at 37°C. Following incubation, sperm were treated with Proteinase K at 55°C for 2 hr, and gDNA was isolated by phenol:chloroform:isoamyl alcohol and EtOH precipitation.
Library amplification was done with 2x KAPA HiFi mix and 1.25 µM indexed primers using the following PCR conditions: 72°C for 5 min; 98°C for 30 s; and 10-11 cycles at 98°C for 10 s, 63°C for 30 s, and 72°C for 1 min.

tag: strain background

value: sperm


title: whole tumor control


title: HUMAN_SPERM_ATACseq_Rep2


title: 1122-iPSCs rep1

value: idiopathic azoospermia


title: Uninfected_WT

first: Maxim

middle: N.

last: Artyomov

state: MO

address: 660 S. Euclid Avenue, Campus Box 8118

department: Immunology&Pathology

institute: Washington University in St.Louis

description: Sorted CD45.1+ CD3– CD19– NK1.1+ NKp46+ cells from livers of uninfected WT:Tbx21-/- mixed bone marrow chimeras

data_processing:
quality control, Cell Ranger Single Cell Software Suite (v1.3.1)
sample de-multiplexing,  Cell Ranger Single Cell Software Suite (v1.3.1)
barcode processing, Cell Ranger Single Cell Software Suite (v1.3.1)
single-cell 3′ gene counting , Cell Ranger Single Cell Software Suite (v1.3.1)
alignment, Cell Ranger Single Cell Software Suite (v1.3.1)
Genome_build: mm10
Supplementary_files_format_and_content: Filtered matrix of gene by cell expression, file with barcodes and file with expressed genes. Files are ready to be imported to Seurat package.

source_name: Liver of WT:Tbx21-/- mixed bone marrow chimera

extract_protocol:
WT:Tbx21-/- mixed bone marrow chimeras were generated by combining CD45.1+ WT bone marrow cells with CD45.2+ Tbx21-/- bone marrow cells in a 1:1 ratio and transplanting mixture of cells into a lethally irradiated CD45.1+ WT recipient. 8 weeks later, some mice were infected with 200 tachyzoites of the Prugniaud strain of T. gondii. Thirty-five days post infection, liver lymphocytes were isolated. CD3– CD19– NK1.1+ NKp46+ that were either CD45.1+ or CD45.2+ were separately FACS sorted
Droplet-based 3′ end massively parallel single-cell RNA sequencing (scRNAseq) was performed by encapsulating sorted live CD45+ tumor infiltrating cells into droplets and libraries were prepared using Chromium Single Cell 3′ Reagent Kits v1 according to manufacturer’s protocol (10x Genomics).  The generated scRNAseq libraries were sequenced using an Illumina HiSeq2500. 14x98nt read lengths were used.

tag: cells genotype

value: Liver


title: RARb3_IEC KO

city: Pittsburgh

first: Zheng

last: Kuang

state: PA

address: 4400 Fifth Avenue

department: Biological Sciences

institute: Carnegie Mellon University

data_processing:
Sequence data were mapped against the mm10 genome using TopHat and FPKMs were generated using Cuffdiff with parameters FPKM >10 (50), fold change >2, and adjusted p value <0.01.
Genome_build: mm10
Supplementary_files_format_and_content: Comma-delimited file with expression values for each sample

source_name: Ileum

extract_protocol:
RNA was extracted from whole mouse ileum
RNA quality was assessed by Agilent 2100 Bioanalyzer. Truseq RNA sample preparation kit v2 (Illumina) was used for the preparation of sequencing libraries.
Sequencing was performed on an Illumina HiSeq 2500 for single end 50 bp length reads.

treatment_protocol: No treatment

value: Ileum

tag: diet

value: Chow diet

value: RARb (intestinal epithelial cell specific-)KO


title: RARb1_IEC KO


title: WT1_RARb fl/fl

value: RARb floxed mice (wildtype)


title: Ctr rep1

first: Shasha

last: Zhu

address: Jixi

institute: 1st affiliated hospital of Anhui medical university

data_processing:
Sequencing performed with HiSeq 2500 (Illumina)
RNA-seq reads were aligned to the mm9 genome with bowtie2 allowing one mismatch
Differential analyses were carried out with DEseq2 with the following setting: Fold change (2), P value (0.05)
Genome_build: mm9
Supplementary_files_format_and_content: tab-delimited text files include reads values for each Sample

source_name: liver

growth_protocol: NKT Cells, gating as GFPhi cells, were sorted from livers or spleens of Va14 Tg.cxcr6gfp/+ mice by FACS Aria (BD Biosciences).

extract_protocol:
total RNA was extracted from islets using TRIzol reagent (Invitrogen , cat. no. 15596018). Poly(A+) RNAs were enriched with biotinylated oligo(dT) and converted to cDNA with Superscript III (Invitrogen, cat. no. 18080-051)
A ddNTP was added to the 3’-end of the cDNA by a terminal transferase. After second-strand cDNA synthesis, 23 cycles of PCR were performed to amplify the cDNA with lengths of 200-400 nt

treatment_protocol: Purified NKT cells were stimulated with plate-coated mCD1d-PBS57 tetramers or keep resting

value: liver

tag: age


title: Ctr rep2

tag: tissue


title: Tet rep2


title: MutantFibroblasts_1,25VitD_rep3

city: Sao Paulo

first: Bruno

last: Ferraz-de-Souza

address: Av Dr Arnaldo, 455 sala 4344

department: Endocrinology

country: Brazil

institute: Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo

description: Gene expression of human fibroblasts with a null p.Arg30* VDR after 24-h treatment with 1,25-vitamin D

hyb_protocol: Samples of labeled fragmented cDNA were hybridized to GeneChip Human Gene 2.0 ST Arrays (Affymetrix) following manufacturers protocol

scan_protocol: Arrays were scanned on GeneChip Scanner 3000 7G System (Affymetrix)

data_processing: Array quality control and data analysis was performed using Partek Genomics Suite®. A Benjamini-Hochberg-corrected p-value cut-off of 0.05 was used for selecting significant differentially expressed genes.

source_name: Human mutant VDR fibroblasts

label_protocol: Samples were processed using Ambion® WT Expression kit (Thermo Fisher) and GeneChip WT Terminal Labeling (Affymetrix) kits according to manufacturer’s instructions, starting with 200 ng total RNA

growth_protocol: Human skin fibroblasts from an individual with hereditary vitamin D-resistant rickets bearing a homozygous p.Arg30* VDR mutation [Damiani et al., Osteoporos Int 2015; 26(6):1819-23] and from an age/sex-matched control were obtained from 4-mm punch biopsies of the forearm skin, after institutional board approval and with informed consent. Skin explants were fragmented in 6-well tissue culture plates and covered in complete AmnioMAX™ C-100 medium until attachment to surface; after approximately 12 days fibroblasts grown out of explants covered well surfaces completely. Secondary fibroblast cultures were maintained in high glucose DMEM supplemented with 10% FBS and 1% P/S. Fibroblasts were used for experiments between passages five to fifteen.

extract_protocol: Total RNA extraction was performed using the RNeasy Plus Mini Kit (Qiagen, cat no 74134) according to manufacturers instructions

treatment_protocol: Cells were grown in 6-well plates and treated with 10 nM 1,25D or ethanol (1 ul/ml of medium) for 24 hours

value: skin

value: fibroblasts

tag: vitamin d receptor status

value: Mutant p.Arg30* VDR


title: MutantFibroblasts_Vehicle_rep3

description: Gene expression of human fibroblasts with a null p.Arg30* VDR after 24-h treatment with vehicle


title: ControlFibroblasts_1,25-VitD_rep2

description: Gene expression of human fibroblasts with a wild-type VDR after 24-h treatment with 1,25-vitamin D

source_name: Human control fibroblasts

value: Wild-type VDR


title: MOUSE_SPERM_HiC_Rep2

data_processing:
For Hi-C, The paired end Hi-C reads were mapped independently using BWA mem and paired. Reads mapping to more than two genomic locations (one for each paired end or two for one paired end in agreement with its partner) were discarded. Reads were further filtered and normalized using HiFive v1.2.2 (Sauria et al., 2015) using the Express algorithm and the settings "--min-interactions 5 --min-distance 100000 --min-binsize 500 --num-bins 50 --express-iterations 5000 --min-change 0.00005 --remove-distance --express-reads cis".
Genome_build: mm9

extract_protocol: We generated in-situ Hi-C libraries using DpnII restriction enzyme following the protocol as described (Rao et al., 2014). Briefly, 10 million sperm were crosslinked with 1% formaldehyde, quenched with glycine, washed with PBS, and permeabilized to obtain intact nuclei. Nuclear DNA was then digested with DpnII, the 5’-overhangs were filled with biotinylated dCTPs and dA/dT/dGTPs to make blunt-end fragments, which were then ligated, reverse-crosslinked, and purified by standard DNA ethanol precipitation. Purified DNA was sonicated to 200-500 bp small fragments and captured with streptavidin beads. Standard Illumina TruSeq library preparation steps including end-repairing, A-tailing, and ligation with universal adaptors were all performed on beads, washing twice in TWB between each step. DNA on the beads was PCR amplified with barcoded primers using KAPA SYBR FAST qPCR Master Mix for 5~12 PCR cycles to obtain enough DNA for sequencing. Generated libraries were paired-end sequenced on Illumina HiSeq2500 v4. Two sets of biological replicates were generated, and replicates were combined after ensuring high correlation.


