{'fraction_non_alphanumeric': 0.03493258074631546, 'fraction_numerical': 0.02345562872373785, 'mean_word_length': 5.076981707317073, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Library-free Methylation Sequencing with Bisulfite Padlock Probes

We previously developed Bisulfite Padlock Probes (BSPPs) for the specific and parallel digital quantification of DNA methylation1. Here we report the second-generation of BSPPs with a design algorithm to generate more efficient padlock probes, a library-free protocol that dramatically reduces the time and cost of sample preparation and is compatible with automation, and an efficient bioinformatics pipeline that accurately obtains both methylation levels and genotypes from bisulfite sequencing data.

( Supplementary Fig. 1). We had previously trained this network using data from probes for exomic targets 6 based on seven properties. Using bisulfite capture data, we have refined the network with two additional factors. ppDesigner can explain ~50% of the variance in capturing efficiency for genomic DNA and ~20% of the variance in capturing efficiency for bisulfite converted DNA; additional variation could be due to factors such as variability in oligonucleotide synthesis and sample DNA quality. ppDesigner is extremely flexible, and has been used to design a variety of genomic and bisulfite probes for Homo sapiens 2,3 , Mus musculus 4 , and Drosophila melanogaster 7 .
Key requirements for methylation analysis on large sample sizes include low cost, simple workflow, and automation compatibility. As DNA sequencing cost has rapidly decreased, sample processing has become a bottleneck in terms of cost and throughput. A complicated workflow increases variability between samples, and reduces power in large-scale studies. To address these issues, we extended a "library-free" protocol 8 to multiplexed BSPP capture (Fig. 1). This method eliminates five steps from Illumina's library construction protocol, such that multiplexed libraries can be generated from DNA in only four steps (Supplementary Table 1). Using multiplexed primers with 6 bp barcodes, we have routinely generated libraries for 96 samples in 96-well plates and sequenced all at once in a single Illumina HiSeq flowcell. Additional primers have been designed to process 384 samples per batch. As sample-specific barcodes were added, barcoded libraries can be pooled for sizeselection, which is the most time consuming, contamination-prone, and error-prone step if performed individually. The protocol is compatible with multi-channel pipettes or liquid handling devices. It dramatically reduced experimental cost and time, and improved reproducibility and read mapping rates (Supplementary Tables 1 and 2). For large sample sizes, the library preparation cost (including probes) is comparable to that of the Restricted Representation and Whole Genome Bisulfite Sequencing (RRBS, WGBS) protocols, while the sequencing cost is much lower than that of WGBS due to targeting of CpG sites of interest. RRBS is more cost-effective than BSPP, but there is little flexibility in selecting specific sites or regions.
Another bottleneck in bisulfite sequencing is a lack of computational tools to efficiently analyze sequencing data generated from hundreds of samples. To overcome this issue, we developed an analysis pipeline for read mapping and methylation quantification called bisReadMapper ( Supplementary Fig. 2). In previous padlock probe studies, reads were mapped only against target regions due to the computational requirements of sequence alignment 1 . In contrast, bisReadMapper maps to the full genome sequence, allowing processing of both targeted and whole genome bisulfite data. bisReadMapper also determines the origin strand of the read based on base composition and maps reads as if they were fully bisulfite-converted to a fully bisulfite-converted genome sequence, allowing mapping of both bi-and uni-directional bisulfite libraries in an unbiased manner. Another feature is the capability to call single nucleotide polymorphisms from bisulfite sequencing data; this feature not only allows for analysis of allele-specific methylation 9 , but also allows accurate sample tracking in large-scale experiments. Finally, bisReadMapper can call methylation levels at both CpG and non-CpG sites.
To demonstrate the effectiveness of our assay, we generated a new genome-scale probe set based on our previous results and new information about differential methylation 1,10-12 . Our new design was targeted to evaluate the methylation level at a set of genomic locations known to contain differentially methylated regions (DMRs) or sites (DMSs) [10][11][12][13] , CTCF binding sites, and DNase I hypersensitive regions. In addition, all microRNA genes and all promoters for human NCBI Reference Sequence (RefSeq) genes were targeted. Using ppDesigner, we successfully designed ~330,000 padlock probes that covered 140,749 nonoverlapping regions with a total size of 34 megabases. We performed capturing experiments and end-sequencing, and found that these probes were slightly more specific (~96% ontarget) and uniform than previous probes 1 (Supplementary Fig. 3). These probes were further normalized using subsetting and suppressor oligonucleotides as described previously 1 to improve uniformity. Roughly 500,000 CpG sites were characterizable with ~4 gigabases of sequencing reads, and additional sites became callable with deeper sequencing (Supplementary Fig. 4

