Introduction

Contamination of TS-like cells detected in RNA-seq data from FI-SC

Dr. Endo analysed the RNA-seq dataset associated with the original STAP papers and found that FI stem cell (FI-SC) samples were the mixture of 2 different cell types which have distinct expression profiles [1]. His findings include:

• Only FI-SC samples have a skewed allele frequency distribution whose peak is at around 95% B6-type allele
• Only FI-SC samples have different allele heterozygosity depending on gene types; their TSC-specific genes have less B6 homo SNPs and conversely ESC-specific genes have more B6 homo SNPs
• Most of the TSC-specific genes in FI-SC samples are expressed in approxmately 10% of TSC samples in the same dataset

These results supported that the samples were prepared from the mixture of about 10% TS-like cells and 90% ES-like cells having almost pure B6 genetic background. An independent analysis of the same RNA-seq data by RIKEN Scientific Investigation Team has also concluded that FI-SC samples were contaminated by 5-10% cells that closely resembled TS cells [2].

Here I reproduced most of the figures in Figure 2 and Figure 4 in Dr. Endo's paper and confirmed his results.

Note: I am a beginner for NGS data analysis (about 3 month experience). The result presented in this page is only preliminary and in non-prefessional quality.

1st. trial

For data retrieval and software setup/usage, see Trisomy-8 detection.

Mapping

It took 7-9h when using 1 CPU core for TruSeq sequences below. I could run 2 mappings (rep1 and rep2) at the same time.

Accesion	Name	accepted_hits.bam	Index of .bam	align_summary.txt
SRR1171565	FI-SC-rep1	SRR1171565.bam (2.6GB) , stats.	SRR1171565.bam.bai (2MB)	align_summary.txt
SRR1171566	FI-SC-rep2	SRR1171566.bam (2.4GB) , stats.	SRR1171565.bam.bai (2MB)	align_summary.txt
SRR1171590	TSC-rep1	SRR1171590.bam (2.8GB) , stats.	SRR1171590.bam.bai (2MB)	align_summary.txt
SRR1171591	TSC-rep2	SRR1171591.bam (2.6GB) , stats.	SRR1171591.bam.bai (2MB)	align_summary.txt
SRR1171560	ESC-rep1a	SRR1171560.bam (2.2GB) , stats.	SRR1171560.bam.bai (2MB)	align_summary.txt
SRR1171561	ESC-rep2a	SRR1171561.bam (2.5GB) , stats.	SRR1171561.bam.bai (2MB)	align_summary.txt
SRR1171580	STAP-rep1a	SRR1171580.bam (2.6GB) , stats.	SRR1171580.bam.bai (2MB)	align_summary.txt
SRR1171581	STAP-rep2a	SRR1171581.bam (2.3GB) , stats.	SRR1171581.bam.bai (2MB)	align_summary.txt

SNP analysis

$ ~/snpexp/src/snpexp -o snpexp.out -G ~/stap/genome/Mus_musculus/NCBI/GRCm38/Annotation/Genes/genes.gtf \
-V ~/stap/snps/dbSNP/mgp.v3.snps.rsIDdbSNPv137.vcf \
-m 20 -s C57BL6NJ,129P2,129S1,129S5 \
SRR1171565.bam
$ python ~/afV2.py snpexp.out > snpexp.129B6.out
$ grep -Fw -f Endo-fig2B-SNPs-ID.list snpexp.out > SRR1171565.fig2B.out

Allele frequency distribution

Ref: Endo-2014, Fig.2A
Allele frequencies of SNPs in total chromosome

ESC-rep1a, ESC-rep2a

FI-SC-rep1, FI-SC-rep2

SNPs IDs used in the figures in Endo-2014:

