scReadSim for 10x scATAC-seq
This tutorial’s main steps and corresponding estimated time usage are as follows (tested on a server with the 256x Intel Xeon Phi CPU 7210 at 1.30 GHz):
Step 1: Import packages and data files: < 1 min
Step 2: Generate features: < 1 min
Step 3: Generate real count matrices: < 1 min
Step 4: Simulate synthetic count matrix: ~ 3 mins
Step 5: Output synthetic read: ~ 2 mins
By default, this tutorial uses Python (Python >= 3.8). However, we also include code chunks using bash commands to preprocess necessary files. To avoid users’ confusion, bash commands start with a symbol $. We also indicate when a following code chunk is using bash commands.
Required softwares for scReadSim
scReadSim requires users to pre-install the following softwares:
Depending on users’ choices, the following softwares are optional:
Pre-process BAM file before scReadSim
Note: This tutorial does not need this pre-process step since the processed BAM file is provided by the scReadSim package (see below Step 1: Import packages and data files).
Input BAM file for scReadSim needs pre-processing to add the cell barcode in front of the read name. For example, in 10x sequencing data, cell barcode TGGACCGGTTCACCCA-1
is stored in the field CB:Z:TGGACCGGTTCACCCA-1
.
The following code chunk (bash commands) outputs a read record from the original BAM file.
$ samtools view unprocess.bam | head -n 1
A00836:472:HTNW5DMXX:1:1372:16260:18129 83 chr1 4194410 60 50M = 4193976 -484 TGCCTTGCTACAGCAGCTCAGGAAATGTCTTTGTGCCCACAGTCTGTGGT :FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF NM:i:0 MD:Z:50 AS:i:50 XS:i:0 CR:Z:TCCGGGACAGCTAACA CY:Z:FFFFFFFFFFFFFFF: CB:Z:TGGACCGGTTCACCCA-1 BC:Z:AAACTCAT QT:Z::FFFFFFF RG:Z:e18_mouse_brain_fresh_5k:MissingLibrary:1:HTNW5DMXX:1
The following code chunk (bash commands) adds the cell barcodes in front of the read names.
$ # extract the header file
$ mkdir tmp
$ samtools view unprocess.bam -H > tmp/unprocess.header.sam
$ # create a bam file with the barcode embedded into the read name
$ time(cat <( cat tmp/unprocess.header.sam ) \
<( samtools view unprocess.bam | awk '{for (i=12; i<=NF; ++i) { if ($i ~ "^CB:Z:"){ td[substr($i,1,2)] = substr($i,6,length($i)-5); } }; printf "%s:%s\n", td["CB"], $0 }' ) \
| samtools view -bS - > processed.bam)
$ rm -dr tmp
$ samtools view processed.bam | head -n 1
TGGACCGGTTCACCCA-1:A00836:472:HTNW5DMXX:1:1372:16260:18129 83 chr1 4194410 60 50M = 4193976 -484 TGCCTTGCTACAGCAGCTCAGGAAATGTCTTTGTGCCCACAGTCTGTGGT :FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF NM:i:0 MD:Z:50 AS:i:50 XS:i:0 CR:Z:TCCGGGACAGCTAACA CY:Z:FFFFFFFFFFFFFFF: CB:Z:TGGACCGGTTCACCCA-1 BC:Z:AAACTCAT QT:Z::FFFFFFF RG:Z:e18_mouse_brain_fresh_5k:MissingLibrary:1:HTNW5DMXX:1
Download reference genome for test example
The example deploys scReadSim on the 10x single cell ATAC-seq dataset. For user convienience, we prepared the indexed reference genome files (by bowtie2), which can be downloaded using the following bash commands:
GENCODE reference genome FASTA file and index file(indexed by bowtie2): reference.genome.chr1.tar.gz
GENCODE genome annotation gtf file: gencode.vM10.annotation.gtf
Note: users may need to edit the code by using their own path. The following code chunk is using bash commands.
