Package: noise2read
Turn ‘noise’ to signal: accurately rectify millions of erroneous short reads through graph learning on edit distancesnoise2read, originated in a computable rule translated from PCR erring mechanism that: a rare read is erroneous if it has a neighboring read of high abundance, turns erroneous reads into their original state without bringing up any non-existing sequences into the short read set(&lt 300bp) including DNA and RNA sequencing (DNA/RNA-seq), small RNA, unique molecular identifiers (UMI) and amplicon sequencing data.Clicknoise2readto jump to its documentationNote: All the experimental results obtained in this study utilised version 0.2.7 of noise2read.Quick-run exampleQuick-run example for testingnoise2readby setting only 1 trial for Optuna and 10 estimators for xGboost which are not the parameters used in our paper.noise2readinstallationPlease refer toQuickStartorInstallation.Clone the codes with datasets in githubgit clone https://github.com/Jappy0/noise2read
cd noise2read/Examples/simulated_miRNAsQuick-run testingnoise2readon D14with high ambiguous errors correction and using GPU for training (running about 4 mins with 26 cores and GPU)noise2read -m correction -c ../../config/quick_test.ini -a True -g gpu_histExamples for correcting simulated miRNAs data with mimic UMIs bynoise2readTake data setsD14 and D16as examples.noise2readinstallationPlease refer toQuickStartorInstallation.Clone the codes with datasets in githubgit clone https://github.com/Jappy0/noise2read
cd noise2read/Examples/simulated_miRNAsReproduce the evaluation results for D14 and D16 from raw, true and corrected datasetsnoise2read -m evaluation -i ./raw/D14_umi_miRNA_mix.fa -t ./true/D14_umi_miRNA_mix.fa -r ./correct/D14_umi_miRNA_mix.fasta -d ./D14
noise2read -m evaluation -i ./raw/D16_umi_miRNA_subs.fa -t ./true/D16_umi_miRNA_subs.fa -r ./correct/D16_umi_miRNA_subs.fasta -d ./D16correcting D14with high ambiguous errors correction and using GPU for trainingnoise2read -m correction -c ./configs/D14.iniwithout high ambiguous errors correction and using GPU for trainingnoise2read -m correction -c ./configs/D14_without_high.inicorrecting D16with high ambiguous errors correction and using GPU for trainingnoise2read -m correction -c ./configs/D16.iniwithout high ambiguous errors correction and using GPU for trainingnoise2read -m correction -c ./configs/D16_without_high.iniExpected ResultsPlease find the expected log files and correction results at the folder noise2read ofbenchmarkfor correcting data sets of D14-D16. The results under noise2read and noise2read-1 represent the corrected results with and without high ambiguous errors’ prediction, respectively.Note: Noise2read may produce slightly different corrected result from these results under Examples/simulated_miRNAs/correct andcorrection. This is because the easy-usable and automatic tuning of the classifiers’ parameters facilitates wide-range explorations, different best models are obtained for each training, but the final prediction results are stable within a certain range. We have discussed this in the Discussion section of our paper.Examples for correcting outcome sequence of ABEs and CBEs bynoise2readClone the codesgit clone https://github.com/Jappy0/noise2read
cd noise2read/CaseStudies
mkdir ABEs_CBEs
cd ABEs_CBEsDownload datasets under the folder of data ofD32_D33.Usingnoise2readto correct the datasets. The running time of each experiment is about 13 minutes using 26 cores and GPU for training.noise2read -m correction -i ./data/D32_ABE_outcome_seqs.fasta -a False -d ./ABE/
noise2read -m correction -i ./data/D33_CBE_outcome_seqs.fasta -a False -d ./CBE/Expected ResultsPlease find the expected log files and correction results at the folderD32_D33. The results for correcting D32 and D33 are presented under the folders of ABE and CBE, respectively.Note: Noise2read may produce slightly different corrected result from these under D32_ABE and D33_CBE ofD32_D33. This is because the easy-usable and automatic tuning of the classifiers’ parameters facilitates wide-range explorations, different best models are obtained for each training, but the final prediction results are stable within a certain range. We have discussed this in the Discussion section of our paper.More examples for reproducing our experiments in this paper can be found at theExamplesof the documentationFeel free to contact me if you have any questions on running noise2read or are interested in noise2read.