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

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



Pileup file

Figure 3 \| Schematic of the bioinformatic analysis procedures of the scRRBS
Step 4: DNA methylation
data. The raw sequencing reads generated from the HiSeq 2000 sequencer 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.
 CRITICAL 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>reserved.
rights AThisfinalcommandalignmentwillreportgeneratewill abeSAMgeneratedfile namedupon‘read1_val_1.fq.gz_bismark_pe.sam’completion of the Bismark alignment.containingIn this report,all the themappingnumberoutputs.of total
All 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
Inc. 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)
context in the λ-DNA genome are definitely unmethylated. The nonconversion rate of scRRBS is calculated as the number ofAmerica, sequenced cytosines in non-CG contexts divided by all the covered cytosines in non-CG contexts in the λ-DNA genome.

47\| Once the alignment is complete, use SortSam.jar in the Picard toolkit to sort the mapping result in coordinate order toNature prepare for the next step.
2015 Example command:
© java -Djava.io.tmpdir=<TMP> SortSam.jar I=<read1_val_1.fq.gz_bismark_pe.sam>
O=<read1_val_1.sort.bam> SORT_ORDER=coordinate VALIDATION_STRINGENCY=LENIENT
VERBOSITY=ERROR TMP_DIR=<TMP>
This command will generate a sorted BAM file.

48\| Create a pileup file of mapped data prepared for DNA methylation–level calculation. This step will give a pileup result for
each covered locus in the genome. Example command:

samtools mpileup -f <genome.fa> <read1_val_1.sort.bam> > <read1_val_1.pileup>

49\| Calculate the DNA methylation level for each covered CpG and non-CpG site. The DNA methylation level of each covered
cytosine is calculated as the number of reported C divided by the total number of reported C and T at the same genome
position. Example command:

Perl SingleC_MetLevel.pl <genome.fa> <read1_val_1.pileup> > <read1_val_1.SingleCmet>
The output file is tab-delimited, and it contains the chromosome, base position, reference genome, chain, total coverage
depth, number of methylated reads, number of unmethylated reads, methylation level and reference context (CpG, CHH or
CHG). This output file contains well-processed data that can be used to calculate the average DNA methylation of the
samples or the DNA methylation levels of any interesting annotated regions.





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

## Page 13

protocol

? TROUBLESHOOTING
Troubleshooting advice can be found in Table 3.

Table 3 \| Troubleshooting table.

Step Problem Possible reason Solution

7 Cell transfer failure Single cells may stick to the inside First, suck a small volume of PBA-BSA into the pipette, and then
wall of the mouth pipette pick the single cell into it. Ensure that the cell is already in the
pipette but near the tip of the pipette. Push all of the carryover
PBA-BSA out of the pipette, together with the cell

9 Incomplete cell lysis A single cell is not loaded into the During cell transfer, ensure that the cell is seeded into the

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

Pileup file

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

Step 4: DNA methylation

data. The raw sequencing reads generated from the HiSeq 2000 sequencer 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.

 CRITICAL 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>reserved.

rights AThisfinalcommandalignmentwillreportgeneratewill abeSAMgeneratedfile namedupon‘read1_val_1.fq.gz_bismark_pe.sam’completion of the Bismark alignment.containingIn this report,all the themappingnumberoutputs.of total

All 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

Inc. 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)

context in the λ-DNA genome are definitely unmethylated. The nonconversion rate of scRRBS is calculated as the number ofAmerica, sequenced cytosines in non-CG contexts divided by all the covered cytosines in non-CG contexts in the λ-DNA genome.

47| Once the alignment is complete, use SortSam.jar in the Picard toolkit to sort the mapping result in coordinate order toNature prepare for the next step.

2015 Example command:

O=<read1_val_1.sort.bam> SORT_ORDER=coordinate VALIDATION_STRINGENCY=LENIENT

VERBOSITY=ERROR TMP_DIR=<TMP>

This command will generate a sorted BAM file.

48| Create a pileup file of mapped data prepared for DNA methylation–level calculation. This step will give a pileup result for

each covered locus in the genome. Example command:

samtools mpileup -f <genome.fa> <read1_val_1.sort.bam> > <read1_val_1.pileup>

49| Calculate the DNA methylation level for each covered CpG and non-CpG site. The DNA methylation level of each covered

cytosine is calculated as the number of reported C divided by the total number of reported C and T at the same genome

position. Example command:

Perl SingleC_MetLevel.pl <genome.fa> <read1_val_1.pileup> > <read1_val_1.SingleCmet>

The output file is tab-delimited, and it contains the chromosome, base position, reference genome, chain, total coverage

depth, number of methylated reads, number of unmethylated reads, methylation level and reference context (CpG, CHH or

CHG). This output file contains well-processed data that can be used to calculate the average DNA methylation of the

samples or the DNA methylation levels of any interesting annotated regions.

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

## Page 13

protocol

? TROUBLESHOOTING

Troubleshooting advice can be found in Table 3.

Table 3 | Troubleshooting table.

Step Problem Possible reason Solution

7 Cell transfer failure Single cells may stick to the inside First, suck a small volume of PBA-BSA into the pipette, and then

wall of the mouth pipette pick the single cell into it. Ensure that the cell is already in the

pipette but near the tip of the pipette. Push all of the carryover

PBA-BSA out of the pipette, together with the cell

9 Incomplete cell lysis A single cell is not loaded into the During cell transfer, ensure that the cell is seeded into the