Ubuntu Manpage: ipdSummary - Detect DNA base-modifications from kinetic signatures.

Provided by: kineticstools_0.5.1+20150828+git3aa3d96+dfsg-1_all

NAME

       ipdSummary - Detect DNA base-modifications from kinetic signatures.

DESCRIPTION

kineticsTool loads IPDs observed at each position in the genome, and compares those IPDs
to value expected for unmodified DNA, and outputs the result of this statistical test.
The expected IPD value for unmodified DNA can come from either an in-silico control or an
amplified control. The in silico control is trained by PacBio and shipped with the
package. It predicts predicts the IPD using the local sequence context around the current
position. An amplified control dataset is generated by sequencing unmodified DNA with the
same sequence as the test sample. An amplified control sample is usually generated by
whole-genome amplification of the original sample.

Modification Detection
The basic mode of kineticsTools does an independent comparison of IPDs at each position on
the genome, for each strand, and emits various statistics to CSV and GFF (after applying a
significance filter).

Modifications Identification
kineticsTools also has a Modification Identification mode that can decode multi-site IPD
'fingerprints' into a reduced set of calls of specific modifications. This feature has the
following benefits:

• Different modifications occuring on the same base can be distinguished (for
example m5C and m4C)

• The signal from one modification is combined into one statistic, improving
sensitivity, removing extra peaks, and correctly centering the call

OPTIONS

       Please call this program with --help to see the available options.

ALGORITHM

   Synthetic Control
       Studies of the relationship between IPD and sequence  context  reveal  that  most  of  the
       variation  in  mean  IPD  across a genome can be predicted from a 12-base sequence context
       surrounding the active site of the DNA polymerase. The  bounds  of  the  relevant  context
       window  correspond  to  the  window  of  DNA  in  contact  with the polymerase, as seen in
       DNA/polymerase crystal structures.  To simplify the process of finding  DNA  modifications
       with  PacBio  data,  the  tool  includes  a  pre-trained  lookup  table mapping 12-mer DNA
       sequences to mean IPDs observed in C2 chemistry.

   Filtering and Trimming
       kineticsTools uses the Mapping QV generated by BLASR and stored  in  the  cmp.h5  file  to
       ignore  reads  that aren't confidently mapped.  The default minimum Mapping QV required is
       10, implying that BLASR has 90\% confidence that the read is correctly mapped. Because  of
       the  range  of  readlengths  inherent  in  PacBio  dataThis  can  be  changed in using the
       --mapQvThreshold command line argument, or via the  SMRTPortal  configuration  dialog  for
       Modification Detection.

       There are a few features of PacBio data that require special attention in order to acheive
       good modification detection performance.  kineticsTools inspects the alignment between the
       observed  bases  and  the  reference  sequence  --  in  order for an IPD measurement to be
       included in the analysis, the PacBio read sequence must match the reference sequence for k
       around  the  cognate base. In the current module k=1 The IPD distribution at some locus be
       thought of as a mixture between the 'normal' incorporation process IPD, which is sensitive
       to  the  local  sequence context and DNA modifications and a contaminating 'pause' process
       IPD which have a much longer duration (mean >10x longer than normal),  but  happen  rarely
       (~1%  of  IPDs).   Note:  Our  current  understanding  is  that pauses do not carry useful
       information about the methylation state of the DNA, however a more careful analysis may be
       warranted.  Also  note  that  modifications  that  drastically  increase the Roughly 1% of
       observed IPDs are generated by pause events. Capping observed  IPDs  at  the  global  99th
       percentile  is motivated by theory from robust hypothesis testing.  Some sequence contexts
       may have naturally longer IPDs, to avoid capping too much data at those contexts, the  cap
       threshold   is   adjusted   per   context   as   follows:   capThreshold  =  max(global99,
       5*modelPrediction, percentile(ipdObservations, 75))

   Statistical Testing
       We test the hypothesis that IPDs observed at a particular  locus  in  the  sample  have  a
       longer means than IPDs observed at the same locus in unmodified DNA.  If we have generated
       a Whole Genome Amplified dataset, which removes DNA modifications, we use a  case-control,
       two-sample  t-test.   This  tool  also provides a pre-calibrated 'synthetic control' model
       which predicts the unmodified IPD, given a 12 base  sequence  context.  In  the  synthetic
       control  case  we  use a one-sample t-test, with an adjustment to account for error in the
       synthetic control model.

INPUTS

   aligned_reads.cmp.h5
       A standard cmp.h5 file contain alignments and IPD information supplies  the  kinetic  data
       used  to perform modification detection.  The standard cmp.h5 file of a SMRTportal jobs is
       data/aligned_read.cmp.h5.

   Reference Sequence
       The tool requires the reference sequence used to perform alignments.  Currently this  must
       be supplied via the path to a SMRTportal reference repository entry.

OUTPUTS