title: mouse_YFPpos_8w_cell35

description:
Single cell from mouse pancreas
D8.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell35

value: beta-like


title: MeDIP_input_3

status: Public on Feb 16 2017

type: SRA

city: Cambridge

first: Gavin

last: Kelsey

address: Babraham Research Campus

country: United Kingdom

institute: The Babraham Institute

description:
TruSeq3
processed data file: MeDIP_Input_1kb_tiles_readcount.txt

library_source: genomic

data_processing:
Libraries were sequenced on an Illumina HiSeq using the default RTA analysis software.
Raw Bisulfite-Seq reads were trimmed to remove both poor quality calls and adapters using Trim Galore version 0.2.5 (default parameters; www.bioinformatics.babraham.ac.uk/projects/trim_galore/)
Remaining Bisulfite-Seq reads were mapped using Bismark version 0.13.1 (default parameters), and CpG methylation calls were extracted using the Bismark methylation extractor (Krueger and Andrews, 2011).
MeDIP-Seq: Trimmed sequences were mapped to the mouse NCBIM37 genome using Bowtie 2 with default parameters.
Genome_build: NCBIM37
Supplementary_files_format_and_content: The Bismark CpG coverage report is tab-delimited, uses 1-based genomic coordinates for every covered cytosine position in the experiment and is in the following format: <chromosome>  <start position>  <end position>  <methylation percentage>  <count methylated>  <count non-methylated>
Supplementary_files_format_and_content: Quantitated MeDIP Input data file is tab delimited and contains read count over 1kb tiles within the following 12 columns: (1) Chromosome; (2) Start; (3) End; (4-12) log2 readcount for each sample

molecule: genomic DNA

organism: Mus musculus

source_name: MEF_non-bisulfute converted genomic DNA control

growth_protocol: ESCs were cultured without feeders in standard serum-containing media (DMEM 4500 mg/L glucose, 4 mM L-glutamine and 110 mg/L sodium pyruvate, 15% fetal bovine serum, 1 U/ml penicillin, 1 μg/ml streptomycin, 0.1mM nonessential amino acids, 50 uM beta-mercaptoethanol, 103 U/ml LIF ESGRO).

extract_protocol:
Genomic DNA was extracted with DNeasy Blood and Tissue Kit (Qiagen) following manufacturer’s instructions.
Full details can be found in Olova et al. (2016). BS-seq: DNA samples were fragmented by sonication (Covaris) and adaptor ligated (using Illumina supplied methylated adaptors and NEB Next library preparation kit). Subsequently, DNA was bisulfite-treated using the Imprint kit (Sigma), EpiTect kit (Qiagen), EZ DNA Methylation Kit (Zymo). Final library amplification (15 cycles) was done using Pfu Turbo Cx (Agilent) and iPCRtag primers, after which the libraries were purified with AMPure XP beads (Agencourt).
MeDIP-seq: DNA samples were fragmented by sonication (Covaris) and adaptor ligated (using Illumina TruSeq indexed adaptors and NEB Next library preparation kit). Subsequently, DNA samples were pooled, denatured and the input sample was sequenced directly without immuno-precipitation, after 6 cycles of amplification with NEB Next 2x Phusion mix and purification with AMPure XP beads (Agencourt).

treatment_protocol: In vitro DNA methylation with M.CviPI (NEB) was performed on 0.5 – 1.0 µg of TKO mESC genomic DNA, incubated for 2 hours at 37°C, purified with GeneJet PCR Purification kit (Thermo).

tag: strain background

value: pMEF (C57BL/6J x CBA/Ca F1 x F1)

tag: genotype/variation

value: Wild type

tag: cell type

value: Mouse embryonic fibroblasts


title: mouse_YFPpos_8w_cell34

city: Stanford

first: Martin

last: Enge

state: CA

address: James H. Clark Center, 318 Campus Drive,

department: Bioengineering

country: USA

institute: Stanford University

description:
Single cell from mouse pancreas
B11.1000200103

library_source: transcriptomic

data_processing:
Sequencing reads were trimmed, adapter sequences removed and the reads aligned using STAR with default parameters.
Duplicate reads were removed using picard. Transcript counts were obtained using HT-Seq and mm10 UCSC exon/transcript annotations.
Genome_build: mm10
Supplementary_files_format_and_content: tab-delimited text files with raw read values for each sample

molecule: total RNA

source_name: Single cell from pancreas, YFPpos_8w_cell34

extract_protocol:
Isolated mouse islets were dissociated into single cells by enzymatic digestion using 0.05% Trypsin (25300-120; Life Technologies). 7-aminoactinomycin D (7-AAD, eBiosciences) was used at 1:500 dilution as Live/Dead stain to exclude dead cells. Sort gates were adjusted with reference to negative controls (wild-type islets without YFP labeling). We collected cells from YFP+, lineage-traced population and also YFPNeg, non-labeled population. Cells were sorted on a special order 5-laser FACS Aria II (BD Biosciences) using a 100 µm nozzle at a flow rate of 1 following doublet removal. Sorted single-cells were collected directly into 96-well plates (Bio-Rad cat #: HSP9601) containing 4 µL of lysis buffer with dNTPs37 for downstream single-cell RNA-Seq assays.
Single-cells were collected  in lysis buffer in 96-well plates, followed by reverse transcription with template-switch using an LNA-modified template switch oligo to generate cDNA. After 21 cycles of pre-amplification, DNA was purified and analyzed on an automated Fragment Analyzer (Advanced Analytical). Each cell’s cDNA fragment profile was individually inspected and only wells with successful amplification products (concentration higher than 0.06 ng/ul) and with no detectable RNA degradation were selected for final library preparation. Tagmentation assays and barcoded sequencing libraries were prepared using Nextera XT kit (FC-131-1024; Illumina) according to the manufacturer’s instructions. Barcoded libraries were pooled and subjected to 75 bp paired-end sequencing on the Illumina NextSeq instrument.

value: YFPpos

tag: time (weeks after arx and dnmt1 depletion)

value: endocrine pancreatic islet cell

tag: inferred cell type

value: alpha-like


title: MeDIP_input_1

description:
TruSeq1
processed data file: MeDIP_Input_1kb_tiles_readcount.txt

source_name: ES cells_non-bisulfute converted genomic DNA control

value: ES cells


title: mouse_YFPpos_8w_cell32

description:
Single cell from mouse pancreas
A10.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell32


title: mouse_YFPpos_8w_cell31

description:
Single cell from mouse pancreas
A4.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell31


title: MeDIP_input_2

description:
TruSeq2
processed data file: MeDIP_Input_1kb_tiles_readcount.txt

value: E14 (129P2/OlaHsda)


title: mouse_YFPpos_8w_cell30

description:
Single cell from mouse pancreas
B7.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell30


title: mouse_YFPpos_8w_cell33

description:
Single cell from mouse pancreas
B11.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell33


title: meTKO_2

source_name: ES cells_in vitro methylated with M.CvIPI

value: 129S4/SvJae derived

tag: treatment

value: in vitro methylated with M.CvIPI

value: Dnmt1,Dnmt3a,Dnmt3b-KO (triple KO)


title: meTKO_1


title: mouse_YFPpos_8w_cell29

description:
Single cell from mouse pancreas
B2.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell29

value: delta-like


title: mouse_YFPpos_8w_cell28

description:
Single cell from mouse pancreas
C1.1000200102

source_name: Single cell from pancreas, YFPpos_8w_cell28


title: Heat_2

source_name: ES cells_heat denaturation

tag: bisulfite conversion protocol

value: heat denaturation


title: Heat_3


title: mouse_YFPpos_8w_cell27

description:
Single cell from mouse pancreas
D2.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell27


title: mouse_YFPpos_8w_cell26

description:
Single cell from mouse pancreas
A8.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell26


title: Alkaline_3

source_name: ES cells_alkaline denaturation

value: alkaline denaturation


title: Alkaline_2


title: Heat_1


title: mouse_YFPpos_8w_cell24

description:
Single cell from mouse pancreas
A8.1000200102

source_name: Single cell from pancreas, YFPpos_8w_cell24


title: mouse_YFPpos_8w_cell22

description:
Single cell from mouse pancreas
D1.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell22


title: mouse_YFPpos_8w_cell20

description:
Single cell from mouse pancreas
B1.1000200102

source_name: Single cell from pancreas, YFPpos_8w_cell20


title: mouse_YFPpos_8w_cell42

description:
Single cell from mouse pancreas
C4.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell42


title: CHD8+/- C

city: Bronx

first: Deyou

last: Zheng

state: NY

address: 1301 Morris Park Ave

institute: Albert Einstein College of Medicine

data_processing:
We obtained 100 bp paired-end RNAseq reads from an Illumina HiSeq 2500 instrument. Adapters and low quality bases in reads were trimmed by trim_galore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). We employed Kallisto (v0.42.5) to determine the read count for each transcript and quantified transcript abundance as transcripts per kilobase million (TPM), using gene annotation in the GENCODE database (v18). Then we summed the read counts and TPM of all alternative splicing transcripts of a gene to obtain gene expression levels.
Genome_build: hg19
Supplementary_files_format_and_content: tab-delimited text files include raw read counts for each sample

organism: Homo sapiens

source_name: cerebral organoids

growth_protocol: iPS cell colonies were maintained on matrigel in mTesr1. To induce cerebral organoid differentiation, iPS cells were pretreated with 50 µM Y27632 in mTesr1 for one hour at 37oC. Wells were rinsed with DMEM/F12 and iPS cell colonies were dissociated with accutase for 10min at 37oC. Cells were rinsed with DMEM/F12, collected and counted for aggregate formation. Following the Stem Cell Technologies protocol, 3.0 x 106 cells were used to create 10,000 cell aggregates using an AggreWellTM plate. For the first 6 days, aggregates were cultured in mTesr1 supplemented with 500ng/ml DKK-1, 1.5 µg/ml BMPRIA-Fc and 10µM SB431542. On day 6, aggregates were removed from the AggreWellTM plate, according to the Stem Cell Technology protocol, and transferred to a 24-well ultra-low attachment plate. On day 18, 1% N2 supplement was added to the medium. On day 25, aggregates were plated onto a 4-well chamber slide coated with 10 µg/ml PORN, 2.5 µg/ml laminin and 50 µg/ml fibronectin, and cultured in Neurobasal medium supplemented with 2% B27 and 2 mM L-glutamine until day 50. Organoids were detached, and RNA was extracted.

extract_protocol:
Total RNA was isolated using miRNeasy (Qiagen).
The blunt, phosphorylated ends were treated with Klenow fragment (32 to 52 exo minus) and dATP to yield a protruding 3- 'A' base for ligation of Illumina's adapters which have a single 'T' base overhang at the 3’ end. After adapter ligation DNA was PCR amplified with Illumina primers for 15 cycles and library fragments of ~250 bp (insert plus adaptor and PCR primer sequences) were band isolated from an agarose gel. The purified cDNA was captured on an Illumina flow cell for cluster generation. Libraries were sequenced on the Genome Analyzer following the manufacturer's protocols.

value: ips-derived cerebral organoids

tag: genotype


title: CHD8+/- A


title: mouse_YFPpos_8w_cell18

description:
Single cell from mouse pancreas
A9.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell18


title: mouse_YFPpos_8w_cell19

description:
Single cell from mouse pancreas
A12.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell19


title: CHD8+/+ A


title: mouse_YFPpos_8w_cell17

description:
Single cell from mouse pancreas
D10.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell17


title: CHD8+/+ B


title: mouse_YFPneg_8w_cell16

description:
Single cell from mouse pancreas
E8.1000200101

source_name: Single cell from pancreas, YFPneg_8w_cell16

value: YFPneg


title: mouse_YFPpos_8w_cell14

description:
Single cell from mouse pancreas
C2.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell14


title: Calorie Restricted (22 month) Rep 3

city: Glasgow

first: Neil

middle: A

last: Robertson

state: Scotland

address: Switchback Road

department: Beatson Institute for Cancer Research

institute: University of Glasgow

description: Bisulfite sequencing of old (22 month) calorie restriction treated mice (UM-HET3)

data_processing:
Basecalls performed in phred64 (solexa1.3-quals).
Sequenced reads were mapped to mm9 whole genome using bismark v0.10.1 (bowtie2 v2.1.0) with directional setting and chunkmbs set to 256.  Remaining default settings.
Multiple reads where both ends of the fragment align to the same genomic positions on the same strand were reduced to a single instance.  Reads with more than 3 methylated cytosines in non-CpG contexts are discarded.
Reads are summarised on a per-CpG basis.
Genome_build: mm9
Supplementary_files_format_and_content: Summary files represent the number of reads supporting methylated/unmethylated bases at each CpG sequenced in that sample (Format is: chr <tab>  position  <tab>  methylated reads  <tab>   unmethylated reads).

