Detection of RNA modifications from Oxford Nanopore direct RNA sequencing reads
- Upgrades
- About EpiNano
- Getting the code
- Running EpiNano
- Further Documentation
- Citing this work
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EpiNano 1.2 - current version
- Includes pretrained m6A models derived from sequences base-called with Guppy v 3.1.5.
- Pretrained models can also be used to detect other RNA modifications (tested for pseudouridine, other modifications: not tested).
- This version of EpiNano allows to make predictions using two different strategies: EpiNano-Error and EpiNano-SVM.
- This version now includes modules for visualizing your RNA modification predictions (EpiNano_Plot)
EpiNano-Error can only be run in pairwise mode (e.g. WT and KO or KD). It combines the different types of base-calling errors that appear in a given dataset (mismatches, deletions, insertions) as well as alterations in per-base-calling qualities. RNA modification predictions are based on the differences in error patterns observed in two matched samples. This strategy can be used with FASTQ data base-called with any given base-calling algorithm version.
EpiNano-SVM can be run using either pre-trained models for a given RNA modification, or by building your own models. However, we should note that using a matched control (e.g. KO or KD) is still highly recommended, due to the noisy nature of direct RNA sequencing reads, which are 'error'-rich. Moreover, in addition to SVM models trained with "raw" base-calling 'error' features (same as in EpiNano 1.0 and 1.1), in EpiNano 1.2 we now provide SVM models trained with features that capture differences between samples (i.e. difference in mismatch, rather than absolute mismatch frequency), which we find have improved performance.
EpiNano 1.1 - a slimmer version of version 1.0, written in python3 is available here.
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This version is the one currently implemented in MasterOfPores, a workflow to analyze direct RNA sequencing data.
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The major differences with EpiNano 1.0 are (i) it is faster (ii) Uses python3 instead of python2 (iii) Does not extract current intensity in the feature table, as this feature was not used to train the final models.
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Includes pre-trained m6A models base-called with Albacore version 2.1.7.
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Works both with Guppy and Albacore basecalled data, but the SVM predictions will be only accurate if your data has been base-called using Albacore 2.1.7.
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Regardless of the basecallers used, EpiNano can be used as a toolkit to extract per k-mer base-calling 'errors' (mismatch, insertion, deletion, quality), which are a proxy of RNA modifications present in a given dataset. We recommend running EpiNano in paired mode, i.e. computing the features in two datasets (WT-KO) to then accurately predict the RNA modified sites (i.e. those showing largest differences in their base-calling 'error' features).
EpiNano 1.0 - original code used in Liu, Begik et al., Nature Comm 2019, which is available here.
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Includes pre-trained m6A models base-called with Albacore version 2.1.7.
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It extracts both base-calling 'errors' (mismatch, insertion, seletion, per-base quality) as well as current intensity values
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Current intensity information is extracted from the base-called Albacore FAST5 files.
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Does not have models trained with Guppy base-called datasets.
EpiNano is a tool to identify RNA modifications present in direct RNA sequencing reads.
EpiNano will extract a set of 'features' from direct RNA sequencing reads, which will be in turn used to predict whether the 'error' is caused by the presence of an RNA modification or not. Features directly extracted and derived include:
- current intensity and duration
- read quality
- base quality scores
- mismatch frequency
- deletion frequency
- insertion frequency
- sumErr
These features can be organized in per base and per kmer formats
In EpiNano 1.2, we introduce delta-features, features capturing difference between modified and un-modified sites and sum_err, a metric computed by combining different types of errors and even base quality scores. These new metrics represent our attempt to steer around the limitation related to the fact that different types of RNA base modifications tend to introduce different types of sequencing errors.
EpiNano version 1.2 can predict RNA-modified sites in two different ways:
- EpiNano-Error
- Base-calling algorithm independent.
- Applicable to any given RNA modification that affects the base-calling features.
- EpiNano-SVM
- Base-calling algorithm dependent (data must be base-called with Guppy 3.1.5)
- Can use both base-calling error features as well as current signals features
- It can be used to train your own models as well as be applied to datasets for which a pre-trained model is available (m6A)
- The available m6A SVM models has been trained and tested upon a set of 'unmodified' and 'modified' sequences containing m6A at known sites or A.
- We also offer SVM models trained with delta features, i.e., features capturing difference between modified and un-modified samples. These models can be applied to detect other RNA modifications apart from m6A (tested on pseudouridine).
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EpiNano relies on the use of base-calling 'errors' to detect RNA modifications; however, direct RNA sequencing base-calling produces a significant amount of 'errors' in unmodified sequences. Therefore, to obtain higher confidence m6A-modified sites, we recommend to sequence both modified and unmodified datasets (e.g. treated with demethylase, or comparing a wild-type vs knockout/knockdown). Coupling a "control" (KD/KO) is not required in earlier Epinano versions, but is highly recommended.