$ mkdir /home/users/example/refgenome_dir # may use users' own path
$ cd /home/users/example/refgenome_dir
$ wget http://compbio10data.stat.ucla.edu/repository/gayan/Projects/scReadSim/reference.genome.chr1.tar.gz # 292 MB
$ wget http://compbio10data.stat.ucla.edu/repository/gayan/Projects/scReadSim/gencode.vM10.annotation.gtf # 765 MB
$ tar -xf reference.genome.chr1.tar.gz
Step 1: Import packages and data files
Import modules.
import sys, os
import scReadSim.Utility as Utility
import scReadSim.GenerateSyntheticCount as GenerateSyntheticCount
import scReadSim.scATAC_GenerateBAM as scATAC_GenerateBAM
import pkg_resources
The real BAM file and other input files are listed and can be accessed by simply loading the code chunk below:
BAM file: 10X_ATAC_chr1_4194444_4399104.bam
cell barcode file: barcodes.tsv
chromosome size file: mm10.chrom.sizes
INPUT_cells_barcode_file = pkg_resources.resource_filename("scReadSim", 'data/barcodes.tsv')
filename = "10X_ATAC_chr1_4194444_4399104"
INPUT_bamfile = pkg_resources.resource_filename("scReadSim", 'data/%s.bam' % filename)
INPUT_genome_size_file = pkg_resources.resource_filename("scReadSim", 'data/mm10.chrom.sizes')
Step 2: Generate features
To pre-process real scATAC-seq data for training, scReadSim segments the reference genome into trustworthy peaks, trustworthy non-peaks and gray ares. First scReadSim prepares the trustworthy peaks and non-peaks for the input BAM file. Then scReadSim defines gray areas as the genomic regions complementary to the trustworthy peaks and non-peaks. Three bed files recording peaks, non-peaks and gray areas will be prepared by scReadSim for following analysis.
Specify output directory
Note: users may need to edit the code by using their own path.
outdirectory = "/home/users/example/outputs" # may use user's own path
os.mkdir(outdirectory)
Specify pre-installed software paths
Note: users may need to edit the code by using their own path.
samtools_directory="/home/users/Tools/samtools"
macs3_directory="/home/users/Tools/MACS3/bin"
bedtools_directory="/home/users/Tools/bedtools/bedtools2/bin"
seqtk_directory="/home/users/Tools/seqtk"
fgbio_jarfile="/home/users/Tools/fgbio/target/scala-2.13/fgbio-2.0.1-e884860-SNAPSHOT.jar"
Prepare Features
To prepare features for the following analysis, scReadSim utilizes function Utility.scATAC_CreateFeatureSets
with following arguments
INPUT_bamfile
: Input BAM file for anlaysis.samtools_directory
: Directory of software samtools.bedtools_directory
: Directory of software bedtools.outdirectory
: Output directory of the prepared features.genome_size_file
: Directory of Genome sizes file. The file should be a tab delimited text file with two columns: first column for the chromosome name, second column indicates the size.peak_mode
: (Optional, default: “macs3”) Specify mode for trustworthy peak and non-peak generation, must be one of the following: “macs3”, “user”, and “superset”.macs3_directory
: (Optional, default: None) Path to software MACS3. Must be specified ifINPUT_peakfile
andINPUT_nonpeakfile
are None.INPUT_peakfile
: (Optional, default: None) Directory of user-specified input peak file.INPUT_nonpeakfile
: (Optional, default: None) Directory of user-specified input non-peak file.superset_peakfile
: (Optional, default: None) Directory of a superset of potential chromatin open regions, including sources such as ENCODE cCRE (Candidate Cis-Regulatory Elements) collection. Must be specified under peak_mode “superset”.OUTPUT_peakfile
: (Optional, default: None) Directory of user-specified output peak file. Synthetic scATAC-seq reads will be generated takingOUTPUT_peakfile
as ground truth peaks. Note thatOUTPUT_peakfile
does not name the generated feature files by functionscATAC_CreateFeatureSets
.
Three modes are supported by scReadSim to prepare features: “macs3” (default), “user” and “superset”.