-5).
We used this probe set to analyze H1 embryonic stem cells (H1 ESCs), PGP1 fibroblasts (PGP1F), and two technical replicates of PGP1 fibroblast-derived induced pluripotent stem cells (PGP1-iPSC). For each sample, we sequenced on average ~3.66 gigabases and measured the methylation level for an average of 480,904 CpG sites. In order to assess whether this data could identify potential epigenetic regulation of transcription, we utilized GREAT 14 to predict the cis-regulatory potential of regions around captured CpG sites. In total, the padlock probes captured CpG sites in regions predicted to regulate 98% of RefSeq genes ( Supplementary Fig. 6).
The data generated by BSPP accurately represented the methylation status of the target regions. Methylation levels for the two technical replicates of PGP1-iPSC were consistent both within a single batch and between separate batches (Pearson's correlation coefficient R = 0.97 -0.98, Supplementary Fig. 7a,b). Additionally, when methylation levels were compared between technical replicates, no CpG site was found to be significantly different by a Fisher Exact Test with Benjamini-Hochberg multiple testing correction (FDR = 0.01, n = 439,090). In comparison, large fractions of sites were found differentially methylated due to either the process of nuclear reprogramming (27.9% DMS between PGP1-iPSC and PGP1F) or the difference in cell type (31.3% DMS between PGP1F and H1) with the same criteria (FDR = 0.01, n = 444,111 and 359,290, respectively). Our BSPP results on H1 ESCs are highly consistent with the published whole genome bisulfite sequencing data 12 (Pearson's correlation coefficient R = 0.95, Supplementary Fig. 8).
Our assay has very low technical variability. We have performed the assay on over 150 samples in 96-well plates; the yield for each was similar ( Supplementary Fig. 9). Approximately 10% of CpG sites are targeted separately on each strand, allowing lowquality data sets with poor correlation between these built-in technical replicates to be identified ( Supplementary Fig. 7c,d,e). As our BSPP assay measures absolute methylation levels, no normalization is necessary as long as the internal replicates are consistent. Therefore, a large number of datasets, even generated from different laboratories, can be directly compared without batch effects, which is important for case-control studies on large samples or meta-analyses. Additionally, the SNP-calling feature of bisReadMapper allowed us to characterize roughly 20,000 SNPs for each sample at an accuracy of 96% or better. This allowed us to unambiguously track samples, which is crucial for projects involving large sample sizes.
Our library-free BSPP method is flexible for different study designs. While our genomescale probe set allows global profiling on thousands of samples, a focused assay is often necessary to follow up on tens to hundreds of candidate regions identified in genome-scale scanning. Such an assay needs to be customizable to different genomic targets, scalable to a very large sample size (1,000-100,000), and inexpensive. To further demonstrate the flexibility, we designed a second set of 3,918 probes to evaluate the methylation state 1 kbp upstream and downstream of 120 genomic regions previously known and confirmed by BSPP to carry aberrant methylation in induced pluripotent stem cells 15 . We acquired the oligonucleotides from a second vendor (LC Sciences). Even with shorter capturing sequences (40 bp total for capturing arms rather than 50 bp on average, Supplementary Figure 10) and a 100-fold smaller target size, an average of 56% of mappable bases were ontarget, equivalent to an enrichment factor of ~6,500. With the data from three cell lines (H1 ESCs, PGP1F, and PGP1-iPSCs) we were able to identify regions of aberrant methylation in iPSCs ( Supplementary Fig. 11), and demonstrated that aberrant methylation continues further upstream and downstream than observed previously. This analysis demonstrates that a focused probe set can be used to validate specific regions of interest identified in global scanning using either our genome-wide probe set or other methods.
This method can be implemented to aid in the identifying the effects of DNA methylation in any organism by using the computational tools made available on the supporting website for this paper (http://genome-tech.ucsd.edu/public/Gen2_BSPP/).

ONLINE METHODS
Probe design and read mapping algorithms, as well as probe sequences and additional information are available at http://genome-tech.ucsd.edu/public/Gen2_BSPP/. Schematic for the padlock probes is illustrated in Supplementary Fig. 10.

Bisulfite padlock probe production (Oligonucleotides from Agilent)
Libraries of oligonucleotides (~150 nt) were synthesized by ink-jet printing on programmable microarrays (Agilent Technologies) and released to form a combined library of 330,000 oligonucleotides. The oligonucleotides were amplified by PCR in 96 reactions (100 μl each) with 0.02 nM template oligonucleotide, 400 nM each of pAP1V61U primer and AP2V6 primer (Supplementary Table 3 Table 3) and denatured at 94 °C for 2 min decreased the temperature to 37 °C and incubated at 37 °C for 3 min. The mixture was digested with 50 units DpnII (10U/μl, NEB) in NEBuffer DpnII at 37 °C for 2 h. Then the mixture was further digested with 5 units USER at 37 °C for 2 h followed by enzyme inactivation at 75 °C for 20 min. The USER/DpnII digested DNAs were purified with Qiaquick PCR purification column. The single-strand 102 nucleotide probes were purified with 6% denaturing PAGE (6% TB-urea 2D gel; Invitrogen).