• Endo-fig2B-SNPs-ID.list
• Endo-fig2C-SNPs-ID.list
• Endo-fig4B-SNPs-ID.list

SNP allele counts (in read)

for ES-abundant genes in Endo-2014, Fig2B

Ref: Endo-2014, Fig.2B

ESC

FI-SC

TSC

STAP

for TSC-specific genes in Endo-2014, Fig2C

Ref: Endo-2014, Fig.2C

ESC

FI-SC

TSC

STAP

for SNPs listed in Endo-2014, Fig4B

Ref: Endo-2014, Fig.4B

Gene expression analysis

$ ~/cufflinks-2.2.1.Linux_x86_64/cuffdiff -o truseq4.cuffdiff ../genome/Mus_musculus/NCBI/GRCm38/Annotation/Genes/genes.gtf ESC-rep1a/SRR1171560.tophat/SRR1171560.bam,ESC-rep2a/SRR1171561.tophat/SRR1171561.bam STAP-rep1a/SRR1171580.tophat/SRR1171580.bam,STAP-rep2a/SRR1171581.tophat/SRR1171581.bam FI-SC-rep1/SRR1171565.tophat/SRR1171565.bam,FI-SC-rep2/SRR1171566.tophat/SRR1171566.bam TSC-rep1/SRR1171590.tophat/SRR1171590.bam,TSC-rep2/SRR1171591.tophat/SRR1171591.bam

$ head -n1 genes.read_group_tracking > ~/fig4A.genes.read_group_tracking
$ grep -Fw -f ~/Endo-Fig4A-genes.list genes.read_group_tracking >> ~/fig4A.genes.read_group_tracking

• truseq4.cuffidff/read_groups.info
• truseq4.cuffdiff/genes.read_gruop_tracking (4MB)
• truseq4.cuffdiff/genes.fpkm_tracking (8MB)

Ref: Endo-2014, Fig.4A

Extraction of cell-type specific genes

• ESC specific: q_value < 0.05 and TSC/ESC < 1/8
• TSC specific: q_value < 0.05 and TSC/ESC > 8
• others

specific-genes.py by Expo70

import sys

# extract cell-type specific genes according to Endo 2014, Fig.2D from gene_exp.diff
# by Expo70
def main():
	minRPKM = 1e-2
	colnum_gene = 2
	colnum_sample_1 = 4
	colnum_sample_2 = 5
	colnum_status = 6
	colnum_value_1 = 7
	colnum_value_2 = 8
	colnum_log2FC = 9
	colnum_q_value = 12
	
	if len(sys.argv) != 3:
		print >> sys.stderr, "usage: python specific-genes.py [ES|TS|other] gene_exp.diff"
		return 1
	filename = sys.argv[2]
	cell_type = sys.argv[1]
	lineno = 0
	
	with open(filename, "r") as fin:
		for line in fin:
			lineno = lineno + 1
			li = line.rstrip()
			ls = li.split("\t")
			if lineno == 1: #check file header
				labels = { colnum_gene:"gene", colnum_sample_1:"sample_1", colnum_sample_2:"sample_2", colnum_status:"status", colnum_value_1:"value_1", colnum_value_2:"value_2", colnum_log2FC:"log2(fold_change)", colnum_q_value:"q_value" }
				for colnum in labels.keys():
					if ls[colnum] != labels[colnum]:
						print >> sys.stderr, "column '" + labels[colnum] + "' could not be found"
						return 2
			else:
				gene = ls[colnum_gene]
				sample_1 = ls[colnum_sample_1]
				sample_2 = ls[colnum_sample_2]
				status = ls[colnum_status]
				value_1 = ls[colnum_value_1]
				value_2 = ls[colnum_value_2]
				if ls[colnum_log2FC] == '-inf':
					log2FC = -100.0
				elif  ls[colnum_log2FC] == 'inf':
					log2FC = 100.0
				else:
					log2FC = float(ls[colnum_log2FC])
				q_value = float(ls[colnum_q_value])
				
				if (status == 'OK') and (sample_1 == 'q1') and (sample_2 == 'q4'): #q1: ESC, q4: TSC 
					if (q_value < 0.05) and (log2FC > 3.0):
						if cell_type == 'TS':
							print gene
					elif (q_value < 0.05) and (log2FC < -3.0):
						if cell_type == 'ES':
							print gene
					else:
						if cell_type == 'other':
							print gene


if __name__ == '__main__':
	main()