Under default mode “macs3” (by setting argument peak_mode
as the default values “macs3”), scReadSim uses MACS3 with the stringent criteria to call trustworthy peaks (q-value 0.01
) and non-peaks (q-value 0.1
) from the input BAM file. This function will generate the following three bed files into directory outdirectory
for following analysis:
peak bed file: scReadSim.MACS3.peak.bed
non-peak bed file: scReadSim.MACS3.nonpeak.bed
gray area bed file: scReadSim.grayareas.bed
# Mode: macs3
Utility.scATAC_CreateFeatureSets(peak_mode="macs3", INPUT_bamfile=INPUT_bamfile, samtools_directory=samtools_directory, bedtools_directory=bedtools_directory, outdirectory=outdirectory, genome_size_file=INPUT_genome_size_file, macs3_directory=macs3_directory, INPUT_peakfile=None, INPUT_nonpeakfile=None)
Note: This tutorial provides an example with the default peak_mode
“macs3”. Thus the following two code chunks with peak_mode
set to “user” or “superset” do not need to be implemented.
Under mode “user”, scReadSim requires user-specified trustworthy peaks and non-peaks (by setting argument peak_mode
as “user” and INPUT_peakfile
and INPUT_nonpeakfile
as the paths to the trustworthy peak and non-peak bed files) for the input BAM file. These peaks and non-peaks could be obtained by users using their preferred peak calling methods. scReadSim further preprocesses the bed files and generates the following three bed files into directory outdirectory
for following analysis:
peak bed file: scReadSim.UserInput.peak.bed
non-peak bed file: scReadSim.UserInput.nonpeak.bed
gray area bed file: scReadSim.grayareas.bed
# Mode: user
# Load our demo trustworthy peak and non-peak bed files
INPUT_peakfile = pkg_resources.resource_filename("scReadSim", 'data/10x_ATAC_chr1_4194444_4399104.input.peak.bed')
INPUT_nonpeakfile = pkg_resources.resource_filename("scReadSim", 'data/10x_ATAC_chr1_4194444_4399104.input.nonpeak.bed')
Utility.scATAC_CreateFeatureSets(peak_mode="user", INPUT_bamfile=INPUT_bamfile, samtools_directory=samtools_directory, bedtools_directory=bedtools_directory, outdirectory=outdirectory, genome_size_file=INPUT_genome_size_file, INPUT_peakfile=INPUT_peakfile, INPUT_nonpeakfile=INPUT_nonpeakfile)
Under mode “superset” (by setting argument peak_mode
as “superset” and superset_peakfile
as the path to the superset of potential open chromatin region bed files), scReadSim requires user to specify a super set of potential open chromatin regions, including sources such as ENCODE cCRE (Candidate Cis-Regulatory Elements) collection. Then the trustworthy peaks and non-peaks will be selected from the superset peaks and the inter-genomic-regions between superset peaks, respectively. scReadSim further generates the following three bed files into directory outdirectory
for following analysis:
peak bed file: scReadSim.superset.peak.bed
non-peak bed file: scReadSim.superset.nonpeak.bed
gray area bed file: scReadSim.grayareas.bed
# Mode: superset
# Load our demo superset open chromatin region bed file
superset_peakfile=pkg_resources.resource_filename("scReadSim", 'data/cCRE_mm10_chr1_4194444_4399104.txt')
Utility.scATAC_CreateFeatureSets(peak_mode="superset", INPUT_bamfile=INPUT_bamfile, samtools_directory=samtools_directory, bedtools_directory=bedtools_directory, outdirectory=outdirectory, genome_size_file=INPUT_genome_size_file, macs3_directory=macs3_directory, superset_peakfile=superset_peakfile, INPUT_peakfile=None, INPUT_nonpeakfile=None)
Step 3: Generate real count matrices
Based on the feature sets output in Step 2, scReasSim constructs the count matrices for both foreground feautures and background features through function Utility.scATAC_bam2countmat_paral
. This function needs user to specify
cells_barcode_file
: Cell barcode file corresponding to the input BAM file.bed_file
: Features’ bed file to generate the count matrix (Generated by functionUtility.scATAC_CreateFeatureSets
).INPUT_bamfile
: Input BAM file for anlaysis.outdirectory
: Specify the output directory of the count matrix file.count_mat_filename
: Specify the base name of output count matrix.n_cores
: (Optional, default: ‘1’) Specify the number of cores for parallel computing when generating count matrix.
