262 lines
16 KiB
BibTeX
262 lines
16 KiB
BibTeX
@article{10.1093/molbev/msy224,
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author = {Flagel, Lex and Brandvain, Yaniv and Schrider, Daniel R},
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title = "{The Unreasonable Effectiveness of Convolutional Neural
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Networks in Population Genetic Inference}",
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journal = {Molecular Biology and Evolution},
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volume = 36,
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number = 2,
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pages = {220-238},
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year = 2018,
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month = 12,
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abstract = "{Population-scale genomic data sets have given researchers
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incredible amounts of information from which to infer
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evolutionary histories. Concomitant with this flood of data,
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theoretical and methodological advances have sought to extract
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information from genomic sequences to infer demographic events
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such as population size changes and gene flow among closely
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related populations/species, construct recombination maps, and
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uncover loci underlying recent adaptation. To date, most
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methods make use of only one or a few summaries of the input
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sequences and therefore ignore potentially useful information
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encoded in the data. The most sophisticated of these
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approaches involve likelihood calculations, which require
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theoretical advances for each new problem, and often focus on
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a single aspect of the data (e.g., only allele frequency
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information) in the interest of mathematical and computational
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tractability. Directly interrogating the entirety of the input
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sequence data in a likelihood-free manner would thus offer a
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fruitful alternative. Here, we accomplish this by representing
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DNA sequence alignments as images and using a class of deep
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learning methods called convolutional neural networks (CNNs)
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to make population genetic inferences from these images. We
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apply CNNs to a number of evolutionary questions and find that
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they frequently match or exceed the accuracy of current
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methods. Importantly, we show that CNNs perform accurate
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evolutionary model selection and parameter estimation, even on
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problems that have not received detailed theoretical
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treatments. Thus, when applied to population genetic
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alignments, CNNs are capable of outperforming expert-derived
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statistical methods and offer a new path forward in cases
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where no likelihood approach exists.}",
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issn = {0737-4038},
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doi = {10.1093/molbev/msy224},
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url = {https://doi.org/10.1093/molbev/msy224},
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eprint = {https://academic.oup.com/mbe/article-pdf/36/2/220/27736968/msy224.pdf},
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}
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@Article{pmid19706884,
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Author = "Robins, H. S. and Campregher, P. V. and Srivastava, S. K.
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and Wacher, A. and Turtle, C. J. and Kahsai, O. and Riddell,
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S. R. and Warren, E. H. and Carlson, C. S. ",
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Title = "{{C}omprehensive assessment of {T}-cell receptor beta-chain
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diversity in alphabeta {T} cells}",
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Journal = "Blood",
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Year = 2009,
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Volume = 114,
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Number = 19,
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Pages = "4099--4107",
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Month = "Nov"
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}
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@article {Nurk2021.05.26.445798,
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author = {Nurk, Sergey and Koren, Sergey and Rhie, Arang and
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Rautiainen, Mikko and Bzikadze, Andrey V. and Mikheenko, Alla
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and Vollger, Mitchell R. and Altemose, Nicolas and Uralsky,
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Lev and Gershman, Ariel and Aganezov, Sergey and Hoyt,
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Savannah J. and Diekhans, Mark and Logsdon, Glennis A. and
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Alonge, Michael and Antonarakis, Stylianos E. and Borchers,
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Matthew and Bouffard, Gerard G. and Brooks, Shelise Y. and
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Caldas, Gina V. and Cheng, Haoyu and Chin, Chen-Shan and Chow,
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William and de Lima, Leonardo G. and Dishuck, Philip C. and
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Durbin, Richard and Dvorkina, Tatiana and Fiddes, Ian T. and
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Formenti, Giulio and Fulton, Robert S. and Fungtammasan,
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Arkarachai and Garrison, Erik and Grady, Patrick G.S. and
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Graves-Lindsay, Tina A. and Hall, Ira M. and Hansen, Nancy F.
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and Hartley, Gabrielle A. and Haukness, Marina and Howe,
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Kerstin and Hunkapiller, Michael W. and Jain, Chirag and Jain,
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Miten and Jarvis, Erich D. and Kerpedjiev, Peter and Kirsche,
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Melanie and Kolmogorov, Mikhail and Korlach, Jonas and
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Kremitzki, Milinn and Li, Heng and Maduro, Valerie V. and
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Marschall, Tobias and McCartney, Ann M. and McDaniel, Jennifer
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and Miller, Danny E. and Mullikin, James C. and Myers, Eugene
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W. and Olson, Nathan D. and Paten, Benedict and Peluso, Paul
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and Pevzner, Pavel A. and Porubsky, David and Potapova, Tamara
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and Rogaev, Evgeny I. and Rosenfeld, Jeffrey A. and Salzberg,
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Steven L. and Schneider, Valerie A. and Sedlazeck, Fritz J.
