Our analysis of 13 PacBio datasets showed characteristic features of PacBio reads (e.g., the read length of PacBio reads follows a log-normal distribution). We have developed a read simulator, PBSIM, that captures these features using either a model-based or sampling-based method. Using PBSIM, we conducted several hybrid error correction and assembly tests for PacBio reads, suggesting that a CLR coverage depth of at least 15 in combination with a CCS coverage depth of at least 30 achieved extensive assembly results.
Availability - PBSIM is freely available from the web under the GNU GPL v2 license.
Open challenge - who can write a simulator for PacBio stock? :)
We came across another paper (ht: Jason Chin) that may interest researchers working on the same topic. This one attempts to derive a statistical model for nucleotide variation in PacBio data.
Modeling kinetic rate variation in third generation DNA sequencing data to detect putative modifications to DNA bases
Genome Res. published online October 23, 2012
Eric Schadt, Onureena Banerjee, Gang Fang, et al.
Current generation DNA sequencing instruments are moving closer to seamlessly sequencing
genomes of entire populations as a routine part of scientific investigation. However, while
significant inroads have been made identifying small nucleotide variation and structural
variations in DNA that impact phenotypes of interest, progress has not been as dramatic
regarding epigenetic changes and base-level damage to DNA, largely due to technological
limitations in assaying all known and unknown types of modifications at genome scale. Recently
single molecule real time (SMRT) sequencing has been reported to identify kinetic variation
(KV) events that have been demonstrated to reflect epigenetic changes of every known type,
providing a path forward for detecting base modifications as a routine part of sequencing.
However, to date, no statistical framework has been proposed to enhance the power to detect
these events while also controlling for false positive events. By modeling enzyme kinetics in the
neighborhood of an arbitrary location in a genomic region of interest as a conditional random
field, we provide a statistical framework for incorporating kinetic information at a test positions
of interest as well as at neighboring sites that help enhance the power to detect KV events. The
performance of this and related models is explored, with the best performing model applied to
plasmid DNA isolated from Escherichia coli and mitochondrial DNA isolated from human brain
tissue. We highlight widespread kinetic variation events, some of which strongly associate with
known modification events while others represent putative chemically modified sites of