$ cd ~/stap/reads/truseq4.cuffdiff
$ python specific-genes.py ES gene_exp.diff > es-specific-genes.list
$ python specific-genes.py TS gene_exp.diff > ts-specific-genes.list
$ python specific-genes.py other gene_exp.diff > other-genes.list

• es-specific-genes.list
• ts-specific-genes.list
• other-genes.list

$ wc -l *.list

  414 es-specific-genes.list
12713 other-genes.list
  429 ts-specific-genes.list
13556 total

Homozygous/heterozygous compositions of SNPs for cell-type specific gene sets

$ grep -Fw -f ts-specific-genes.list snpexp.129B6.out > snpexp.cell-type-TS.out

Ref: Endo-2014, Fig.2D

Extraction of SNPs that have TSC-specific alleles

tsSNPs.py by Expo70

import sys

filename = sys.argv[1]
base_pos = { "A":0, "C":1, "G":2, "T":3 }
homozygous_min_ratio = 0.95 #from M&M "SNP identification and heterozygosity tests" in Endo-2014

with open(filename, "r") as fin:
	for line in fin:
		li = line.rstrip()
		ls = li.split("\t")
		strains = ls[6-1]
		strain_bases = set(list(strains))
		acgt = ls[7-1].split(",")
		if len(acgt) == 4:
			if len(strain_bases) == 1:
				common_base = strain_bases.pop()
				common_base_count = int(acgt[base_pos[common_base]])
				bases = {"A","C","G","T"}
				bases.remove(common_base)
				for b in bases:
					b_count = int(acgt[base_pos[b]])
					total_count = common_base_count + b_count
					if total_count == 0:
						continue
					if float(b_count)/total_count >= homozygous_min_ratio:
						print li
						break

$ cd TSC-rep1/*.tophat
$ python ~/tsSNPs.py snpexp.out | awk '$3!="." {print $3}' | sort -u > SRR1171590.tsSNPs.list
$ cd ../../TSC-rep2/*.tophat
$ python ~/tsSNPs.py snpexp.out | awk '$3!="." {print $3}' | sort -u > SRR1171591.tsSNPs.list
$ cd ../..
$ cat TSC-rep1/*.tophat/SRR1171590.tsSNPs.list TSC-rep2/*.tophat/SRR1171591.tsSNPs.list | sort -u > tsSNPs.list

tsSNPs.list

or common SNPs among TSC replicates

$ comm -1 -2 TSC-rep1/*.tophat/SRR1171590.tsSNPs.list TSC-rep2/*.tophat/SRR1171591.tsSNPs.list > tsSNPs2.list

tsSNPs2.list

or common SNPs among TSC replicates including those having no rs IDs

$ python ~/tsSNPs.py snpexp.out | awk '{print $1 "\t" $2}' | sort -u > SRR1171590.tsSNPs3.list

$ comm -1 -2 TSC-rep1/*.tophat/SRR1171590.tsSNPs3.list TSC-rep2/*.tophat/SRR1171591.tsSNPs3.list > tsSNPs3.list

tsSNPs3.list in format of "#chr pos"

Whole SNPs analysis of the ratio of heterozygous/homogygous SNPs having TSC-specific alleles