title: ControlFibroblasts_1,25-VitD_rep1


title: MutantFibroblasts_1,25VitD_rep1


title: ControlFibroblasts_1,25-VitD_rep4


title: MutantFibroblasts_1,25VitD_rep4


title: MutantFibroblasts_1,25VitD_rep2


title: ControlFibroblasts_1,25-VitD_rep3


title: ControlFibroblasts_Vehicle_rep4

description: Gene expression of human fibroblasts with a wild-type VDR after 24-h treatment with vehicle


title: Infected_KO

description: Sorted CD45.2+ CD3– CD19– NK1.1+ NKp46+ cells from livers of T. gondii-infected WT:Tbx21-/- mixed bone marrow chimeras


title: MutantFibroblasts_Vehicle_rep1


title: ControlFibroblasts_Vehicle_rep3


title: ControlFibroblasts_Vehicle_rep2


title: Infected_WT

description: Sorted CD45.1+ CD3– CD19– NK1.1+ NKp46+ cells from livers of T. gondii-infected WT:Tbx21-/- mixed bone marrow chimeras


title: WT_CM_Rep3

city: San Francisco

first: Benoit

last: Bruneau

state: CA

address: 1650 Owens Street

institute: Gladstone Institute of Cardiovascular Disease

data_processing:
Reads were trimmed using the fastq-mcf program (Aronesty, 2011), and then aligned to the mm9 mouse genome assembly using tophat2 (RNA-seq) (Kim et al., 2013). For each gene, reads were counted, and counts-per-million (CPM) calculated using featureCounts (Liao et al., 2014). Finally, edgeR was used to perform differential expression analyses (Robinson et al., 2010).
Genome_build: mm9
Supplementary_files_format_and_content: bigwig

source_name: cardiac myocyte

growth_protocol: ES cells differentiated to cardiac myocytes

extract_protocol:
Total RNA was isolated from 2 mill cells using TRizol Reagent and Qiagen microRNAeasy kit
Libraries were prepared according to NuGEN Ultralow V2 kit and paired-end 100bp sequenced on an Illumina HiSeq 2500 instrument.

value: ESC-derived cardiac myocytes

value: Brg1f/f; Actin-CreER

value: wild type


title: RARb4_IEC KO


title: Tet rep1


title: Uninfected_KO

description: Sorted CD45.2+ CD3– CD19– NK1.1+ NKp46+ cells from livers of uninfected WT:Tbx21-/- mixed bone marrow chimeras


title: Brg1KO_CP_Rep3

source_name: cardiac progenitor cells

value: ESC-derived cardiac progenitors

value: Brg1 KO


title: WT_CP_Rep2


title: Brg1KO_CP_Rep1


title: WT2_RARb fl/fl


title: WT3_RARb fl/fl


title: MOUSE_SPERM_HiC_Rep1

value: sperm


title: BAF170_CM_R1

data_processing:
bigwig and bed files
Genome_build: mm9
Supplementary_files_format_and_content: bigwig and bed

source_name: cardiomyoctes

growth_protocol: ES cells were differentiated following methods described in Walmstad et al., 2012 and Kattman et al., 2011.  Briefly, Cells were grown to from embroid bodies in presence of VEGF, Activin and BMP4 and cultured in monolayers in presence of FGFs to from cardiac precursor and subsequently to cardiomyocytes.

extract_protocol:
ChIP was performed following  O'Geen et al., 2011, Wamstad et al., 2012 with modifications. Briefly, 50 million cells were crosslinked with disuccinimidyl gulatarate  (45 min) followed by  formaldehyde (15min) and quenched, chromatin was digested with MNase to mono and di-nucleosomes, BRG1 was immuniprecipitated using anti-BRG1 antibody (ab110641, lot no. )and washed extensively and DNA eluted followed by reverese crosslinking, proteinase  and RNAse treatment and ampure purification of DNA (Serandour et al., 2013).
Libraries were prepared mannually.  Briefly, DNA was end-repaired using the End-IP repair kit (Epicenter, # ER81050). DNA were A-tailed with Klenow fragment (3' to5' exo minus) and dATP.  Barcoded Illumina  adapters were ligated and DNA was PCR amplified  with Illumina primers for 14 cycles. 300- 500bp library fragments were size selected in an agarose gel and purified.   Libraries were quantified by Qubit and analyzed in a Bioanalyser. Libraries were diluted and sequenced in a NEBNextSeq sequencing system following manufacturer's instructutions.

value: BAF170 KO

tag: passage

tag: chip antibody


title: Input_BAF60cKO_CM_R1

value: BAF60c KO

value: input


title: BrgKO_CM_Rep2


title: WT_CM_Rep1


title: BRG1_CM_R1


title: MutantFibroblasts_Vehicle_rep2


title: Brg1KO_CP_Rep2


title: WT_CP_Rep3


title: BRG1_CP_R2

source_name: Cardiac progenitor cells


title: Input_BAF170KO_CM_R1


title: Input_CM_R1


title: Input_BAF170KO_CP

description: BRG1_ChIPSeq_BAF170KO_CP_Average_Density


title: ControlFibroblasts_Vehicle_rep1


title: BAF170KO_CM Rep2

source_name: cardiac myocytes

value: BRG1-3XFLAG; BAF170 KO

value: ES cell derived cardiac myocytes


title: BAF60c_CM_R1


title: WT_CP_Rep1


title: BAF170KO_CM Rep3


title: BAF170KO_CP Rep1

source_name: Cardiac progenitors

value: ES cell derived Cardiac progenitors


title: BAF60cKO_CP Rep3

value: BRG1-3XFLAG; BAF60c KO


title: BRG1_CP_R1


title: BAF170KO_CM Rep1


title: BAF170KO_CP Rep2


title: BAF60cKO_CP Rep2


title: BAF170KO_CP Rep3


title: BAF60cKO_CM Rep3


title: BAF60cKO_CM Rep1


title: WT_CM Rep2


title: BAF60cKO_CP Rep1


title: WT_CP Rep1


title: BAF170KO_CP_R1


title: Input_CP


title: WT_CP Rep3


title: WT_CM Rep1

value: ES cell derivedcardiac myocytes


title: WT_CP Rep2


title: BAF170KO_CP_R2


title: TF_40_S40

first: Caleb

last: Lareau

state: New York

address: 417 E 68th St, Zuckerman - ZRC 1132

institute: Memorial Sloan Kettering

data_processing:
RNA sequencing data was aligned to the human reference genome (GRCh37/hg19) using STAR using standard two-pass approach.
Gene were quantified using the STAR --quantMode GeneCounts flag over the Gencode v19 annotation with a custom chromosome to map the lentiviral infected GFP+ random 30-mer barcode
Processed bams were genotyped for mitochondria using a custom python script.
Samples were filtered such that individual cells each had at least 100x coverage at heteroplasmic variants. No nuclear genome QC was performed. Samples were further filtered to ensure that each cell at least one unique barcode
Genome_build: hg19
Supplementary_files_format_and_content: High confidence mitochondrial variants for 3 clones assayed by 3 separate single cell technologies

