Welcome to Tombo’s documentation!¶
Tombo is a suite of tools primarily for the identification of modified nucleotides from nanopore sequencing data.
Tombo also provides tools for the analysis and visualization of raw nanopore signal.
Basic tombo installation (python 2.7 and 3.4+ support)
# install via bioconda environment conda install -c bioconda ont-tombo # or install pip package (numpy install required before tombo for cython optimization) pip install numpy pip install ont-tombo[full]
See Tombo Tutorials for common workflows.
This quick start guides the steps to perform some common modified base detection analyses using the Tombo command line interface.
The first step in any Tombo analysis is to re-squiggle (raw signal to reference sequence alignment) raw nanopore reads. This creates an index and stores the information necessary to perform downstream analyses.
In this example, an E. coli sample is tested for dam and dcm methylation (present in lab E. coli; CpG model also available for human analysis). Using these results, raw signal is plotted at the most significantly modified dcm positions and the dam results are output to a wiggle file for use in downstream processing or visualization in a genome browser.
tombo resquiggle path/to/fast5s/ genome.fasta --processes 4 --num-most-common-errors 5 tombo detect_modifications alternative_model --fast5-basedirs path/to/fast5s/ \ --statistics-file-basename native.e_coli_sample \ --alternate-bases dam dcm --processes 4 # plot raw signal at most significant dcm locations tombo plot most_significant --fast5-basedirs path/to/fast5s/ \ --statistics-filename native.e_coli_sample.dcm.tombo.stats \ --plot-standard-model --plot-alternate-model dcm \ --pdf-filename sample.most_significant_dcm_sites.pdf # produces "estimated fraction of modified reads" genome browser files tombo text_output browser_files --statistics-filename native.e_coli_sample.dam.tombo.stats \ --file-types dampened_fraction --browser-file-basename native.e_coli_sample.dam # also produce successfully processed reads coverage file for reference tombo text_output browser_files --fast5-basedirs path/to/fast5s/ \ --file-types coverage --browser-file-basename native.e_coli_sample
While motif models (
dam; most accurate) and all-context specific alternate base models (
6mA; more accurate) are preferred, Tombo also allows users to investigate other or even unknown base modifications.
Here are two example commands running the
de_novo method (detect deviations from expected cannonical base signal levels) and the
level_sample_compare method (detect deviation in signal levels between two samples of interest; works best with high >50X coverage).
tombo detect_modifications de_novo --fast5-basedirs path/to/fast5s/ \ --statistics-file-basename sample.de_novo_detect --processes 4 tombo text_output browser_files --statistics-filename sample.de_novo_detect.tombo.stats \ --browser-file-basename sample.de_novo_detect --file-types dampened_fraction tombo detect_modifications level_sample_compare --fast5-basedirs path/to/fast5s/ \ --control-fast5-basedirs path/to/control/fast5s/ --minimum-test-reads 50 \ --processes 4 --statistics-file-basename sample.level_samp_comp_detect tombo text_output browser_files --statistics-filename sample.level_samp_comp_detect.tombo.stats \ --browser-file-basename sample.level_samp_comp_detect --file-types statistic
All Tombo commands work for direct RNA nanopore reads as well, but a transcriptome reference sequence must be provided for spliced transcripts.
tombo -h to see all Tombo command groups, run
tombo [command-group] -h to see all commands within each group and run
tombo [command-group] [comand] -h for help with arguments to each Tombo command.
Detailed documentation for all Tombo algorithms and commands can be found through the links here.
Tombo Ahi is a Japanese name for albacore (the name of an Oxford Nanopore Technologies basecaller). So use albacore to identify canonical bases and then use Tombo to detect more exotic, non-canonical bases.
- Tombo Tutorials
- Tombo Commands
- Re-squiggle Algorithm
- Modified Base Detection
- Text Outputs
- Plotting Commands
- Read Filtering Commands
- RNA Processing
- Model Training (Advanced Users Only)