Bisulfite padlock probe production (Oligonucleotides from LC Sciences)
The oligonucleotides (100 nt) were synthesized using a programmable microfluidic microarray platform (LC Sciences) and released to form a mix of 3,918 oligoucleotides. The oligonucleotides were amplified by two-step PCR in a 200 μl reaction with 1nM template oligonucleotides, 400 nM each of eMIP_CA1_F primer and eMIP_CA1_R primer (Supplementary Table 3 The probe molecules (with size of approximately 70 bases) were purified by 6% denaturing PAGE (6% TB-urea 2D gel).

Sample preparation and capture
Genomic DNA was extracted using the AllPrep DNA/RNA Mini kit (Qiagen) and bisulfite converted with the EZ-96 DNA methylation Gold kit (Zymoresearch) in 96-well plate. Normalized amount of padlock probes, 200 ng of bisulfite converted gDNA, and 4.2 nM oligo suppressor were mixed in 25 μl 1× Ampligase Buffer (Epicentre) in 96-well plate, denatured at 95 °C for 10 min, gradually lowered the temperature at 0.02 °C/s to 55 °C in a thermocycler, and hybridized at 55 °C for 20 h. 2.5 μl of SLN mix (100 μM dNTP, 2 U/μl AmpliTaq Stoffel Fragment (ABI) and 0.5 U/μl Ampligase (Epicentre) in 1 × Ampligase buffer) was added to the reaction for gap-filling reaction. For circularization, the reactions were incubated at 55 °C for 20 h, followed by enzyme inactivation at 94 °C for 2 min. To digest linear DNA after circularization, 2 μl of exonuclease mix (10 U/μl exonuclease I and 100 U/μl exonuclease III, USB) was added to the reactions, and the reactions were incubated at 37 °C for 2 h then inactivated at 94 °C for 2 min.

Primer barcode design for multiplexing
An in-house perl script was written to randomly generate 6 nt long sequences. A sequence was kept if it does not have more than two matching positions with another accepted barcode and if it has between two to four guanine or cytosine. The script reiterates until the desired number of barcodes have been obtained. A total of 384 primers were designed (Supplementary Table 4).

Bisulfite read mapping and data analysis
Bisulfite converted data was processed as previously described. Reference genome is computationally converted by changing all C's to T's on Watson and Crick strands separately. FASTQ reads are encoded by 1) predicting the mapping orientation, 2) converting all predicted forward mapping reads by changing all C's to T's and converting all predicted reverse complementary mapping reads by changing all G's to A's, the original reads are maintained. The bisulfite reads are then mapped to the converted reference separately using SOAP2Align (http://soap.genomics.org.cn/soapaligner.html) with the parameters r = 0, v = 2 (one mismatch per 40bp sequenced), Paired-End: m = 0, × = 400. Alignment files are then combined, and one alignment per read was selected. Original C calls were placed back into the alignment information. Alignments are then converted to pileup format using SamTools (http://samtools.sourceforge.net/). Raw SNPs and methylation frequency files were computed from pileup counts. Methylation frequencies and SNPs were called using a method described previously 1 .

Correlation of methylation levels between two samples
To check if methylation levels were similar between two samples, the Pearson's correlation was calculated on all CpG sites characterizable in both. First, a list of CpG sites with read depth of at least 10 in both samples was generated. The methylation frequencies at these sites were obtained from bisReadMapper output, and input into the statistical package R. Finally, Pearson's correlation for the two samples was computed using the cor() function.

Analysis of differential methylation
From the bisReadMapper output, the raw read counts showing methylation and lack of methylation were assembled for each line. Using these counts, a Fisher-Exact Test with Benjamini-Hochberg Multiple Testing Correction (FDR = 0.01) was carried out on each CpG site with minimum 10x depth coverage. This resulted in a set of differentially methylated sites (DMSs) between the two lines; at each of these sites, the methylation levels were statistically significantly different with at least 0.1 methylation level difference. Technical replicates did not show any differential methylation, while different cell types showed a large degree (~33%). Schematic of library-free BSPP protocol. Each padlock probe has a common linker sequence flanked by two target-specific capturing arms (red) that anneal to bisulfite converted genomic DNA (black). The 3′ end is extended and ligated with the 5′ end to form circularized DNA. After removal of linear DNA, all circularized captured targets are PCRamplified with barcoded primers and directly sequenced with an Illumina sequencing platform (GA II(x) or HiSeq). Amplicon size is 363 bp, which includes captured target (180 bp), capturing arms (55 bp), and amplification primers and adapters (128 bp). The inserts can be read through with paired-end 120 bp sequencing reads.