bachelor-thesis/docs/assets/bibliography.bib

@article{10.1093/molbev/msy224,
  author          = {Flagel, Lex and Brandvain, Yaniv and Schrider, Daniel R},
  title           = "{The Unreasonable Effectiveness of Convolutional Neural
                  Networks in Population Genetic Inference}",
  journal         = {Molecular Biology and Evolution},
  volume          = 36,
  number          = 2,
  pages           = {220-238},
  year            = 2018,
  month           = 12,
  abstract        = "{Population-scale genomic data sets have given researchers
                  incredible amounts of information from which to infer
                  evolutionary histories. Concomitant with this flood of data,
                  theoretical and methodological advances have sought to extract
                  information from genomic sequences to infer demographic events
                  such as population size changes and gene flow among closely
                  related populations/species, construct recombination maps, and
                  uncover loci underlying recent adaptation. To date, most
                  methods make use of only one or a few summaries of the input
                  sequences and therefore ignore potentially useful information
                  encoded in the data. The most sophisticated of these
                  approaches involve likelihood calculations, which require
                  theoretical advances for each new problem, and often focus on
                  a single aspect of the data (e.g., only allele frequency
                  information) in the interest of mathematical and computational
                  tractability. Directly interrogating the entirety of the input
                  sequence data in a likelihood-free manner would thus offer a
                  fruitful alternative. Here, we accomplish this by representing
                  DNA sequence alignments as images and using a class of deep
                  learning methods called convolutional neural networks (CNNs)
                  to make population genetic inferences from these images. We
                  apply CNNs to a number of evolutionary questions and find that
                  they frequently match or exceed the accuracy of current
                  methods. Importantly, we show that CNNs perform accurate
                  evolutionary model selection and parameter estimation, even on
                  problems that have not received detailed theoretical
                  treatments. Thus, when applied to population genetic
                  alignments, CNNs are capable of outperforming expert-derived
                  statistical methods and offer a new path forward in cases
                  where no likelihood approach exists.}",
  issn            = {0737-4038},
  doi             = {10.1093/molbev/msy224},
  url             = {https://doi.org/10.1093/molbev/msy224},
  eprint          = {https://academic.oup.com/mbe/article-pdf/36/2/220/27736968/msy224.pdf},
}

@Article{pmid19706884,
  Author          = "Robins, H. S. and Campregher, P. V. and Srivastava, S. K.
                  and Wacher, A. and Turtle, C. J. and Kahsai, O. and Riddell,
                  S. R. and Warren, E. H. and Carlson, C. S. ",
  Title           = "{{C}omprehensive assessment of {T}-cell receptor beta-chain
                  diversity in alphabeta {T} cells}",
  Journal         = "Blood",
  Year            = 2009,
  Volume          = 114,
  Number          = 19,
  Pages           = "4099--4107",
  Month           = "Nov"
}

