Human Stem Cell Genome Project

Welcome to Human Stem Cell Genome Project data portal. This is an online resource for researchers to search, download and visualize high coverage whole genome sequence data generated from more than a hundred publicly available pluripotent cell lines. The overarching goal of this project is to capture the genetic variation across widely used stem cell lines and to enable rationale selection of these lines for downstream applications of interest.

To use this resource, users can search across cell lines for sequence genetic variants (point mutations and indels) or structural genetic variants (duplications, deletions, and copy number neutral loss of heterozygosity events). Any of these fields can be interrogated for variants of interest, and each cell line can be studied separately. Data resulting from a search can be downloaded as a CSV file. Due to data sharing restrictions, not all cell lines included in the manuscript accompanying this portal could be included here. If a cell line was not assessed for a certain type of variant, "n/a" is returned. For a brief overview of the function of this portal, please see "Tutorial".

In order to gain full access to the features included in this portal, please first apply for access. Once you have been approved, please visit our Terra workspace for accessing sequence data.

Citation: If you use this data portal, please cite our most recent publication

Source code is freely available on GitHub or Zenodo and is free of use. We hope it will be a resource for users that wish to create similar data portals to facilitate the exploration and dissemination of their own data.

Study Summary

Overview

The data below summarises essential information from the 143 human pluripotent stem cell lines included in this study. The number of sequence variants shown below correspond to predicted missense or loss of function (LOF) variants. Similarly, the number of structural variants refers to those large (> 1 MBP) and small (1 KBP - 1 MBP) variants that overlap protein coding genes. Where these variants were not ascertained, N/A is returned. To search for details about variants of interest, please see “Primary Data Access” to apply for permission to access raw and analysed data. Once access has been granted, a searchable “Whole Genome” page will become visible. Please see Tutorial to learn more about the functionality of this searchable resource. Briefly, users can search across all cell lines for a particular gene or genetic variant, or within a cell line of interest to ascertain all variants present in that line.

Note that for all cell lines obtained from University of California Los Angeles, we can only provide summary statistics on this page due to data use limitations.

Objectives

Use whole genome sequencing to map the genetic architecture of stem cell lines at the level of sequence and structural variants
Catalog inherited and acquired, rare and common variation in stem cell lines
Develop a framework for using genetic data to select cell lines for basic and clinical applications