$ grep -Fw -f tsSNPs.list snpexp.out > SRR1171590.tsSNPs.out
$ grep -Fw -f tsSNPs2.list snpexp.out > SRR1171590.tsSNPs2.out
$ grep -Fw -f tsSNPs3.list snpexp.out > SRR1171590.tsSNPs3.out

tsSNPs-count.py by Expo70

import sys

filename = sys.argv[1]
base_pos = { "A":0, "C":1, "G":2, "T":3 }
homozygous_min_ratio = 0.95

with open(filename, "r") as fin:
	nonTsSNPs_homo_count = 0
	hetero_count = 0
	tsSNPs_homo_count = 0
	for line in fin:
		li = line.rstrip()
		ls = li.split("\t")
		strains = ls[6-1]
		strain_bases = set(list(strains))
		acgt = ls[7-1].split(",")
		if len(acgt) == 4:
			if len(strain_bases) == 1:
				common_base = strain_bases.pop()
				common_base_count = int(acgt[base_pos[common_base]])
				bases = {"A","C","G","T"}
				bases.remove(common_base)
				b_counts = list()
				for b in bases:
					b_counts.append(int(acgt[base_pos[b]]))
				m_count = max(b_counts)
				if float(common_base_count)/(common_base_count + m_count) >= homozygous_min_ratio:
					nonTsSNPs_homo_count = nonTsSNPs_homo_count + 1
				elif float(m_count)/(common_base_count + m_count) >= homozygous_min_ratio:
					tsSNPs_homo_count = tsSNPs_homo_count + 1
				else:
					hetero_count = hetero_count + 1
	print "# file\tNon-TSC homo\tTSC/non-TSC homo\tTSC-type homo\tTotal #SNPs"
	print "%s\t%d\t%d\t%d\t%d" %(filename, nonTsSNPs_homo_count, hetero_count, tsSNPs_homo_count, nonTsSNPs_homo_count + hetero_count + tsSNPs_homo_count)

$ for f in $(find -name "*.tsSNPs.out"); do python ~/tsSNPs-count.py $f; done | LC_COLLATE=C sort -u

I got

# file	Non-TSC homo	TSC/non-TSC homo	TSC-type homo	Total #SNPs
./ESC-rep1a/SRR1171560.tophat/SRR1171560.tsSNPs.out	1324	110	20	1454
./ESC-rep2a/SRR1171561.tophat/SRR1171561.tsSNPs.out	1385	120	20	1525
./FI-SC-rep1/SRR1171565.tophat/SRR1171565.tsSNPs.out	285	1339	13	1637
./FI-SC-rep2/SRR1171566.tophat/SRR1171566.tsSNPs.out	261	1320	10	1591
./STAP-rep1a/SRR1171580.tophat/SRR1171580.tsSNPs.out	1270	125	14	1409
./STAP-rep2a/SRR1171581.tophat/SRR1171581.tsSNPs.out	1202	120	16	1338
./TSC-rep1/SRR1171590.tophat/SRR1171590.tsSNPs.out	0	35	1858	1893
./TSC-rep2/SRR1171591.tophat/SRR1171591.tsSNPs.out	0	237	1599	1836

$ for f in $(find -name "*.tsSNPs2.out"); do python ~/tsSNPs-count.py $f; done | LC_COLLATE=C sort -u

I got

# file	Non-TSC homo	TSC/non-TSC homo	TSC-type homo	Total #SNPs
./ESC-rep1a/SRR1171560.tophat/SRR1171560.tsSNPs2.out	1047	82	19	1148
./ESC-rep2a/SRR1171561.tophat/SRR1171561.tsSNPs2.out	1100	83	18	1201
./FI-SC-rep1/SRR1171565.tophat/SRR1171565.tsSNPs2.out	187	1076	10	1273
./FI-SC-rep2/SRR1171566.tophat/SRR1171566.tsSNPs2.out	166	1077	9	1252
./STAP-rep1a/SRR1171580.tophat/SRR1171580.tsSNPs2.out	1000	87	13	1100
./STAP-rep2a/SRR1171581.tophat/SRR1171581.tsSNPs2.out	955	82	15	1052
./TSC-rep1/SRR1171590.tophat/SRR1171590.tsSNPs2.out	0	0	1450	1450
./TSC-rep2/SRR1171591.tophat/SRR1171591.tsSNPs2.out	0	0	1450	1450