For the user specified count_mat_filename
, scReadSim will generate a count matrix named count_mat_filename
.txt to directory outdirectory
.
# Specify the path to bed files generated by Utility.scATAC_CreateFeatureSets
peak_bedfile = outdirectory + "/" + "scReadSim.MACS3.peak.bed"
nonpeak_bedfile = outdirectory + "/" + "scReadSim.MACS3.nonpeak.bed"
# Specify the output count matrices' prenames
count_mat_peak_filename = "%s.peak.countmatrix" % filename
count_mat_nonpeak_filename = "%s.nonpeak.countmatrix" % filename
# Construct count matrix for peaks
Utility.scATAC_bam2countmat_paral(cells_barcode_file=INPUT_cells_barcode_file, bed_file=peak_bedfile, INPUT_bamfile=INPUT_bamfile, outdirectory=outdirectory, count_mat_filename=count_mat_peak_filename, n_cores=1)
# Construct count matrix for non-peaks
Utility.scATAC_bam2countmat_paral(cells_barcode_file=INPUT_cells_barcode_file, bed_file=nonpeak_bedfile, INPUT_bamfile=INPUT_bamfile, outdirectory=outdirectory, count_mat_filename=count_mat_nonpeak_filename, n_cores=1)
Step 4: Simulate synthetic count matrix
Detect doublet (optional)
Before generating synthetic count matrices, we recommend users to detect doublets/multiplets using the real count matrices generated from previous step Utility.scATAC_bam2countmat_paral
. This step could help remove the potential artifact effects generated from the combined profiles. scReadSim implicitly implements R package scDblFinder to identify doublets/multiplets. Use function DoubletDetection.detectDouble
to detect the doublets/multiplets with following paramters
count_mat_filename
: Base name of the count matrix output by functionUtility.scATAC_bam2countmat_paral
orUtility.scRNA_bam2countmat_paral
.directory
: Path to the count matrix.outdirectory
: Specify the output directory of the synthetic count matrix file.omic_choice
: Specify the omic choice for doublet detection procedure: “ATAC” or “RNA”.
The doublet detection result doublet_classification.Rdata will be generated to path outdirectory
.
Note: Although by implementing function DoubletDetection.detectDoublet
, scReadSim implicitly helps install the R package scDblFinder
. However, the installation of scDblFinder
may take a while, we recommend users to pre-install it independently in R before implementing our function DoubletDetection.detectDoublet
.
# Import module
import scReadSim.DoubletDetection as DoubletDetection
# Detect doublets
DoubletDetection.detectDoublet(count_mat_filename=count_mat_peak_filename, directory=outdirectory, outdirectory=outdirectory, omic_choice= "ATAC")
Simulate
In this tutorial, scReadSim implements scDesign2 to generate synthetic count matrix based on the constructed count matrix from the input BAM file. Use function GenerateSyntheticCount.scATAC_GenerateSyntheticCount
to generate synthetic count matrix with following paramters
count_mat_filename
: Base name of the count matrix output by functionUtility.scATAC_bam2countmat_paral
.directory
: Path to the count matrix.outdirectory
: Specify the output directory of the synthetic count matrix file.doub_classification_label_file
: (Optional, default: ‘None’) Specify the absolute path to the doublet classification resultdoublet_classification.Rdata
generated by functionDoubletDetection.detectDoublet
.n_cell_new
: (Optional, default: ‘None’) Number of synthetic cells. If not specified, scReadSim uses the number of real cells.total_count_new
: (Optional, default: ‘None’) Number of (expected) sequencing depth. If not specified, scReadSim uses the real sequencing depth.celllabel_file
: (Optional, default: ‘None’) Specify the one-column text file containing the predefined cell labels. Make sure that the order of cell labels correspond to the cell barcode file. If no cell labels are specified, scReadSim performs a Louvain clustering before implementing scDesign2.n_cores
: (Optional, default: ‘1’) Specify the number of cores for parallel computing when generating count matrix.