source_name: Hepatocytes

growth_protocol: Genetically hetrogenous UM-HET3 female mice were bred and housed at the university of Michigan. They are the offspring of (BALB/cByJ x C57BL/6J)F1 mothers and (C3H/HeJ x DBA/2J)F1 fathers.  They were housed in a specific-pathogen free colony at four mice/cage and given a diet based on Purina 5LG6.

extract_protocol:
DNA was extracted by Dneasy blood and tissue kit  (Quiagen)
Libraries were prepared by BGI, Shenzhen.  Briefly, DNA is fragmented and methylated adapters are ligated to DNA fragments.  DNA is bisulphite treated and sequenced on Illumina platforms as per manufacturers instructions.

treatment_protocol: Some mice were given encapsulated rapamycin at a drug dose of 42 ppm from 4 months of age.  For other mice, a calorie restricted diet was initiated at 4 months; these mice received 60% of the food consumed by age-matched controls, after a two week run-in period at 80%.

tag: sample type

value: Calorie Restriction Treated


title: Calorie Restricted (22 month) Rep 2


title: mouse_YFPpos_8w_cell25

description:
Single cell from mouse pancreas
B9.1000200102

source_name: Single cell from pancreas, YFPpos_8w_cell25


title: CHD8+/- D


title: mouse_YFPpos_8w_cell23

description:
Single cell from mouse pancreas
A3.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell23


title: mouse_YFPpos_8w_cell13

description:
Single cell from mouse pancreas
A7.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell13

value: beta-like


title: mouse_YFPpos_8w_cell12

description:
Single cell from mouse pancreas
C10.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell12


title: mouse_YFPneg_8w_cell21

description:
Single cell from mouse pancreas
F9.1000200101

source_name: Single cell from pancreas, YFPneg_8w_cell21


title: CHD8+/- B


title: mouse_YFPpos_12w_cell147

description:
Single cell from mouse pancreas
C7.1000200104

source_name: Single cell from pancreas, YFPpos_12w_cell147


title: Rapamycin (22 month) Rep 4

description: Bisufite sequencing of old (22 month) rapamycin treated mice (UM-HET3)

value: Rapamycin Treated


title: mouse_YFPpos_8w_cell114

description:
Single cell from mouse pancreas
D9.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell114


title: mouse_YFPpos_8w_cell9

description:
Single cell from mouse pancreas
D4.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell9


title: mouse_YFPpos_8w_cell10

description:
Single cell from mouse pancreas
A1.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell10


title: mouse_YFPpos_8w_cell8

description:
Single cell from mouse pancreas
C10.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell8


title: mouse_YFPpos_8w_cell7

description:
Single cell from mouse pancreas
C9.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell7


title: Old (22 month) Rep 4

description: Bisulfite sequencing of old (22 month) untreated mice (UM-HET3)

value: Untreated


title: Old (22 month) Rep 3


title: Rapamycin (22 month) Rep 2


title: Rapamycin (22 month) Rep 1


title: mouse_YFPpos_8w_cell15

description:
Single cell from mouse pancreas
C11.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell15

value: YFPpos


title: Calorie Restricted (22 month) Rep 4


title: Old (22 month) Rep 1


title: mouse_YFPpos_8w_cell6

description:
Single cell from mouse pancreas
B7.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell6

value: endocrine pancreatic islet cell


title: Old (22 month) Rep 2


title: mouse_YFPpos_8w_cell11

description:
Single cell from mouse pancreas
C12.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell11


title: mouse_YFPpos_8w_cell5

description:
Single cell from mouse pancreas
B12.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell5


title: mouse_YFPpos_12w_cell140

description:
Single cell from mouse pancreas
B9.1000200104

source_name: Single cell from pancreas, YFPpos_12w_cell140

value: alpha-like


title: mouse_YFPpos_12w_cell135

description:
Single cell from mouse pancreas
B11.1000200104

source_name: Single cell from pancreas, YFPpos_12w_cell135


title: mouse_YFPpos_8w_cell4

description:
Single cell from mouse pancreas
D12.1000200101

source_name: Single cell from pancreas, YFPpos_8w_cell4


title: Calorie Restricted (22 month) Rep 1

description: Bisulfite sequencing of old (22 month) calorie restriction treated mice (UM-HET3)

source_name: Hepatocytes


title: Young (2 Month) Rep 2

description: Bisulfite sequencing of young (2 month) untreated mice (UM-HET3)


title: mouse_YFPpos_8w_cell2

description:
Single cell from mouse pancreas
B9.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell2


title: Rapamycin (22 month) Rep 3

description: Bisufite sequencing of old (22 month) rapamycin treated mice (UM-HET3)


title: WTY Rep4 – Ames Prop1 Het – 2 month

description: Bisulfite sequencing of young (2 month) mice

growth_protocol: Ames dwarf mice were derived from a closed colony with a heterogeneous background (over 25 years) at the University of North Dakota. Dwarf mice were generated by mating either homozygous (df/df) or heterozygous (df/+) dwarf males with carrier females (df/+).

treatment_protocol: All mice were bred and maintained at the University of North Dakota Center for Biomedical Research under controlled conditions of photoperiod (12:12 h light/dark cycle) and temperature (22 -/+1 °C) and ad libitum access to food (Teklad #8640) and water.  Animal procedures were reviewed and approved by the UND Institutional Animal Care and Use Committee. Liver tissue from male WT and Ames dwarf mice was collected at 2 and 22 months of age (n=4/genotype/age).

tag: strain

value: Ames Dwarf

value: Prop1 Het


title: 390c-3d

city: Zürich

first: Alexis

last: Sarazin

address: Universitätstrasse 2

country: Switzerland

institute: ETH

data_processing:
Basecalling pipeline: HiSeq Control Software 2.2.58 ; RTA 1.18.64.0 ; CASAVA-1.8.2
FASTQ file generation, demultiplexing and adapter removal were done by Fasteris.
Small RNA reads from both lanes were merged and filtered to 15-35nt long reads. Reads with the same sequence have been regrouped using the processReads function from ncPRO-seq pipeline (Chen et al. 2012) and then matched against both the Arabidopsis thaliana genome (TAIR10) and as well as the transgenes sequences using Bowtie (Langmead B and Salzberg SL 2012) using the options "--wrapper basic-0 -e 50 -a -m 5000 --best --strata --nomaqround -y --phred33-quals".
Genome_build: TAIR10
Supplementary_files_format_and_content: For each sample, tab-delimited text files including distinct sequences, reads count values and whether they were mapped to Arabidopsis thalaina genome and/or transgene sequence.

organism: Arabidopsis thaliana

source_name: Rosette

growth_protocol: Plants were grown in 16h light/8h dark condition at 21ºC for 4 weeks.

extract_protocol:
Total cellular RNA (10 µl, 200-300 ng/µL), extracted using TRIzol reagent (Invitrogen)
Extracted RNA was processed into sequencing libraries using adapted Illumina protocols and sequenced at Fasteris (http://www.fasteris.com, Switzerland) using the Illumina HiSeq 2500 sequencer. This was done on two lanes for each samples.

tag: ecotype

value: 390C-3D transgene

tag: tissue

value: Rosette

tag: development stage

value: 4 weeks old


title: 390-3d

value: 390-3D transgene


title: mouse_YFPpos_8w_cell3

description:
Single cell from mouse pancreas
B4.1000200103

source_name: Single cell from pancreas, YFPpos_8w_cell3


title: 390c-3d-390c

value: 390C-3D-390C transgene


title: Young (2 Month) Rep 3


title: WTY Rep3 – Ames Prop1 Het – 2 month


title: Young (2 Month) Rep 4


title: WTY Rep2 – Ames Prop1 Het – 2 month


title: Young (2 Month) Rep 1


title: mouse_YFPpos_8w_cell1

description:
Single cell from mouse pancreas
C7.1000200102

source_name: Single cell from pancreas, YFPpos_8w_cell1


title: DY Rep2 – Ames Prop1 Hom – 2 month

value: Prop1 Hom


title: DY Rep1 – Ames Prop1 Hom – 2 month


title: MDS-3.1

city: South San Francisco

first: Han

last: Yuan

address: 1170 Veterans Blvd

institute: Calico Life Sciences

description:
Day 12 S.EB
bc43

data_processing:
NextSeq 500 standard
Single-ended reads were aligned to GRCh37.75 (hg19) using STAR alignment tool.
Aligned bam files were filtered for uniquely aligned reads with MAQ > 10.
Reads were counted using GRCh37.75 annotation with tRNA and rRNA removed. Read counting was done using SummarizeOverlaps function from R-package “GenomicAlignments” with IntersectionNotEmpty mode.
Genome_build: hg19
Supplementary_files_format_and_content: tab-deliminated count table

source_name: sorted CD34+ cells

growth_protocol: In vitro hematopoietic differentiation of iPSCs

extract_protocol:
Total RNA extracted using QIAGEN Rneasy mini kit following manufacturer's instructions    RNeasy Mini Kit     RNeasy Mini Kit  following manufacturer's instructions
Magnetic cell sorting of CD34+ cells was performed on day 9 and 12 of hematopoietic differentiation. PolyA-tailed mRNA was selected  from 1µg total RNA using the NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs). cDNAs were generated using random hexamers and ligated to barcoded Illumina adaptors with the NEXTflex Rapid Directional RNA-Seq Library Prep Kit (Bioo Scientific).
Sequencing of 80 nucleotide-long single-end reads was performed in a NextSeq-500 (Illumina).

treatment_protocol: no treatment

tag: disease state

value: High risk MDS

value: CD34+ cells derived from iPSCs


title: AML-4.24

description:
Day 9 S.EB
bc36

value: MDS/AML


title: mouse_YFPpos_12w_cell142

description:
Single cell from mouse pancreas
A11.1000200104

source_name: Single cell from pancreas, YFPpos_12w_cell142

value: delta-like


title: AML-4.16

description:
Day 9 S.EB
bc35


title: WTY Rep1 – Ames Prop1 Het – 2 month


title: Expression measurements

city: Rehovot,

first: Michal

last: Levo

address: Herzl Street 234

country: Israel

institute: Weizmann Institute of Science

data_processing:
Mean expression per sequence variant was obtained based on a weighted average of the variant’s reads in each expression bin, as previously described (Sharon et al. Nature Biotechnology, 2012). Expression per variant can be found in suppTable2.
Occuapncy patterns were obtained in the following maner: Sequence reads were mapped to library variants initially based on the 10bp barcode included in the design (this is performed for reads that were found to include the 5’ primer sequence after bisulfite conversion). We then compute an alignment of each read to the reference (i.e. designed) variant, assuming full conversion (that is every C in the reference sequence is expected to appear as a T in the corresponding read), and disregarding the status of Cs within CpGs. Reads with more than 1.5% sequencing errors (including mutations, gaps or Ns) are discarded. Reads with less than 95% conversion efficiently are discarded. We then call a methylation status for each CpG; methylated if we find a C in that position, unmethylated if we find T, or NaN if there is uncertainty (N in the sequencing or disagreement between the paired-end reads). Notably a large fraction of the variably designed region (120bp out 163bp) is covered by reads from both ends and we use this information to improve the quality of our alignment and methylation status calling. We then compute for each CpG position in each variant the fraction of reads in which it was unmethylated, and use this as a proxy of occupancy in that position. To obtain full occupancy pattern along the promoter we interpolate this data. The resulting vector encompasses values for 456bp, from the first occurrence of a CpG to the last occurrence. This data, per variant, can also be found in suppTable2.
Supplementary_files_format_and_content: suppTable2.xlsx contains the designed library of sequences as well as the mean expression and the occupancy measured along each the sequence.

