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Max(global99, 5*modelPrediction, percentile(ipdObservations, 75)) Statistical Testing ¶ Naturally longer IPDs, to avoid capping too much data at those contexts, theĬap threshold is adjusted per context as follows: capThreshold = Capping observed IPDs at the global 99th percentile is motivated by Also note that modifications thatĭrastically increase the Roughly 1% of observed IPDs are generated by pauseĮvents. Useful information about the methylation state of the DNA, however a moreĬareful analysis may be warranted. Note: Our current understanding is that pauses do not carry Much longer duration (mean >10x longer than normal), but happen rarely Incorporation process IPD, which is sensitive to the local sequence contextĪnd DNA modifications and a contaminating 'pause' process IPD which have a In the current module k=1 The IPDĭistribution at some locus be thought of as a mixture between the 'normal' Reference sequence - in order for an IPD measurement to be included in theĪnalysis, the PacBio read sequence must match the reference sequence for KineticsTools inspects the alignment between the observed bases and the There are a few features of PacBio data that require specialĪttention in order to achieve good modification detection performance. mapQvThreshold command line argument, or via the SMRTPortal configuration Readlengths inherent in PacBio dataThis can be changed in using the Theĭefault minimum Mapping QV required is 10, implying that BLASR has 90\%Ĭonfidence that the read is correctly mapped. KineticsTools uses the Mapping QV generated by BLASR and stored in the cmp.h5 orīAM file (or AlignmentSet) to ignore reads that aren't confidently mapped. Pre-trained lookup table mapping 12-mer DNA sequences to mean IPDs observed in Process of finding DNA modifications with PacBio data, the tool includes a The polymerase, as seen in DNA/polymerase crystal structures. Of the relevant context window correspond to the window of DNA in contact with Sequence context surrounding the active site of the DNA polymerase. The variation in mean IPD across a genome can be predicted from a 12-base Studies of the relationship between IPD and sequence context reveal that most of
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Please call this program with -help to see the available options. Sensitivity, removing extra peaks, and correctly centering the call The signal from one modification is combined into one statistic, improving.Different modifications occurring on the same base can be distinguished.Position on the genome, for each strand, and emits various statistics to CSVĪnd GFF (after applying a significance filter). The basic mode of kineticsTools does an independent comparison of IPDs at each Generated by whole-genome amplification of the original sample. Predicts the IPD using the local sequence context around the current position.Īn amplified control dataset is generated by sequencing unmodified DNA with Silico control is trained by PacBio and shipped with the package. The expected IPD value for unmodified DNA can come fromĮither an in-silico control or an amplified control. Those IPDs to value expected for unmodified DNA, and outputs the result of KineticsTool loads IPDs observed at each position in the genome, and compares IpdSummary - Detect DNA base-modifications from kinetic signatures.