The inputs of NanoMod are two groups of FAST5 files and a known sequence. One group of FAST5 files are from a tested sample with modifications, while the other group of FAST5 files are from a control sample without modifications. FAST5 files are the output of Nanopore sequencing equipment.
NanoMod needs Events data in FAST5 files. If there is no Event data in FAST5 files, one needs to run Albacore (1.0<version<v2.0 is strongly recommended) on their data before running NanoMod. Please refer to Nanopore community for help about how to download/install/run Albacore.
First of all, run python NanoMod.py Annotate to obtain signal annotation information for the known sequence. The parameters for this command is described below. One usually need to provide where is the FAST5 files (specified by --wrkBase1) and a reference sequence(specified by --Ref), and keep other parameters default. If you need less or more threads to run this command, --threads should be changed. By default 12 threads will be used.
usage: NanoMod.py [-h] {detect,Annotate} ...
Detect nucleotide modification from nanopore signals data.
optional arguments:
-h, --help show this help message and exit
For example,
python NanoMod.py detect: detect the modificaton of nucleotides
python NanoMod.py Annotate: annotate a known sequence using fast5
usage: NanoMod.py Annotate [-h] [--outLevel {0,1,2,3}] [--wrkBase1 WRKBASE1]
[--Ref REF] [--kmer_model_file KMER_MODEL_FILE]
[--Resegment_wind RESEGMENT_WIND]
[--Resegment_signal_wind RESEGMENT_SIGNAL_WIND]
[--threads THREADS]
[--files_per_thread FILES_PER_THREAD]
[--basecall_1d BASECALL_1D]
[--basecall_2strand BASECALL_2STRAND]
[--MinNumSignal MINNUMSIGNAL] [--recursive {0,1}]
Annotate a known sequence using each fast5
optional arguments:
-h, --help show this help message and exit
--outLevel {0,1,2,3} The level for output: 0 for DEBUG, 1 for INFO, 2 for WARNING, 3 for ERROR. Default: 2
--wrkBase1 WRKBASE1 The working base folder for the first group.
--Ref REF The known sequence
--kmer_model_file KMER_MODEL_FILE
The kmer_model_file
--Resegment_wind RESEGMENT_WIND
The number of neighbor nucleotides used for resegmentation. Default:4
--Resegment_signal_wind RESEGMENT_SIGNAL_WIND
The window size of raw signals used to calculate the difference of consecutive regions. Default:4
--threads THREADS The number of threads used. Default:12
--files_per_thread FILES_PER_THREAD
The number of fast5 files for each thread. Default:300
--basecall_1d BASECALL_1D
Path for basecall_1d. Default: Basecall_1D_000
--basecall_2strand BASECALL_2STRAND
Path for basecall_2strand. Default: BaseCalled_template
--MinNumSignal MINNUMSIGNAL
Mininum number of signals for an event. Default:4
--recursive {0,1} Recurise to find fast5 files. Default:1
For example,
python NanoMod.py Annotate --wrkBase1 A488_Click_SpeI_cut_data/workspace --Ref ref/trainseq3.fa
python NanoMod.py Annotate --wrkBase1 A488_Click_SpeI_cut_data/workspace --Ref ref/trainseq3.fa --threads 24
For example,
python NanoMod.py detect --wrkBase1 ctrl_oligo_SpeI_cut --wrkBase2 Nicotine_SpeI_cut
The annotation information will be writen back to each FAST5 file. One can use h5dump FAST5file or h5ls -r FAST5file to see the detail.
How to conduct insertion and deletion correction is introduced in the figure below:
An example of the deletion correction procedure in NanoMod. X axis represents time of signal acquisition, and y axis denotes detected signal values by Nanopore sequencers before standardization. ‘Albacore’ represents a sequence of bases called based on original events before error correction, and ‘Known’ represents the known sequence. Each red horizontal bar represents an event split by vertical lines. ‘-’ in ‘Albacore’ suggests a deletion. The region shadowed in green shows the deleted bases together with one upstream and one downstream neighbors.
An example of the insertion correction procedure in NanoMod. The region shadowed in yellow shows the insertion base together with one upstream and one downstream neighbors. For other notations, see the figure above.
