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. 2021 Aug 11;2(4):100153. doi: 10.1016/j.xinn.2021.100153

Table 2.

Tools developed for analysis of Nanopore sequencing data

Tool Description Algorithm Advantages Rate Disadvantages Link Reference (PMID)
LAST Alignment adaptive seeds Adaptive seeds are matches that are chosen based on their rareness, instead of using fixed-length matches - the running time increases linearly with sequence length and short DNA reads https://gitlab.com/mcfrith/last 21209072
Minimap2 Alignment split-read alignment DNA or long mRNA, higher accuracy, faster, and full length of reads - not suitable for chimeric alignments https://github.com/lh3/minimap2 29750242
GraphMap Alignment candidate alignments and fast graph traversal long reads with speed, high sensitivity - large-memory https://github.com/isovic/graphmap 27079541
UNCALLED Alignment Ferragina-Manzini index mapping during sequencing and the leftmost mapping - not full length https://github.com/skovaka/UNCALLED 33257863
tailfindr poly(A) measures poly(A) tail length - - - https://github.com/adnaniazi/tailfindr 31266821, 33835460
NaS Assembly illumina hybrid entirely and with no error - Not suitable for large genomes https://www.genoscope.cns.fr/externe/nas/ 25927464
LQS Assembly multiple-alignment corrected corrected by a multiple-alignment and 99.5% nucleotide identity - Not suitable for large genomes https://github.com/jts/nanopore-paper-analysis 26076426
Canu Assembly tf-idf weighted MinHash and graph construction halves depth-of-coverage requirements, improves assembly continuity and reduces runtime on large genomes - accuracy depends on signal-level polishing https://github.com/marbl/canu 28298431
Miniasm Assembly No correction magnitude faster - error rate is as high as raw reads https://github.com/lh3/miniasm 27153593
Nanopolish Variant caller/Methylation detection Hidden Markov Model calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs and indels - signal-level analysis https://github.com/jts/nanopolish 26076426
Clairvoyante Variant caller/SV caller convolutional neural network SV calling, small variants and genotype - higher sequencing depth https://github.com/aquaskyline/Clairvoyante 30824707
Clair Variant caller Deep neural network faster and complex variants with multiple alternative alleles - accuracy depends on pileup data and greater computational demands https://github.com/quay/clair
NanoSV SV caller split- and gapped-aligned reads genotyping - non-detectable inversion, complex repeat regions and segmental duplications https://github.com/mroosmalen/nanosv 29109544
Picky SV caller seed-and-extend process and split-read micro-insertions and phased SV - high specificity https://github.com/TheJacksonLaboratory/Picky 29713081
NanoVar SV caller artificial neural network low-depth (8X) - the alignment profile of each read requires re-training https://github.com/benoukraflab/nanovar 32127024
SENSV SV caller Deep neural network low-depth - balanced translocation missed https://github.com/HKU-BAL/SENSV
CAMPHOR SV caller SV breakpoints polymorphic SVs and somatic SVs - removed indels in short repeats, the average read length 5 kbps and non-detectable indels < 100 bp https://github.com/afujimoto/CAMPHOR 33910608
NanoMod Methylation detection signal intensities raw signal data and 5mC - two pair sample reads https://github.com/WGLab/NanoMod 30712508
DeepSignal Methylation detection deep learning 6mA/5mC, lower coverage, and predict methylation states - train DeepSignal to detect more types of base modification https://github.com/bioinfomaticsCSU/deepsignal 30994904
mCaller Methylation detection neural network 6mA and detect known or confirm suspected methyltransferase target motifs - only bacteria genome https://github.com/al-mcintyre/mCaller_analysis_scripts 30718479
DeepMod Methylation detection recurrent neural network 6mA/5mC,strand-sensitive and has single-base resolution - non-detectable other types of modifications or other different motifs, not suitable for RNA, neighboring bases inflence, elied on alignment tool to find correct reference positions of bases https://github.com/WGLab/DeepMod 31164644
MINES Methylation detection random forest m6A sites within DRACH motifs - lost small difference modification sites and not suitable for DNA https://github.com/YeoLab/MINES 31624092
Nanom6A Methylation detection XGBoost model m6A at single-base resolution and quantified abundance of m6A sites - not suitable for DNA https://github.com/gaoyubang/nanom6A 33413586
FLAIR Isoform detection correct and realign assessing 3′ poly(A) tail length, base modifications, and transcript haplotypes - combined short Illumina reads https://github.com/BrooksLabUCSC/flair 31740818
TrackCluster Isoform detection read tracks read classification, a transcript isoform with numerous exons, stage-specific or cell-specific expression of isoforms - not suitable for large genomes https://github.com/Runsheng/trackcluster 32024662