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You can use EpiNano as a feature extractor to predict RNA modifications based on alterations in base-called features (i.e., EpiNano-Error, as used here), as well as use the pre-trained SVMs to detect m6A RNA modifications (i.e., EpiNano-SVM, as used here).
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EpiNano does not have per-read resolution. We are currently working on an improved version of EpiNano to obtain predictions at per-read level.
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The performance of the algorithm is dependent on the stoichiometry of the site (i.e. sites with very low stoichiometry will be often missed by the algorithm)
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Pre-trained models to predict m6A sites are included in each release. Please note that if you use pre-trained m6A models, your data should be base-called with the SAME base-calling algorithm and version (i.e. Guppy 3.1.5 if you use EpiNano 1.2, and Albacore 2.1.7 if you use EpiNano 1.0 or 1.1).
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If you are using a different base-calling algorithm version, we recommend you to use EpiNano-Error rather than EpiNano-SVM.
The following softwares and packages were used by EpiNano
| Software/Packages | Version |
|---|---|
| java openjdk | 1.8.0 |
| minimap2 | 2.14-r886 |
| samtools | 0.1.19 |
| sam2tsv | a779a30d6af485d9cd669aa3752465132cf21eec |
| python | 3.6.7 |
| h5py | 2.8.0 |
| numpy | 1.15.4 |
| pandas | 0.23.4 |
| scikit-learn | 0.20.2 |
| nanopolish | 0.12.4 |
| dask | 2.5.2 |
| biopython | 1.76 |
| pysam | 0.15.3+ |
| R | 3.6.0 (2019-04-26) |
| R packages: | |
| forcats | 0.4.0 |
| optparse | 1.6.6 |
| stringr | 1.4.0 |
| dplyr | 1.0.1 |
| purrr | 0.3.2 |
| readr | 1.3.1 |
| tidyr | 0.8.3 |
| tibble | 3.0.3 |
| tidyverse | 1.2.1 |
| ggrepel | 0.8.1 |
| car | 3.0-3 |
| ggplot2 | 3.1.1 |
| reshape2 | 1.4.3 |
| outliers | 0.14 |
To download the latest version of EpiNano , you just need to clone the repo:
git clone [email protected]:enovoa/EpiNano.git
You can choose to download EpiNano 1.1 HERE
You can choose to download EpiNano 1.0 HERE
To train models and assess prediction accuracies, please refer to commands in test_data/train_models/train_test.sh.
To make predictions with pre-trained models, please refer to commands in /test_data/make_predictions.
We will also update in wiki with specific examples of using different Epinano components.
Below is a simple introduction of programs' usage information.
Epinano_Variants, outputs a feature table sample.per.site.var.csv, which contains base-calling ‘error’ information for each reference position. Please note that by default, the feature table sample.per_site.5mer.csv that was generated by default in EpiNano 1.1 (which contains the same base-called features organized in 5-mer windows) is not generated by default any more. If you want to generate this file, please use the script Slide_Variants.py.
python Epinano_Variants.py -h
usage: Epinano_Variants.py [-h] [-r REFERENCE] [-b BAM]
[-c NUMBER_CPUS]
optional arguments:
-h, --help show this help message and exit
-c NUMBER_CPUS, --number_cpus NUMBER_CPUS
number of CPUs
Required Arguments:
-r REFERENCE, --reference REFERENCE
reference file indexed with samtools faidx and with
sequence dictionary created using picard
CreateSequenceDictionary
-b BAM, --bam BAM bam file; if given; no need to offer reads file;
mapping will be skipped
Example:
python $EPINANO_HOME/Epinano_Variants.py -c 6 -r reference.fasta -b sample.reads.bam
Note 1: it is possible to organize the variants in any kmer length using $EPINANO_HOME/misc/Slide_Variants.py.
Note 2: the users should split the computations for each reference sequences if the reference genome is large.
Note 3: UPDATE in EpiNano 1.2.2: We have now released an improved version of EpiNano_variants.py that requires less resources, so can be used with whole genomes. You can find this script here, and use it instead of EpiNano_Variants.py.
This is optional. Users who are interested in exploring the electric signals including current intensities and duration time can rely on this to extract the relevant information. User can also use the extracted features to train SVM models.
Epinano_Current uses Nanopolish to extract current signal level information and then collapses it on a single position basis.
Note 1: Please add the /path/to/nanopolish to environmental $PATH variable, otherwise the script will fail.
$ sh Epinano_Current.sh -h
Epinano_Current.sh [-h] [-b bam -r reads -f genome/transriptome reference -d fast5dir -t threads -m bam_file_mapping_type]
it runs nanopolish eventalign; aggreagets current intensity values associated with single positions
nanopolish, samtools have to be installed and added to environmental paths!!