Given the input count matrix count_mat_filename
.txt, scReadSim generates the syntheitic count matrix file to outdirectory
for following analysis:
Synthetic count matrix:
count_mat_filename
.scDesign2Simulated.txtSynthetic cell cluster/type labels:
count_mat_filename
.scDesign2Simulated.CellTypeLabel.txt
Additionaly, if no celllabel_file
is specified, scReadSim automatically performs Louvain clustering from Seurat and outputs clustering labels to outdirectory
:
Real cells’ Louvain clustering labels:
count_mat_filename
.LouvainClusterResults.txt
# Generate synthetic count matrix for peak-by-cell count matrix
GenerateSyntheticCount.scATAC_GenerateSyntheticCount(count_mat_filename=count_mat_peak_filename, directory=outdirectory, outdirectory=outdirectory)
# Specify cluster labels obtained from peak-by-cell matrix
celllabel_file = outdirectory + "/" + "10X_ATAC_chr1_4194444_4399104.peak.countmatrix.LouvainClusterResults.txt"
# Generate synthetic count matrix for nonpeak-by-cell count matrix
GenerateSyntheticCount.scATAC_GenerateSyntheticCount(count_mat_filename=count_mat_nonpeak_filename, directory=outdirectory, outdirectory=outdirectory, celllabel_file=celllabel_file)
Step 5: Output synthetic read
Generate synthetic reads in BED format
Based on the synthetic count matrix, scReadSim generates synthetic reads by randomly sampling from the real BAM file input by users. First use function scATAC_GenerateBAM.scATAC_GenerateBAMCoord
to create the synthetic reads and output in BED file storing the coordinates information. Function scATAC_GenerateBAM.scATAC_GenerateBAMCoord
takes following input arguments:
bed_file
: Features’ bed file to generate the synthetic reads (Generated by functionUtility.scATAC_CreateFeatureSets
).count_mat_file
: The path to the synthetic count matrix generated byGenerateSyntheticCount.scATAC_GenerateSyntheticCount
.synthetic_cell_label_file
: Synthetic cell label file generated byscATAC_GenerateSyntheticCount
.read_bedfile_prename
: Specify the base name of output bed file.INPUT_bamfile
: Input BAM file for anlaysis.outdirectory
: Specify the output directory for synthetic reads bed file.OUTPUT_cells_barcode_file
: Specify the file name storing the synthetic cell barcodes.jitter_size
: (Optional, default: ‘5’) Specify the range of random shift to avoid replicate synthetic reads. Default value is 5 bp.read_len
: (Optional, default: ‘50’) Specify the length of synthetic reads. Default value is 50 bp.random_noise_mode
: (Optional, default: ‘False’) Specify whether to use a uniform distribution of reads.GrayAreaModeling
: (Optional, default: ‘False’) Specify whether to generate synthetic reads for Gray Areas when generaing reads for non-peaks. Do not specify ‘True’ when generating reads for peaks.
This function will output two bed files read_bedfile_prename
.read1.bed and read_bedfile_prename
.read2.bed storing the coordinates information of synthetic reads and its cell barcode file OUTPUT_cells_barcode_file
in directory outdirectory
.
After generation of synthetic reads for both peaks and non-peaks, combine the their bed files using function scATAC_GenerateBAM.scATAC_CombineBED
, which takes following input arguments:
outdirectory
: Directory ofpeak_read_bedfile_prename
.txt andnonpeak_read_bedfile_prename
.txt.peak_read_bedfile_prename
: Base name of the bed file containig synthetic reads for peaks (generated by functionscATAC_GenerateBAM.scATAC_GenerateBAMCoord
).nonpeak_read_bedfile_prename
: Base name of the bed file containig synthetic reads for non-peaks (generated by functionscATAC_GenerateBAM.scATAC_GenerateBAMCoord
).BED_filename_combined_pre
: Specify the base name for the combined syntehtic reads bed file. The combined bed file will be output tooutdirectory
.