@article {Nurk2021.05.26.445798,
  author          = {Nurk, Sergey and Koren, Sergey and Rhie, Arang and
                  Rautiainen, Mikko and Bzikadze, Andrey V. and Mikheenko, Alla
                  and Vollger, Mitchell R. and Altemose, Nicolas and Uralsky,
                  Lev and Gershman, Ariel and Aganezov, Sergey and Hoyt,
                  Savannah J. and Diekhans, Mark and Logsdon, Glennis A. and
                  Alonge, Michael and Antonarakis, Stylianos E. and Borchers,
                  Matthew and Bouffard, Gerard G. and Brooks, Shelise Y. and
                  Caldas, Gina V. and Cheng, Haoyu and Chin, Chen-Shan and Chow,
                  William and de Lima, Leonardo G. and Dishuck, Philip C. and
                  Durbin, Richard and Dvorkina, Tatiana and Fiddes, Ian T. and
                  Formenti, Giulio and Fulton, Robert S. and Fungtammasan,
                  Arkarachai and Garrison, Erik and Grady, Patrick G.S. and
                  Graves-Lindsay, Tina A. and Hall, Ira M. and Hansen, Nancy F.
                  and Hartley, Gabrielle A. and Haukness, Marina and Howe,
                  Kerstin and Hunkapiller, Michael W. and Jain, Chirag and Jain,
                  Miten and Jarvis, Erich D. and Kerpedjiev, Peter and Kirsche,
                  Melanie and Kolmogorov, Mikhail and Korlach, Jonas and
                  Kremitzki, Milinn and Li, Heng and Maduro, Valerie V. and
                  Marschall, Tobias and McCartney, Ann M. and McDaniel, Jennifer
                  and Miller, Danny E. and Mullikin, James C. and Myers, Eugene
                  W. and Olson, Nathan D. and Paten, Benedict and Peluso, Paul
                  and Pevzner, Pavel A. and Porubsky, David and Potapova, Tamara
                  and Rogaev, Evgeny I. and Rosenfeld, Jeffrey A. and Salzberg,
                  Steven L. and Schneider, Valerie A. and Sedlazeck, Fritz J.
                  and Shafin, Kishwar and Shew, Colin J. and Shumate, Alaina and
                  Sims, Yumi and Smit, Arian F. A. and Soto, Daniela C. and
                  Sovi{\'c}, Ivan and Storer, Jessica M. and Streets, Aaron and
                  Sullivan, Beth A. and Thibaud-Nissen, Fran{\c c}oise and
                  Torrance, James and Wagner, Justin and Walenz, Brian P. and
                  Wenger, Aaron and Wood, Jonathan M. D. and Xiao, Chunlin and
                  Yan, Stephanie M. and Young, Alice C. and Zarate, Samantha and
                  Surti, Urvashi and McCoy, Rajiv C. and Dennis, Megan Y. and
                  Alexandrov, Ivan A. and Gerton, Jennifer L. and
                  O{\textquoteright}Neill, Rachel J. and Timp, Winston and Zook,
                  Justin M. and Schatz, Michael C. and Eichler, Evan E. and
                  Miga, Karen H. and Phillippy, Adam M.},
  title           = {The complete sequence of a human genome},
  elocation-id    = {2021.05.26.445798},
  year            = 2021,
  doi             = {10.1101/2021.05.26.445798},
  publisher       = {Cold Spring Harbor Laboratory},
  abstract        = {In 2001, Celera Genomics and the International Human Genome
                  Sequencing Consortium published their initial drafts of the
                  human genome, which revolutionized the field of genomics.
                  While these drafts and the updates that followed effectively
                  covered the euchromatic fraction of the genome, the
                  heterochromatin and many other complex regions were left
                  unfinished or erroneous. Addressing this remaining 8\% of the
                  genome, the Telomere-to-Telomere (T2T) Consortium has finished
                  the first truly complete 3.055 billion base pair (bp) sequence
                  of a human genome, representing the largest improvement to the
                  human reference genome since its initial release. The new
                  T2T-CHM13 reference includes gapless assemblies for all 22
                  autosomes plus Chromosome X, corrects numerous errors, and
                  introduces nearly 200 million bp of novel sequence containing
                  2,226 paralogous gene copies, 115 of which are predicted to be
                  protein coding. The newly completed regions include all
                  centromeric satellite arrays and the short arms of all five
                  acrocentric chromosomes, unlocking these complex regions of
                  the genome to variational and functional studies for the first
                  time.Competing Interest StatementAF and CSC are employees of
                  DNAnexus; IS, JK, MWH, PP, and AW are employees of Pacific
                  Biosciences; FJS has received travel funds to speak at events
                  hosted by Pacific Biosciences; SK and FJS have received travel
                  funds to speak at events hosted by Oxford Nanopore
                  Technologies. WT has licensed two patents to Oxford Nanopore
                  Technologies (US 8748091 and 8394584).},
  URL             = {https://www.biorxiv.org/content/early/2021/05/27/2021.05.26.445798},
  eprint          = {https://www.biorxiv.org/content/early/2021/05/27/2021.05.26.445798.full.pdf},
  journal         = {bioRxiv}
}