organism: Saccharomyces cerevisiae

source_name: synthetic DNA construct

extract_protocol: 10 ng were used for library preparation for sequencing (protocol adopted from(Blecher-Gonen et al. 2013)). The DNA amplified using 14 amplification cycles and sequenced on a 50 bp single read flowcell on Illumina HiSeq-2000 sequencer

tag: measurment type

value: Reporter expression


title: WTO Rep4 – Ames Prop1 Het – 22 month

description: Bisulfite sequencing of old (22 month) mice


title: MDS-2.13

description:
Day 12 S.EB
bc42


title: WTO Rep3 – Ames Prop1 Het – 22 month


title: 390-5d-390c

value: 390-5D-390C transgene


title: AML-4.10

description:
Day 9 S.EB
bc34


title: DY Rep3 – Ames Prop1 Hom – 2 month


title: MDS-3.4

description:
Day 12 S.EB
bc44


title: 390c-5d-390c

value: 390C-5D-390C transgene


title: DO Rep3 - Ames Prop1 Hom – 22 month


title: WTO Rep2 – Ames Prop1 Het – 22 month


title: MDS-1.12

description:
Day 9 S.EB
bc33

value: Low risk MDS


title: MDS-1.4

description:
Day 9 S.EB
bc32


title: DY Rep4 – Ames Prop1 Hom – 2 month


title: Binding measurements

value: DNA binding


title: Growth with daptomycin (0.5 mg/L) - sample 1

city: Rennes

first: Vincent

last: Cattoir

address: 2 rue Henri Le Guilloux

country: France

institute: CHU de Rennes

description:
D12
Barcode: GCCAAT

data_processing:
The reads were mapped against the genomic sequence of E. faecium Aus0004 (Genbank accession no. CP003351) using the TopHat software v.2.1.0.
The number of reads uniquely aligned to each genomic position was determined using the HTSeq program v.0.6.0.
The expression of genes in different RNA-seq samples was compared using using the R package DESeq R package.
Genome_build: E. faecium Aus0004 (Genbank accession no. CP003351)
Supplementary_files_format_and_content: Tab-delimited text files generated by the HTSeq program v.0.6.0.

organism: Enterococcus faecium Aus0004

source_name: Bacteria

growth_protocol: Bacterial cells of E. faecium Aus0004 bacteria were cultured at 37°C to the late-exponential-growth phase in BHI broth

extract_protocol:
Total RNA was extracted from using the ZR Fungal/Bacterial RNA Miniprep kit (Zymo Research, Irvine, CA). Residual chromosomal DNA was removed by treating samples with the TURBO DNA-free kit (Life Technologies). The Ribo-Zero Magnetic Kit Gram-Positive Bacteria (Epicentre, Madison, WI) was used according to the manufacturer’s recommendations to remove the 23S, 16S, and 5S rRNAs from the total RNA samples.
The strand-specific library preparation was performed using the NEXTflex Directional RNA-Seq Kit (dUTP-Based) (Bio Scientific, Austin, TX) and the samples were sequenced using the Illumina HiSeq 2500 platform (multiplex protocol, single-end 50-bp reads).

tag: agent

value: daptomycin


title: H1

description:
Day 9 S.EB
bc31

value: Normal


title: Growth with daptomycin (0.5 mg/L) - sample 2

description:
D13
Barcode: TTAGGC


title: N-8.2

description:
Day 9 S.EB
bc37


title: Growth with no daptomycin - sample 1

description:
TC12
Barcode: TGACCA

value: control


title: DO Rep1 - Ames Prop1 Hom – 22 month

value: Ames Dwarf


title: S_MeDIP_2r

status: Public on Feb 15 2017

city: Richmond

first: Edwin

last: van den Oord

address: 1112 East Clay Street

department: Center Biomarker Research and Precision Med

institute: Virginia Commonwealth University

description: processed data file: S_MeDIP_2_mCG.csv.gz; S_MeDIP_2_mCH.csv.gz

data_processing:
library strategy: MeDIP
Bisulfite sequencing and enrichment sequencing
Bisulfite data: BS-Seeker2(Guo, Fiziev, Yan, Cokus, Sun, Zhang, Chen, and Pellegrini 2013) was used to align data and call methylation levels. BS-Seeker2 converts both reads and reference to align reads (build hg19/GRCh37) in a 3-letter base space using Bowtie2(Langmead and Salzberg 2012). We used local (i.e. removal of terminal mismatches from the reads) and gapped alignment (e.g. allowing small indels) to increase mapping rates(Guo et al. 2013). The local alignment algorithm also automatically truncates adapter sequences. The maximum number of mismatches allowed per read was three. Only successfully mapped read-pairs were used to call methylation levels with correction for overlaps between read-pairs. To avoid unreliable estimates, for the bisulfite data we used methylation calls that were based on five or more sequencing reads.
*Guo, W., P. Fiziev, W. Yan, S. Cokus, X. Sun, M. Q. Zhang, P. Y. Chen, and M. Pellegrini. 2013. "BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data." BMC Genomics 14:774.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
Enrichment data: Reads were aligned (build hg19/GRCh37) with Bowtie2(Langmead and Salzberg 2012) using a seed-and-extend approach combined with local alignment while allowing for gaps. Specifically, we used a 20 bp seed with zero mismatches. Rather than considering the entire read, local alignment was used to improve sensitivity by finding the maximum similarity score between the reference sequence and a substring of the extension that may be "trimmed" at both ends. Gaps were allowed to account for small indels.
We quality controlled (QC) multi- and duplicate-reads. Reads often map to multiple genomic locations but in most cases a single alignment can be selected because it is clearly better than the others. In the case of multi-reads, multiple alignments are about equally good. When Bowtie2 encounters a set of equally good alignments, it uses a pseudo-random number to select one primary alignment. Duplicate-reads are reads that start at the same nucleotide positions. When sequencing a whole genome, duplicate-reads often arise from template preparation or amplification artifacts. In our context of sequencing an enriched genomic fraction, duplicate-reads are increasingly likely to occur because reads align to a much smaller fraction of the genome. In instances where >3 (duplicate) reads start at the same position, we reset the read count to 1 implicitly assuming that these reads all tagged a single clonal fragment.
A natural way to quantify enrichment is to count the number of fragments covering a cytosine. In contrast to bisulfite sequencing where precision depends on the number of sequenced bases covering each putative methylation site, the number of sequenced fragments determines the precision of enrichment methods. For this reason, single-end libraries are in principle more cost-effective then paired end libraries. However, with single-end libraries the fragment sizes are not observed. Counting the number of reads covering the putative methylation site instead, seriously underestimates coverage as the fragment that generated the read is usually longer than the read. To remedy this, we estimated the fragment size distributions from the empirical sequencing data using isolated cytosines(van den Oord, Bukszar, Rudolf, Nerella, McClay, Xie, and Aberg 2013). To evaluate this approach, we showed that methylation estimates obtained from paired end data (where the fragment size distribution is observed) are almost identical to those obtained from single end data with our estimator (correlation was 0.999). The estimated fragment size distributions is used to calculate the probability that a sequenced fragment will cover the cytosine under consideration. Coverage for each cytosine was then calculated by taking the sum of probabilities for all fragments aligning within proximity of the cytosine. For example, this probability is 1.0 for fragments with reads starting within one read-length of the cytosine, but is ≤1.0 for fragments with reads starting more than one read-length away.
Coverage is also affected by the total number of used reads per sample that is a function of sequencer loading and output, rather than differences in methylation. Therefore, coverage estimates were standardized using the total number of reads that remained after quality control.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
*van den Oord, E. J., J. Bukszar, G. Rudolf, S. Nerella, J. L. McClay, L. Y. Xie, and K. A. Aberg. 2013. "Estimation of CpG coverage in whole methylome next-generation sequencing studies." BMC Bioinformatics 14:50.
Genome_build: build hg19/GRCh37

Supplementary_files_format_and_content:
CGmap.tar.gz: BSseeker2 CGmap files
mCG.csv.gz: Panel mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
hmCG.csv.gz: Panel hmCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
mCH.csv.gz: Panel mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCG.csv.gz: MeDIP mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCH.csv.gz: MeDIP mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)

source_name: methylated DNA immunoprecipitation sequencing replicate 2 rerun of same library to add more reads

extract_protocol:
Human brain tissue was homogenized in Buffer RTL (Qiagen, # 79216) with a 1 mL syringe fitted with a 20G needle. Genomic DNA was extracted from the homogenate using the AllPrep DNA/RNA/miRNA Universal Kit (Qiagen, # 80224).
Bisulfite sequencing
DNA fragmentation
Genomic DNA was sonicated to 550 bp using a Covaris S2 ultra-sonicator (1 cycle, 2 cycles per burst, 45 seconds, 10% duty cycle, intensity 2.0). Multiple sonications were performed for each sample (2 μg DNA maximum per 100 μL fragmentation). The fragmented aliquots from each sample were pooled together to meet the input needs of both WGB and TAB, prior to any downstream assays.
Bisulfite and TET1 treatment
For whole genome bisulfite (WGB) sequencing the fragmented DNA was bisulfite treated using reduced conversion times with the EZ DNA Methylation-Lightning™ Kit (Zymo Research) to convert non-methylated cytosines to uracil. For Tet-assisted bisulfite (TAB(44)) sequencing we used the WiseGene kit to treat the fragmented DNA with T4 β-glucosyltransferase (16 hours at 30°C) to protect hmCs from oxidation by installation of a glucose moiety. Treatment with TET1 then oxidized only mCs to 5-carboxylcytosine. Finally, the TET1-oxidized DNA is bisulfite treated using the EZ DNA Methylation-Lightning™ Kit (Zymo Research) to convert non-hydroxymethylated cytosines to uracil. To assess conversion and protection rates, fragmented genomic DNA was spiked with commercial methylated and hydroxymethylated non-mammalian control DNAs (WiseGene). The mC control consisted of SssI treated lambda DNA, which should theoretically contain 100% methylated CpGs and non-methylated non-CpG cytosines. In reality, we observed a 96.3% methylation level for CpGs in the mC control, and likely the result of incomplete methylation during the production of the control. The hmC control consisted of a 1.64 kb amplicon from pUC19, intended to contain 100% hydroxymethylcytosines at all locations. The observed hydroxymethylation level for the hmC control was also reduced at 91.1%, but is not unexpected considering past evidence of contamination of commercial 5-hmCTP with dCTP(45-46). Conversion and protection rates are presented in Supplementary Table 1.
Sequencing
We used the Accel-NGS™ Methyl-Seq DNA Library Kit (Swift Biosciences) to create indexed Illumina libraries directly from the bisulfite and TET1-oxidized/bisulfite converted DNA.  Libraries were pooled with PhiX (15%) to compensate for lowered cytosine signal and 2x150 bp paired-end sequenced on an Illumina NextSeq 500 using v2 chemistry.
Enrichment panel
DNA fragmentation
Genomic DNA was sonicated to 100-150 bp using a Covaris S2 ultra-sonicator (10 cycles, 1 cycle per burst, 60 seconds, 10% duty cycle, intensity 5.0). Multiple sonications were performed for each sample (2 μg DNA maximum per 100 μL fragmentation). The fragmented aliquots from each sample were pooled together prior to any downstream assays.
MBD
We used the MethylMiner™ Kit (Invitrogen) to enrich for mCG via affinity purification with a methyl-CG binding domain protein (MBD2). For each sample, 15 μL of stock Dynabeads ® M-280 Streptavidin (10 μL of beads per 1 μg of DNA input) was coupled to 5.25 μg of MBD-Biotin Protein according to the vendor supplied protocol. Prepared MBD-beads were added to each input sample of 1.5 μg of fragmented DNA, and adjusted to a final volume of 200 μL in 1x Bind/Wash Buffer. The MBD-beads were incubated with DNA on an orbital shaker at 650 rpm for 1 hour at room temperature in a 96-well 1.2 mL square well plate. Following incubation the plate was placed on a magnetic rack and the supernatant containing non-captured, mCG-depleted DNA was retained for MBD-DIP. Beads were then washed three times by addition of 200 μL of 1x Bind/Wash Buffer followed by 3 minute incubations at room temperature on an orbital shaker at 650 rpm. Methylated DNA was eluted from the beads with three treatments with 200 μL low salt elution buffer (12.5% High Salt Elution Buffer + 87.5% Low Salt Elution Buffer [v/v]; 500 mM NaCl final), following the same incubation paradigm as the wash steps. The combined 600 μL mCG-enriched eluate was purified by ethanol precipitation.
After optimization, we have empirically determined that a low salt elution improves the specificity of the assay for loci with moderate numbers of CpG sites, giving better methylome-wide representation(41). Additionally, shearing genomic DNA to as small fragments as the downstream sequencing protocol allows (100-150 bp) further increases both the resolution and sensitivity of the MBD enrichment assay (i.e. small, sparsely methylated fragments require less energy to be captured than larger fragments).
MBD-DIP
The mCG-depleted DNA fraction was retained after MBD enrichment and purified by ethanol precipitation. Methylated DNA immunoprecipitation (MeDIP) was used to enrich for mCH from the mCG-depleted fraction of each sample. We used the MagMeDIP kit (Diagenode, # C02010021) with 1.0 μg of mCG-depleted DNA as input. Briefly, input DNA was heat denatured and incubated with anti-mC antibody and magnetic beads overnight at 4°C with end-over-end rotation. After incubation, beads were washed and mCH-enriched DNA liberated with proteinase K treatment. Reagent volumes and concentrations were prepared according to the vendor supplied protocol. The crude mCH enriched fractions were cleaned by column purification (ChIP DNA Clean & Concentrator, Zymo, # D5201) prior to library generation.
hMe-Seal
Selective chemical labeling and enrichment of hmC (hMe-Seal)(37) was performed using components of the Hydroxymethyl Collector™ kit (Active Motif). We substituted the enzyme in the kit with T4 β-glucosyltransferase from New England Biosciences (# M0357) to improve labeling performance. In our experience, using high-quality and high-activity enzyme is critical for successful hmC enrichment. 1.5 μg of 150bp fragmented gDNA was used as input for each assay. Briefly, T4 β-glucosyltransferase (10 U per sample, 16 hour incubation at 30°C) was used to selectively label hmC residues with 150 μM final UDP-azide-glucose. Each azo-glucosylated hmC was next biotinylated via dibenzocyclooctyl click chemistry by addition of the provided Biotin Conjugate Solution (likely DBCO-S-S-PEG3-Biotin at 150 μM final) with a 2 hour incubation at 37°C. After biotinylation, the labeled DNA was column purified, enriched with paramagnetic streptavidin beads, washed, and eluted following the vendor supplied protocol, with the substitution of end-over-end rotation with agitation on an orbital shaker at 700 rpm in a 96-well 1.2 mL square well plate. In an independent study(38) this chemical capture-based approach compared favorably to hmC enrichment methods using proteins or antibodies.
MeDIP
MeDIP was performed with 1.0 μg fragmented gDNA using the MagMeDIP Kit (Diagenode, # C02010021). Capture protocol, purification, library preparation, and sequencing were carried out identically to that of the MBD-DIP samples.
Sequencing
The MBD and hMe-Seal enriched fractions are used to create indexed sequencing libraries using the TruSeq Nano DNA HT Library Prep Kit (Illumina). For MBD-DIP and MeDIP enriched fractions, the Accel-NGS Methyl-Seq DNA Library Kit (Swift Biosciences) was used to generate indexed libraries directly from single-stranded DNA. Libraries were size-selected using SPRI beads to obtain a mean insert size of 150 bp. Libraries were pooled and 75-cycle single-end sequenced on an Illumina NextSeq 500 using v2 chemistry.