source_name: ATCC

growth_protocol: RPMI, 10%FBS, Pen/Strep, 2ng/ml GM-CSF

extract_protocol:
Qiagen TCL lysis buffer
Smart-seq2

value: TF1 hematopoietic cell line


title: TF_42_S42


title: TF_39_S39


title: TF_38_S38


title: BAF60cKO_CM Rep2


title: TF_379_S379


title: TF_37_S37


title: TF_378_S378


title: TF_377_S377


title: TF_383_S383


title: TF_376_S376


title: TF_384_neg_S384


title: TF_375_S375


title: TF_374_S374


title: WT_CM Rep3


title: TF_382_S382


title: TF_381_S381


title: TF_371_S371


title: TF_369_S369


title: BrgKO_CM_Rep3


title: TF_370_S370


title: TF_364_S364


title: TF_380_S380


title: TF_361_S361


title: TF_372_S372


title: TF_36_S36


title: TF_41_S41


title: TF_359_S359


title: TF_358_S358


title: TF_35_S35


title: TF_356_S356


title: TF_355_S355


title: TF_354_S354


title: TF_373_S373


title: TF_367_S367


title: TF_353_S353


title: TF_352_S352


title: TF_363_S363


title: TF_365_S365


title: TF_366_S366


title: TF_362_S362


title: TF_360_S360


title: TF_348_S348


title: TF_346_S346


title: TF_357_S357


title: TF_351_S351


title: TF_343_S343


title: TF_341_S341


title: TF_368_S368


title: TF_34_S34


title: TF_335_S335


title: TF_349_S349


title: TF_350_S350


title: TF_334_S334


title: TF_337_S337


title: TF_345_S345


title: BrgKO_CM_Rep1


title: TF_344_S344


title: TF_328_S328


title: TF_342_S342


title: TF_33_S33


title: TF_330_S330


title: TF_339_S339


title: TF_338_S338


title: TF_340_S340


title: TF_336_S336


title: TF_322_S322


title: TF_323_S323


title: TF_324_S324


title: TF_333_S333


title: TF_321_S321


title: TF_31_S31


title: TF_332_S332


title: TF_32_S32


title: TF_320_S320


title: TF_331_S331


title: TF_347_S347


title: TF_315_S315


title: TF_314_S314


title: WT_CM_Rep2


title: TF_327_S327


title: TF_325_S325


title: TF_326_S326


title: TF_319_S319


title: TF_310_S310


title: TF_316_S316


title: TF_317_S317


title: TF_30_S30


title: TF_309_S309


title: TF_329_S329


title: TF_318_S318


title: TF_305_S305


title: TF_306_S306


title: TF_313_S313


title: TF_308_S308


title: TF_301_S301


title: TF_312_S312


title: TF_311_S311


title: TF_29_S29


title: TF_302_S302


title: TF_307_S307


title: TF_299_S299


title: TF_300_S300


title: TF_298_S298


title: TF_297_S297


title: TF_303_S303


title: TF_304_S304


title: TF_293_S293


title: TF_294_S294


title: TF_290_S290


title: TF_295_S295


title: TF_291_S291


title: TF_292_S292


title: TF_286_S286


title: TF_288_neg_S288


title: TF_289_pos_S289


title: TF_285_S285


title: TF_282_S282


title: TF_28_S28


title: TF_284_S284


title: TF_296_S296


title: TF_287_S287


title: TF_283_S283


title: TF_276_S276


title: TF_272_S272


title: TF_27_S27


title: TF_281_S281


title: TF_277_S277


title: TF_275_S275


title: TF_273_S273


title: TF_271_S271


title: TF_279_S279


title: TF_26_S26


title: TF_269_S269


title: TF_268_S268


title: TF_266_S266


title: TF_274_S274


title: TF_267_S267


title: TF_265_S265


title: TF_270_S270


title: TF_264_S264


title: TF_262_S262


title: TF_263_S263


title: TF_278_S278


title: TF_280_S280


title: TF_260_S260


title: TF_259_S259


title: TF_258_S258


title: TF_25_S25


title: TF_257_S257


title: TF_256_S256


title: TF_255_S255


title: TF_254_S254


title: TF_251_S251


title: TF_253_S253


title: TF_252_S252


title: TF_250_S250


title: TF_247_S247


title: TF_249_S249


title: TF_24_S24


title: TF_245_S245


title: TF_244_S244


title: TF_242_S242


title: TF_248_S248


title: TF_243_S243


title: TF_246_S246


title: TF_240_S240


title: TF_261_S261


title: TF_241_S241


title: TF_238_S238


title: TF_236_S236


title: TF_239_S239


title: TF_233_S233


title: TF_230_S230


title: TF_231_S231


title: TF_232_S232


title: TF_235_S235


title: TF_22_S22


title: TF_234_S234


title: TF_228_S228


title: TF_224_S224


title: TF_226_S226


title: TF_225_S225


title: TF_223_S223


title: TF_227_S227


title: TF_220_S220


title: TF_222_S222


title: TF_21_S21


title: TF_219_S219


title: TF_218_S218


title: TF_23_S23


title: TF_217_S217


title: TF_215_S215


title: TF_213_S213


title: TF_216_S216


title: TF_237_S237


title: TF_214_S214


title: TF_211_S211


title: TF_20_S20


title: TF_208_S208


title: TF_209_S209


title: TF_229_S229


title: TF_206_S206


title: TF_205_S205


title: TF_207_S207


title: TF_221_S221


title: TF_202_S202


title: TF_203_S203


title: TF_19_S19


title: TF_198_S198


title: TF_196_S196


title: TF_194_S194


title: TF_193_pos_S193


title: TF_212_S212


title: TF_191_S191


title: TF_190_S190


title: TF_199_S199


title: TF_210_S210


title: TF_188_S188


title: TF_189_S189


title: TF_187_S187


title: TF_186_S186


title: TF_183_S183


title: TF_184_S184


title: TF_182_S182


title: TF_201_S201


title: TF_185_S185


title: TF_200_S200


title: TF_204_S204


title: TF_180_S180


title: TF_177_S177


title: TF_181_S181


title: TF_179_S179


title: TF_17_S17


title: TF_195_S195


title: TF_197_S197


title: TF_173_S173


title: TF_174_S174


title: TF_192_neg_S192


title: TF_175_S175


title: TF_170_S170


title: TF_18_S18


title: TF_172_S172


title: TF_16_S16


title: TF_171_S171


title: TF_169_S169


title: TF_168_S168


title: TF_176_S176


title: TF_164_S164


title: TF_166_S166


title: TF_165_S165


title: TF_178_S178


title: TF_162_S162


title: TF_161_S161


title: TF_163_S163


title: TF_167_S167


title: TF_158_S158


title: TF_160_S160


title: TF_15_S15


title: TF_159_S159


title: TF_156_S156


title: TF_157_S157


title: TF_155_S155


title: TF_151_S151


title: TF_152_S152


title: TF_154_S154


title: TF_148_S148


title: TF_150_S150


title: TF_14_S14


title: TF_147_S147


title: TF_145_S145


title: TF_143_S143


title: TF_153_S153


title: TF_146_S146


title: TF_141_S141


title: TF_142_S142


title: TF_13_S13


title: TF_139_S139


title: TF_138_S138


title: TF_136_S136


title: TF_140_S140


title: TF_135_S135


title: TF_137_S137


title: TF_133_S133


title: TF_134_S134


title: TF_130_S130


title: TF_132_S132


title: TF_131_S131


title: TF_129_S129


title: TF_128_S128


title: TF_127_S127


title: TF_126_S126


title: TF_144_S144


title: TF_125_S125


title: TF_149_S149


title: TF_122_S122


title: TF_123_S123


title: TF_116_S116


title: TF_117_S117


title: TF_119_S119


title: TF_121_S121


title: TF_11_S11


title: TF_115_S115


title: TF_118_S118


title: TF_112_S112


title: TF_110_S110


title: TF_111_S111


title: TF_12_S12


title: TF_114_S114


title: TF_113_S113


title: TF_10_S10


title: TF_108_S108


title: TF_106_S106


title: TF_107_S107


title: TF_105_S105


title: TF_104_S104


title: TF_100_S100


title: TF_09_S9


title: TF_124_S124


title: TF_120_S120


title: TF_102_S102


title: TF_101_S101


title: TF_08_S8


title: TF_07_S7


title: TF_06_S6


title: TF_05_S5


title: TF_03_S3


title: TF_02_S2


title: TF_109_S109


title: CC100_479_S95 [scRNA-seq]

data_processing:
RNA sequencing data was aligned to the human reference genome (GRCh37/hg19) using STAR using standard two-pass approach.
Gene were quantified using the STAR --quantMode GeneCounts flag over the Gencode v19 annotation.
Processed bams were genotyped for mitochondria using a custom python script.
Samples were filtered such that individual cells each had at least 100x coverage over the full mitochondria.
Genome_build: hg19
Supplementary_files_format_and_content: Heteroplasmy of high confidence mitochondrial variants assayed in high quality single cells

source_name: CD34+ hematopoietic cells

growth_protocol: StemSpan II supplemented with CC100

extract_protocol:
Qiagen TCL lysis buffer
Smart-seq2; Single cell RNA-seq

value: primary hematopoietic cells

tag: supplemented with


title: CC100_480_neg_S96 [scRNA-seq]


title: CC100 culture

data_processing:
Adapter sequences were trimmed from FASTQs using custom python scripts to enable mapping fragments with sequences containing adapters. Paired-end reads were aligned to hg19 or mm10 using BOWTIE2 using the parameter –X2000 allowing fragments of up to 2 kb to align. Duplicates were removed using PICARD tools for reads uniquely mapping to the nuclear genome.
Mitochondrial genotypes were inferred for each ATAC-seq sample using a custom python script.
All samples/libraries passed QC (none were filtered in downstream analyses)
NA
Genome_build: hg19
Supplementary_files_format_and_content: Heteroplasmy estimations for bulk populations assayed through ATAC-seq and single cells through scATAC + scRNA-seq