Cell Lines

Cell Line Sample	Institution	NIH Reg. #	Sex Chromosome Genotype	Mean Seq. Coverage	Median Seq. Coverage	Banked?	Sequence Variants	Structural Variants (all)
Cell Line	Institution	NIH Reg. #	Sex Chromosome Genotype	Mean Seq. Coverage	Median Seq. Coverage	Banked?	Sequence Variants	Structural Variants (all)
CHB10 CHB10_P24_140728	Children's Hospital Corporation	9	XY	32.296	33	true	8956	339
CHB11 CHB11_P25_140722	Children's Hospital Corporation	10	XX	36.149	36	true	8972	345
CHB12 CHB12_P23_140809	Children's Hospital Corporation	11	XX	33.416	34	true	8960	317
CHB4 CHB4_P22_150729	Children's Hospital Corporation	4	XY	33.582	34	true	8834	N/A
CHB5 CHB5_P25_140801	Children's Hospital Corporation	5	XX	32.404	32	true	9223	412
CHB6 CHB6_P37_140722	Children's Hospital Corporation	6	XX	32.132	32	true	9036	366
CHB8 CHB8_P26_140723	Children's Hospital Corporation	7	XX	31.752	32	true	8719	358
CHB9 CHB9_P24_140728	Children's Hospital Corporation	8	XY	27.504	28	true	8961	365
CSES12 CSES12_P14_140616	Cedars-Sinai Medical Center	111	XY	26.662	27	true	8897	453
CSES15 CSES15_P26_140611	Cedars-Sinai Medical Center	114	XY	31.061	31	true	8981	1366
CSES2 CSES2_P18_140611	Cedars-Sinai Medical Center	106	XX	50.256	51	true	8802	323
CSES25 CSES25_P26_150119	Cedars-Sinai Medical Center	122	XX	30.581	30	true	8957	556
CSES4 CSES4_P27_140616	Cedars-Sinai Medical Center	107	XY	33.911	35	true	9119	338
CSES6 CSES6_P15_140809	Cedars-Sinai Medical Center	134	XX	32.13	32	true	8981	361
CSES7 CSES7_P14_140722	Cedars-Sinai Medical Center	108	XX	31.268	31	true	9029	335
CT1 CT1_P40_140809	University of Connecticut Sch. of Med./Dnt.	68	XX	32.707	33	true	9101	331
CT2 CT2_P11_140622	University of Connecticut Sch. of Med./Dnt.	69	XX	35.368	36	true	9097	332
CT3 CT3_P17_140809	University of Connecticut Sch. of Med./Dnt.	70	XX	25.674	26	true	8908	365
CT4 CT4_P15_140622	University of Connecticut Sch. of Med./Dnt.	71	XX	32.94	33	true	8965	342
ESI017 ESI017_P31_140619	BioTime, Inc.	93	XX	40.61	41	true	8898	373
ESI035 ESI035_P36_140617	BioTime, Inc.	129	XX	33.306	34	true	8905	466
ESI049 ESI049_P33_140612	BioTime, Inc.	130	XY	32.181	33	true	9074	341
ESI051 ESI051_P35_140610	BioTime, Inc.	131	XX	31.484	32	true	8835	344
ESI053 ESI053_P28_140611	BioTime, Inc.	132	XX	32.541	33	true	8866	314
Elf1 Elf1_P12_140624	University of Washington	156	XX	31.845	32	true	8889	371
Genea15 Genea15_P20_150110	Genea Biocells	228	XY	26.457	27	true	8773	330
Genea16 Genea16_P17_150104	Genea Biocells	229	XX	29.437	29	true	8884	348
Genea2 Genea2_P19_150119	Genea Biocells	151	XY	26.184	27	true	8794	343
Genea42 Genea42_P19_150107	Genea Biocells	231	XX	29.507	30	true	9301	391
Genea43 Genea43_P12_150104	Genea Biocells	232	XY	26.007	26	true	9173	1449
Genea47 Genea47_P10_150104	Genea Biocells	230	XX	27.771	28	true	8928	305
Genea48 Genea48_P8_150110	Genea Biocells	152	XY	26.979	27	true	8911	3795
Genea52 Genea52_P11_150110	Genea Biocells	234	XY	25.713	26	true	8867	344
Genea57 Genea57_P13_150110	Genea Biocells	233	XX	27.985	28	true	8820	315
HS1001 HS1001_P14	Karolinska Institute	NA	XY	26.916	26	false	8841	346
HS346 HS346_P25_140704	Karolinska Institute	201	XX	32.438	33	true	8807	331
HS401 HS401_P25_140619	Karolinska Institute	202	XY	30.109	31	true	8853	3342
HS420 HS420_P24_140623	Karolinska Institute	203	XY	32.977	34	true	8713	338
HS975 HS975_P8	Karolinska Institute	NA	XX	29.182	29	false	9015	363
HS980 HS980_P8	Karolinska Institute	NA	XX	29.253	29	false	9177	373
HS983a HS983a_P7	Karolinska Institute	NA	XY	30.873	31	false	9033	385
HS999 HS999_P12	Karolinska Institute	NA	XX	28.305	28	false	9117	690
HUES42 HUES42_P22_131008	Harvard University	157	XY	31.803	32	true	8933	362
HUES44 HUES44_P16_131008	Harvard University	158	XX	33.355	33	true	8845	299
HUES45 HUES45_P21_131206	Harvard University	76	XX	31.863	32	true	8912	356
HUES48 HUES48_P23_150902	Harvard University	53	XX	36.268	36	true	8869	4832
HUES49 HUES49_P25_130903	Harvard University	54	XX	35.19	35	true	9086	396