# Specify the names of synthetic count matrices (generated by GenerateSyntheticCount.scATAC_GenerateSyntheticCount)
synthetic_countmat_peak_file = count_mat_peak_filename + ".scDesign2Simulated.txt"
synthetic_countmat_nonpeak_file = count_mat_nonpeak_filename + ".scDesign2Simulated.txt"
# Specify the base name of bed files containing synthetic reads
OUTPUT_cells_barcode_file = "synthetic_cell_barcode.txt"
peak_read_bedfile_prename = "%s.syntheticBAM.peak" % filename
nonpeak_read_bedfile_prename = "%s.syntheticBAM.nonpeak" % filename
BED_filename_combined_pre = "%s.syntheticBAM.combined" % filename
synthetic_cell_label_file = count_mat_peak_filename + ".scDesign2Simulated.CellTypeLabel.txt"
# Create synthetic read bed file for peaks
scATAC_GenerateBAM.scATAC_GenerateBAMCoord(bed_file=peak_bedfile, count_mat_file=outdirectory + "/" + synthetic_countmat_peak_file, synthetic_cell_label_file=outdirectory + "/" + synthetic_cell_label_file, read_bedfile_prename=peak_read_bedfile_prename, INPUT_bamfile=INPUT_bamfile, outdirectory=outdirectory, OUTPUT_cells_barcode_file=OUTPUT_cells_barcode_file, jitter_size=5, read_len=50)
# Create synthetic read bed file for non-peaks
scATAC_GenerateBAM.scATAC_GenerateBAMCoord(bed_file=nonpeak_bedfile, count_mat_file=outdirectory + "/" + synthetic_countmat_nonpeak_file, synthetic_cell_label_file=outdirectory + "/" + synthetic_cell_label_file, read_bedfile_prename=nonpeak_read_bedfile_prename, INPUT_bamfile=INPUT_bamfile, outdirectory=outdirectory, OUTPUT_cells_barcode_file=OUTPUT_cells_barcode_file, jitter_size=5, read_len=50, GrayAreaModeling=True)
# Combine bed files
scATAC_GenerateBAM.scATAC_CombineBED(outdirectory=outdirectory, peak_read_bedfile_prename=peak_read_bedfile_prename, nonpeak_read_bedfile_prename=nonpeak_read_bedfile_prename, BED_filename_combined_pre=BED_filename_combined_pre)
Convert BED files to FASTQ files
Use function scATAC_BED2FASTQ
to convert BED file to FASTQ file. This function takes the following arguments:
bedtools_directory
: Path to software bedtools.seqtk_directory
: Path to software seqtk.referenceGenome_file
: Reference genome FASTA file that the synthteic reads should align.outdirectory
: Output directory of the synthteic bed file and its corresponding cell barcodes file.BED_filename_combined
: Base name of the combined bed file output by functionscATAC_CombineBED
.synthetic_fastq_prename
: Specify the base name of the output FASTQ files.
This function will output paired-end reads in FASTQ files named as synthetic_fastq_prename
.read1.bed2fa.sorted.fq, synthetic_fastq_prename
.read2.bed2fa.sorted.fq to directory outdirectory
.
Note: users may need to edit the code by using their own path.
referenceGenome_name = "chr1"
referenceGenome_dir = "/home/users/example/refgenome_dir" # may use users' own path
referenceGenome_file = "%s/%s.fa" % (referenceGenome_dir, referenceGenome_name)
synthetic_fastq_prename = BED_filename_combined_pre
# Convert combined bed file into FASTQ files
scATAC_GenerateBAM.scATAC_BED2FASTQ(bedtools_directory=bedtools_directory, seqtk_directory=seqtk_directory, referenceGenome_file=referenceGenome_file, outdirectory=outdirectory, BED_filename_combined=BED_filename_combined_pre, synthetic_fastq_prename=synthetic_fastq_prename)
Introduce Error to synthetic data
Use function scATAC_ErrorBase
to introduce random error to synthetic reads.