extract_protocol:
NP 40 lysis
TF1-barcoding-ATAC

tag: celltype


title: CC100_477_S93 [scRNA-seq]


title: TF_103_S103


title: CC100_473_S89 [scRNA-seq]


title: CC100_470_S86 [scRNA-seq]


title: CC100_478_S94 [scRNA-seq]


title: CC100_474_S90 [scRNA-seq]


title: CC100_472_S88 [scRNA-seq]


title: CC100_476_S92 [scRNA-seq]


title: CC100_469_S85 [scRNA-seq]


title: TF_01_pos_S1


title: CC100_467_S83 [scRNA-seq]


title: CC100_464_S80 [scRNA-seq]


title: CC100_465_S81 [scRNA-seq]


title: CC100_463_S79 [scRNA-seq]


title: CC100_462_S78 [scRNA-seq]


title: CC100_461_S77 [scRNA-seq]


title: TF_04_S4


title: CC100_466_S82 [scRNA-seq]


title: CC100_459_S75 [scRNA-seq]


title: CC100_458_S74 [scRNA-seq]


title: CC100_457_S73 [scRNA-seq]


title: CC100_460_S76 [scRNA-seq]


title: CC100_456_S72 [scRNA-seq]


title: CC100_455_S71 [scRNA-seq]


title: CC100_453_S69 [scRNA-seq]


title: CC100_475_S91 [scRNA-seq]


title: CC100_471_S87 [scRNA-seq]


title: CC100_452_S68 [scRNA-seq]


title: CC100_446_S62 [scRNA-seq]


title: CC100_447_S63 [scRNA-seq]


title: CC100_448_S64 [scRNA-seq]


title: CC100_451_S67 [scRNA-seq]


title: CC100_445_S61 [scRNA-seq]


title: CC100_468_pos_S84 [scRNA-seq]


title: CC100_450_S66 [scRNA-seq]


title: CC100_454_S70 [scRNA-seq]


title: CC100_449_S65 [scRNA-seq]


title: CC100_438_S54 [scRNA-seq]


title: CC100_442_S58 [scRNA-seq]


title: CC100_440_S56 [scRNA-seq]


title: CC100_439_S55 [scRNA-seq]


title: CC100_437_S53 [scRNA-seq]


title: CC100_436_S52 [scRNA-seq]


title: CC100_441_S57 [scRNA-seq]


title: CC100_443_S59 [scRNA-seq]


title: CC100_435_S51 [scRNA-seq]


title: CC100_444_S60 [scRNA-seq]


title: CC100_434_S50 [scRNA-seq]


title: CC100_432_S48 [scRNA-seq]


title: CC100_431_S47 [scRNA-seq]


title: CC100_429_S45 [scRNA-seq]


title: CC100_430_S46 [scRNA-seq]


title: CC100_425_S41 [scRNA-seq]


title: CC100_426_S42 [scRNA-seq]


title: CC100_428_S44 [scRNA-seq]


title: CC100_421_S37 [scRNA-seq]


title: CC100_427_S43 [scRNA-seq]


title: CC100_422_S38 [scRNA-seq]


title: CC100_420_S36 [scRNA-seq]


title: CC100_419_S35 [scRNA-seq]


title: CC100_417_S33 [scRNA-seq]


title: CC100_418_S34 [scRNA-seq]


title: CC100_416_S32 [scRNA-seq]


title: CC100_415_S31 [scRNA-seq]


title: CC100_424_S40 [scRNA-seq]


title: CC100_423_S39 [scRNA-seq]


title: CC100_414_S30 [scRNA-seq]


title: CC100_412_S28 [scRNA-seq]


title: CC100_409_S25 [scRNA-seq]


title: CC100_408_S24 [scRNA-seq]


title: CC100_407_S23 [scRNA-seq]


title: CC100_404_S20 [scRNA-seq]


title: CC100_410_S26 [scRNA-seq]


title: CC100_405_S21 [scRNA-seq]


title: CC100_411_S27 [scRNA-seq]


title: CC100_403_S19 [scRNA-seq]


title: CC100_402_S18 [scRNA-seq]


title: CC100_400_S16 [scRNA-seq]


title: CC100_406_S22 [scRNA-seq]


title: CC100_433_S49 [scRNA-seq]


title: CC100_413_S29 [scRNA-seq]


title: CC100_399_S15 [scRNA-seq]


title: CC100_398_S14 [scRNA-seq]


title: CC100_397_S13 [scRNA-seq]


title: CC100_401_S17 [scRNA-seq]


title: CC100_395_S11 [scRNA-seq]


title: CC100_391_S7 [scRNA-seq]


title: CC100_390_S6 [scRNA-seq]


title: CC100_392_S8 [scRNA-seq]


title: CC100_389_S5 [scRNA-seq]


title: CC100_388_S4 [scRNA-seq]


title: CC100_387_S3 [scRNA-seq]


title: CC100_393_S9 [scRNA-seq]


title: CC100_386_S2 [scRNA-seq]


title: CC100_384_neg_S384 [scRNA-seq]


title: CC100_383_S383 [scRNA-seq]


title: CC100_385_S1 [scRNA-seq]


title: CC100_382_S382 [scRNA-seq]


title: CC100_380_S380 [scRNA-seq]


title: CC100_379_S379 [scRNA-seq]


title: CC100_381_S381 [scRNA-seq]


title: CC100_376_S376 [scRNA-seq]


title: CC100_378_S378 [scRNA-seq]


title: CC100_375_S375 [scRNA-seq]


title: CC100_372_pos_S372 [scRNA-seq]


title: CC100_373_S373 [scRNA-seq]


title: CC100_374_S374 [scRNA-seq]


title: CC100_371_S371 [scRNA-seq]


title: CC100_370_S370 [scRNA-seq]


title: CC100_369_S369 [scRNA-seq]


title: CC100_396_S12 [scRNA-seq]


title: CC100_394_S10 [scRNA-seq]


title: CC100_368_S368 [scRNA-seq]


title: CC100_366_S366 [scRNA-seq]


title: CC100_367_S367 [scRNA-seq]


title: CC100_377_S377 [scRNA-seq]


title: CC100_359_S359 [scRNA-seq]


title: CC100_365_S365 [scRNA-seq]


title: CC100_362_S362 [scRNA-seq]


title: CC100_363_S363 [scRNA-seq]


title: CC100_358_S358 [scRNA-seq]


title: CC100_357_S357 [scRNA-seq]


title: CC100_356_S356 [scRNA-seq]


title: CC100_364_S364 [scRNA-seq]


title: CC100_354_S354 [scRNA-seq]


title: CC100_355_S355 [scRNA-seq]


title: CC100_352_S352 [scRNA-seq]


title: CC100_351_S351 [scRNA-seq]


title: CC100_353_S353 [scRNA-seq]


title: CC100_344_S344 [scRNA-seq]


title: CC100_347_S347 [scRNA-seq]


title: CC100_349_S349 [scRNA-seq]


title: CC100_345_S345 [scRNA-seq]


title: CC100_346_S346 [scRNA-seq]


title: CC100_348_S348 [scRNA-seq]


title: CC100_343_S343 [scRNA-seq]


title: CC100_342_S342 [scRNA-seq]


title: CC100_341_S341 [scRNA-seq]


title: CC100_340_S340 [scRNA-seq]


title: CC100_361_S361 [scRNA-seq]


title: CC100_360_S360 [scRNA-seq]


title: CC100_338_S338 [scRNA-seq]


title: CC100_337_S337 [scRNA-seq]


title: CC100_339_S339 [scRNA-seq]


title: CC100_335_S335 [scRNA-seq]


title: CC100_334_S334 [scRNA-seq]


title: CC100_333_S333 [scRNA-seq]


title: CC100_336_S336 [scRNA-seq]


title: CC100_332_S332 [scRNA-seq]


title: CC100_329_S329 [scRNA-seq]


title: CC100_330_S330 [scRNA-seq]


title: CC100_331_S331 [scRNA-seq]


title: CC100_328_S328 [scRNA-seq]


title: CC100_350_S350 [scRNA-seq]


title: CC100_327_S327 [scRNA-seq]


title: CC100_324_S324 [scRNA-seq]


title: CC100_323_S323 [scRNA-seq]


title: CC100_325_S325 [scRNA-seq]


title: CC100_322_S322 [scRNA-seq]


title: CC100_321_S321 [scRNA-seq]


title: CC100_319_S319 [scRNA-seq]


title: CC100_317_S317 [scRNA-seq]


title: CC100_318_S318 [scRNA-seq]


title: CC100_316_S316 [scRNA-seq]


title: CC100_315_S315 [scRNA-seq]


title: CC100_314_S314 [scRNA-seq]


title: CC100_320_S320 [scRNA-seq]


title: CC100_313_S313 [scRNA-seq]


title: CC100_312_S312 [scRNA-seq]