-h [this help text]
-b [bam file]
-d [fast5 folder]
-r [fastq reads file]
-f [reference file used to produce bam file]
-t [number of threads]
-m [t: reads mapping performed using reference transcriptome; g: reads mapping performed with reference genome]
EpiNano offers two alternative methods to predict RNA modifications: i) EpiNano-Error uses of the variants/error frequencies computed above ii) EpiNano-SVM uses an SVM algorithm to train models and predict modifications.
Epinano_DiffErr.R fits a linear regression model between paired unmodified and modified samples, and then detects outliers, i.e., observations with large residuals, which tend to be underlined by base modifications.
Note 1: different types of RNA base modification show distinct biases toward the spefic types of errors. Thus, offered Epinano_sumErr.py to combine mismatches, indels and even quality scores. Just like the independent types of errors, the combined error is internally performed when running Epinano_ErrDiff.R.
$ Rscript Epinano_DiffErr.R -h
Usage:
DiffErr.R v0.1 compares a given feature between two samples. It predicts potential modified sites mainly through two methods:
1. Compute deviance of selected featuers between samples and then calculate z-scores. Outliers or potential modified sites will then
be determined based on user-defined threshold. Note that this is not suited for 'curlcake' data because they are highly modified (~25% of the bases
in the RNA molecules are modified bases).
2. Fit a linear regression model between two samples.
1) detect residuals outliers of the given linear model.
2) compute z-scores of residuals for each observation and determine outliers using user-defined z-score threshold.
Examples:
1 compare sum_err between two samples
Rscript Epinano_DiffErr.R -k ko.csv -w wt.csv -t 3 -o Test -c 30 -f sum_err -d 0.1
2 same as above, but generate plots, one for each reference.
Rscript Epinano_DiffErr.R -k ko.csv -w wt.csv -t 3 -o Test -c 30 -f sum_err -d 0.1 -p
Options:
-c COVERAGE, --coverage=COVERAGE
minimum coverage/depth; default: 30
-t THRESHOLD, --threshold=THRESHOLD
minimum z-score (i.e., number of standard deviation from mean) to determine modified sites; default: 3
-d DEVIANCE, --deviance=DEVIANCE
minimum deviance of selected feature between two samples; default: 0.1
-f FEATURE, --feature=FEATURE
the feature (column name(s) in from input file) to use to predict modifications
-k KO_SAMPLE, --ko_sample=KO_SAMPLE
knockout/unmodified sample
-w WT_SAMPLE, --wt_sample=WT_SAMPLE
wildtype/modified sample
-o OUT_PREFIX, --out_prefix=OUT_PREFIX
output prefix
-p, --plot
whether or not generate plots; default: no plots will be generated because Epinano_Plot.R can do the job
-h, --help
Show this help message and exit
EpiNano 1.2 includes pre-trained models (found in $EPINANO_HOME/models/), which have been trained using synthetic molecules (curlcakes) with and without introduced m6A modifications. However, the user can train their own models using EpiNano_Predict, employing the features generated with EpiNano_Variants.py and/or EpiNano_Current.py as shown in the previous steps. The relevant commands can be found in test_data/train_models/.
$ python Epinano_Predict.py -h
Command: Epinano_Predict.py -h
usage: Epinano_Predict.py [-h] [-k KERNEL] [-o OUT_PREFIX] [-a] [-M MODEL]
[-t TRAIN] [-mc MODIFICATION_STATUS_COLUMN] -p
PREDICT -cl COLUMNS
optional arguments:
-h, --help show this help message and exit
-k KERNEL, --kernel KERNEL
kernel used for training SVM, choose any one from
'linear', 'poly', 'rbf', 'sigmoid'; if no choice made,
all 4 kernels will be used
-o OUT_PREFIX, --out_prefix OUT_PREFIX
output file prefix
-a, --accuracy_estimation
'-a' performs accuracy estimation with known modified
status from --predict file; only feasible when there
is prior knolwdge of modificaton status indiciated by
--modification_status_column
-M MODEL, --model MODEL
pre-trained model that can ben used for prediction
-t TRAIN, --train TRAIN
file name of feature table used for training; can be
gzipped
-mc MODIFICATION_STATUS_COLUMN, --modification_status_column MODIFICATION_STATUS_COLUMN
column number from (input file1, i.e, traing file)
that contains modification status information
required arguments:
-p PREDICT, --predict PREDICT
file name of feature table used for making predictions
or testing accuracy; can be gzipped. when this file is
the same the one used for training, half of the data
will be chosen for training.