value: Postmortem brain tissue

tag: region

value: Brodmann area 10


title: S_MeDIP_1r

description: processed data file: S_MeDIP_1_mCG.csv.gz; S_MeDIP_1_mCH.csv.gz

source_name: methylated DNA immunoprecipitation sequencing replicate 1 rerun of same library to add more reads


title: WTO Rep1 – Ames Prop1 Het – 22 month

value: Prop1 Het


title: S_MeDIP_1

source_name: methylated DNA immunoprecipitation sequencing replicate 1


title: S_MeDIP_2

source_name: methylated DNA immunoprecipitation sequencing replicate 2


title: S_HMS_1

data_processing:
library strategy: hMe-Seal
Bisulfite sequencing and enrichment sequencing
Bisulfite data: BS-Seeker2(Guo, Fiziev, Yan, Cokus, Sun, Zhang, Chen, and Pellegrini 2013) was used to align data and call methylation levels. BS-Seeker2 converts both reads and reference to align reads (build hg19/GRCh37) in a 3-letter base space using Bowtie2(Langmead and Salzberg 2012). We used local (i.e. removal of terminal mismatches from the reads) and gapped alignment (e.g. allowing small indels) to increase mapping rates(Guo et al. 2013). The local alignment algorithm also automatically truncates adapter sequences. The maximum number of mismatches allowed per read was three. Only successfully mapped read-pairs were used to call methylation levels with correction for overlaps between read-pairs. To avoid unreliable estimates, for the bisulfite data we used methylation calls that were based on five or more sequencing reads.
*Guo, W., P. Fiziev, W. Yan, S. Cokus, X. Sun, M. Q. Zhang, P. Y. Chen, and M. Pellegrini. 2013. "BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data." BMC Genomics 14:774.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
Enrichment data: Reads were aligned (build hg19/GRCh37) with Bowtie2(Langmead and Salzberg 2012) using a seed-and-extend approach combined with local alignment while allowing for gaps. Specifically, we used a 20 bp seed with zero mismatches. Rather than considering the entire read, local alignment was used to improve sensitivity by finding the maximum similarity score between the reference sequence and a substring of the extension that may be "trimmed" at both ends. Gaps were allowed to account for small indels.
We quality controlled (QC) multi- and duplicate-reads. Reads often map to multiple genomic locations but in most cases a single alignment can be selected because it is clearly better than the others. In the case of multi-reads, multiple alignments are about equally good. When Bowtie2 encounters a set of equally good alignments, it uses a pseudo-random number to select one primary alignment. Duplicate-reads are reads that start at the same nucleotide positions. When sequencing a whole genome, duplicate-reads often arise from template preparation or amplification artifacts. In our context of sequencing an enriched genomic fraction, duplicate-reads are increasingly likely to occur because reads align to a much smaller fraction of the genome. In instances where >3 (duplicate) reads start at the same position, we reset the read count to 1 implicitly assuming that these reads all tagged a single clonal fragment.
A natural way to quantify enrichment is to count the number of fragments covering a cytosine. In contrast to bisulfite sequencing where precision depends on the number of sequenced bases covering each putative methylation site, the number of sequenced fragments determines the precision of enrichment methods. For this reason, single-end libraries are in principle more cost-effective then paired end libraries. However, with single-end libraries the fragment sizes are not observed. Counting the number of reads covering the putative methylation site instead, seriously underestimates coverage as the fragment that generated the read is usually longer than the read. To remedy this, we estimated the fragment size distributions from the empirical sequencing data using isolated cytosines(van den Oord, Bukszar, Rudolf, Nerella, McClay, Xie, and Aberg 2013). To evaluate this approach, we showed that methylation estimates obtained from paired end data (where the fragment size distribution is observed) are almost identical to those obtained from single end data with our estimator (correlation was 0.999). The estimated fragment size distributions is used to calculate the probability that a sequenced fragment will cover the cytosine under consideration. Coverage for each cytosine was then calculated by taking the sum of probabilities for all fragments aligning within proximity of the cytosine. For example, this probability is 1.0 for fragments with reads starting within one read-length of the cytosine, but is ≤1.0 for fragments with reads starting more than one read-length away.
Coverage is also affected by the total number of used reads per sample that is a function of sequencer loading and output, rather than differences in methylation. Therefore, coverage estimates were standardized using the total number of reads that remained after quality control.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
*van den Oord, E. J., J. Bukszar, G. Rudolf, S. Nerella, J. L. McClay, L. Y. Xie, and K. A. Aberg. 2013. "Estimation of CpG coverage in whole methylome next-generation sequencing studies." BMC Bioinformatics 14:50.
Genome_build: build hg19/GRCh37

Supplementary_files_format_and_content:
CGmap.tar.gz: BSseeker2 CGmap files
mCG.csv.gz: Panel mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
hmCG.csv.gz: Panel hmCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
mCH.csv.gz: Panel mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCG.csv.gz: MeDIP mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCH.csv.gz: MeDIP mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)

source_name: 5-hydroxymethylcytosine (5-hmC) DNA methylation enrichment sequencing sample 1


title: S_MBDIP_1

data_processing:
library strategy: MBD-DIP
Bisulfite sequencing and enrichment sequencing
Bisulfite data: BS-Seeker2(Guo, Fiziev, Yan, Cokus, Sun, Zhang, Chen, and Pellegrini 2013) was used to align data and call methylation levels. BS-Seeker2 converts both reads and reference to align reads (build hg19/GRCh37) in a 3-letter base space using Bowtie2(Langmead and Salzberg 2012). We used local (i.e. removal of terminal mismatches from the reads) and gapped alignment (e.g. allowing small indels) to increase mapping rates(Guo et al. 2013). The local alignment algorithm also automatically truncates adapter sequences. The maximum number of mismatches allowed per read was three. Only successfully mapped read-pairs were used to call methylation levels with correction for overlaps between read-pairs. To avoid unreliable estimates, for the bisulfite data we used methylation calls that were based on five or more sequencing reads.
*Guo, W., P. Fiziev, W. Yan, S. Cokus, X. Sun, M. Q. Zhang, P. Y. Chen, and M. Pellegrini. 2013. "BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data." BMC Genomics 14:774.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
Enrichment data: Reads were aligned (build hg19/GRCh37) with Bowtie2(Langmead and Salzberg 2012) using a seed-and-extend approach combined with local alignment while allowing for gaps. Specifically, we used a 20 bp seed with zero mismatches. Rather than considering the entire read, local alignment was used to improve sensitivity by finding the maximum similarity score between the reference sequence and a substring of the extension that may be "trimmed" at both ends. Gaps were allowed to account for small indels.
We quality controlled (QC) multi- and duplicate-reads. Reads often map to multiple genomic locations but in most cases a single alignment can be selected because it is clearly better than the others. In the case of multi-reads, multiple alignments are about equally good. When Bowtie2 encounters a set of equally good alignments, it uses a pseudo-random number to select one primary alignment. Duplicate-reads are reads that start at the same nucleotide positions. When sequencing a whole genome, duplicate-reads often arise from template preparation or amplification artifacts. In our context of sequencing an enriched genomic fraction, duplicate-reads are increasingly likely to occur because reads align to a much smaller fraction of the genome. In instances where >3 (duplicate) reads start at the same position, we reset the read count to 1 implicitly assuming that these reads all tagged a single clonal fragment.
A natural way to quantify enrichment is to count the number of fragments covering a cytosine. In contrast to bisulfite sequencing where precision depends on the number of sequenced bases covering each putative methylation site, the number of sequenced fragments determines the precision of enrichment methods. For this reason, single-end libraries are in principle more cost-effective then paired end libraries. However, with single-end libraries the fragment sizes are not observed. Counting the number of reads covering the putative methylation site instead, seriously underestimates coverage as the fragment that generated the read is usually longer than the read. To remedy this, we estimated the fragment size distributions from the empirical sequencing data using isolated cytosines(van den Oord, Bukszar, Rudolf, Nerella, McClay, Xie, and Aberg 2013). To evaluate this approach, we showed that methylation estimates obtained from paired end data (where the fragment size distribution is observed) are almost identical to those obtained from single end data with our estimator (correlation was 0.999). The estimated fragment size distributions is used to calculate the probability that a sequenced fragment will cover the cytosine under consideration. Coverage for each cytosine was then calculated by taking the sum of probabilities for all fragments aligning within proximity of the cytosine. For example, this probability is 1.0 for fragments with reads starting within one read-length of the cytosine, but is ≤1.0 for fragments with reads starting more than one read-length away.
Coverage is also affected by the total number of used reads per sample that is a function of sequencer loading and output, rather than differences in methylation. Therefore, coverage estimates were standardized using the total number of reads that remained after quality control.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
*van den Oord, E. J., J. Bukszar, G. Rudolf, S. Nerella, J. L. McClay, L. Y. Xie, and K. A. Aberg. 2013. "Estimation of CpG coverage in whole methylome next-generation sequencing studies." BMC Bioinformatics 14:50.
Genome_build: build hg19/GRCh37