HUES53 HUES53_P16_131008	Harvard University	55	XY	25.187	25	true	8748	359
HUES62 HUES62_P18_131007	Harvard University	65	XX	28.444	28	true	8966	326
HUES63 HUES63_P15_140416	Harvard University	66	XY	33.521	34	true	8928	326
HUES64 HUES64_P14_131009	Harvard University	67	XY	28.813	29	true	8871	340
HUES65 HUES65_P15_131027	Harvard University	56	XY	34.053	35	true	9011	340
HUES66 HUES66_P15_131015	Harvard University	57	XX	27.341	27	true	8946	1427
HUES68 HUES68_P27_131112	Harvard University	176	XX	23.289	23	true	8915	853
HUES69 HUES69_P21_130922	Harvard University	178	XX	27.219	27	true	8859	328
HUES70 HUES70_P27_131019	Harvard University	177	XX	26.109	26	true	8844	310
HUES71 HUES71_P28_150210	Harvard University	281	XX	28.906	29	true	9158	6214
HUES72 HUES72_P20_150119	Harvard University	282	XX	28.833	29	true	8916	2210
HUES73 HUES73_P28_150210	Harvard University	283	XY	27.759	28	true	8984	2490
HUES74 HUES74_P7_150201	Harvard University	304	XY	25.709	26	true	8996	343
HUES75 HUES75_P7_150124	Harvard University	280	XX	26.144	26	true	9000	335
I3 I3_P27_150210	Technion R&D Foundation	204	XX	27.265	28	true	9037	297
I4 I4_P29_150210	Technion R&D Foundation	205	XX	27.626	28	true	9028	266
KCL019 KCL019_P8_141010	King's College London	270	XX	30.992	31	false	8870	329
KCL020 KCL020_P30_111121	King's College London	271	XY	28.914	29	false	8868	329
KCL022 KCL022_P22_111209	King's College London	264	XY	29.464	29	false	8867	8057
KCL031 KCL031_P17_120710	King's College London	263	XY	26.884	27	false	8923	322
KCL032 KCL032_P7_141015	King's College London	266	XX	27.101	27	false	8906	346
KCL033 KCL033_P17_120408	King's College London	267	XX	26.94	26	false	8909	339
KCL034 KCL034_P17_131020	King's College London	268	XY	25.662	26	false	8772	331
KCL037 KCL037_P7_141015	King's College London	269	XY	29.83	30	false	8863	322
KCL038 KCL038_P9_141015	King's College London	265	XY	60.914	62	false	8988	327
KCL039 KCL039_P8_141015	King's College London	274	XY	28.798	28	false	8957	349
KCL040 KCL040_P22_120905	King's College London	272	XX	29.378	29	false	8961	344
MShef11 MShef11_P18_140715	University of Sheffield	NA	XY	26.999	27	false	8937	370
MShef12 MShef12_P16	University of Sheffield	NA	XX	30.924	31	false	9023	362
MShef13 MShef13_P16_140715	University of Sheffield	NA	XY	26.463	27	false	8896	352
MShef14 MShef14_P13_130816	University of Sheffield	NA	XX	30.452	30	false	8767	308
MShef2 MShef2_P18_111121	University of Sheffield	NA	XX	28.302	28	false	8838	290
MShef3 MShef3_P10_140803	University of Sheffield	NA	XX	30.139	30	false	8954	2275
MShef4 MShef4_P35_130524	University of Sheffield	NA	XY	30.212	30	false	8859	341
MShef5 MShef5_P8_140803	University of Sheffield	NA	XY	24.821	25	false	8852	365
MShef7 MShef7_P13_140715	University of Sheffield	NA	XY	30.134	30	false	8736	342
MShef8 MShef8_P14_140715	University of Sheffield	NA	XY	29.31	29	false	8871	218
Man11 Man11_P27	University of Manchester	NA	XX	26.693	27	false	8831	392
Man12 Man12_P24	University of Manchester	NA	XY	27.666	28	false	8799	359
Mel1 Mel1_P20_150107	University of Queensland	139	XY	25.96	26	true	8782	331
Mel2 Mel2_P28_150121	University of Queensland	140	XX	26.531	27	true	8989	566
Mel3 Mel3_P38_150121	University of Queensland	141	XX	29.055	29	true	8918	2156
Mel4 Mel4_P37_150119	University of Queensland	142	XX	28.203	28	true	8894	340
Mshef10 Mshef10_P22	University of Sheffield	NA	XY	30.591	31	false	8851	414
RUES1 RUES1_P25_140730	The Rockefeller University	12	XY	33.097	33	true	8912	392
RUES2 RUES2_P18_170730	The Rockefeller University	13	XX	30.891	31	true	8989	2332
Shef3-2 Shef3-2_P13_140801	University of Sheffield	77	XY	32.263	32	true	8869	359
Shef6-1 Shef6-1_P12_140725	University of Sheffield	78	XX	49.628	50	true	8853	5034
UCLA1 UCLA1_P15_150817	University of California Los Angeles	58	XX	32.071	30	true	8961	N/A
UCLA10 UCLA10_P15_150817	University of California Los Angeles	146	XY	33.473	34	true	9144	N/A
