sequencing/scg-protocols-v1 · Datasets at Hugging Face

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protocol

Box 2 \| ‘Crush and soak’ method ● tIMInG 12–14 h

1. Use a needle from a 1-ml syringe to make several holes in the bottom of 0.5-ml thin-walled PCR tubes.
 cr ItIcal step Handle the needle with appropriate protection.
2. Transfer the gel slices into 0.5-ml thin-walled PCR tubes, place the tubes into 1.5-ml Eppendorf DNA LoBind tubes and centrifuge
them at 13,000g for at least 1 min at room temperature until the gel slice collects in the bottom of the 1.5-ml tubes.
3. Add diffusion buffer (~350 µl, Reagent Setup) to the 1.5-ml tubes until all the gel debris is covered with buffer.
4. Incubate the slurry on a Thermomixer by shaking for 2–12 h at 50 °C. Longer incubations give better recoveries.
 cr ItIcal step The slurry should be incubated for no less than 2 h.
5. Transfer the eluate and the gel debris to the top of a 10-µm filter spin, and then centrifuge the filter at 3,000g for 0.5–1 min at
room temperature to ensure that all of the eluate passes through the filter and flows to the bottom of the collection tubes.
6. Transfer the flow-through to new 1.5-ml Eppendorf DNA LoBind tubes.
 cr ItIcal step The flow-through should not be discarded.




Quality control and high-throughput Dna sequencing ● tIMInG 10–18 d
42\| Quantify the final single-cell RRBS library from Step 41 with a Qubit fluorometer and the Qubit dsDNA HS assay kit.
Use the qPCR assay to determine the concentration of each single-cell RRBS sample.
 cr ItIcal step The typical yield of the scRRBS libraries is ~20–30 ng (using the Qubit fluorometer for quantification) after
gel-based size selection and AMPure XP beads purification, with <1 ng in the pick-buffer-only negative controls.
 cr ItIcal step The standard curve–based qPCR assay is a standard quantification assessment for the Illumina libraries
before deep sequencing. First, prepare serial tenfold dilutions of the standard Illumina libraries to generate the standard
curve; on the basis of this curve the number of adapter-insert-adapter molecules of the scRRBS libraries can be determined.

43\| Assess the final libraries using a Fragment Analyzer (Advanced Analytical Technologies) to check the size distributions
(Fig. 2b).
 cr ItIcal step If available, an Agilent Bioanalyzer 2100 can be used as an alternative to the Fragment Analyzer to
evaluate the quality and the size distributions of the final libraries.
 cr ItIcal step Typical DNA size distribution in scRRBS libraries ranges from 160 to 350 bp, with visible peaks
corresponding to MspI fragments for some repetitive elements (Fig. 2b,c). If the Fragment Analyzer results show that
there are still primer dimers present, perform another clean-up step with 1:1-fold AMPure XP beads as described in Box 1.

44\| Sequence the libraries using HiSeq 2000/2500 sequencers
with cluster densities at 75–85% of that used in regular bulk Raw data
DNA or RNA sequencing.

Step 1: quality control (Step 45)
Data analysis for single-cell rr Bs data ● tIMInG 2–3 d
 cr ItIcal An overview of the major procedures involved
Clean data
in scRRBS data analysis is summarized in Figure 3. An
archive containing custom scripts used in this protocol is
available as supplementary Data. The overview of these Step 2: alignment (Step 46)
custom scripts, including the script names, functions and
the corresponding protocol steps in which they should be SAM file
used, is listed in table 2.
Step 3: sort and pileup (Steps 47 and 48)



Pileup file

Figure 3 \| Schematic of the bioinformatic analysis procedures of the scRRBS
data. The raw sequencing reads generated from the HiSeq 2000 sequencer Step 4: DNA methylation
level calculation (Step 49)
are trimmed to remove reads containing low-quality bases or adapter
contaminants. Next, the cleaned reads are used to align to the reference DNA methylation profiles
genome (bisulfite converted in silico) using the default parameters. SAM
files are obtained after bismark mapping, sorted into BAM files and used to
generate the standard pileup files. Customized Perl scripts are applied to
calculate the DNA methylation levels of each covered cytosine based on the
reported C (methylated reads) divided by the total number of reported C and Downstream analyses
T (total reads) at the same genomic positions.


