novoalab/yeast_RNA_Mod

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novoalab/yeast_RNA_Mod

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Analysis of Direct RNA Sequencing of Yeast Ribosomal RNA

EpiNano

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

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

m6ABasecaller

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

Jupyter Notebook

Nano3P_Seq

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

Nano-tRNAseq

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

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

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).