The second step of modification detection is to run python NanoMod.py detect. Similar to running python NanoMod.py Annotate, one can use the default values for majority of parameters. Only two parameters are necessary: --wrkBase1 and --wrkBase2 which specify where are the FAST5 files for the tested sample and the control sample. One can also use --testMethod to tell NanoMod which statistical method should be used. By deault, Kolmogorov-Smirnov test is used. --FileID should be given to different values if you run NanoMod multiple times at the same time. By default, it is 'mod'
usage: NanoMod.py [-h] {detect,Annotate} ...
Detect nucleotide modification from nanopore signals data.
optional arguments:
-h, --help show this help message and exit
For example,
python NanoMod.py detect: detect the modificaton of nucleotides
python NanoMod.py Annotate: annotate a known sequence using fast5
usage: NanoMod.py detect [-h] [--outLevel {0,1,2,3}] [--wrkBase1 WRKBASE1]
[--window WINDOW] [--FileID FILEID]
[--outFolder OUTFOLDER] [--MinCoverage MINCOVERAGE]
[--topN TOPN] [--neighborPvalues NEIGHBORPVALUES]
[--WeightsDif WEIGHTSDIF]
[--testMethod {fisher,stouffer,ks}]
[--rankUse {st,pv}] [--SaveTest {0,1}]
[--RegionRankbyST {0,1}] [--percentile PERCENTILE]
[--WindOvlp {0,1}] [--NA {,A,C,G,T}] [--Pos POS]
[--wrkBase2 WRKBASE2]
Detect nucleotide modifications from nanopore signal data
optional arguments:
-h, --help show this help message and exit
--Pos POS The position of interest: chr:pos. Default: ''
--wrkBase2 WRKBASE2 The working base folder for the second group.
Common options for the comparison between two groups of signals.:
--outLevel {0,1,2,3} The level for output: 0 for DEBUG, 1 for INFO, 2 for WARNING, 3 for ERROR. Default: 2
--wrkBase1 WRKBASE1 The working base folder for the first group.
--window WINDOW The window size to plot. It is better to be an odd number, such as 11, 21, 31, ... Default: 21
--FileID FILEID The unique string for intermediate files and final output files. Default: 'mod'
--outFolder OUTFOLDER
The default folder for outputing the results. Default: 'mRes/'
--MinCoverage MINCOVERAGE
The minimum coverage for the base of interest and its neighbors. Default: 5.
--topN TOPN The topN most significance test. Default: 30
--neighborPvalues NEIGHBORPVALUES
The number of neighbor p-values are used for combining p-values using fisher's or stouffer's methods. Default:2.
--WeightsDif WEIGHTSDIF
The difference of two adjacent neighbors when weight stouffer's methods are used. Maximum weight for the center position is 100, and the weights of a position are obtained by dividing the weights of position closest to the center by WeightsDif. Default:2.0.
--testMethod {fisher,stouffer,ks}
Which method is used for test statistics: fisher, stouffer or KS-test(default).
--rankUse {st,pv} Which criterion is used for ranking: 'st':statistics; 'pv':p-value(default)
--SaveTest {0,1} Whether significant test would be save. Default: 0 (not save)
--RegionRankbyST {0,1}
Rank region of window according to the stastitcs of the region.
--percentile PERCENTILE
The smallest percentile of p-values in the region is used for ranking. Only used when RegionRankbyST=1.
--WindOvlp {0,1} Whether two windows are overlapped. Default: 0 (not save). The overlapping size will be half of window. Only used when RegionRankbyST=1.
--NA {,A,C,G,T} The nucleotide type of interest. Default: ''(all 4 nucleotides). Only used when RegionRankbyST=1.
For example,
python NanoMod.py detect --wrkBase1 ctrl_oligo_SpeI_cut --wrkBase2 Nicotine_SpeI_cut
The final results could be found under the folder of --outFolder. The txt file could be --FileID*.txt if --SaveTest is set to 1. The pdf file could be rplot_--FileID*.pdf which saves top --topN positions of the ranking.