nature protocols \| VOL.10 NO.5 \| 2015 \| 655

## Page 12

protocol


45\| Perform quality control to filter low-quality reads and reads containing adapter sequences, as well as to remove
additional bases that contain cytosines, which were artificially introduced during library preparation. This step can be
conducted using the trim_galore tool with the following command line:
trim_galore --quality 20 --phred33 --stringency 3 --gzip --length 36 --rrbs --paired
--trim1 --output_dir <output_dir> <read1.fastq.gz> <read2.fastq.gz>
This command will generate two files suffixed with ‘_val_1.fq.gz’ and ‘_val_2.fq.gz’ for paired-end sequencing data. We call
these two files ‘clean data’ here.
 cr ItIcal step The parameter ‘--rrbs’ used here will remove the artificial bases introduced in the end-repair step from the
3′ ends of the reads.

46\| When clean data have been obtained, they can be mapped to the reference genome using the Bismark alignment tool35.
For the first-time use of a reference genome and before alignment, ensure that the genome is bisulfite-converted in silico and
indexed well using bismark_genome_preparation, which is attached with the Bismark software package. Example command
line:
bismark_genome_preparation --verbose –path_to_bowtie bowtie-1.0.0 <genome_folder>
This command will create a transformed reference genome index for bisulfite data alignment.
bismark –path_to_bowtie bowtie-1.0.0 –quiet -o <output_dir> --temp_dir <tmp_dir>
<reference_folder> -1 <read1_val_1.fq.gz> -2 <read2_val_2.fq.gz>
This command will generate a SAM file named ‘read1_val_1.fq.gz_bismark_pe.sam’ containing all the mapping outputs.
A final alignment report will be generated upon completion of the Bismark alignment. In this report, the number of total
sequencing reads, the number of uniquely mapped reads and the mapping ratio of the sample are included. This report will
show the average DNA methylation levels of cytosines in the CpG, CHG and CHH contexts (where H stands for adenine, cyto-
sine or thymine nucleotide), respectively.
Because the samples were spiked with trace amounts of unmethylated λ-DNA, the cytosines in a non-CG (CHH and CHG)

protocol

Box 2 | ‘Crush and soak’ method ● tIMInG 12–14 h

1. Use a needle from a 1-ml syringe to make several holes in the bottom of 0.5-ml thin-walled PCR tubes.

 cr ItIcal step Handle the needle with appropriate protection.

2. Transfer the gel slices into 0.5-ml thin-walled PCR tubes, place the tubes into 1.5-ml Eppendorf DNA LoBind tubes and centrifuge

them at 13,000g for at least 1 min at room temperature until the gel slice collects in the bottom of the 1.5-ml tubes.

3. Add diffusion buffer (~350 µl, Reagent Setup) to the 1.5-ml tubes until all the gel debris is covered with buffer.

4. Incubate the slurry on a Thermomixer by shaking for 2–12 h at 50 °C. Longer incubations give better recoveries.

 cr ItIcal step The slurry should be incubated for no less than 2 h.

5. Transfer the eluate and the gel debris to the top of a 10-µm filter spin, and then centrifuge the filter at 3,000g for 0.5–1 min at

room temperature to ensure that all of the eluate passes through the filter and flows to the bottom of the collection tubes.

6. Transfer the flow-through to new 1.5-ml Eppendorf DNA LoBind tubes.

 cr ItIcal step The flow-through should not be discarded.

Quality control and high-throughput Dna sequencing ● tIMInG 10–18 d

42| Quantify the final single-cell RRBS library from Step 41 with a Qubit fluorometer and the Qubit dsDNA HS assay kit.

Use the qPCR assay to determine the concentration of each single-cell RRBS sample.

 cr ItIcal step The typical yield of the scRRBS libraries is ~20–30 ng (using the Qubit fluorometer for quantification) after

gel-based size selection and AMPure XP beads purification, with <1 ng in the pick-buffer-only negative controls.

 cr ItIcal step The standard curve–based qPCR assay is a standard quantification assessment for the Illumina libraries

before deep sequencing. First, prepare serial tenfold dilutions of the standard Illumina libraries to generate the standard

curve; on the basis of this curve the number of adapter-insert-adapter molecules of the scRRBS libraries can be determined.