$ for f in $(find -name "*.tsSNPs3.out"); do python ~/tsSNPs-count.py $f; done | LC_COLLATE=C sort -u

I got

# file	Non-TSC homo	TSC/non-TSC homo	TSC-type homo	Total #SNPs
./ESC-rep1a/SRR1171560.tophat/SRR1171560.tsSNPs3.out	1293	106	38	1437
./ESC-rep2a/SRR1171561.tophat/SRR1171561.tsSNPs3.out	1357	107	36	1500
./FI-SC-rep1/SRR1171565.tophat/SRR1171565.tsSNPs3.out	249	1317	20	1586
./FI-SC-rep2/SRR1171566.tophat/SRR1171566.tsSNPs3.out	224	1318	19	1561
./STAP-rep1a/SRR1171580.tophat/SRR1171580.tsSNPs3.out	1211	102	29	1342
./STAP-rep2a/SRR1171581.tophat/SRR1171581.tsSNPs3.out	1157	94	29	1280
./TSC-rep1/SRR1171590.tophat/SRR1171590.tsSNPs3.out	0	0	1803	1803
./TSC-rep2/SRR1171591.tophat/SRR1171591.tsSNPs3.out	0	0	1803	1803

Ref: Endo-2014, Fig.4C

---

Raw outputs

for Fig2B (ESC specific genes)

#Chr	Position	ID_REF	Ref	Alt	C57BL6NJ,129P2,129S1,129S5	#A,C,G,T	Genes

SRR1171565.fig2B.out (FI-SC-rep1)

2	168749753	rs33341561	C	T	CCTTTTTT	0,321,0,4	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168749777	rs32916595	T	A	TTAAAAAA	0,0,0,334	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750038	rs33175525	A	G	AAGGGGGG	172,0,2,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750384	rs33232114	G	A	GGAAAAAA	3,0,228,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168754522	rs32978167	A	G	AAGGGGGG	274,0,3,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168755443	rs13476906	T	C	TTCCCCCC	0,3,0,190	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
4	55527553	rs49458812	G	A	GGAAAAAA	6,0,296,0	NM_010637.3 // Klf4
4	55527858	rs52406488	A	C	AACCCCCC	317,1,0,1	NM_010637.3 // Klf4
4	55527950	rs245328394	A	G	AAGGGGGG	174,3,2,23	NM_010637.3 // Klf4
4	55528182	rs32168644	A	G	AAGGGGGG	298,1,5,3	NM_010637.3 // Klf4

SRR1171566.fig2B.out (FI-SC-rep2)

2	168749753	rs33341561	C	T	CCTTTTTT	1,261,0,3	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168749777	rs32916595	T	A	TTAAAAAA	5,0,0,292	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750038	rs33175525	A	G	AAGGGGGG	175,0,2,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750384	rs33232114	G	A	GGAAAAAA	2,1,176,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168754522	rs32978167	A	G	AAGGGGGG	257,0,3,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168755443	rs13476906	T	C	TTCCCCCC	0,2,0,202	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
4	55527553	rs49458812	G	A	GGAAAAAA	10,0,304,0	NM_010637.3 // Klf4
4	55527858	rs52406488	A	C	AACCCCCC	326,2,0,0	NM_010637.3 // Klf4
4	55527950	rs245328394	A	G	AAGGGGGG	166,1,0,12	NM_010637.3 // Klf4
4	55528182	rs32168644	A	G	AAGGGGGG	290,0,4,0	NM_010637.3 // Klf4

SRR1171590.fig2B.out (TSC-rep1)

2	168749753	rs33341561	C	T	CCTTTTTT	0,45,0,24	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168749777	rs32916595	T	A	TTAAAAAA	24,0,0,48	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750038	rs33175525	A	G	AAGGGGGG	32,0,11,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750384	rs33232114	G	A	GGAAAAAA	20,0,21,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168754522	rs32978167	A	G	AAGGGGGG	28,0,15,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168755443	rs13476906	T	C	TTCCCCCC	0,29,0,23	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
4	55528182	rs32168644	A	G	AAGGGGGG	0,0,32,0	NM_010637.3 // Klf4