UCLA11 UCLA11_P16_150821	University of California Los Angeles	185	XY	51.72	44	true	8775	N/A
UCLA12 UCLA12_P16_150819	University of California Los Angeles	186	XX	48.328	49	true	8995	N/A
UCLA13 UCLA13_P16_150817	University of California Los Angeles	293	XY	47.479	48	true	9041	N/A
UCLA14 UCLA14_P20_150817	University of California Los Angeles	294	XX	44.79	45	true	9008	N/A
UCLA15 UCLA15_P19_150823	University of California Los Angeles	295	XX	45.304	46	true	8961	N/A
UCLA16 UCLA16_P16_150817	University of California Los Angeles	296	XX	48.243	49	true	8951	N/A
UCLA17 UCLA17_P18_150823	University of California Los Angeles	297	XX	40.265	41	true	8960	N/A
UCLA18 UCLA18_P26_150817	University of California Los Angeles	298	XX	47.789	47	true	8879	N/A
UCLA2 UCLA2_P14_150817	University of California Los Angeles	59	XY	46.456	43	true	8903	N/A
UCLA3 UCLA3_P16_150817	University of California Los Angeles	60	XX	35.986	36	true	9081	N/A
UCLA4 UCLA4_P16_150819	University of California Los Angeles	87	XX	32.598	31	true	9160	N/A
UCLA5 UCLA5_P26_150819	University of California Los Angeles	88	XX	35.975	34	true	8808	N/A
UCLA6 UCLA6_P20_150817	University of California Los Angeles	89	XY	43.127	44	true	8837	N/A
UCLA7 UCLA7_P21_150817	University of California Los Angeles	143	XX	51.041	52	true	8956	N/A
UCLA8 UCLA8_P15_150817	University of California Los Angeles	144	XX	35.998	35	true	8972	N/A
UCLA9 UCLA9_P16_150821	University of California Los Angeles	145	XX	43.273	41	true	8920	N/A
UCSF4 UCSF4_P19_140613	University of California San Francisco	44	XX	28.793	29	true	8999	318
UM121-7 UM121-7_P10_150925	University of Michigan	291	XY	44.911	46	true	8879	1695
UM14-1 UM14-1_P26_150115	University of Michigan	162	XY	27.329	28	true	8765	340
UM22-2 UM22-2_P26_150107	University of Michigan	209	XX	28.397	29	true	8957	374
UM33-4 UM33-4_P25_150107	University of Michigan	279	XX	28.752	29	true	8879	354
UM4-6 UM4-6_P15_150116	University of Michigan	147	XY	31.755	32	true	8960	382
UM77-2 UM77-2_P12_150107	University of Michigan	278	XX	30.007	30	true	9113	331
UM78-2 UM78-2_P13_150922	University of Michigan	288	XY	32.436	32	true	9052	1699
WA01 WA01_P23_140528	WiCell Research Institute	43	XY	29.39	30	true	8809	1125
WA09 WA09_P25_140528	WiCell Research Institute	62	XX	27.878	28	true	9178	365
WA13 WA13_P33_140530	WiCell Research Institute	63	XY	31.771	32	true	8949	341
WA14 WA14_P21_131206	WiCell Research Institute	64	XY	32.657	33	true	8935	352
WA15 WA15_P31_140529	WiCell Research Institute	96	XY	34.231	35	true	8829	343
WA17 WA17_P12_140530	WiCell Research Institute	98	XY	31.759	32	true	8875	314
WA18 WA18_P9_140530	WiCell Research Institute	99	XY	35.953	37	true	8935	361
WA19 WA19_P11_140529	WiCell Research Institute	100	XY	33.876	35	true	8845	308
WA20 WA20_P23_140613	WiCell Research Institute	101	XX	31.841	32	true	8798	5012
WA21 WA21_P28_140528	WiCell Research Institute	102	XY	27.507	28	true	8802	360
WA22 WA22_P14_140530	WiCell Research Institute	103	XX	31.617	32	true	9035	358
WA23 WA23_P20_140606	WiCell Research Institute	104	XY	30.548	31	true	8822	10590
WA24 WA24_P15_140528	WiCell Research Institute	105	XY	28.215	29	true	8768	352
WA25 WA25_P14_140606	WiCell Research Institute	196	XX	26.874	27	true	8847	320
WA26 WA26_P15_140606	WiCell Research Institute	197	XX	24.057	24	true	8852	336
WA27 WA27_P15_140613	WiCell Research Institute	198	XX	31.943	32	true	8982	552
WA7 WA7_P33_140529	WiCell Research Institute	61	XX	32.126	32	true	9097	339
WIBR1 WIBR1_P34_140608	Whitehead Institute for Biomedical Research	74	XX	31.364	32	true	9018	1007
WIBR2 WIBR2_P33_140617	Whitehead Institute for Biomedical Research	75	XX	31.879	32	true	8971	328
WIBR3 WIBR3_P23_140624	Whitehead Institute for Biomedical Research	79	XX	32.186	33	true	8840	342
WIBR5 WIBR5_P13_140611	Whitehead Institute for Biomedical Research	81	XX	32.151	32	true	8787	346
WIBR6 WIBR6_P11_140617	Whitehead Institute for Biomedical Research	82	XX	34.679	35	true	8678	423