43| Assess the final libraries using a Fragment Analyzer (Advanced Analytical Technologies) to check the size distributions

(Fig. 2b).

 cr ItIcal step If available, an Agilent Bioanalyzer 2100 can be used as an alternative to the Fragment Analyzer to

evaluate the quality and the size distributions of the final libraries.

 cr ItIcal step Typical DNA size distribution in scRRBS libraries ranges from 160 to 350 bp, with visible peaks

corresponding to MspI fragments for some repetitive elements (Fig. 2b,c). If the Fragment Analyzer results show that

there are still primer dimers present, perform another clean-up step with 1:1-fold AMPure XP beads as described in Box 1.

44| Sequence the libraries using HiSeq 2000/2500 sequencers

with cluster densities at 75–85% of that used in regular bulk Raw data

DNA or RNA sequencing.

Step 1: quality control (Step 45)

Data analysis for single-cell rr Bs data ● tIMInG 2–3 d

 cr ItIcal An overview of the major procedures involved

Clean data

in scRRBS data analysis is summarized in Figure 3. An

archive containing custom scripts used in this protocol is

available as supplementary Data. The overview of these Step 2: alignment (Step 46)

custom scripts, including the script names, functions and

the corresponding protocol steps in which they should be SAM file

used, is listed in table 2.

Step 3: sort and pileup (Steps 47 and 48)

Pileup file

Figure 3 | Schematic of the bioinformatic analysis procedures of the scRRBS

data. The raw sequencing reads generated from the HiSeq 2000 sequencer Step 4: DNA methylation

level calculation (Step 49)

are trimmed to remove reads containing low-quality bases or adapter

contaminants. Next, the cleaned reads are used to align to the reference DNA methylation profiles

genome (bisulfite converted in silico) using the default parameters. SAM

files are obtained after bismark mapping, sorted into BAM files and used to

generate the standard pileup files. Customized Perl scripts are applied to

calculate the DNA methylation levels of each covered cytosine based on the

reported C (methylated reads) divided by the total number of reported C and Downstream analyses

T (total reads) at the same genomic positions.

nature protocols | VOL.10 NO.5 | 2015 | 655

## Page 12

protocol

45| Perform quality control to filter low-quality reads and reads containing adapter sequences, as well as to remove

additional bases that contain cytosines, which were artificially introduced during library preparation. This step can be

conducted using the trim_galore tool with the following command line:

trim_galore --quality 20 --phred33 --stringency 3 --gzip --length 36 --rrbs --paired

--trim1 --output_dir <output_dir> <read1.fastq.gz> <read2.fastq.gz>

This command will generate two files suffixed with ‘_val_1.fq.gz’ and ‘_val_2.fq.gz’ for paired-end sequencing data. We call

these two files ‘clean data’ here.

 cr ItIcal step The parameter ‘--rrbs’ used here will remove the artificial bases introduced in the end-repair step from the

3′ ends of the reads.

46| When clean data have been obtained, they can be mapped to the reference genome using the Bismark alignment tool35.

For the first-time use of a reference genome and before alignment, ensure that the genome is bisulfite-converted in silico and

indexed well using bismark_genome_preparation, which is attached with the Bismark software package. Example command

line:

bismark_genome_preparation --verbose –path_to_bowtie bowtie-1.0.0 <genome_folder>

This command will create a transformed reference genome index for bisulfite data alignment.

bismark –path_to_bowtie bowtie-1.0.0 –quiet -o <output_dir> --temp_dir <tmp_dir>

<reference_folder> -1 <read1_val_1.fq.gz> -2 <read2_val_2.fq.gz>

This command will generate a SAM file named ‘read1_val_1.fq.gz_bismark_pe.sam’ containing all the mapping outputs.

A final alignment report will be generated upon completion of the Bismark alignment. In this report, the number of total

sequencing reads, the number of uniquely mapped reads and the mapping ratio of the sample are included. This report will

show the average DNA methylation levels of cytosines in the CpG, CHG and CHH contexts (where H stands for adenine, cyto-

sine or thymine nucleotide), respectively.

Because the samples were spiked with trace amounts of unmethylated λ-DNA, the cytosines in a non-CG (CHH and CHG)