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and Shafin, Kishwar and Shew, Colin J. and Shumate, Alaina and
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Sims, Yumi and Smit, Arian F. A. and Soto, Daniela C. and
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Sovi{\'c}, Ivan and Storer, Jessica M. and Streets, Aaron and
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Sullivan, Beth A. and Thibaud-Nissen, Fran{\c c}oise and
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Torrance, James and Wagner, Justin and Walenz, Brian P. and
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Wenger, Aaron and Wood, Jonathan M. D. and Xiao, Chunlin and
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Yan, Stephanie M. and Young, Alice C. and Zarate, Samantha and
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Surti, Urvashi and McCoy, Rajiv C. and Dennis, Megan Y. and
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Alexandrov, Ivan A. and Gerton, Jennifer L. and
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O{\textquoteright}Neill, Rachel J. and Timp, Winston and Zook,
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Justin M. and Schatz, Michael C. and Eichler, Evan E. and
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Miga, Karen H. and Phillippy, Adam M.},
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title = {The complete sequence of a human genome},
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elocation-id = {2021.05.26.445798},
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year = 2021,
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doi = {10.1101/2021.05.26.445798},
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publisher = {Cold Spring Harbor Laboratory},
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abstract = {In 2001, Celera Genomics and the International Human Genome
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Sequencing Consortium published their initial drafts of the
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human genome, which revolutionized the field of genomics.
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While these drafts and the updates that followed effectively
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covered the euchromatic fraction of the genome, the
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heterochromatin and many other complex regions were left
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unfinished or erroneous. Addressing this remaining 8\% of the
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genome, the Telomere-to-Telomere (T2T) Consortium has finished
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the first truly complete 3.055 billion base pair (bp) sequence
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of a human genome, representing the largest improvement to the
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human reference genome since its initial release. The new
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T2T-CHM13 reference includes gapless assemblies for all 22
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autosomes plus Chromosome X, corrects numerous errors, and
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introduces nearly 200 million bp of novel sequence containing
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2,226 paralogous gene copies, 115 of which are predicted to be
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protein coding. The newly completed regions include all
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centromeric satellite arrays and the short arms of all five
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acrocentric chromosomes, unlocking these complex regions of
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the genome to variational and functional studies for the first
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time.Competing Interest StatementAF and CSC are employees of
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DNAnexus; IS, JK, MWH, PP, and AW are employees of Pacific
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Biosciences; FJS has received travel funds to speak at events
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hosted by Pacific Biosciences; SK and FJS have received travel
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funds to speak at events hosted by Oxford Nanopore
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Technologies. WT has licensed two patents to Oxford Nanopore
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Technologies (US 8748091 and 8394584).},
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URL = {https://www.biorxiv.org/content/early/2021/05/27/2021.05.26.445798},
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eprint = {https://www.biorxiv.org/content/early/2021/05/27/2021.05.26.445798.full.pdf},
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journal = {bioRxiv}
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}
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@ARTICLE{10.3389/fgene.2020.00900,
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AUTHOR = {Wang, Luotong and Qu, Li and Yang, Longshu and Wang, Yiying
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and Zhu, Huaiqiu},
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TITLE = {NanoReviser: An Error-Correction Tool for Nanopore
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Sequencing Based on a Deep Learning Algorithm},
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JOURNAL = {Frontiers in Genetics},
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VOLUME = 11,
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PAGES = 900,
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YEAR = 2020,
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URL = {https://www.frontiersin.org/article/10.3389/fgene.2020.00900},
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DOI = {10.3389/fgene.2020.00900},
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ISSN = {1664-8021},
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ABSTRACT = {Nanopore sequencing is regarded as one of the most
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promising third-generation sequencing (TGS) technologies.