title: CC100_308_S308 [scRNA-seq]


title: CC100_309_S309 [scRNA-seq]


title: CC100_310_S310 [scRNA-seq]


title: CC100_311_S311 [scRNA-seq]


title: CC100_306_S306 [scRNA-seq]


title: CC100_305_S305 [scRNA-seq]


title: CC100_304_S304 [scRNA-seq]


title: CC100_302_S302 [scRNA-seq]


title: CC100_326_S326 [scRNA-seq]


title: CC100_301_S301 [scRNA-seq]


title: CC100_300_S300 [scRNA-seq]


title: CC100_299_S299 [scRNA-seq]


title: CC100_298_S298 [scRNA-seq]


title: CC100_296_S296 [scRNA-seq]


title: CC100_293_S293 [scRNA-seq]


title: CC100_292_S292 [scRNA-seq]


title: CC100_291_S291 [scRNA-seq]


title: CC100_290_S290 [scRNA-seq]


title: CC100_294_S294 [scRNA-seq]


title: CC100_287_S287 [scRNA-seq]


title: CC100_289_S289 [scRNA-seq]


title: CC100_286_S286 [scRNA-seq]


title: CC100_285_S285 [scRNA-seq]


title: CC100_297_S297 [scRNA-seq]


title: CC100_283_S283 [scRNA-seq]


title: CC100_288_neg_S288 [scRNA-seq]


title: CC100_303_S303 [scRNA-seq]


title: CC100_307_S307 [scRNA-seq]


title: CC100_282_S282 [scRNA-seq]


title: CC100_281_S281 [scRNA-seq]


title: CC100_279_S279 [scRNA-seq]


title: CC100_278_S278 [scRNA-seq]


title: CC100_276_pos_S276 [scRNA-seq]


title: CC100_273_S273 [scRNA-seq]


title: CC100_274_S274 [scRNA-seq]


title: CC100_275_S275 [scRNA-seq]


title: CC100_271_S271 [scRNA-seq]


title: CC100_270_S270 [scRNA-seq]


title: CC100_272_S272 [scRNA-seq]


title: CC100_295_S295 [scRNA-seq]


title: CC100_267_S267 [scRNA-seq]


title: CC100_261_S261 [scRNA-seq]


title: CC100_263_S263 [scRNA-seq]


title: CC100_259_S259 [scRNA-seq]


title: CC100_258_S258 [scRNA-seq]


title: CC100_257_S257 [scRNA-seq]


title: CC100_262_S262 [scRNA-seq]


title: CC100_277_S277 [scRNA-seq]


title: CC100_280_S280 [scRNA-seq]


title: CC100_251_S251 [scRNA-seq]


title: CC100_254_S254 [scRNA-seq]


title: CC100_260_S260 [scRNA-seq]


title: CC100_252_S252 [scRNA-seq]


title: CC100_256_S256 [scRNA-seq]


title: CC100_269_S269 [scRNA-seq]


title: CC100_247_S247 [scRNA-seq]


title: CC100_250_S250 [scRNA-seq]


title: CC100_249_S249 [scRNA-seq]


title: CC100_246_S246 [scRNA-seq]


title: CC100_255_S255 [scRNA-seq]


title: CC100_245_S245 [scRNA-seq]


title: CC100_248_S248 [scRNA-seq]


title: CC100_268_S268 [scRNA-seq]


title: CC100_266_S266 [scRNA-seq]


title: CC100_242_S242 [scRNA-seq]


title: CC100_264_S264 [scRNA-seq]


title: CC100_243_S243 [scRNA-seq]


title: CC100_240_S240 [scRNA-seq]


title: CC100_265_S265 [scRNA-seq]


title: CC100_239_S239 [scRNA-seq]


title: CC100_238_S238 [scRNA-seq]


title: CC100_241_S241 [scRNA-seq]


title: CC100_253_S253 [scRNA-seq]


title: CC100_235_S235 [scRNA-seq]


title: CC100_236_S236 [scRNA-seq]


title: CC100_237_S237 [scRNA-seq]


title: CC100_231_S231 [scRNA-seq]


title: CC100_229_S229 [scRNA-seq]


title: CC100_230_S230 [scRNA-seq]


title: CC100_232_S232 [scRNA-seq]


title: CC100_234_S234 [scRNA-seq]


title: CC100_228_S228 [scRNA-seq]


title: CC100_233_S233 [scRNA-seq]


title: CC100_244_S244 [scRNA-seq]


title: CC100_222_S222 [scRNA-seq]


title: CC100_224_S224 [scRNA-seq]


title: CC100_221_S221 [scRNA-seq]


title: CC100_226_S226 [scRNA-seq]


title: CC100_219_S219 [scRNA-seq]


title: CC100_225_S225 [scRNA-seq]


title: CC100_223_S223 [scRNA-seq]


title: CC100_220_S220 [scRNA-seq]


title: CC100_216_S216 [scRNA-seq]


title: CC100_217_S217 [scRNA-seq]


title: CC100_218_S218 [scRNA-seq]


title: CC100_215_S215 [scRNA-seq]


title: CC100_214_S214 [scRNA-seq]


title: CC100_212_S212 [scRNA-seq]


title: CC100_210_S210 [scRNA-seq]


title: CC100_211_S211 [scRNA-seq]


title: CC100_207_S207 [scRNA-seq]


title: CC100_209_S209 [scRNA-seq]


title: CC100_227_S227 [scRNA-seq]


title: CC100_208_S208 [scRNA-seq]


title: CC100_204_S204 [scRNA-seq]


title: CC100_206_S206 [scRNA-seq]


title: CC100_203_S203 [scRNA-seq]


title: CC100_199_S199 [scRNA-seq]


title: CC100_205_S205 [scRNA-seq]


title: CC100_200_S200 [scRNA-seq]


title: CC100_201_S201 [scRNA-seq]


title: CC100_202_S202 [scRNA-seq]


title: CC100_196_S196 [scRNA-seq]


title: CC100_197_S197 [scRNA-seq]


title: CC100_198_S198 [scRNA-seq]


title: CC100_194_S194 [scRNA-seq]


title: CC100_195_S195 [scRNA-seq]


title: CC100_213_S213 [scRNA-seq]


title: CC100_190_S190 [scRNA-seq]


title: CC100_191_S191 [scRNA-seq]


title: CC100_187_S187 [scRNA-seq]


title: CC100_188_S188 [scRNA-seq]


title: CC100_192_neg_S192 [scRNA-seq]


title: CC100_185_S185 [scRNA-seq]


title: CC100_189_S189 [scRNA-seq]


title: CC100_183_S183 [scRNA-seq]


title: CC100_184_S184 [scRNA-seq]


title: CC100_186_S186 [scRNA-seq]


title: CC100_181_S181 [scRNA-seq]


title: CC100_182_S182 [scRNA-seq]


title: CC100_179_S179 [scRNA-seq]


title: CC100_178_S178 [scRNA-seq]


title: CC100_284_S284 [scRNA-seq]


title: CC100_193_S193 [scRNA-seq]


title: CC100_175_S175 [scRNA-seq]


title: CC100_174_S174 [scRNA-seq]


title: CC100_172_S172 [scRNA-seq]


title: CC100_170_S170 [scRNA-seq]


title: CC100_171_S171 [scRNA-seq]


title: CC100_168_S168 [scRNA-seq]


title: CC100_167_S167 [scRNA-seq]


title: CC100_165_S165 [scRNA-seq]


title: CC100_160_S160 [scRNA-seq]


title: CC100_158_S158 [scRNA-seq]


title: CC100_163_S163 [scRNA-seq]


title: CC100_161_S161 [scRNA-seq]


title: CC100_164_S164 [scRNA-seq]


title: CC100_157_S157 [scRNA-seq]


title: CC100_169_S169 [scRNA-seq]


title: CC100_156_S156 [scRNA-seq]


title: CC100_176_S176 [scRNA-seq]


title: CC100_154_S154 [scRNA-seq]


title: CC100_155_S155 [scRNA-seq]


title: CC100_150_S150 [scRNA-seq]


title: CC100_159_S159 [scRNA-seq]


title: CC100_177_S177 [scRNA-seq]


title: CC100_173_S173 [scRNA-seq]


title: CC100_149_S149 [scRNA-seq]


title: CC100_180_pos_S180 [scRNA-seq]


title: CC100_148_S148 [scRNA-seq]


title: CC100_151_S151 [scRNA-seq]


title: CC100_147_S147 [scRNA-seq]


title: CC100_146_S146 [scRNA-seq]


title: CC100_166_S166 [scRNA-seq]


title: CC100_145_S145 [scRNA-seq]


title: CC100_144_S144 [scRNA-seq]


title: CC100_142_S142 [scRNA-seq]


title: CC100_141_S141 [scRNA-seq]


title: CC100_139_S139 [scRNA-seq]