Supplementary_files_format_and_content:
CGmap.tar.gz: BSseeker2 CGmap files
mCG.csv.gz: Panel mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
hmCG.csv.gz: Panel hmCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
mCH.csv.gz: Panel mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCG.csv.gz: MeDIP mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCH.csv.gz: MeDIP mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)

source_name: methylated DNA immunoprecipitation after depletion by methyl-CG binding domain replicate 1


title: S_MBDIP_2

source_name: methylated DNA immunoprecipitation after depletion by methyl-CG binding domain replicate 2


title: N-6.1

description:
Day 9 S.EB
bc38


title: S_MBD_2

data_processing:
library strategy: MBD-seq
Bisulfite sequencing and enrichment sequencing
Bisulfite data: BS-Seeker2(Guo, Fiziev, Yan, Cokus, Sun, Zhang, Chen, and Pellegrini 2013) was used to align data and call methylation levels. BS-Seeker2 converts both reads and reference to align reads (build hg19/GRCh37) in a 3-letter base space using Bowtie2(Langmead and Salzberg 2012). We used local (i.e. removal of terminal mismatches from the reads) and gapped alignment (e.g. allowing small indels) to increase mapping rates(Guo et al. 2013). The local alignment algorithm also automatically truncates adapter sequences. The maximum number of mismatches allowed per read was three. Only successfully mapped read-pairs were used to call methylation levels with correction for overlaps between read-pairs. To avoid unreliable estimates, for the bisulfite data we used methylation calls that were based on five or more sequencing reads.
*Guo, W., P. Fiziev, W. Yan, S. Cokus, X. Sun, M. Q. Zhang, P. Y. Chen, and M. Pellegrini. 2013. "BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data." BMC Genomics 14:774.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
Enrichment data: Reads were aligned (build hg19/GRCh37) with Bowtie2(Langmead and Salzberg 2012) using a seed-and-extend approach combined with local alignment while allowing for gaps. Specifically, we used a 20 bp seed with zero mismatches. Rather than considering the entire read, local alignment was used to improve sensitivity by finding the maximum similarity score between the reference sequence and a substring of the extension that may be "trimmed" at both ends. Gaps were allowed to account for small indels.
We quality controlled (QC) multi- and duplicate-reads. Reads often map to multiple genomic locations but in most cases a single alignment can be selected because it is clearly better than the others. In the case of multi-reads, multiple alignments are about equally good. When Bowtie2 encounters a set of equally good alignments, it uses a pseudo-random number to select one primary alignment. Duplicate-reads are reads that start at the same nucleotide positions. When sequencing a whole genome, duplicate-reads often arise from template preparation or amplification artifacts. In our context of sequencing an enriched genomic fraction, duplicate-reads are increasingly likely to occur because reads align to a much smaller fraction of the genome. In instances where >3 (duplicate) reads start at the same position, we reset the read count to 1 implicitly assuming that these reads all tagged a single clonal fragment.
A natural way to quantify enrichment is to count the number of fragments covering a cytosine. In contrast to bisulfite sequencing where precision depends on the number of sequenced bases covering each putative methylation site, the number of sequenced fragments determines the precision of enrichment methods. For this reason, single-end libraries are in principle more cost-effective then paired end libraries. However, with single-end libraries the fragment sizes are not observed. Counting the number of reads covering the putative methylation site instead, seriously underestimates coverage as the fragment that generated the read is usually longer than the read. To remedy this, we estimated the fragment size distributions from the empirical sequencing data using isolated cytosines(van den Oord, Bukszar, Rudolf, Nerella, McClay, Xie, and Aberg 2013). To evaluate this approach, we showed that methylation estimates obtained from paired end data (where the fragment size distribution is observed) are almost identical to those obtained from single end data with our estimator (correlation was 0.999). The estimated fragment size distributions is used to calculate the probability that a sequenced fragment will cover the cytosine under consideration. Coverage for each cytosine was then calculated by taking the sum of probabilities for all fragments aligning within proximity of the cytosine. For example, this probability is 1.0 for fragments with reads starting within one read-length of the cytosine, but is ≤1.0 for fragments with reads starting more than one read-length away.
Coverage is also affected by the total number of used reads per sample that is a function of sequencer loading and output, rather than differences in methylation. Therefore, coverage estimates were standardized using the total number of reads that remained after quality control.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
*van den Oord, E. J., J. Bukszar, G. Rudolf, S. Nerella, J. L. McClay, L. Y. Xie, and K. A. Aberg. 2013. "Estimation of CpG coverage in whole methylome next-generation sequencing studies." BMC Bioinformatics 14:50.
Genome_build: build hg19/GRCh37

Supplementary_files_format_and_content:
CGmap.tar.gz: BSseeker2 CGmap files
mCG.csv.gz: Panel mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
hmCG.csv.gz: Panel hmCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
mCH.csv.gz: Panel mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCG.csv.gz: MeDIP mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCH.csv.gz: MeDIP mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)

source_name: methyl-CG binding domain sequencing replicate 2


title: S_WGB_1

data_processing:
library strategy: WGB-seq
Bisulfite sequencing and enrichment sequencing
Bisulfite data: BS-Seeker2(Guo, Fiziev, Yan, Cokus, Sun, Zhang, Chen, and Pellegrini 2013) was used to align data and call methylation levels. BS-Seeker2 converts both reads and reference to align reads (build hg19/GRCh37) in a 3-letter base space using Bowtie2(Langmead and Salzberg 2012). We used local (i.e. removal of terminal mismatches from the reads) and gapped alignment (e.g. allowing small indels) to increase mapping rates(Guo et al. 2013). The local alignment algorithm also automatically truncates adapter sequences. The maximum number of mismatches allowed per read was three. Only successfully mapped read-pairs were used to call methylation levels with correction for overlaps between read-pairs. To avoid unreliable estimates, for the bisulfite data we used methylation calls that were based on five or more sequencing reads.
*Guo, W., P. Fiziev, W. Yan, S. Cokus, X. Sun, M. Q. Zhang, P. Y. Chen, and M. Pellegrini. 2013. "BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data." BMC Genomics 14:774.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
Enrichment data: Reads were aligned (build hg19/GRCh37) with Bowtie2(Langmead and Salzberg 2012) using a seed-and-extend approach combined with local alignment while allowing for gaps. Specifically, we used a 20 bp seed with zero mismatches. Rather than considering the entire read, local alignment was used to improve sensitivity by finding the maximum similarity score between the reference sequence and a substring of the extension that may be "trimmed" at both ends. Gaps were allowed to account for small indels.
We quality controlled (QC) multi- and duplicate-reads. Reads often map to multiple genomic locations but in most cases a single alignment can be selected because it is clearly better than the others. In the case of multi-reads, multiple alignments are about equally good. When Bowtie2 encounters a set of equally good alignments, it uses a pseudo-random number to select one primary alignment. Duplicate-reads are reads that start at the same nucleotide positions. When sequencing a whole genome, duplicate-reads often arise from template preparation or amplification artifacts. In our context of sequencing an enriched genomic fraction, duplicate-reads are increasingly likely to occur because reads align to a much smaller fraction of the genome. In instances where >3 (duplicate) reads start at the same position, we reset the read count to 1 implicitly assuming that these reads all tagged a single clonal fragment.
A natural way to quantify enrichment is to count the number of fragments covering a cytosine. In contrast to bisulfite sequencing where precision depends on the number of sequenced bases covering each putative methylation site, the number of sequenced fragments determines the precision of enrichment methods. For this reason, single-end libraries are in principle more cost-effective then paired end libraries. However, with single-end libraries the fragment sizes are not observed. Counting the number of reads covering the putative methylation site instead, seriously underestimates coverage as the fragment that generated the read is usually longer than the read. To remedy this, we estimated the fragment size distributions from the empirical sequencing data using isolated cytosines(van den Oord, Bukszar, Rudolf, Nerella, McClay, Xie, and Aberg 2013). To evaluate this approach, we showed that methylation estimates obtained from paired end data (where the fragment size distribution is observed) are almost identical to those obtained from single end data with our estimator (correlation was 0.999). The estimated fragment size distributions is used to calculate the probability that a sequenced fragment will cover the cytosine under consideration. Coverage for each cytosine was then calculated by taking the sum of probabilities for all fragments aligning within proximity of the cytosine. For example, this probability is 1.0 for fragments with reads starting within one read-length of the cytosine, but is ≤1.0 for fragments with reads starting more than one read-length away.
Coverage is also affected by the total number of used reads per sample that is a function of sequencer loading and output, rather than differences in methylation. Therefore, coverage estimates were standardized using the total number of reads that remained after quality control.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
*van den Oord, E. J., J. Bukszar, G. Rudolf, S. Nerella, J. L. McClay, L. Y. Xie, and K. A. Aberg. 2013. "Estimation of CpG coverage in whole methylome next-generation sequencing studies." BMC Bioinformatics 14:50.
Genome_build: build hg19/GRCh37

Supplementary_files_format_and_content:
CGmap.tar.gz: BSseeker2 CGmap files
mCG.csv.gz: Panel mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
hmCG.csv.gz: Panel hmCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
mCH.csv.gz: Panel mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCG.csv.gz: MeDIP mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCH.csv.gz: MeDIP mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)

source_name: whole genome bisulfite sequencing replicate 1


title: S_HMS_2

source_name: 5-hydroxymethylcytosine (5-hmC) DNA methylation enrichment sequencing sample 2


title: S_WGB_2

source_name: whole genome bisulfite sequencing replicate 2


title: DO Rep2 - Ames Prop1 Hom – 22 month

source_name: Hepatocytes

value: Prop1 Hom


title: S_TAB_1

data_processing:
library strategy: TABE-seq
Bisulfite sequencing and enrichment sequencing
Bisulfite data: BS-Seeker2(Guo, Fiziev, Yan, Cokus, Sun, Zhang, Chen, and Pellegrini 2013) was used to align data and call methylation levels. BS-Seeker2 converts both reads and reference to align reads (build hg19/GRCh37) in a 3-letter base space using Bowtie2(Langmead and Salzberg 2012). We used local (i.e. removal of terminal mismatches from the reads) and gapped alignment (e.g. allowing small indels) to increase mapping rates(Guo et al. 2013). The local alignment algorithm also automatically truncates adapter sequences. The maximum number of mismatches allowed per read was three. Only successfully mapped read-pairs were used to call methylation levels with correction for overlaps between read-pairs. To avoid unreliable estimates, for the bisulfite data we used methylation calls that were based on five or more sequencing reads.
*Guo, W., P. Fiziev, W. Yan, S. Cokus, X. Sun, M. Q. Zhang, P. Y. Chen, and M. Pellegrini. 2013. "BS-Seeker2: a versatile aligning pipeline for bisulfite sequencing data." BMC Genomics 14:774.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
Enrichment data: Reads were aligned (build hg19/GRCh37) with Bowtie2(Langmead and Salzberg 2012) using a seed-and-extend approach combined with local alignment while allowing for gaps. Specifically, we used a 20 bp seed with zero mismatches. Rather than considering the entire read, local alignment was used to improve sensitivity by finding the maximum similarity score between the reference sequence and a substring of the extension that may be "trimmed" at both ends. Gaps were allowed to account for small indels.
We quality controlled (QC) multi- and duplicate-reads. Reads often map to multiple genomic locations but in most cases a single alignment can be selected because it is clearly better than the others. In the case of multi-reads, multiple alignments are about equally good. When Bowtie2 encounters a set of equally good alignments, it uses a pseudo-random number to select one primary alignment. Duplicate-reads are reads that start at the same nucleotide positions. When sequencing a whole genome, duplicate-reads often arise from template preparation or amplification artifacts. In our context of sequencing an enriched genomic fraction, duplicate-reads are increasingly likely to occur because reads align to a much smaller fraction of the genome. In instances where >3 (duplicate) reads start at the same position, we reset the read count to 1 implicitly assuming that these reads all tagged a single clonal fragment.
A natural way to quantify enrichment is to count the number of fragments covering a cytosine. In contrast to bisulfite sequencing where precision depends on the number of sequenced bases covering each putative methylation site, the number of sequenced fragments determines the precision of enrichment methods. For this reason, single-end libraries are in principle more cost-effective then paired end libraries. However, with single-end libraries the fragment sizes are not observed. Counting the number of reads covering the putative methylation site instead, seriously underestimates coverage as the fragment that generated the read is usually longer than the read. To remedy this, we estimated the fragment size distributions from the empirical sequencing data using isolated cytosines(van den Oord, Bukszar, Rudolf, Nerella, McClay, Xie, and Aberg 2013). To evaluate this approach, we showed that methylation estimates obtained from paired end data (where the fragment size distribution is observed) are almost identical to those obtained from single end data with our estimator (correlation was 0.999). The estimated fragment size distributions is used to calculate the probability that a sequenced fragment will cover the cytosine under consideration. Coverage for each cytosine was then calculated by taking the sum of probabilities for all fragments aligning within proximity of the cytosine. For example, this probability is 1.0 for fragments with reads starting within one read-length of the cytosine, but is ≤1.0 for fragments with reads starting more than one read-length away.
Coverage is also affected by the total number of used reads per sample that is a function of sequencer loading and output, rather than differences in methylation. Therefore, coverage estimates were standardized using the total number of reads that remained after quality control.
*Langmead, B. and S. L. Salzberg. 2012. "Fast gapped-read alignment with Bowtie 2." Nat Methods 9:357-9.
*van den Oord, E. J., J. Bukszar, G. Rudolf, S. Nerella, J. L. McClay, L. Y. Xie, and K. A. Aberg. 2013. "Estimation of CpG coverage in whole methylome next-generation sequencing studies." BMC Bioinformatics 14:50.
Genome_build: build hg19/GRCh37

