This repository was created because the Google Code repository provided by the original authors is not being maintained, and Google Code is now defunct.
PacBio sequencers produce two types of characteristic reads as below.
Continuous Long Read (CLR) : long and high error rate. Circular consensus Read (CCS) : short and low error rate.
We have developed a PacBio reads simulater (called PBSIM) in which sampling-based and model-based simulations are implemented.
To build PBSIM run;
autoreconf -i
./configure
makeA new executable pbsim will be available in the src/ directory
To run model-based simulation:
pbsim --data-type CLR --depth 20 --model_qc data/model_qc_clr sample/sample.fasta
In the example above, simulated read sequences are randomly sampled from a reference sequence ("sample/sample.fasta") and differences (errors) of the sampled reads are introduced. Data type is CLR, and coverage depth is 20. If the reference sequence is multi-FASTA file, the simulated data is created for each FASTA. Three output files are created for each FASTA. "sd_0001.ref" is a single-FASTA file which is copied from the reference sequence. "sd_0001.fastq" is a simulated read dataset in the FASTQ format. "sd_0001.maf" is a list of alignments between reference sequence and simulated reads in the MAF format. The length and accuracy of reads are simulated based on our model of PacBio read.
To run sampling-based simulation:
pbsim --data-type CLR --depth 20 --sample-fastq sample/sample.fastq sample/sample.fasta
In the sampling-based simulation, read length and quality score are the same as those of a read taken randomly in the sample PacBio dataset ("sample/sample.fastq").
If you want to create several simulated data with different coverage depths using the same PacBio sample, you would be better to use --sample-profile-id option as below. You can save time to parse "sample/sample.fastq".
(1) storing profile
pbsim --data-type CLR
--depth 20
--prefix depth20
--sample-fastq sample/sample.fastq
--sample-profile-id pf1
sample/sample.fasta
(2) reusing profile
pbsim --data-type CLR
--depth 30
--prefix depth30
--sample-profile-id pf1
sample/sample.fasta
pbsim --data-type CLR
--depth 40
--prefix depth40
--sample-profile-id pf1
sample/sample.fasta
For each read, the length is randomly drawn from the log-normal distribution with given mean and standard deviation.
How to simulate the accuracy of each read is different between CLR and CCS read. For CLR reads, the accuracy is randomly drawn from the normal distribution with given mean and standard deviation. For CCS reads, an exponential function which is fit to the the real distribution is utilized to simulate with fixed mean and standard deviation.
Errors from single molecule sequencing which generates PacBio reads are considered to be stochastical, therefore quality scores are randomly chosen from a frequency table of quality scores (named "quality profile") for each accuracy of a read. For accuracies of 0-59% and 86-100% of CLR readsi and 0-84% of CCS reads, uniform distributions are used because real PacBio datasets are not sufficiently large. "data/model_qc_clr" is quality profile for CLR and "data/model_qc_ccs" is for CCS.
Simulated read sequences are randomly sampled from a reference sequence.
The percentage of both directions of reads is same. Differences (errors)
of the sampled reads are introduced as follows.
The substitutions and insertions are introduced according to the quality
scores. Their probabilities are computed for each positions of a simulated
read from the error probability of the position (computed from the quality
score of the position) and the ratios of differences given by the user.
Patterns of substitutions are randomly sampled.
We observed that inserted nucleotides are often the same as their following
nucleotides. According to the observed bias, half of inserted nucleotides
are chosen to be the same as their following nucleotides, and the other
half are randomly chosen.
The deletion probability is uniform for all positions of all simulated
reads, which is computed from the mean error probability of the read set
and the ratios of differeces.
By setting minimum and maximum of the length, the range of length chosen from the distribution model can be restricted. Note that mean and standard deviation of the chosen length are influenced by this restriction. The accuracy can be restricted in the same way, however unlike the length, the restriction of accuracy is not strict, and can be used in only case of CLR reads.
The lengths and quality scores of reads are simulated by randomly sampling them in a real library of PacBio reads provided by the user. randomly in a real PacBio dataset given by user. Subsequently, their nucleotide sequences are simulated by the same method with the model- based simulation. The restriction of length and accuracy are also the same as model-based simulation.
PBSIM requires reference sequences in the single- or multi-FASTA Format.
A real PacBio read data is required for sampling-based simulation, specified with the --sample-fastq option. FASTQ format must be Sanger standard (fastq-sanger).
If a reference sequence file is multi-FASTA format, simulated datasets are generated for each reference sequence numbered sequentially. Three output files are created for each reference sequence.
"sd_.ref" is a single-FASTA file which is copied from the reference sequence. "sd_.fastq" is a simulated read dataset in the FASTQ format. "sd_.maf" is a list of alignments between reference sequence and simulated reads in the MAF format.
"sd" is prefix which can be specified with the --prefix option.
Quality profiles are derived from frequencies of real quality scores for each accuracy of a read. "data/model_qc_clr" is quality profile for CLR, "data/model_qc_ccs" is for CCS. In "data/model_qc_clr", 1st column is accuracies of a read, and 2nd-23th columns are proportions of phred quality scores (0-21). In "data/model_qc_ccs", 1st column is accuracies of a read, and 2nd-95th columns are proportions of phred quality scores (0-93).
When a coverage depth is 100x and a length of reference sequence is about 10M, PBSIM generates simulated dataset in several minutes. The runtime is roughly proportional to the coverage depth and the length of reference sequence.
PBSIM requires memory of the length of reference sequence plus several mega bytes.
@kiwiroy - autotools and warning corrections
@jumpinsky - fixed memory leaks