Discover novoalab/m6ABasecaller Open Source project

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An m6A-aware basecalling model to detect m6A modifications at single nucleotide resolution in individual reads (Cruciani, Delgado-Tejedor, Pryszcz et al., BioRxiv 2023)

EpiNano

Detection of RNA modifications from Oxford Nanopore direct RNA sequencing reads (Liu*, Begik* et al., Nature Comm 2019)

Python

108

nanoRMS

Prediction of RNA modifications and their stoichiometry from per-read features: current intensity, dwell time and trace (Begik*, Lucas* et al., Nature Biotech 2021)

Jupyter Notebook

modPhred

modPhred is a pipeline for detection of DNA/RNA modifications from raw ONT data

Python

Nano3P_Seq

Nanopore 3' end-capture sequencing (Begik et al., bioRxiv 2021)

Python

Nano-tRNAseq

Quantitative analysis of native tRNA populations using direct RNA nanopore sequencing (Lucas*, Pryszcz* et al., Nat Biotech 2023)

Python

Best_Practices_dRNAseq_analysis

Best practices for the Analysis of Oxford Nanopore Direct RNA sequencing Data (Begik*, Lucas*, et al., bioRxiv 2020)

basecalling_models

Benchmarked base calling models for nanopore sequencing providing super high accuracy and improved RNA modification detection

yeast_RNA_Mod

Analysis of Direct RNA Sequencing of Yeast Ribosomal RNA

codonAutocorrelation

Compute codon autocorrelation from fasta CDS sequences (Novoa et al., Mol Biol Evol 2019)

RNAModMachinery

Analyses from manuscript: Integrative analyses of the RNA modification machinery reveal tissue- and cancer-specific signatures (Begik et al. Genome Biology 2020).

novoalab/m6ABasecaller

novoalab

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