List of software packages for Nanopore sequencing data analysis, including basecalling, DNA/RNA modifications, etc. Contributions welcome...
- Dorado - [C++] - Production basecaller from 2022, successor to Guppy.
- Nanocall - [C++] - Nanocall: an open source basecaller for Oxford Nanopore sequencing data
- PoreSeq - [C++] - De novo sequencing and variant calling with nanopores using PoreSeq
- Nanonet - [C++] - Nanonet - Development version of RNN basecaller
- DeepNano - [Python] - DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads
- BasecRAWller - [Close sourced (May request code by emailing IPO@lbl.gov)] - BasecRAWller: Streaming Nanopore Basecalling Directly from Raw Signal
- Chiron - [Python] - Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning
- Causalcall - [Python] - Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network
- Bonito - [Python] - A PyTorch Basecaller for Oxford Nanopore Reads (research, not production basecaller)
- Readfish - [Python] - Readfish enables targeted nanopore sequencing of gigabase-sized genomes
- UNCALLED - [C++] - Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED
- SquiggleNet - [Python] - Real-Time, Direct Classification of Nanopore Signals with SquiggleNet
- Sigmap - [C/C++] - Real-time mapping of nanopore raw signals
- cwDTW - [C/C++] - An accurate and rapid continuous wavelet dynamic time warping algorithm for end-to-end mapping in ultra-long nanopore sequencing
- OpenDBA - [C++/CUDA] - GPU-accelerated Dynamic Time Warp (DTW) Barycenter Averaging
- Magenta & Maxwell - [C++/CUDA] - Fast signal-level matching for direct RNA nanopore sequencing
- tombo resquiggle - [Python] - Re-squiggle Algorithm.
- nanopolish eventalign - [C++] - Detecting DNA cytosine methylation using nanopore sequencing.
- f5c eventalign - [C/C++/CUDA] - GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis
- SquiggleKit Segmenter/MotifSeq - [Python] - SquiggleKit: a toolkit for manipulating nanopore signal data
- Megalodon - [C++] - Research modified base caller which uses rerio, remora and a genome.
- modbam2bed - [C++] - Convert modified base calls from megalodon etc to bedMethyl format
- nanopolish call-methylation - [C++] - Detecting DNA cytosine methylation using nanopore sequencing.
- f5c call-methylation - [C/C++/CUDA] - GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis
- nanoNOMe - [Python] - Simultaneous profiling of chromatin accessibility and methylation on human cell lines with nanopore sequencing.
- signalAlign - [C] - Mapping DNA methylation with high-throughput nanopore sequencing.
- mCaller - [Python] - Single-molecule sequencing detection of N6-methyladenine in microbial reference materials.
- DeepSignals - [Python] - DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning.
- tombo detect_modifications - [Python] - (previously nanoraw)De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing.
- NanoMod - [Python] - NanoMod: a computational tool to detect DNA modifications using Nanopore long-read sequencing data.
- tombo detect_modifications - [Python] - Modified Base Detection.
- MINES - [Python] - Direct RNA sequencing enables m6A detection in endogenous transcript isoforms at base specific resolution.
- EpiNano - [Python] - Accurate detection of m6A RNA modifications in native RNA sequences.
- Nanom6A - [Python] - Quantitative profiling of N6-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing.
- m6anet - [Python] - Detection of m6A from direct RNA-Seq data.
- nano-ID - [R] - Native molecule sequencing by nano-ID reveals synthesis and stability of RNA isoforms.
- nanoRMS - [Python] - Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing.
- Yanocomp - [Python] - Yanocomp: robust prediction of m6A modifications in individual nanopore direct RNA reads.
- DiffErr - [Python] - A tool for detecting modifications from Nanopore DRS errors using a low modification control.
- ELIGOS - [Python] - Decoding the epitranscriptional landscape from native RNA sequences.
- nanoDoc - [Python] - nanoDoc: RNA modification detection using Nanopore raw reads with Deep One-Class Classification.
- nanocompore - [Python] - RNA modifications detection by comparative Nanopore direct RNA sequencing.
- DRUMMER - [Python] - Direct RNA sequencing reveals m6A modifications on adenovirus RNA are necessary for efficient splicing.
- xPore - [Python] - Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore.
- nanoSHAPE - [Python] - Direct detection of RNA modifications and structure using single molecule nanopore sequencing.
- PORE-cupine - [R] - Determination of isoform-specific RNA structure with nanopore long reads.
- nanopolish polya - [C++] - Nanopore native RNA sequencing of a human poly(A) transcriptome.
- tailfindr - [R] - tailfindr: Alignment-free poly(A) length measurement for Oxford Nanopore RNA and DNA sequencing.
- bambu - [R] - Reference-guided isoform reconstruction and quantification for long read RNA-Seq data
- NanoSplicer - [Python] - Identification of splice junctions from nanopore sequencing using raw signal squiggles
- TALON - [Python] - Python package for identifying and quantifying known and novel genes/isoforms in long-read transcriptome data sets Run before TranscriptClean
- slow5lib - [C] - Fast nanopore sequencing data analysis with SLOW5
- pyslow5 - [Python] - pyslow5 python library
- slow5tools - [C/C++] - Toolkit for converting (FAST5 <-> SLOW5), compressing, viewing, indexing and manipulating data in SLOW5 format
- SquiggleKit SquigglePlot - [Python] - SquiggleKit: a toolkit for manipulating nanopore signal data
- MOP2 - [Nextflow] - MasterOfPores: A Workflow for the Analysis of Oxford Nanopore Direct RNA Sequencing Datasets
We welcome contributions and suggestions! Please follow the steps below to contribute:
- Fork this repository
- Make a change to README.md in this format:
[RESOURCE](LINK)- [language(s)] - DESCRIPTION - Submit a pull request.
Wan, Y.K., Hendra, C., Pratanwanich, P.N. & Göke, J. Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal data. Trends in Genetics (2021). https://doi.org/10.1016/j.tig.2021.09.001.
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This repository was created by Yuk Kei Wan.