SRR1171591.fig2B.out (TSC-rep2)

2	168749753	rs33341561	C	T	CCTTTTTT	0,37,0,28	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168749777	rs32916595	T	A	TTAAAAAA	26,0,0,34	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750038	rs33175525	A	G	AAGGGGGG	27,0,13,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750384	rs33232114	G	A	GGAAAAAA	20,0,27,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168754522	rs32978167	A	G	AAGGGGGG	42,0,22,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168755443	rs13476906	T	C	TTCCCCCC	0,19,0,34	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
4	55527553	rs49458812	G	A	GGAAAAAA	34,0,0,0	NM_010637.3 // Klf4
4	55528182	rs32168644	A	G	AAGGGGGG	0,0,58,0	NM_010637.3 // Klf4

SRR1171560.fig2B.out (ESC-rep1a)

2	168749753	rs33341561	C	T	CCTTTTTT	0,107,0,70	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168749777	rs32916595	T	A	TTAAAAAA	60,0,0,113	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750038	rs33175525	A	G	AAGGGGGG	66,0,28,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750384	rs33232114	G	A	GGAAAAAA	41,0,75,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168754522	rs32978167	A	G	AAGGGGGG	76,0,48,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168755443	rs13476906	T	C	TTCCCCCC	0,48,0,42	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
4	55527553	rs49458812	G	A	GGAAAAAA	68,1,81,0	NM_010637.3 // Klf4
4	55527858	rs52406488	A	C	AACCCCCC	81,31,0,0	NM_010637.3 // Klf4
4	55527950	rs245328394	A	G	AAGGGGGG	54,1,16,5	NM_010637.3 // Klf4
4	55528182	rs32168644	A	G	AAGGGGGG	58,0,68,2	NM_010637.3 // Klf4

SRR1171561.fig2B.out (ESC-rep2a)

2	168749753	rs33341561	C	T	CCTTTTTT	0,120,0,80	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168749777	rs32916595	T	A	TTAAAAAA	74,0,0,109	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750038	rs33175525	A	G	AAGGGGGG	39,0,37,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168750384	rs33232114	G	A	GGAAAAAA	63,0,85,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168754522	rs32978167	A	G	AAGGGGGG	90,0,38,0	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
2	168755443	rs13476906	T	C	TTCCCCCC	0,67,0,73	NM_201396.2 // Sall4 /// NM_201395.2 // Sall4 /// NM_175303.3 // Sall4
4	55527553	rs49458812	G	A	GGAAAAAA	68,0,93,0	NM_010637.3 // Klf4
4	55527858	rs52406488	A	C	AACCCCCC	112,45,0,0	NM_010637.3 // Klf4
4	55527950	rs245328394	A	G	AAGGGGGG	53,2,24,6	NM_010637.3 // Klf4
4	55528182	rs32168644	A	G	AAGGGGGG	90,0,80,0	NM_010637.3 // Klf4

for Fig2C (TSC specific genes)

#Chr	Position	ID_REF	Ref	Alt	C57BL6NJ,129P2,129S1,129S5	#A,C,G,T	Genes

SRR1171565.fig2C.out (FI-SC-rep1)

14	118235390	rs215374979	C	T	CCTTTTTT	0,29,0,6	NM_177753.3 // Sox21
14	118236534	rs50457356	C	T	CCTTTTTT	0,37,0,5	NM_177753.3 // Sox21
14	118236794	rs257018347	A	G	AAGGGGGG	21,0,4,0	NM_177753.3 // Sox21
14	118236818	rs249072519	C	T	CCTTTTTT	0,20,0,3	NM_177753.3 // Sox21
2	103439276	rs13469469	C	T	CCTTTTTT	0,52,0,46	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103442829	rs13469464	G	A	GGAAAAAA	40,0,39,0	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103449748	rs52190599	C	T	CCTTTTTT	0,4,0,37	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103449764	rs234337412	C	T	CCTTTTTT	0,2,0,32	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103450038	rs13469472	C	T	CCTTTTTT	0,26,0,12	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5