Supplementary_files_format_and_content:
CGmap.tar.gz: BSseeker2 CGmap files
mCG.csv.gz: Panel mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
hmCG.csv.gz: Panel hmCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
mCH.csv.gz: Panel mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCG.csv.gz: MeDIP mCG (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)
MeDIP_mCH.csv.gz: MeDIP mCH (chr, coordinate, enrichement coverage, bisulfite estimated methylation sum)

source_name: TET-assisted bisulfite sequencing replicate 1


title: TM012_25

city: Philadelphia

first: Pablo

middle: G

last: Camara

state: PA

address: 3700 Hamilton Walk

institute: University of Pennsylvania

data_processing:
Sequenced reads were de-multiplexed, trimmed, and mapped to the UCSC mouse reference (mm10) using tophat v2.1.1 with parameter –no-novel-juncs
Gene expression was quantified using htseq-count v0.6.1 with parameters -s no
Genome_build: mm10
Supplementary_files_format_and_content: tab-delimited text files include read counts for each sample

molecule: polyA RNA

source_name: In vitro differentiation of mESCs into motor neurons, pilot experiment

growth_protocol: mESC based differentiations into motor neurons were performed using the method of Wichterle at al. (Cell 110, 385-397, (2010))

extract_protocol:
Embryoid bodies were dissociated into single cells using the Worthington Biochemical Papain Dissociation System (LK003178). Single cell deposition was accomplished using a Beckman Coulter MoFlo Astrios EQ cell sorter into 96 well plates. Cells were then snap-frozen and subsequently lysed.
Single-cell RNA-seq libraries were prepared using a modified CEL-seq protocol (Hashimshony et al., Cell Reports 2, 666-673 (2012)). Briefly, the mRNA in each cell lysate was reverse transcribed with barcoded primers, the single cell samples were pooled, second strand cDNA was synthesized, and then linearly amplified by in vitro transcription with T7 RNA polymerase. The amplified RNA (aRNA) was chemically fragmented and T4 RNA ligase was used to ligate Illumina 3’-RNA adapters. The aRNA was then run on an Agilent Bioanalyzer to assess proper fragmentation and then reverse transcribed to generate cDNA and subjected to PCR enrichment.

tag: sampling day

value: Day 3


title: TM012_28


title: TM012_30


title: TM012_31


title: TM012_24


title: TM012_27


title: TM012_29


title: TM012_26


title: TM012_23


title: TM012_22


title: TM012_20


title: TM012_21


title: TM012_18


title: TM012_19


title: S_TAB_2

source_name: TET-assisted bisulfite sequencing replicate 2


title: DO Rep4 - Ames Prop1 Hom – 22 month


title: TM012_14


title: TM012_15


title: TM012_16


title: TM012_13


title: TM012_9


title: TM012_8


title: TM012_10


title: TM012_7


title: TM012_4


title: TM012_6


title: TM012_11


title: TM012_12


title: TM012_5


title: S_MBD_1

source_name: methyl-CG binding domain sequencing replicate 1


title: TM012_17


title: TM012_3


title: TM012_2


title: TM011_35


title: TM011_38


title: TM011_32


title: TM011_36


title: TM011_33


title: TM011_30


title: TM011_31


title: TM011_34


title: TM011_28


title: TM011_27


title: TM011_29


title: TM011_39


title: TM012_1


title: TM011_24


title: TM011_22


title: TM011_23


title: TM012_0


title: TM011_25


title: TM011_37


title: TM011_21


title: TM011_19


title: TM011_18


title: TM011_20


title: TM011_16


title: TM011_15


title: TM011_13


title: TM011_11


title: TM011_9


title: TM011_14


title: TM011_10


title: TM011_12


title: Growth with no daptomycin - sample 2

description:
TC13
Barcode: CGATGT

source_name: Bacteria


title: TM011_6


title: TM011_4


title: TM011_8


title: TM011_7


title: TM011_5


title: TM011_26


title: TM011_2


title: TM010_38

value: Day 2


title: TM010_37


title: TM011_1


title: TM010_36


title: TM011_0


title: TM011_17


title: TM010_35


title: TM010_39


title: TM010_32


title: TM010_34


title: TM010_33


title: TM010_29


title: TM010_31


title: TM010_26


title: TM010_24


title: TM010_25


title: TM011_3


title: TM010_22


title: TM010_20


title: TM010_21


title: TM010_18


title: TM010_17


title: TM010_15


title: TM010_19


title: TM010_16


title: TM010_27


title: TM010_14


title: TM010_13


title: TM010_11


title: TM010_12


title: TM010_7


title: TM010_10


title: TM010_8


title: TM010_30


title: TM010_9


title: TM010_28


title: TM010_6


title: TM010_5


title: TM010_4


title: TM010_2


title: TM010_0


title: TM010_23


title: TM010_3


title: TM009_39


title: TM010_1


title: TM009_33


title: TM009_36


title: TM009_35


title: TM009_37


title: TM009_30


title: TM009_29


title: TM009_32


title: TM009_34


title: TM009_31


title: TM009_28


title: TM009_23


title: TM009_27


title: TM009_24


title: TM009_20


title: TM009_21


title: TM009_19


title: TM009_17


title: TM009_18


title: TM009_25


title: TM009_26


title: TM009_22


title: TM009_38


title: TM009_12


title: TM009_15


title: TM009_13


title: TM009_10


title: TM009_11


title: TM009_14


title: TM009_9


title: TM009_7


title: TM009_5


title: TM009_6


title: TM009_8


title: TM009_4


title: TM009_0


title: TM009_1


title: TM009_2


title: TM009_3


title: RPI36_95

source_name: In vitro differentiation of mESCs into motor neurons, main experiment