title: CC100_138_S138 [scRNA-seq]


title: CC100_143_S143 [scRNA-seq]


title: CC100_136_S136 [scRNA-seq]


title: CC100_162_S162 [scRNA-seq]


title: CC100_152_S152 [scRNA-seq]


title: CC100_133_S133 [scRNA-seq]


title: CC100_135_S135 [scRNA-seq]


title: CC100_132_S132 [scRNA-seq]


title: CC100_140_S140 [scRNA-seq]


title: CC100_129_S129 [scRNA-seq]


title: CC100_131_S131 [scRNA-seq]


title: CC100_130_S130 [scRNA-seq]


title: CC100_137_S137 [scRNA-seq]


title: CC100_153_S153 [scRNA-seq]


title: CC100_128_S128 [scRNA-seq]


title: CC100_126_S126 [scRNA-seq]


title: CC100_125_S125 [scRNA-seq]


title: CC100_124_S124 [scRNA-seq]


title: CC100_127_S127 [scRNA-seq]


title: CC100_122_S122 [scRNA-seq]


title: CC100_123_S123 [scRNA-seq]


title: CC100_121_S121 [scRNA-seq]


title: CC100_120_S120 [scRNA-seq]


title: CC100_119_S119 [scRNA-seq]


title: CC100_117_S117 [scRNA-seq]


title: CC100_115_S115 [scRNA-seq]


title: CC100_134_S134 [scRNA-seq]


title: CC100_116_S116 [scRNA-seq]


title: CC100_118_S118 [scRNA-seq]


title: CC100_114_S114 [scRNA-seq]


title: CC100_112_S112 [scRNA-seq]


title: CC100_110_S110 [scRNA-seq]


title: CC100_109_S109 [scRNA-seq]


title: CC100_111_S111 [scRNA-seq]


title: CC100_108_S108 [scRNA-seq]


title: CC100_113_S113 [scRNA-seq]


title: CC100_105_S105 [scRNA-seq]


title: CC100_106_S106 [scRNA-seq]


title: CC100_103_S103 [scRNA-seq]


title: CC100_104_S104 [scRNA-seq]


title: CC100_100_S100 [scRNA-seq]


title: CC100_102_S102 [scRNA-seq]


title: CC100_099_S99 [scRNA-seq]


title: CC100_096_neg_S96 [scRNA-seq]


title: CC100_092_S92 [scRNA-seq]


title: CC100_093_S93 [scRNA-seq]


title: CC100_097_S97 [scRNA-seq]


title: CC100_095_S95 [scRNA-seq]


title: CC100_098_S98 [scRNA-seq]


title: CC100_090_S90 [scRNA-seq]


title: CC100_107_S107 [scRNA-seq]


title: CC100_089_S89 [scRNA-seq]


title: CC100_087_S87 [scRNA-seq]


title: CC100_085_S85 [scRNA-seq]


title: CC100_086_S86 [scRNA-seq]


title: CC100_083_S83 [scRNA-seq]


title: CC100_084_pos_S84 [scRNA-seq]


title: CC100_082_S82 [scRNA-seq]


title: CC100_081_S81 [scRNA-seq]


title: CC100_079_S79 [scRNA-seq]


title: CC100_091_S91 [scRNA-seq]


title: CC100_088_S88 [scRNA-seq]


title: CC100_080_S80 [scRNA-seq]


title: CC100_077_S77 [scRNA-seq]


title: CC100_078_S78 [scRNA-seq]


title: CC100_076_S76 [scRNA-seq]


title: CC100_073_S73 [scRNA-seq]


title: CC100_072_S72 [scRNA-seq]


title: CC100_101_S101 [scRNA-seq]


title: CC100_071_S71 [scRNA-seq]


title: CC100_075_S75 [scRNA-seq]


title: CC100_074_S74 [scRNA-seq]


title: CC100_069_S69 [scRNA-seq]


title: CC100_070_S70 [scRNA-seq]


title: CC100_067_S67 [scRNA-seq]


title: CC100_065_S65 [scRNA-seq]


title: CC100_068_S68 [scRNA-seq]


title: CC100_064_S64 [scRNA-seq]


title: CC100_066_S66 [scRNA-seq]


title: CC100_062_S62 [scRNA-seq]


title: CC100_061_S61 [scRNA-seq]


title: CC100_060_S60 [scRNA-seq]


title: CC100_059_S59 [scRNA-seq]


title: CC100_057_S57 [scRNA-seq]


title: CC100_058_S58 [scRNA-seq]


title: CC100_056_S56 [scRNA-seq]


title: CC100_055_S55 [scRNA-seq]


title: CC100_049_S49 [scRNA-seq]


title: CC100_054_S54 [scRNA-seq]


title: CC100_051_S51 [scRNA-seq]


title: CC100_053_S53 [scRNA-seq]


title: CC100_043_S43 [scRNA-seq]


title: CC100_050_S50 [scRNA-seq]


title: CC100_047_S47 [scRNA-seq]


title: CC100_045_S45 [scRNA-seq]


title: CC100_042_S42 [scRNA-seq]


title: CC100_039_S39 [scRNA-seq]


title: CC100_040_S40 [scRNA-seq]


title: CC100_052_S52 [scRNA-seq]


title: CC100_046_S46 [scRNA-seq]


title: CC100_038_S38 [scRNA-seq]


title: CC100_063_S63 [scRNA-seq]


title: CC100_036_S36 [scRNA-seq]


title: CC100_035_S35 [scRNA-seq]


title: CC100_034_S34 [scRNA-seq]


title: CC100_037_S37 [scRNA-seq]


title: CC100_041_S41 [scRNA-seq]


title: CC100_033_S33 [scRNA-seq]


title: CC100_032_S32 [scRNA-seq]


title: CC100_094_S94 [scRNA-seq]


title: CC100_031_S31 [scRNA-seq]


title: CC100_030_S30 [scRNA-seq]


title: CC100_028_S28 [scRNA-seq]


title: CC100_027_S27 [scRNA-seq]


title: CC100_044_S44 [scRNA-seq]


title: CC100_025_S25 [scRNA-seq]


title: CC100_026_S26 [scRNA-seq]


title: CC100_024_S24 [scRNA-seq]


title: CC100_022_S22 [scRNA-seq]


title: CC100_020_S20 [scRNA-seq]


title: CC100_019_S19 [scRNA-seq]


title: CC100_021_S21 [scRNA-seq]


title: CC100_023_S23 [scRNA-seq]


title: CC100_016_S16 [scRNA-seq]


title: CC100_007_S7 [scRNA-seq]


title: CC100_012_S12 [scRNA-seq]


title: CC100_010_S10 [scRNA-seq]


title: CC100_017_S17 [scRNA-seq]


title: CC100_014_S14 [scRNA-seq]


title: CC100_015_S15 [scRNA-seq]


title: CC100_006_S6 [scRNA-seq]


title: CC100_011_S11 [scRNA-seq]


title: CC100_018_S18 [scRNA-seq]


title: CC100_008_S8 [scRNA-seq]


title: CC100_003_S3 [scRNA-seq]


title: CC100_009_S9 [scRNA-seq]


title: CC100_004_S4 [scRNA-seq]


title: CC100_002_S2 [scRNA-seq]


title: CC100_013_S13 [scRNA-seq]


title: CC100-C1-2-94 [CC100_scATAC-seq]

data_processing:
Adapter sequences were trimmed from FASTQs using custom python scripts to enable mapping fragments with sequences containing adapters. Paired-end reads were aligned to hg19 or mm10 using BOWTIE2 using the parameter –X2000 allowing fragments of up to 2 kb to align. Duplicates were removed using PICARD tools for reads uniquely mapping to the nuclear genome.
Reads were subsequently filtered for alignment quality of >Q20 and were required to be properly paired. We used MACS2 to call all  ATAC-seq peaks. MACS2 was used with the following parameters (--nomodel --nolambda --keep-dup all --call-summits). Peaks were filtered using the consensus excludable ENCODE blacklist (http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeMapability/) and a custom blacklist, designed to remove high-signal causing repeats and mitochondrial homologues.
For all subsequent analysis we discarded peaks falling within these regions. Mitochondrial genotypes were inferred for each ATAC-seq sample using a custom python script.
Samples were filtered such that individual cells each had at least 100x coverage over the full mitochondria.
Genome_build: hg19
Supplementary_files_format_and_content: Heteroplasmy of high confidence mitochondrial variants assayed in high quality single cells

extract_protocol:
NP40 lysis
Single cell ATAC-seq (C1 Fluidigm)


title: CC100-C1-2-93 [CC100_scATAC-seq]


title: CC100-C1-2-96 [CC100_scATAC-seq]


title: CC100-C1-2-9 [CC100_scATAC-seq]


title: CC100_001_S1 [scRNA-seq]


title: CC100-C1-2-95 [CC100_scATAC-seq]


