Dropdown items
My Academies

Personal Library

Account settings

Charting a Future for Sequencing RNA and Its Modifications: A New Era for Biology and Medicine (2024)

Chapter: Appendix A: Tables of Computational Tools

Visit NAP.edu/10766 to get more information about this book, to buy it in print, or to download it as a free PDF.

Previous chapter Next chapter
Page of 248
Search this publication

Previous Chapter: 6 A Bold Vision for the Future of Sequencing RNA and Its Modifications: Conclusions, Recommendations, and a Path Forward

Page 181 Cite

Suggested Citation: "Appendix A: Tables of Computational Tools." National Academies of Sciences, Engineering, and Medicine. 2024. Charting a Future for Sequencing RNA and Its Modifications: A New Era for Biology and Medicine. Washington, DC: The National Academies Press. doi: 10.17226/27165.

Appendix A

Tables of Computational Tools

TABLE A-1 State-of-the-Art Nanopore Basecalling Tools with Principle of Analysis, Input, and Output Along with Resources

Tool	Principle of Analysis	Tool Input	Tool Output	Resource
Albacore	RNN	FAST5	FASTA/FASTQ	https://community.Nanoporetech.com
Guppy	RNN	FAST5	FASTA/FASTQ	https://community.Nanoporetech.com
MinKNOW
Metrichor	RNN	FAST5	FASTA/FASTQ	https://metrichor.com
Nanocall	HMM	FAST5	FASTA	https://github.com/mateidavid/nanocall
DeepNano	RNN	FAST5	FASTA	https://github.com/jeammimi/deepnano
Nanonet	RNN	FAST5	FASTA	https://github.com/ProgramFiles/nanonet
basecRAWller				https://www.osti.gov/biblio/1572483
Chiron	CNNs, RNNs, and CTC decoder	FAST5	FASTA/FASTQ	https://github.com/haotianteng/chiron
Scrappie	RNN	FAST5	FASTA/SAM	https://github.com/Nanoporetech/scrappie
Bonito	QuartzNet13(multiple TCSConv-BNReLU)	FAST5	FASTQ, SAM, BAM, CRAM	https://github.com/Nanoporetech/bonito

Page 182 Cite

Tool	Principle of Analysis	Tool Input	Tool Output	Resource
URNano	Refined U-net model for signal segmentation and basecalling	FAST5	FASTA/FASTQ	https://github.com/yaozhong/URnano
Causalcall	TCN	FAST5	FASTA/FASTQ	https://github.com/scutbioinformatic/causalcall
Heron and Osprey	CNN with dynamic pooling	FAST5	FASTA	https://github.com/fmfi-compbio/heron https://github.com/fmfi-compbio/osprey
DeepNano-coral	Enhanced version of Bonito-CNN that has factorization of a full convolution into smaller operations	FAST5	FASTA	https://github.com/fmfi-compbio/coral-training
DeepNano-blitz	Bi-directional RNN	FAST5	FASTA	https://github.com/fmfi-compbio/deepnano-blitz
BRAWL	Binarization of Chiron basecaller (CNN+RNN+CTC)	FAST5	FASTA/FASTQ	https://github.com/haotianteng/Chiron
SACall	Convolution layers, transformer self-attention layers and a CTC decoder	FAST5	FASTA/FASTQ	https://github.com/huangnengCSU/SACall-basecaller
CATCaller	Convolution-augmented transformer architecture	FAST5	FASTA/FASTQ	https://github.com/biomed-AI/CATCaller
Halcyon	CNN module and RNN-based encoder and decoder	FAST5	FASTA	https://github.com/relastle/halcyon
Tool	Principle of Analysis	Tool Input	Tool Output	Resource
Ravvent	Encoder-decoder architecture with attention mechanism and LSTMs as RNNs	FAST5	FASTA	https://github.com/adamnapieralski/ravventbasecaller
Dorado	Based on libtorch, the C++ API for PyTorch	POD5	HTS format	https://github.com/nanoporetech/dorado

NOTE: CNN = convolutional neural network; CTC = connectionist temporal classification decoder; TCN = temporal convolutional network; LSTM = long short-term memory network; API = application programming interface. Other abbreviations are defined in the Front Matter.