title: RPI36_93


title: RPI36_94


title: TM009_16


title: RPI36_91


title: RPI36_92


title: RPI36_87


title: RPI36_90


title: RPI36_86


title: RPI36_88


title: RPI36_89


title: RPI36_84


title: RPI36_85


title: RPI36_79


title: RPI36_80


title: RPI36_77


title: RPI36_78


title: RPI36_81


title: RPI36_83


title: RPI36_82


title: RPI36_71


title: RPI36_76


title: RPI36_72


title: RPI36_74


title: RPI36_73


title: RPI36_75


title: RPI36_68


title: RPI36_69


title: RPI36_70


title: RPI36_67


title: RPI36_66


title: RPI36_64


title: RPI36_63


title: RPI36_60


title: RPI36_59


title: RPI36_62


title: RPI36_58


title: RPI36_57


title: RPI36_54


title: RPI36_56


title: RPI36_53


title: RPI36_55


title: RPI36_52


title: RPI36_51


title: RPI36_50


title: RPI36_48


title: RPI36_47


title: RPI36_49


title: RPI36_44


title: RPI36_42


title: RPI36_43


title: RPI36_45


title: RPI36_41


title: RPI36_61


title: RPI36_38


title: RPI36_40


title: RPI36_37


title: RPI36_39


title: RPI36_36


title: RPI36_35


title: RPI36_34


title: RPI36_33


title: RPI36_31


title: RPI36_32


title: RPI36_65


title: RPI36_30


title: RPI36_27


title: RPI36_28


title: RPI36_25


title: RPI36_22


title: RPI36_26


title: RPI36_20


title: RPI36_21


title: RPI36_46


title: RPI36_23


title: RPI36_29


title: RPI36_15


title: RPI36_18


title: RPI36_16


title: RPI36_19


title: RPI36_17


title: RPI36_13


title: RPI36_11


title: RPI36_12


title: RPI36_9


title: RPI36_6


title: RPI36_5


title: RPI36_3


title: RPI36_1


title: RPI36_0


title: RPI7_94


title: RPI7_91


title: RPI7_88


title: RPI7_95


title: RPI7_87


title: RPI36_14


title: RPI7_89


title: RPI7_83


title: RPI7_85


title: RPI36_8


title: RPI7_84


title: RPI36_10


title: RPI7_86


title: RPI36_4


title: RPI7_90


title: RPI7_81


title: RPI36_7


title: RPI7_76


title: RPI7_78


title: RPI7_77


title: RPI7_79


title: RPI36_2


title: RPI7_75


title: RPI7_73


title: RPI36_24


title: RPI7_74


title: RPI7_72


title: RPI7_71


title: RPI7_92


title: RPI7_70


title: RPI7_93


title: RPI7_69


title: RPI7_82


title: RPI7_67


title: RPI7_80


title: RPI7_66


title: RPI7_63


title: RPI7_65


title: RPI7_58


title: RPI7_61


title: RPI7_62


title: RPI7_57


title: RPI7_59


title: RPI7_60


title: RPI7_55


title: RPI7_51


title: RPI7_52


title: RPI7_54


title: RPI7_56


title: RPI7_53


title: RPI7_50


title: RPI7_49


title: RPI7_47


title: RPI7_44


title: RPI7_48


title: RPI7_46


title: RPI7_45


title: RPI7_43


title: RPI7_68


title: RPI7_64


title: RPI7_40


title: RPI7_38


title: RPI7_39


title: RPI7_35


title: RPI7_34


title: RPI7_33


title: RPI7_32


title: RPI7_30


title: RPI7_26


title: RPI7_28


title: RPI7_31


title: RPI7_23


title: RPI7_22


title: RPI7_21


title: RPI7_42


title: RPI7_41


title: RPI7_19


title: RPI7_17


title: RPI7_36


title: RPI7_18


title: RPI7_20


title: RPI7_37


title: RPI7_16


title: RPI7_14


title: RPI7_13


title: RPI7_12


title: RPI7_27


title: RPI7_11


title: RPI7_8


title: RPI7_6


title: RPI7_9


title: RPI7_10


title: RPI7_29


title: RPI7_7


title: RPI7_25


title: RPI7_5


title: RPI7_4


title: RPI7_1


title: RPI7_2


title: RPI7_3


title: RPI7_0


title: RPI7_15


title: RPI30_89

value: Day 4


title: RPI30_90


title: RPI30_93


title: RPI30_92


title: RPI30_94


title: RPI30_87


title: RPI30_85


title: RPI30_86


title: RPI30_83


title: RPI30_84


title: RPI30_81


title: RPI30_82


title: RPI30_79


title: RPI30_77


title: RPI30_80


title: RPI30_74


title: RPI30_75


title: RPI30_73


title: RPI30_76


title: RPI30_71


title: RPI30_91


title: RPI30_95


title: RPI30_69


title: RPI30_88


title: RPI30_66


title: RPI30_70


title: RPI30_64


title: RPI30_67


title: RPI30_62


title: RPI30_61


title: RPI30_60


title: RPI30_63


title: RPI30_65


title: RPI30_59


title: RPI30_58


title: RPI30_78


title: RPI30_57


title: RPI30_56


title: RPI30_55


title: RPI30_72


title: RPI30_53


title: RPI30_52


title: RPI30_48


title: RPI30_49


title: RPI30_51


title: RPI30_47


title: RPI30_50


title: RPI30_68


title: RPI30_46


title: RPI30_45


title: RPI30_43


title: RPI30_42


title: RPI30_41


title: RPI30_38


title: RPI30_39


title: RPI30_40


title: RPI30_37


title: RPI30_36


title: RPI30_34


title: RPI30_32


title: RPI30_30


title: RPI30_54


title: RPI30_35


title: RPI30_31


title: RPI30_29


title: RPI30_28


title: RPI30_26


title: RPI30_24


title: RPI30_27


title: RPI30_25


title: RPI30_22


title: RPI30_20


title: RPI30_21


title: RPI30_44


title: RPI30_19


title: RPI30_18


title: RPI30_17


title: RPI30_33


title: RPI30_16


title: RPI30_15


title: RPI30_14


title: RPI30_11


title: RPI30_10


title: RPI30_12


title: RPI30_13


title: RPI30_5


title: RPI30_7


title: RPI30_6


title: RPI30_9


title: RPI30_2


title: RPI30_3


title: RPI30_23


title: RPI30_1


title: RPI42_95


title: RPI42_94


title: RPI30_0


title: RPI42_92


title: RPI42_91


title: RPI42_89


title: RPI30_4


title: RPI42_88


title: RPI42_87


title: RPI42_86


title: RPI42_84


title: RPI42_85


title: RPI42_82


title: RPI42_83


title: RPI42_80


title: RPI42_79


title: RPI42_78


title: RPI42_77


title: RPI42_81


title: RPI42_75


title: RPI42_74


title: RPI42_90


title: RPI42_72


title: RPI42_93


title: RPI42_68


title: RPI42_71


title: RPI42_76


title: RPI42_65


title: RPI42_67


title: RPI42_66


title: RPI42_69


title: RPI42_73


title: RPI42_64


title: RPI42_62


title: RPI42_63


title: RPI42_70


title: RPI42_61


title: RPI42_60


title: RPI42_59


title: RPI42_58


title: RPI42_56


title: RPI42_57


title: RPI42_55


title: RPI42_53


title: RPI42_54


title: RPI42_52


title: RPI42_51


title: RPI42_49


title: RPI42_45


title: RPI42_50


title: RPI42_44


title: RPI42_46


title: RPI42_47


title: RPI42_48


title: RPI7_24


title: RPI42_34


title: RPI42_43


title: RPI42_37


title: RPI42_38


title: RPI42_32


title: RPI42_42


title: RPI42_41


title: RPI42_40


title: RPI42_36


title: RPI42_33


title: RPI42_39


title: RPI42_29


title: RPI42_35


title: RPI42_30


title: RPI42_25


title: RPI42_26


title: RPI42_27


title: RPI42_24


title: RPI42_23


title: RPI42_21


title: RPI42_20


title: RPI42_22


title: RPI42_18


title: RPI42_19


title: RPI42_17


title: RPI42_16


title: RPI42_15


title: RPI42_14


title: RPI42_9


title: RPI42_11


title: RPI42_12


title: RPI42_7


title: RPI42_13


title: RPI42_28


title: RPI42_3


title: RPI42_8


title: RPI42_10


title: RPI42_6


title: RPI42_5


title: RPI42_4


title: RPI42_0


title: RPI28_95

value: Day 5


title: RPI42_2


title: RPI42_1


title: RPI28_93


title: RPI28_92


title: RPI28_91


title: RPI28_88


title: RPI28_89


title: RPI28_90


title: RPI28_87


title: RPI28_86


title: RPI28_85


title: RPI28_84


title: RPI28_94


title: RPI28_80


title: RPI28_78


title: RPI28_72


title: RPI28_74


title: RPI28_71


title: RPI28_79


title: RPI28_67


title: RPI28_66


title: RPI28_65


title: RPI28_69


title: RPI28_68


title: RPI42_31


title: RPI28_61


title: RPI28_77


title: RPI28_60


title: RPI28_81


title: RPI28_64


title: RPI28_62


title: RPI28_59


title: RPI28_63


title: RPI28_57


title: RPI28_82


title: RPI28_83


title: RPI28_58


title: RPI28_76


title: RPI28_55


title: RPI28_56


title: RPI28_75


title: RPI28_50


title: RPI28_51


title: RPI28_52


title: RPI28_53


title: RPI28_48


title: RPI28_49


title: RPI28_47


title: RPI28_54


title: RPI28_42


title: RPI28_44


title: RPI28_41


title: RPI28_46


title: RPI28_35


title: RPI28_39


title: RPI28_37


title: RPI28_36


title: RPI28_34


title: RPI28_32


title: RPI28_31


title: RPI28_30


title: RPI28_45


title: RPI30_8


title: RPI28_28


title: RPI28_73


title: RPI28_26


title: RPI28_27


title: RPI28_29


title: RPI28_23


title: RPI28_43


title: RPI28_24


title: RPI28_22


title: RPI28_40


title: RPI28_38


title: RPI28_25


title: RPI28_20


title: RPI28_33


title: RPI28_19


title: RPI28_18


title: RPI28_17


title: RPI28_13


title: RPI28_12


title: RPI28_15


title: RPI28_14


title: RPI28_11


title: RPI28_8


title: RPI28_9


title: RPI28_6


title: RPI28_21


title: RPI28_10


title: RPI28_7


title: RPI28_5


title: RPI28_4


title: RPI37_94

value: Day 6


title: RPI28_0


title: RPI28_1


title: RPI37_93


title: RPI37_95


title: RPI37_91


title: RPI37_90


title: RPI37_92


title: RPI37_88


title: RPI28_16


title: RPI37_89


title: RPI37_87


title: RPI37_86


title: RPI37_84


title: RPI37_85


title: RPI28_3


title: RPI37_81


title: RPI37_83


title: RPI37_80


title: RPI37_82


title: RPI37_79


title: RPI28_2


title: RPI37_77


title: RPI37_76


title: RPI37_75


title: RPI37_74


title: RPI37_72


title: RPI37_69


title: RPI37_78


title: RPI37_73


title: RPI37_70


title: RPI37_66


title: RPI37_67


title: RPI37_68


title: RPI28_70


title: RPI37_64


title: RPI37_62


title: RPI37_63


title: RPI37_61


title: RPI37_59


title: RPI37_60


title: RPI37_57


title: RPI37_56


title: RPI37_51


title: RPI37_71


title: RPI37_52


title: RPI37_53


title: RPI37_54


title: RPI37_45


title: RPI37_49


title: RPI37_46


title: RPI37_50


title: RPI37_48


title: RPI37_65


title: RPI37_43


title: RPI37_42


title: RPI37_44


title: RPI37_40


title: RPI37_47


title: RPI37_37


title: RPI37_38


title: RPI37_36


title: RPI37_58


title: RPI37_39


title: RPI37_35


title: RPI37_34


title: RPI37_33


title: RPI37_32


title: RPI37_30


title: RPI37_55


title: RPI37_31


title: RPI37_29


title: RPI37_41


title: RPI37_25


title: RPI37_24


title: RPI37_27


title: RPI37_26


title: RPI37_23


title: RPI37_21


title: RPI37_19


title: RPI37_18


title: RPI37_16


title: RPI37_15


title: RPI37_17


title: RPI37_22


title: RPI37_12


title: RPI37_20


title: RPI37_13


title: RPI37_14


title: RPI37_5


title: RPI37_6


title: RPI37_9


title: RPI37_7


title: RPI37_8


title: RPI37_10


title: RPI37_28


title: RPI37_0


title: RPI37_1


title: RPI37_2


title: RPI37_11


title: RPI26_95


title: RPI37_4


title: RPI26_92


title: RPI26_84


title: RPI26_88


title: RPI26_89


title: RPI37_3


title: RPI26_85


title: RPI26_94


title: RPI26_77


title: RPI26_81


title: RPI26_79


title: RPI26_80


title: RPI26_78


title: RPI26_86


title: RPI26_93


title: RPI26_74


title: RPI26_90


title: RPI26_75


title: RPI26_91


title: RPI26_72


title: RPI26_69


title: RPI26_70


title: RPI26_73


title: RPI26_71


title: RPI26_82


title: RPI26_83


title: RPI26_68


title: RPI26_65


title: RPI26_66


title: RPI26_63


title: RPI26_62


title: RPI26_61


title: RPI26_64


title: RPI26_67


title: RPI26_54


title: RPI26_57


title: RPI26_59


title: RPI26_53


title: RPI26_56


title: RPI26_58


title: RPI26_87


title: RPI26_51


title: RPI26_50


title: RPI26_47


title: RPI26_48


title: RPI26_60


title: RPI26_76


title: RPI26_45


title: RPI26_46


title: RPI26_44


title: RPI26_43


title: RPI26_49


title: RPI26_42


title: RPI26_40


title: RPI26_38


title: RPI26_55


title: RPI26_37


title: RPI26_39


title: RPI26_52


title: RPI26_35


title: RPI26_36


title: RPI26_34


title: RPI26_41


title: RPI26_29


title: RPI26_30


title: RPI26_32


title: RPI26_26


title: RPI26_31


title: RPI26_33


title: RPI26_27


title: RPI26_28


title: RPI26_23


title: RPI26_22


title: RPI26_21


title: RPI26_20


title: RPI26_18


title: RPI26_19


title: RPI26_17


title: RPI26_25


title: RPI26_16


title: RPI26_24


title: RPI26_15


title: RPI26_14


title: RPI26_12


title: RPI26_13


title: RPI26_1


title: RPI26_8


title: RPI26_2


title: RPI10_95_eGFP+


title: RPI26_0


title: RPI26_3


title: RPI26_6


title: RPI10_92_eGFP+


title: RPI26_11


title: RPI26_5


title: RPI26_10


title: RPI10_93_eGFP+


title: RPI26_7


title: RPI10_89_eGFP+


title: RPI26_9


title: RPI26_4


title: RPI10_90_eGFP+


title: RPI10_94_eGFP+


title: RPI10_91_eGFP+


title: RPI10_84_eGFP+


title: RPI10_85_eGFP+