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Since 2014, Oxford Nanopore Technologies (ONT) has developed a
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series of devices based on nanopore sequencing to produce very
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long reads, with an expected impact on genomics. However, the
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nanopore sequencing reads are susceptible to a fairly high
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error rate owing to the difficulty in identifying the DNA
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bases from the complex electrical signals. Although several
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basecalling tools have been developed for nanopore sequencing
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over the past years, it is still challenging to correct the
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sequences after applying the basecalling procedure. In this
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study, we developed an open-source DNA basecalling reviser,
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NanoReviser, based on a deep learning algorithm to correct the
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basecalling errors introduced by current basecallers provided
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by default. In our module, we re-segmented the raw electrical
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signals based on the basecalled sequences provided by the
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default basecallers. By employing convolution neural networks
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(CNNs) and bidirectional long short-term memory (Bi-LSTM)
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networks, we took advantage of the information from the raw
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electrical signals and the basecalled sequences from the
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basecallers. Our results showed NanoReviser, as a
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post-basecalling reviser, significantly improving the
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basecalling quality. After being trained on standard ONT
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sequencing reads from public E. coli and human NA12878
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datasets, NanoReviser reduced the sequencing error rate by
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over 5% for both the E. coli dataset and the human dataset.
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The performance of NanoReviser was found to be better than
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those of all current basecalling tools. Furthermore, we
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analyzed the modified bases of the E. coli dataset and added
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the methylation information to train our module. With the
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methylation annotation, NanoReviser reduced the error rate by
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7% for the E. coli dataset and specifically reduced the error
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rate by over 10% for the regions of the sequence rich in
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methylated bases. To the best of our knowledge, NanoReviser is
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the first post-processing tool after basecalling to accurately
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correct the nanopore sequences without the time-consuming
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procedure of building the consensus sequence. The NanoReviser
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package is freely available at <ext-link ext-link-type="uri"
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xlink:href="https://github.com/pkubioinformatics/NanoReviser"
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xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pkubioinformatics/NanoReviser</ext-link>.}
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}
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@Article{Davis2021,
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author = {Davis, Eric M. and Sun, Yu and Liu, Yanling and Kolekar,
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Pandurang and Shao, Ying and Szlachta, Karol and Mulder,
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Heather L. and Ren, Dongren and Rice, Stephen V. and Wang,
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Zhaoming and Nakitandwe, Joy and Gout, Alexander M. and
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Shaner, Bridget and Hall, Salina and Robison, Leslie L. and
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Pounds, Stanley and Klco, Jeffery M. and Easton, John and Ma,
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Xiaotu},
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title = {SequencErr: measuring and suppressing sequencer errors in
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next-generation sequencing data},
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journal = {Genome Biology},
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year = 2021,
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month = {Jan},
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day = 25,
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volume = 22,
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number = 1,
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pages = 37,
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abstract = {There is currently no method to precisely measure the
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errors that occur in the sequencing instrument/sequencer,
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which is critical for next-generation sequencing applications
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aimed at discovering the genetic makeup of heterogeneous
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cellular populations.},
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issn = {1474-760X},
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doi = {10.1186/s13059-020-02254-2},
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url = {https://doi.org/10.1186/s13059-020-02254-2}
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}
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@article{HEATHER20161,
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title = {The sequence of sequencers: The history of sequencing DNA},
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journal = {Genomics},
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volume = 107,
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number = 1,
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pages = {1-8},
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year = 2016,
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issn = {0888-7543},
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doi = {https://doi.org/10.1016/j.ygeno.2015.11.003},
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url = {https://www.sciencedirect.com/science/article/pii/S0888754315300410},
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author = {James M. Heather and Benjamin Chain},
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keywords = {DNA, RNA, Sequencing, Sequencer, History},
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abstract = {Determining the order of nucleic acid residues in
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biological samples is an integral component of a wide variety
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of research applications. Over the last fifty years large
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numbers of researchers have applied themselves to the
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production of techniques and technologies to facilitate this
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feat, sequencing DNA and RNA molecules. This time-scale has
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witnessed tremendous changes, moving from sequencing short
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oligonucleotides to millions of bases, from struggling towards
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the deduction of the coding sequence of a single gene to rapid
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and widely available whole genome sequencing. This article
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traverses those years, iterating through the different
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generations of sequencing technology, highlighting some of the
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key discoveries, researchers, and sequences along the way.}
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}
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@Article{vanDijk2014,
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author = {van Dijk, Erwin L. and Auger, H{\'e}l{\`e}ne and
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Jaszczyszyn, Yan and Thermes, Claude},
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title = {Ten years of next-generation sequencing technology},
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journal = {Trends in Genetics},
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year = 2014,
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month = {Sep},
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day = 01,
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publisher = {Elsevier},
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volume = 30,
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number = 9,
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pages = {418-426},
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issn = {0168-9525},
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doi = {10.1016/j.tig.2014.07.001},
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url = {https://doi.org/10.1016/j.tig.2014.07.001}
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}
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