You can do exclude results in searches using the "!" character before the term.

Methods (Cell Banking and Analysis)

Tutorial

Please watch this short video tutorial to see some basic features of the portal.

Primary Data Access

Whole genome data are accessible via DUOS, listed as dataset DUOS-000121. If you wish to verify variants identified in this whole genome sequencing dataset in an independently generated whole exome sequencing (WES) dataset of most of the samples included in this portal, these data are available in controlled access databases: dgGaP accession phs001343.v1.p1, EGA accession EGAS00001002400, and these data are searchable on the "Whole Genome" page (accessible once logged in).

Contact Us

To obtain access to the primary or analysed data, please request access via DUOS as explained in Primary Data Access. If you have specific questions about the portal architecture or would like to replicate it for your own data, commented code is available on GitHub. If you have specific questions about the analysis or conclusions, please contact one of the corresponding authors at

fm436 [at] medschl.cam.ac.uk
eggan [at] mcb.harvard.edu
mccarroll [at] genetics.med.harvard.edu

Basic Search
View CNV Regions

Whole Genome Search

Sequence variants and large structural variants were ascertained across all 143 cell lines in this study, but variants from the 18 UCLA lines cannot be shown due to data sharing regulations. Small CNVs were ascertained for 125 of the remaining lines that had low intrinsic variability in sequencing depth. Sequence variants shown are those predicted to be missense or loss-of-function mutations in protein coding genes. Data can be sorted by allele frequency (AF) or predicted deleterious effect (CADD or DANN score). Small CNVs (approximately 1kbp to 1Mbp) and large structural variants (>1Mbp) shown are those that overlap with gene bodies. If more than one gene is affected, the same CNV may be listed on multiple rows. Multiple adjacent CNV calls within the same line likely reflect fragmented calls from a single event and should be manually confirmed by the user. Calls are classified as Deletion, Duplication, or CN-LOH based on their copy number, or Mixed when calls are complex. Large structural variants were called and classified by depth of coverage analysis and/or b allele frequency analysis from WGS data, MoChA analysis of microarray data, or a combination of these three methods. Note that a maximum of 4 cell lines may be viewed at a time in the IGV browser. If there are more than 4 possibilities, the 4 most informative lines for your query will be displayed.