SRR1171566.fig2C.out (FI-SC-rep2)

14	118234949	rs253162768	G	C	GGCCCCCC	0,1,27,0	NM_177753.3 // Sox21
14	118235390	rs215374979	C	T	CCTTTTTT	0,23,0,7	NM_177753.3 // Sox21
14	118236534	rs50457356	C	T	CCTTTTTT	0,27,0,0	NM_177753.3 // Sox21
14	118236794	rs257018347	A	G	AAGGGGGG	23,0,3,0	NM_177753.3 // Sox21
14	118236818	rs249072519	C	T	CCTTTTTT	0,22,0,3	NM_177753.3 // Sox21
2	103439276	rs13469469	C	T	CCTTTTTT	0,50,0,46	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103442829	rs13469464	G	A	GGAAAAAA	40,0,45,0	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103449748	rs52190599	C	T	CCTTTTTT	0,2,0,30	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103449764	rs234337412	C	T	CCTTTTTT	0,0,1,28	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103450038	rs13469472	C	T	CCTTTTTT	0,13,0,19	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5

SRR1171590.fig2C.out (TSC-rep1)

14	118234949	rs253162768	G	C	GGCCCCCC	0,15,9,0	NM_177753.3 // Sox21
14	118235390	rs215374979	C	T	CCTTTTTT	0,19,0,22	NM_177753.3 // Sox21
14	118236534	rs50457356	C	T	CCTTTTTT	0,9,0,28	NM_177753.3 // Sox21
14	118236794	rs257018347	A	G	AAGGGGGG	18,0,15,0	NM_177753.3 // Sox21
14	118236818	rs249072519	C	T	CCTTTTTT	0,15,0,13	NM_177753.3 // Sox21
2	103439276	rs13469469	C	T	CCTTTTTT	1,429,3,460	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103442829	rs13469464	G	A	GGAAAAAA	305,0,362,1	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103449748	rs52190599	C	T	CCTTTTTT	0,36,1,277	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103449764	rs234337412	C	T	CCTTTTTT	0,19,0,255	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103450038	rs13469472	C	T	CCTTTTTT	0,152,0,138	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5

SRR1171591.fig2C.out (TSC-rep2)

14	118234949	rs253162768	G	C	GGCCCCCC	0,10,13,0	NM_177753.3 // Sox21
14	118235390	rs215374979	C	T	CCTTTTTT	0,26,0,23	NM_177753.3 // Sox21
14	118236534	rs50457356	C	T	CCTTTTTT	0,16,0,37	NM_177753.3 // Sox21
14	118236794	rs257018347	A	G	AAGGGGGG	12,0,17,0	NM_177753.3 // Sox21
14	118236818	rs249072519	C	T	CCTTTTTT	0,12,0,18	NM_177753.3 // Sox21
2	103439276	rs13469469	C	T	CCTTTTTT	1,371,0,478	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103442829	rs13469464	G	A	GGAAAAAA	299,0,322,0	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103449748	rs52190599	C	T	CCTTTTTT	0,44,1,255	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103449764	rs234337412	C	T	CCTTTTTT	0,22,0,228	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5
2	103450038	rs13469472	C	T	CCTTTTTT	0,145,0,114	NM_001145813.1 // Elf5 /// NM_010125.3 // Elf5

SRR1171560.fig2C.out (ESC-rep1a)

SRR1171561.fig2C.out (ESC-rep2a)

---

Reference

1. Endo 2014, Genes to Cells, "Quality control method for RNA-seq using single nucleotide polymorphism allele frequency"
2. Katsura report (J)(E)