Build reference genome dictionary (optional)
Before using function scATAC_ErrorBase
, please create the reference dictionary for the reference genome with function CreateSequenceDictionary
using software Picard and make sure that the output .dict files are within the same directory to referenceGenome_name
.fa.
Note: For this tutorial, no dictionary building is needed since we have built for chr1.fa in reference.genome.chr1.tar.gz. The following code chunk is using bash commands.
$ cd /home/users/example/refgenome_dir # may use users' own path
$ java -jar /home/users/picard/build/libs/picard.jar CreateSequenceDictionary \
$ -R chr1.fa \
$ -O chr1.fa.dict
Introduce errors to synthetic reads
Function scATAC_ErrorBase
takes the following arguments:
fgbio_jarfile
: Path to software fgbio jar script.INPUT_bamfile
: Input BAM file for anlaysis.referenceGenome_file
: Reference genome FASTA file that the synthteic reads should align.outdirectory
: Specify the output directory of the synthteic FASTQ file with random errors.synthetic_fastq_prename
: Base name of the synthetic FASTQ files output by functionscATAC_BED2FASTQ
.
This function will output synthetic reads with random errors in FASTQ files to directory outdirectory
:
synthetic_fastq_prename
.ErrorIncluded.read1.bed2fa.fqsynthetic_fastq_prename
.ErrorIncluded.read2.bed2fa.fq
# Generate reads with errors in FASTQs
scATAC_GenerateBAM.scATAC_ErrorBase(fgbio_jarfile=fgbio_jarfile, INPUT_bamfile=INPUT_bamfile, referenceGenome_file=referenceGenome_file, outdirectory=outdirectory, synthetic_fastq_prename=synthetic_fastq_prename)
Convert FASTQ files to BAM file (optional)
The current version of scReadSim implicitly uses bowtie2 to align the synthetic reads onto the reference genome. Use function AlignSyntheticBam_Pair
to align FASTQ files onto reference genome. It takes the following arguments:
bowtie2_directory
: Path to software bowtie2.samtools_directory
: Path to software samtools.outdirectory
: Specify the output directory of the synthteic BAM file.referenceGenome_name
: Base name of the reference genome FASTA file. For example, you should input “chr1” for file “chr1.fa”.referenceGenome_dir
: Path to the reference genome FASTA file.synthetic_fastq_prename
: Base name of the synthetic FASTQ files output by functionscATAC_BED2FASTQ
.output_BAM_pre
: Specify the base name of the output BAM file.
Index reference genome (optional)
Before using function AlignSyntheticBam_Pair
, the reference gemome FASTA file should be indexed by bowtie2 through following chunk and make sure the output index files are within the same directory to referenceGenome_name
.fa.
Note: For this tutorial, no indexing is needed since we have indexed chr1.fa in reference.genome.chr1.tar.gz. The following code chunk is using bash commands.
$ cd /home/users/example/refgenome_dir # may use users' own path
$ bowtie2-build chr1.fa chr1
Align synthetic reads
Now align the synthetic reads on to the reference genome with bowtie2.
# Specify bowtie2 path
bowtie2_directory="/home/users/Tools/bowtie2/bin"
# Specify output BAM name
output_BAM_pre = "%s.syntheticBAM.CBincluded" % filename
# Synthetic reads alignment
scATAC_GenerateBAM.AlignSyntheticBam_Pair(bowtie2_directory=bowtie2_directory, samtools_directory=samtools_directory, outdirectory=outdirectory, referenceGenome_name=referenceGenome_name, referenceGenome_dir=referenceGenome_dir, synthetic_fastq_prename=synthetic_fastq_prename, output_BAM_pre=output_BAM_pre)
# Synthetic reads (with sequencing errors) alignment
scATAC_GenerateBAM.AlignSyntheticBam_Pair(bowtie2_directory=bowtie2_directory, samtools_directory=samtools_directory, outdirectory=outdirectory, referenceGenome_name=referenceGenome_name, referenceGenome_dir=referenceGenome_dir, synthetic_fastq_prename=synthetic_fastq_prename + ".ErrorIncluded" , output_BAM_pre=output_BAM_pre+ ".ErrorIncluded")