title: CC100_029_S29 [scRNA-seq]


title: CC100-C1-2-90 [CC100_scATAC-seq]


title: CC100-C1-2-92 [CC100_scATAC-seq]


title: CC100-C1-2-8 [CC100_scATAC-seq]


title: CC100-C1-2-89 [CC100_scATAC-seq]


title: CC100-C1-2-91 [CC100_scATAC-seq]


title: CC100_005_S5 [scRNA-seq]


title: CC100_048_S48 [scRNA-seq]


title: CC100-C1-2-88 [CC100_scATAC-seq]


title: CC100-C1-2-82 [CC100_scATAC-seq]


title: CC100-C1-2-83 [CC100_scATAC-seq]


title: CC100-C1-2-85 [CC100_scATAC-seq]


title: CC100-C1-2-80 [CC100_scATAC-seq]


title: CC100-C1-2-84 [CC100_scATAC-seq]


title: CC100-C1-2-86 [CC100_scATAC-seq]


title: CC100-C1-2-77 [CC100_scATAC-seq]


title: CC100-C1-2-7 [CC100_scATAC-seq]


title: CC100-C1-2-71 [CC100_scATAC-seq]


title: CC100-C1-2-79 [CC100_scATAC-seq]


title: CC100-C1-2-6 [CC100_scATAC-seq]


title: CC100-C1-2-75 [CC100_scATAC-seq]


title: CC100-C1-2-69 [CC100_scATAC-seq]


title: CC100-C1-2-78 [CC100_scATAC-seq]


title: CC100-C1-2-81 [CC100_scATAC-seq]


title: CC100-C1-2-74 [CC100_scATAC-seq]


title: CC100-C1-2-73 [CC100_scATAC-seq]


title: CC100-C1-2-64 [CC100_scATAC-seq]


title: CC100-C1-2-63 [CC100_scATAC-seq]


title: CC100-C1-2-62 [CC100_scATAC-seq]


title: CC100-C1-2-70 [CC100_scATAC-seq]


title: CC100-C1-2-87 [CC100_scATAC-seq]


title: CC100-C1-2-76 [CC100_scATAC-seq]


title: CC100-C1-2-61 [CC100_scATAC-seq]


title: CC100-C1-2-60 [CC100_scATAC-seq]


title: CC100-C1-2-72 [CC100_scATAC-seq]


title: CC100-C1-2-58 [CC100_scATAC-seq]


title: CC100-C1-2-5 [CC100_scATAC-seq]


title: CC100-C1-2-68 [CC100_scATAC-seq]


title: CC100-C1-2-57 [CC100_scATAC-seq]


title: CC100-C1-2-55 [CC100_scATAC-seq]


title: CC100-C1-2-56 [CC100_scATAC-seq]


title: CC100-C1-2-65 [CC100_scATAC-seq]


title: CC100-C1-2-59 [CC100_scATAC-seq]


title: CC100-C1-2-66 [CC100_scATAC-seq]


title: CC100-C1-2-67 [CC100_scATAC-seq]


title: CC100-C1-2-47 [CC100_scATAC-seq]


title: CC100-C1-2-46 [CC100_scATAC-seq]


title: CC100-C1-2-51 [CC100_scATAC-seq]


title: CC100-C1-2-50 [CC100_scATAC-seq]


title: CC100-C1-2-43 [CC100_scATAC-seq]


title: CC100-C1-2-52 [CC100_scATAC-seq]


title: CC100-C1-2-41 [CC100_scATAC-seq]


title: CC100-C1-2-42 [CC100_scATAC-seq]


title: CC100-C1-2-40 [CC100_scATAC-seq]


title: CC100-C1-2-3 [CC100_scATAC-seq]


title: CC100-C1-2-39 [CC100_scATAC-seq]


title: CC100-C1-2-53 [CC100_scATAC-seq]


title: CC100-C1-2-54 [CC100_scATAC-seq]


title: CC100-C1-2-37 [CC100_scATAC-seq]


title: CC100-C1-2-49 [CC100_scATAC-seq]


title: CC100-C1-2-38 [CC100_scATAC-seq]


title: CC100-C1-2-36 [CC100_scATAC-seq]


title: CC100-C1-2-48 [CC100_scATAC-seq]


title: CC100-C1-2-4 [CC100_scATAC-seq]


title: CC100-C1-2-34 [CC100_scATAC-seq]


title: CC100-C1-2-35 [CC100_scATAC-seq]


title: CC100-C1-2-32 [CC100_scATAC-seq]


title: CC100-C1-2-33 [CC100_scATAC-seq]


title: CC100-C1-2-28 [CC100_scATAC-seq]


title: CC100-C1-2-31 [CC100_scATAC-seq]


title: CC100-C1-2-30 [CC100_scATAC-seq]


title: CC100-C1-2-26 [CC100_scATAC-seq]


title: CC100-C1-2-29 [CC100_scATAC-seq]


title: CC100-C1-2-44 [CC100_scATAC-seq]


title: CC100-C1-2-45 [CC100_scATAC-seq]


title: CC100-C1-2-19 [CC100_scATAC-seq]


title: CC100-C1-2-20 [CC100_scATAC-seq]


title: CC100-C1-2-18 [CC100_scATAC-seq]


title: CC100-C1-2-21 [CC100_scATAC-seq]


title: CC100-C1-2-17 [CC100_scATAC-seq]


title: CC100-C1-2-22 [CC100_scATAC-seq]


title: CC100-C1-2-23 [CC100_scATAC-seq]


title: CC100-C1-2-1 [CC100_scATAC-seq]


title: CC100-C1-2-16 [CC100_scATAC-seq]


title: CC100-C1-2-14 [CC100_scATAC-seq]


title: CC100-C1-1-95 [CC100_scATAC-seq]


title: CC100-C1-2-24 [CC100_scATAC-seq]


title: CC100-C1-2-15 [CC100_scATAC-seq]


title: CC100-C1-2-13 [CC100_scATAC-seq]


title: CC100-C1-2-12 [CC100_scATAC-seq]


title: CC100-C1-1-91 [CC100_scATAC-seq]


title: CC100-C1-2-2 [CC100_scATAC-seq]


title: CC100-C1-2-27 [CC100_scATAC-seq]


title: CC100-C1-1-90 [CC100_scATAC-seq]


title: CC100-C1-2-11 [CC100_scATAC-seq]


title: CC100-C1-1-9 [CC100_scATAC-seq]


title: CC100-C1-1-8 [CC100_scATAC-seq]


title: CC100-C1-1-96 [CC100_scATAC-seq]


title: CC100-C1-1-88 [CC100_scATAC-seq]


title: CC100-C1-1-89 [CC100_scATAC-seq]


title: CC100-C1-1-93 [CC100_scATAC-seq]


title: CC100-C1-1-94 [CC100_scATAC-seq]


title: CC100-C1-2-25 [CC100_scATAC-seq]


title: CC100-C1-1-92 [CC100_scATAC-seq]


title: CC100-C1-1-84 [CC100_scATAC-seq]


title: CC100-C1-1-79 [CC100_scATAC-seq]


title: CC100-C1-1-81 [CC100_scATAC-seq]


title: CC100-C1-1-86 [CC100_scATAC-seq]


title: CC100-C1-1-87 [CC100_scATAC-seq]


title: CC100-C1-1-83 [CC100_scATAC-seq]


title: CC100-C1-1-82 [CC100_scATAC-seq]


title: CC100-C1-1-77 [CC100_scATAC-seq]


title: CC100-C1-1-76 [CC100_scATAC-seq]


title: CC100-C1-1-73 [CC100_scATAC-seq]


title: CC100-C1-1-85 [CC100_scATAC-seq]


title: CC100-C1-1-74 [CC100_scATAC-seq]


title: CC100-C1-1-7 [CC100_scATAC-seq]


title: CC100-C1-1-80 [CC100_scATAC-seq]


title: CC100-C1-1-78 [CC100_scATAC-seq]


title: CC100-C1-1-71 [CC100_scATAC-seq]


title: CC100-C1-1-68 [CC100_scATAC-seq]


title: CC100-C1-1-69 [CC100_scATAC-seq]


title: CC100-C1-1-70 [CC100_scATAC-seq]


title: CC100-C1-1-72 [CC100_scATAC-seq]


title: CC100-C1-1-6 [CC100_scATAC-seq]


title: CC100-C1-1-65 [CC100_scATAC-seq]


title: CC100-C1-1-66 [CC100_scATAC-seq]


title: CC100-C1-1-56 [CC100_scATAC-seq]


title: CC100-C1-1-60 [CC100_scATAC-seq]


title: CC100-C1-1-57 [CC100_scATAC-seq]


title: CC100-C1-1-5 [CC100_scATAC-seq]


title: CC100-C1-1-53 [CC100_scATAC-seq]


title: CC100-C1-2-10 [CC100_scATAC-seq]