-cl COLUMNS, --columns COLUMNS
comma separated column number(s) that contain features
used for training and prediction
Example:
python $EPINANO_HOME/Epinano_Predict.py
--train ko_wt_combined.per_site_raw_feature.rrach.5mer.csv
--predict ko_wt_combined.per_site_raw_feature.rrach.5mer.csv --accuracy_estimation
--out_prefix train_and_test
--columns 8,13,23
--modification_status_column 26
While the user can choose to train the algorithm with one sample (--train) and test it on another independent sample (--predict), it is also possible to use the same input file both for training and testing the model, as depicted in the example above. In this scenario, Epinano_Predict will train the models with 50% of the input data, and make predictions with the remaining 50% of the data.
In the above command, ‘--columns’ denotes the column numbers of features that are used for training models (in this case, corresponding to ‘q3’, ’mis3’ and ‘del3’), while ‘--modification_status_column’ indicates the prior knowledge of the modification statuses, i.e., the labels ‘mod’ and ‘unm’. Switching on --accuracy_estimation will report the accuracy of the trained model(s). Unless ‘--kernel’ is used, Epinano_Predict will train models with multiple kernels.
With the trained models, the user can make predictions of modifications.
python $EPINANO_HOME/Epinano_Predict.py
--model q3.mis3.del3.MODEL.linear.model.dump
--predict some_sample.per_site.5mer.csv
--columns 8,13,23
--out_prefix some_sample.modification
In the command above, we employ a previously trained model ‘q3.mis3.del3.MODEL.linear.model.dump’ that will predict m6A modifications in RRACH k-mers on a dataset that is specified with ‘--predict’. Please remember to filt your dataset before or after making predicitons to keep only RRACH k-mers.
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Build feature table (on which predictions will be made)
For step-by-step instructions to build a feature table, please take a look at the Wiki
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To train SVM and perform predictions:
This step includes SVM training, prediction and performance assessment using single and multiple features.
$ python3 SVM.py -h
Command: scripts/SVM.py -h
usage: SVM.py [-h] [-k KERNEL] [-o OUT_PREFIX] [-a] [-M MODEL] [-t TRAIN]
[-mc MODIFICATION_STATUS_COLUMN] -p PREDICT -cl COLUMNS
optional arguments:
-h, --help show this help message and exit
-k KERNEL, --kernel KERNEL
kernel used for training SVM, choose any one from
'linear', 'poly', 'rbf', 'sigmoid'; if no choice made,
all 4 kernels will be used
-o OUT_PREFIX, --out_prefix OUT_PREFIX
ouput file prefix
-a, --accuracy_estimation
-a perform accuracy estimation with known modified
status from --predict file
-M MODEL, --model MODEL
pre-trained model that can ben used for prediction; if
this is not available SVM model will be trained and
dumped; there can be multiple models, which should be
in the same order as kernels applied
-t TRAIN, --train TRAIN
file name of feature table used for training
-mc MODIFICATION_STATUS_COLUMN, --modification_status_column MODIFICATION_STATUS_COLUMN
column number from (input file1, i.e, traing file)
that contains modification status information
required arguments:
-p PREDICT, --predict PREDICT
file name of feature table used for making predictions
or testing accuracy. when this file is the same the
one used for training, half of the data will be chosen
for training.
-cl COLUMNS, --columns COLUMNS
comma seperated column number(s) that contain features
used for training and prediciton
With the example svm input files from example/svm_input folder:
- training with the example feature tables
# the command below will train models using all quality scores are all positions from sample1 and make prediction on sample2
python3 SVM.py -a -t sample1.csv -p sample2.csv -cl 1-5 -mc 11 -o test
# while this command will do the same thing except choosing a 'linear' kernel for SVM training
python3 SVM.py -a -k linear -cl 1-5 -t sample1.csv -p sample2.csv -mc 11
# this 3rd command uses base quality and mismatch frequencies of the centred bases for SVM training
python3 SVM.py -t sample1.csv -p sample2.csv -cl 3,7 -mc 11
- predict modifications
#use previously trained model and epinano-scripts-generated site-wise feature table to make predictions
python3 SVM.py -a -M M6A.mis3.del3.q3.poly.dump -p test.csv -cl 7,12,22 -mc 28 -o pretrained.prediction
Please check HERE for additional information on usage on EpiNano 1.2
Please check the Wiki for additional information on usage on EpiNano 1.1
If you find this work useful, please cite:
Huanle Liu*, Oguzhan Begik*, MorghanC Lucas, Jose Miguel Ramirez, Christopher E. Mason, David Wiener, Schraga Schwartz, John S. Mattick, Martin A. Smith and Eva Maria Novoa. Accurate detection of m6A RNA modifications in native RNA sequences . Nature Communications 2019, 10:4079.
Link to paper: https://www.nature.com/articles/s41467-019-11713-9
See LICENSE.md for details
Please read the Wiki before opening an issue. Also, please go over other issues that may have been previously resolved (check out "closed" issues). If you still have doubts/concerns/suggestions, please open a new Issue. Thanks!
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