Page 183 Cite

TABLE A-2 Various Quality Control Tools Used with Nanopore Sequencing Data

QC Tool	Principle of Analysis	Input	Output	Resource
Poretools	Open-source software and is written in Python	FAST5 files	Tables & Graphs (PNG & PDF)	https://github.com/arq5x/poretools
poRe	Cross-platform tool scripted in R language	FAST5 files	Tables & Graphs	https://github.com/mw55309/poRe_docs
NanoOK	Alignment-based QC tool, written in Java with supporting R scripts	FAST5 files	PDF report	https://github.com/TGAC/NanoOK
NanoPack	Long-read QC tool written in Python language	FASTQ, sorted BAM, & sequencing summary files	HTML, PDF, PNG & JPG	https://github.com/wdecoster/nanopack
ToulligQC	QC tool for guppy basecalled files, written in Python 3	Sequencing summary files	HTML	https://github.com/GenomicParisCentre/toulligQC
MinIONQC	Non-interactive tool written in R	Sequencing summary file	PNG & YAML format	https://github.com/roblanf/minion_qc
PycoQC	Interactive QC tool written in Python language	Sequencing summary file	HTML	https://github.com/a-slide/pycoQC
NanoR	A cross-platform R package	FAST5 file or sequencing summary file	TSV file or HTML file	https://github.com/davidebolo1993/NanoR
PyPore	Open-source interactive software written in Python 2.7	FAST5 files	HTML	https://github.com/rsemeraro/PyPore
LongQC	Automated QC tool written in Python 3	FASTA, FASTQ, or a PacBio BAM file	JSON and HTML	https://github.com/yfukasawa/LongQC
BoardION	Web application based on R	FAST5 file or sequencing summary file	HTML	https://github.com/institut-degenomique/BoardION
IONiseR	R package	FAST5	R plots	https://www.bioconductor.org/packages/release/bioc/html/IONiseR.html

Page 184 Cite

TABLE A-3 ONT Assemblers for Long-Reads

Name of the tool	Principle of analysis	Input	Output	Resource
MaSuRCA	De Bruijn graph and overlap-layout-consensus	FASTA/FASTQ	FASTA	https://github.com/alekseyzimin/masurca
Canu	Detection of overlaps in high-noise sequences using MinHash Alignment Process	FASTA/FASTQ, uncompressed or compressed in gzip, bzip2, or xz	FASTA	https://github.com/marbl/canu
Unicycler	Miniasm+Racon pipeline through a short-read-first hybrid assembly	FASTQ	FASTA	https://github.com/rrwick/Unicycler
HINGE	Variation of the greedy algorithm, called hinging without a preassembly or read correction step	FASTA	FASTA	https://github.com/HingeAssembler/HINGE
Flye	Repeat graphs from the analysis of random paths in an unknown repeat graph, called disjointigs	FASTQ or FASTA format, uncompressed or compressed with gzip	FASTA	https://github.com/fenderglass/Flye
Shasta	Run-length representation of the read sequence	FASTA/FASTQ	FASTA/GFA	https://github.com/chanzuckerberg/shasta
Wtdbg2	FBG to long noisy reads assembly	FASTA/FASTQ	FASTA	https://github.com/ruanjue/wtdbg2

NOTE: FBG = fuzzy De Bruijn graph.

Page 185 Cite

TABLE A-4 Aligners for RNA-Seq Data

Tool	Principle of analysis	Input	Output	Source
HISAT	Mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes as well as to a single reference genome	FASTA/FASTQ/SEQ	SAM	https://github.com/DaehwanKimLab/hisat2
Minimap2	Overlapping read-to-read sequences with k-mers	FASTA/FASTQ	SAM/PAF	https://github.com/lh3/minimap2
DART	Separation of sequence into segments to replace the seed extension step	FASTA/FASTQ	SAM/BAM	https://github.com/hsinnan75/Dart
Magic-BLAST	Selection of the best scoring alignment from sequences found in the NCBI	FASTA	SAM/BAM	https://github.com/ncbi/magicblast/tree/master/magicblast-tools
GraphMap2	Subdivision of k-mer similarity	FASTA/FASTQ	SAM	https://github.com/lbcb-sci/graphmap2
deSALT`	De Bruijn graph-based spliced aligner for long-reads	FASTA/FASTQ	SAM	https://github.com/ydLiuHIT/deSALT
uLTRA	Alignment of reads to a genome through guided annotation of exons	FASTA/FASTQ	SAM	https://github.com/ksahlin/ultra
mapAlign	Map long-reads against a reference genome or a prebuilt index file	FASTA/FASTQ	SAM	https://github.com/yw575/mapAlign