title: CC100-C1-1-64 [CC100_scATAC-seq]


title: CC100-C1-1-51 [CC100_scATAC-seq]


title: CC100-C1-1-63 [CC100_scATAC-seq]


title: CC100-C1-1-62 [CC100_scATAC-seq]


title: CC100-C1-1-58 [CC100_scATAC-seq]


title: CC100-C1-1-75 [CC100_scATAC-seq]


title: CC100-C1-1-59 [CC100_scATAC-seq]


title: CC100-C1-1-55 [CC100_scATAC-seq]


title: CC100-C1-1-54 [CC100_scATAC-seq]


title: CC100-C1-1-52 [CC100_scATAC-seq]


title: CC100-C1-1-67 [CC100_scATAC-seq]


title: CC100-C1-1-4 [CC100_scATAC-seq]


title: CC100-C1-1-49 [CC100_scATAC-seq]


title: CC100-C1-1-48 [CC100_scATAC-seq]


title: CC100-C1-1-36 [CC100_scATAC-seq]


title: CC100-C1-1-40 [CC100_scATAC-seq]


title: CC100-C1-1-3 [CC100_scATAC-seq]


title: CC100-C1-1-47 [CC100_scATAC-seq]


title: CC100-C1-1-35 [CC100_scATAC-seq]


title: CC100-C1-1-34 [CC100_scATAC-seq]


title: CC100-C1-1-61 [CC100_scATAC-seq]


title: CC100-C1-1-46 [CC100_scATAC-seq]


title: CC100-C1-1-31 [CC100_scATAC-seq]


title: CC100-C1-1-42 [CC100_scATAC-seq]


title: CC100-C1-1-43 [CC100_scATAC-seq]


title: CC100-C1-1-45 [CC100_scATAC-seq]


title: CC100-C1-1-30 [CC100_scATAC-seq]


title: CC100-C1-1-41 [CC100_scATAC-seq]


title: CC100-C1-1-38 [CC100_scATAC-seq]


title: CC100-C1-1-37 [CC100_scATAC-seq]


title: CC100-C1-1-44 [CC100_scATAC-seq]


title: CC100-C1-1-50 [CC100_scATAC-seq]


title: CC100-C1-1-2 [CC100_scATAC-seq]


title: CC100-C1-1-28 [CC100_scATAC-seq]


title: CC100-C1-1-26 [CC100_scATAC-seq]


title: CC100-C1-1-39 [CC100_scATAC-seq]


title: CC100-C1-1-25 [CC100_scATAC-seq]


title: CC100-C1-1-33 [CC100_scATAC-seq]


title: CC100-C1-1-24 [CC100_scATAC-seq]


title: CC100-C1-1-23 [CC100_scATAC-seq]


title: CC100-C1-1-32 [CC100_scATAC-seq]


title: CC100-C1-1-22 [CC100_scATAC-seq]


title: CC100-C1-1-20 [CC100_scATAC-seq]


title: CC100-C1-1-1 [CC100_scATAC-seq]


title: CC100-C1-1-18 [CC100_scATAC-seq]


title: CC100-C1-1-17 [CC100_scATAC-seq]


title: CC100-C1-1-21 [CC100_scATAC-seq]


title: CC100-C1-1-19 [CC100_scATAC-seq]


title: CC100-C1-1-16 [CC100_scATAC-seq]


title: CC100-C1-1-14 [CC100_scATAC-seq]


title: CC100-C1-1-12 [CC100_scATAC-seq]


title: CC100-C1-1-13 [CC100_scATAC-seq]


title: CC100-C1-1-11 [CC100_scATAC-seq]


title: CC100-C1-1-15 [CC100_scATAC-seq]


title: CC100-C1-1-29 [CC100_scATAC-seq]


title: CC100-C1-1-27 [CC100_scATAC-seq]


title: C34_15_S191 [colonies_scRNA-seq]

data_processing:
RNA sequencing data was aligned to the human reference genome (GRCh37/hg19) using STAR using standard two-pass approach.
Gene were quantified using the STAR --quantMode GeneCounts flag over the Gencode v19 annotation.
Processed bams were genotyped for mitochondria using a custom python script.
Samples were filtered based on the following criterion: 1) a cell had to have 500 genes detected with at least 2000 total counts and 2) minimum mean mitochondria coverage of 100x
Genome_build: hg19
Supplementary_files_format_and_content: Heteroplasmy for variants that separated colonies as well as single-cell gene expression counts for filtered genes and cells used in analysis

source_name: donor 2_CD34+ hematopoietic cells

tag: donor id

value: donor 2


title: C34_13_S179 [colonies_scRNA-seq]


title: CC100-C1-1-10 [CC100_scATAC-seq]


title: C34_14_pos_S180 [colonies_scRNA-seq]


title: C34_16_neg_S192 [colonies_scRNA-seq]


title: C34_12_S168 [colonies_scRNA-seq]


title: CC100_bulkATAC_scRNA_12_06_S14

data_processing:
Adapter sequences were trimmed from FASTQs using custom python scripts to enable mapping fragments with sequences containing adapters. Paired-end reads were aligned to hg19 or mm10 using BOWTIE2 using the parameter –X2000 allowing fragments of up to 2 kb to align. Duplicates were removed using PICARD tools for reads uniquely mapping to the nuclear genome.
Mitochondrial genotypes were inferred for each ATAC-seq sample using a custom python script.
All samples/libraries passed QC (none were filtered in downstream analyses)
Genome_build: hg19
Supplementary_files_format_and_content: Heteroplasmy estimations for bulk populations assayed through ATAC-seq and single cells through scATAC + scRNA-seq

extract_protocol:
NP 40 lysis
ATAC-seq


title: CC100_bulkATAC_scATAC_12_07_S15


title: C34_11_S167 [colonies_scRNA-seq]


title: C34_07_S143 [colonies_scRNA-seq]


title: C34_09_S155 [colonies_scRNA-seq]


title: C34_10_S156 [colonies_scRNA-seq]


title: C34_06_S132 [colonies_scRNA-seq]


title: C34_02_S108 [colonies_scRNA-seq]


title: C34_04_S120 [colonies_scRNA-seq]


title: C34_03_S119 [colonies_scRNA-seq]


title: C34_05_S131 [colonies_scRNA-seq]


title: C33_16_S190 [colonies_scRNA-seq]


title: C34_01_S107 [colonies_scRNA-seq]


title: C33_12_S166 [colonies_scRNA-seq]


title: C33_15_S189 [colonies_scRNA-seq]


title: C33_13_S177 [colonies_scRNA-seq]


title: C33_09_S153 [colonies_scRNA-seq]


title: C33_08_S142 [colonies_scRNA-seq]


title: C33_10_S154 [colonies_scRNA-seq]


title: C33_07_S141 [colonies_scRNA-seq]


title: C33_11_S165 [colonies_scRNA-seq]


title: C33_03_S117 [colonies_scRNA-seq]


title: C34_08_S144 [colonies_scRNA-seq]


title: C33_05_S129 [colonies_scRNA-seq]


title: C32_16_S188 [colonies_scRNA-seq]


title: C33_01_S105 [colonies_scRNA-seq]


title: C33_04_S118 [colonies_scRNA-seq]


title: C33_06_S130 [colonies_scRNA-seq]


title: C33_02_S106 [colonies_scRNA-seq]


title: C32_15_S187 [colonies_scRNA-seq]


title: C32_13_S175 [colonies_scRNA-seq]


title: C32_10_S152 [colonies_scRNA-seq]


title: C32_08_S140 [colonies_scRNA-seq]


title: C32_09_S151 [colonies_scRNA-seq]


title: C32_11_S163 [colonies_scRNA-seq]


title: C32_07_S139 [colonies_scRNA-seq]


title: C32_12_S164 [colonies_scRNA-seq]


title: C32_02_S104 [colonies_scRNA-seq]


title: C32_06_S128 [colonies_scRNA-seq]


title: C31_16_S186 [colonies_scRNA-seq]


title: C32_05_S127 [colonies_scRNA-seq]


title: C32_14_S176 [colonies_scRNA-seq]


title: C32_03_S115 [colonies_scRNA-seq]


title: C32_01_S103 [colonies_scRNA-seq]


title: C31_12_S162 [colonies_scRNA-seq]


title: C31_10_S150 [colonies_scRNA-seq]


title: C31_15_S185 [colonies_scRNA-seq]


title: C31_04_S114 [colonies_scRNA-seq]


title: C31_06_S126 [colonies_scRNA-seq]


title: C31_11_S161 [colonies_scRNA-seq]


title: C31_05_S125 [colonies_scRNA-seq]


title: C31_09_S149 [colonies_scRNA-seq]


title: C31_14_S174 [colonies_scRNA-seq]


title: C31_03_S113 [colonies_scRNA-seq]


title: C31_08_S138 [colonies_scRNA-seq]