title: RPI10_87_eGFP+


title: RPI10_83_eGFP+


title: RPI10_88_eGFP+


title: RPI10_80_eGFP+


title: RPI10_82_eGFP+


title: RPI10_81_eGFP+


title: RPI10_79_eGFP+


title: RPI10_78_eGFP+


title: RPI10_86_eGFP+


title: RPI10_76_eGFP+


title: RPI10_73_eGFP+


title: RPI10_74_eGFP+


title: RPI10_72_eGFP+


title: RPI10_70_eGFP+


title: RPI10_68_eGFP+


title: RPI10_75_eGFP+


title: RPI10_67_eGFP+


title: RPI10_71_eGFP+


title: RPI10_60_eGFP+


title: RPI10_62_eGFP+


title: RPI10_63_eGFP+


title: RPI10_66_eGFP+


title: RPI10_65_eGFP+


title: RPI10_59_eGFP+


title: RPI10_61_eGFP+


title: RPI10_58_eGFP+


title: RPI10_57_eGFP+


title: RPI10_53_eGFP+


title: RPI10_54_eGFP+


title: RPI10_56_eGFP+


title: RPI10_55_eGFP+


title: RPI10_52_eGFP+


title: RPI10_51_eGFP+


title: RPI10_50_eGFP+


title: RPI10_49_eGFP+


title: RPI10_64_eGFP+


title: RPI10_46_eGFP+


title: RPI10_45_eGFP+


title: RPI10_48_eGFP+


title: RPI10_40_eGFP+


title: RPI10_43_eGFP+


title: RPI10_39_eGFP+


title: RPI10_38_eGFP+


title: RPI10_35_eGFP+


title: RPI10_32_eGFP+


title: RPI10_33_eGFP+


title: RPI10_37_eGFP+


title: RPI10_47_eGFP+


title: RPI10_36_eGFP+


title: RPI10_30_eGFP+


title: RPI10_31_eGFP+


title: RPI10_34_eGFP+


title: RPI10_25_eGFP+


title: RPI10_28_eGFP+


title: RPI10_27_eGFP+


title: RPI10_24_eGFP+


title: RPI10_77_eGFP+


title: RPI10_29_eGFP+


title: RPI10_26_eGFP+


title: RPI10_23_eGFP+


title: RPI10_44_eGFP+


title: RPI10_41_eGFP+


title: RPI10_69_eGFP+


title: RPI10_42_eGFP+


title: RPI10_20_eGFP+


title: RPI10_22_eGFP+


title: RPI10_19_eGFP+


title: RPI10_21_eGFP+


title: RPI10_17_eGFP+


title: RPI10_18_eGFP+


title: RPI10_11_eGFP+


title: RPI10_14_eGFP+


title: RPI10_12_eGFP+


title: RPI10_2_eGFP+


title: RPI10_1_eGFP+


title: RPI45_95


title: RPI45_94


title: RPI45_93


title: RPI10_15_eGFP+


title: RPI45_92


title: RPI10_16_eGFP+


title: RPI45_91


title: RPI45_90


title: RPI45_89


title: RPI45_85


title: RPI45_86


title: RPI45_87


title: RPI45_88


title: RPI10_13_eGFP+


title: RPI45_83


title: RPI45_81


title: RPI45_82


title: RPI45_80


title: RPI10_0_eGFP+


title: RPI10_10_eGFP+


title: RPI45_77


title: RPI10_7_eGFP+


title: RPI10_8_eGFP+


title: RPI10_6_eGFP+


title: RPI10_3_eGFP+


title: RPI10_9_eGFP+


title: RPI45_79


title: RPI10_5_eGFP+


title: RPI10_4_eGFP+


title: RPI45_75


title: RPI45_72


title: RPI45_74


title: RPI45_73


title: RPI45_70


title: RPI45_71


title: RPI45_66


title: RPI45_68


title: RPI45_69


title: RPI45_65


title: RPI45_64


title: RPI45_84


title: RPI45_63


title: RPI45_62


title: RPI45_78


title: RPI45_61


title: RPI45_59


title: RPI45_76


title: RPI45_60


title: RPI45_56


title: RPI45_51


title: RPI45_53


title: RPI45_58


title: RPI45_57


title: RPI45_48


title: RPI45_54


title: RPI45_55


title: RPI45_52


title: RPI45_50


title: RPI45_46


title: RPI45_49


title: RPI45_44


title: RPI45_45


title: RPI45_67


title: RPI45_41


title: RPI45_42


title: RPI45_43


title: RPI45_38


title: RPI45_47


title: RPI45_37


title: RPI45_35


title: RPI45_34


title: RPI45_36


title: RPI45_32


title: RPI45_33


title: RPI45_29


title: RPI45_31


title: RPI45_30


title: RPI45_23


title: RPI45_20


title: RPI45_21


title: RPI45_25


title: RPI45_18


title: RPI45_26


title: RPI45_22


title: RPI45_24


title: RPI45_19


title: RPI45_15


title: RPI45_12


title: RPI45_16


title: RPI45_13


title: RPI45_14


title: RPI45_27


title: RPI45_17


title: RPI45_11


title: RPI45_28


title: RPI45_39


title: RPI45_9


title: RPI45_6


title: RPI45_3


title: RPI45_7


title: RPI45_1


title: RPI45_2


title: RPI45_0


title: RPI45_8


title: RPI34_95


title: RPI34_92


title: RPI34_93


title: RPI34_94


title: RPI34_88


title: RPI34_82


title: RPI34_91


title: RPI34_85


title: RPI34_86


title: RPI34_83


title: RPI34_81


title: RPI34_87


title: RPI45_4


title: RPI34_79


title: RPI34_75


title: RPI34_89


title: RPI34_76


title: RPI34_72


title: RPI34_74


title: RPI34_80


title: RPI34_90


title: RPI34_71


title: RPI34_77


title: RPI34_68


title: RPI34_66


title: RPI34_69


title: RPI34_59


title: RPI34_62


title: RPI34_57


title: RPI34_63


title: RPI34_61


title: RPI34_78


title: RPI34_65


title: RPI34_84


title: RPI34_70


title: RPI34_58


title: RPI34_56


title: RPI34_60


title: RPI45_10


title: RPI34_73


title: RPI34_55


title: RPI34_53


title: RPI34_52


title: RPI34_54


title: RPI34_49


title: RPI34_67


title: RPI34_50


title: RPI34_51


title: RPI34_48


title: RPI34_47


title: RPI34_42


title: RPI34_43


title: RPI34_41


title: RPI34_40


title: RPI34_45


title: RPI34_64


title: RPI34_39


title: RPI34_36


title: RPI34_32


title: RPI34_35


title: RPI34_38


title: RPI34_31


title: RPI34_37


title: RPI34_33


title: RPI34_30


title: RPI34_34


title: RPI34_29


title: RPI34_44


title: RPI34_46


title: RPI34_27


title: RPI34_24


title: RPI34_28


title: RPI34_26


title: RPI34_21


title: RPI34_22


title: RPI34_25


title: RPI34_20


title: RPI34_23


title: RPI34_17


title: RPI34_18


title: RPI34_15


title: RPI34_13


title: RPI34_14


title: RPI34_19


title: RPI34_11


title: RPI34_12


title: RPI34_7


title: RPI34_8


title: RPI34_5


title: RPI34_9


title: RPI34_6


title: RPI45_40


title: RPI34_10


title: RPI34_0


title: RPI40_95


title: RPI34_4


title: RPI34_2


title: RPI34_3


title: RPI34_1


title: RPI40_94


title: RPI34_16


title: RPI40_93


title: RPI40_92


title: RPI40_91


title: RPI40_89


title: RPI40_90


title: RPI40_88


title: RPI40_87


title: RPI40_83


title: RPI40_84


title: RPI40_82


title: RPI40_78


title: RPI40_80


title: RPI40_77


title: RPI40_75


title: RPI40_73


title: RPI40_74


title: RPI40_81


title: RPI40_76


title: RPI40_72


title: RPI40_69


title: RPI40_67


title: RPI40_68


title: RPI40_71


title: RPI40_70


title: RPI40_85


title: RPI40_64


title: RPI40_60


title: RPI40_86


title: RPI40_63


title: RPI40_61


title: RPI40_62


title: RPI40_59


title: RPI40_56


title: RPI40_53


title: RPI40_55


title: RPI40_54


title: RPI40_57


title: RPI40_58


title: RPI40_47


title: RPI40_51


title: RPI40_50


title: RPI40_49


title: RPI40_52


title: RPI40_66


title: RPI40_46


title: RPI40_45


title: RPI40_44


title: RPI40_41


title: RPI40_79


title: RPI40_40


title: RPI40_43


title: RPI40_39


title: RPI40_42


title: RPI40_38


title: RPI40_35


title: RPI40_37


title: RPI40_36


title: RPI40_33


title: RPI40_48


title: RPI45_5


title: RPI40_34


title: RPI40_31


title: RPI40_29


title: RPI40_28


title: RPI40_24


title: RPI40_23


title: RPI40_25


title: RPI40_22


title: RPI40_26


title: RPI40_27


title: RPI40_20


title: RPI40_21


title: RPI40_18


title: RPI40_17


title: RPI40_19


title: RPI40_13


title: RPI40_16


title: RPI40_15


title: RPI40_14


title: RPI40_11


title: RPI40_12


title: RPI40_9


title: RPI40_10


title: RPI40_8


title: RPI40_7


title: RPI40_32


title: RPI40_30


title: RPI40_6


title: RPI40_5


title: RPI40_2


title: RPI40_1


title: RPI40_3


title: RPI40_0


title: RPI43_92


title: RPI40_4


title: RPI43_95


title: RPI43_93


title: RPI43_94


title: RPI43_91


title: RPI43_90


title: RPI43_86


title: RPI43_89


title: RPI43_84


title: RPI43_83


title: RPI43_85


title: RPI43_88


title: RPI43_87


title: RPI43_81


title: RPI43_80


title: RPI43_75


title: RPI43_78


title: RPI43_79


title: RPI43_74


title: RPI43_73


title: RPI43_72


title: RPI43_70


title: RPI43_71


title: RPI43_68


title: RPI43_67


title: RPI43_66


title: RPI43_62


title: RPI43_64


title: RPI43_69


title: RPI43_65


title: RPI43_63


title: RPI43_82


title: RPI43_60


title: RPI43_56


title: RPI43_59


title: RPI43_57


title: RPI43_77


title: RPI43_55


title: RPI43_53


title: RPI43_52


title: RPI43_54


title: RPI43_76


title: RPI43_51


title: RPI43_48


title: RPI43_49


title: RPI43_47


title: RPI43_46


title: RPI43_61


title: RPI43_43


title: RPI43_39


title: RPI43_42


title: RPI43_36


title: RPI43_45


title: RPI43_37


title: RPI43_38


title: RPI43_33


title: RPI43_40


title: RPI43_35


title: RPI43_58


title: RPI43_34


title: RPI43_50


title: RPI43_32


title: RPI43_28


title: RPI43_31


title: RPI43_24


title: RPI43_25


title: RPI43_27


title: RPI43_29


title: RPI43_21


title: RPI43_23


title: RPI43_22


title: RPI43_30


title: RPI43_20


title: RPI43_44


title: RPI43_41


title: RPI43_19


title: RPI43_18


title: RPI43_17


title: RPI43_16


title: RPI43_13


title: RPI43_14


title: RPI43_11


title: RPI43_12


title: RPI43_9


title: RPI43_8


title: RPI43_10


title: RPI43_7


title: RPI43_3


title: RPI43_5


title: RPI43_26


title: RPI43_4


title: RPI43_2


title: RPI43_1


title: RPI43_6


title: RPI43_0


title: RPI38_94


title: RPI38_92


title: RPI38_93


title: RPI38_95


title: RPI38_91


title: RPI38_90


title: RPI38_89


title: RPI43_15


title: RPI38_87


title: RPI38_88


title: RPI38_84


title: RPI38_82


title: RPI38_85


title: RPI38_80


title: RPI38_79


title: RPI38_76


title: RPI38_78


title: RPI38_74


title: RPI38_75


title: RPI38_73


title: RPI38_71


title: RPI38_70


title: RPI38_67


title: RPI38_69


title: RPI38_68


title: RPI38_66


title: RPI38_64


title: RPI38_63


title: RPI38_65


title: RPI38_62


title: RPI38_86


title: RPI38_60


title: RPI38_83


title: RPI38_61


title: RPI38_58


title: RPI38_59


title: RPI38_56


title: RPI38_81


title: RPI38_77


title: RPI38_53


title: RPI38_52


title: RPI38_51


title: RPI38_72


title: RPI38_49


title: RPI38_48


title: RPI38_47


title: RPI38_46


title: RPI38_45


title: RPI38_44


title: RPI38_50


title: RPI38_41


title: RPI38_42


title: RPI38_40


title: RPI38_38


title: RPI38_39


title: RPI38_37


title: RPI38_36


title: RPI38_35


title: RPI38_34


title: RPI38_32


title: RPI38_33


title: RPI38_55


title: RPI38_29


title: RPI38_54


title: RPI38_57


title: RPI38_28


title: RPI38_26


title: RPI38_25


title: RPI38_21


title: RPI38_23


title: RPI38_30


title: RPI38_43


title: RPI38_24


title: RPI38_22


title: RPI38_18


title: RPI38_19


title: RPI38_16


title: RPI38_20


title: RPI38_15


title: RPI38_13


title: RPI38_31


title: RPI38_10


title: RPI38_12


title: RPI38_11


title: RPI38_8


title: RPI38_7


title: RPI38_9


title: RPI38_6


title: RPI38_27


title: RPI38_5


title: RPI38_4


title: RPI38_3


title: RPI38_1


title: RPI46_95_eGFP++


title: RPI38_0


title: RPI38_2


title: RPI46_90_eGFP++


title: RPI46_94_eGFP++


title: RPI46_93_eGFP++


title: RPI46_91_eGFP++


title: RPI46_92_eGFP++


title: RPI46_89_eGFP++


title: RPI38_17


title: RPI46_88_eGFP++


title: RPI46_87_eGFP++


title: RPI46_84_eGFP++


title: RPI38_14


title: RPI46_85_eGFP++


title: RPI46_86_eGFP++


title: RPI46_82_eGFP++


title: RPI46_83_eGFP++


title: RPI46_79_eGFP++


title: RPI46_80_eGFP++


title: RPI46_77_eGFP++


title: RPI46_81_eGFP++


title: RPI46_76_eGFP++


title: RPI46_78_eGFP++


title: RPI46_75_eGFP++


title: RPI46_74_eGFP++


title: RPI46_72_eGFP++


title: RPI46_73_eGFP++


title: RPI46_71_eGFP++


title: RPI46_70_eGFP++


title: RPI46_69_eGFP++


title: RPI46_66_eGFP++


title: RPI46_67_eGFP++


title: RPI46_65_eGFP++


title: RPI46_63_eGFP++


title: RPI46_62_eGFP++


title: RPI46_60_eGFP++


title: RPI46_61_eGFP++


title: RPI46_57_eGFP++


title: RPI46_58_eGFP++


title: RPI46_68_eGFP++


title: RPI46_55_eGFP++


title: RPI46_56_eGFP++


title: RPI46_49_eGFP++


title: RPI46_51_eGFP++


title: RPI46_53_eGFP++


title: RPI46_52_eGFP++


title: RPI46_50_eGFP++


title: RPI46_54_eGFP++


title: RPI46_47_eGFP++


title: RPI46_45_eGFP++


title: RPI46_46_eGFP++


title: RPI46_44_eGFP++


title: RPI46_48_eGFP++


title: RPI46_64_eGFP++


title: RPI46_43_eGFP++


title: RPI46_39_eGFP++


title: RPI46_42_eGFP++


title: RPI46_38_eGFP++


title: RPI46_40_eGFP++


title: RPI46_34_eGFP++


title: RPI46_36_eGFP++


title: RPI46_37_eGFP++


title: RPI46_59_eGFP++


title: RPI46_33_eGFP++


title: RPI46_32_eGFP++