@ARTICLE{10.3389/fgene.2020.00900,
  AUTHOR          = {Wang, Luotong and Qu, Li and Yang, Longshu and Wang, Yiying
                  and Zhu, Huaiqiu},
  TITLE           = {NanoReviser: An Error-Correction Tool for Nanopore
                  Sequencing Based on a Deep Learning Algorithm},
  JOURNAL         = {Frontiers in Genetics},
  VOLUME          = 11,
  PAGES           = 900,
  YEAR            = 2020,
  URL             = {https://www.frontiersin.org/article/10.3389/fgene.2020.00900},
  DOI             = {10.3389/fgene.2020.00900},
  ISSN            = {1664-8021},
  ABSTRACT        = {Nanopore sequencing is regarded as one of the most
                  promising third-generation sequencing (TGS) technologies.
                  Since 2014, Oxford Nanopore Technologies (ONT) has developed a
                  series of devices based on nanopore sequencing to produce very
                  long reads, with an expected impact on genomics. However, the
                  nanopore sequencing reads are susceptible to a fairly high
                  error rate owing to the difficulty in identifying the DNA
                  bases from the complex electrical signals. Although several
                  basecalling tools have been developed for nanopore sequencing
                  over the past years, it is still challenging to correct the
                  sequences after applying the basecalling procedure. In this
                  study, we developed an open-source DNA basecalling reviser,
                  NanoReviser, based on a deep learning algorithm to correct the
                  basecalling errors introduced by current basecallers provided
                  by default. In our module, we re-segmented the raw electrical
                  signals based on the basecalled sequences provided by the
                  default basecallers. By employing convolution neural networks
                  (CNNs) and bidirectional long short-term memory (Bi-LSTM)
                  networks, we took advantage of the information from the raw
                  electrical signals and the basecalled sequences from the
                  basecallers. Our results showed NanoReviser, as a
                  post-basecalling reviser, significantly improving the
                  basecalling quality. After being trained on standard ONT
                  sequencing reads from public E. coli and human NA12878
                  datasets, NanoReviser reduced the sequencing error rate by
                  over 5% for both the E. coli dataset and the human dataset.
                  The performance of NanoReviser was found to be better than
                  those of all current basecalling tools. Furthermore, we
                  analyzed the modified bases of the E. coli dataset and added
                  the methylation information to train our module. With the
                  methylation annotation, NanoReviser reduced the error rate by
                  7% for the E. coli dataset and specifically reduced the error
                  rate by over 10% for the regions of the sequence rich in
                  methylated bases. To the best of our knowledge, NanoReviser is
                  the first post-processing tool after basecalling to accurately
                  correct the nanopore sequences without the time-consuming
                  procedure of building the consensus sequence. The NanoReviser
                  package is freely available at <ext-link ext-link-type="uri"
                  xlink:href="https://github.com/pkubioinformatics/NanoReviser"
                  xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pkubioinformatics/NanoReviser</ext-link>.}
}



@Article{Davis2021,
  author          = {Davis, Eric M. and Sun, Yu and Liu, Yanling and Kolekar,
                  Pandurang and Shao, Ying and Szlachta, Karol and Mulder,
                  Heather L. and Ren, Dongren and Rice, Stephen V. and Wang,
                  Zhaoming and Nakitandwe, Joy and Gout, Alexander M. and
                  Shaner, Bridget and Hall, Salina and Robison, Leslie L. and
                  Pounds, Stanley and Klco, Jeffery M. and Easton, John and Ma,
                  Xiaotu},
  title           = {SequencErr: measuring and suppressing sequencer errors in
                  next-generation sequencing data},
  journal         = {Genome Biology},
  year            = 2021,
  month           = {Jan},
  day             = 25,
  volume          = 22,
  number          = 1,
  pages           = 37,
  abstract        = {There is currently no method to precisely measure the
                  errors that occur in the sequencing instrument/sequencer,
                  which is critical for next-generation sequencing applications
                  aimed at discovering the genetic makeup of heterogeneous
                  cellular populations.},
  issn            = {1474-760X},
  doi             = {10.1186/s13059-020-02254-2},
  url             = {https://doi.org/10.1186/s13059-020-02254-2}
}

@article{HEATHER20161,
  title           = {The sequence of sequencers: The history of sequencing DNA},
  journal         = {Genomics},
  volume          = 107,
  number          = 1,
  pages           = {1-8},
  year            = 2016,
  issn            = {0888-7543},
  doi             = {https://doi.org/10.1016/j.ygeno.2015.11.003},
  url             = {https://www.sciencedirect.com/science/article/pii/S0888754315300410},
  author          = {James M. Heather and Benjamin Chain},
  keywords        = {DNA, RNA, Sequencing, Sequencer, History},
  abstract        = {Determining the order of nucleic acid residues in
                  biological samples is an integral component of a wide variety
                  of research applications. Over the last fifty years large
                  numbers of researchers have applied themselves to the
                  production of techniques and technologies to facilitate this
                  feat, sequencing DNA and RNA molecules. This time-scale has
                  witnessed tremendous changes, moving from sequencing short
                  oligonucleotides to millions of bases, from struggling towards
                  the deduction of the coding sequence of a single gene to rapid
                  and widely available whole genome sequencing. This article
                  traverses those years, iterating through the different
                  generations of sequencing technology, highlighting some of the
                  key discoveries, researchers, and sequences along the way.}
}



@Article{vanDijk2014,
  author          = {van Dijk, Erwin L. and Auger, H{\'e}l{\`e}ne and
                  Jaszczyszyn, Yan and Thermes, Claude},
  title           = {Ten years of next-generation sequencing technology},
  journal         = {Trends in Genetics},
  year            = 2014,
  month           = {Sep},
  day             = 01,
  publisher       = {Elsevier},
  volume          = 30,
  number          = 9,
  pages           = {418-426},
  issn            = {0168-9525},
  doi             = {10.1016/j.tig.2014.07.001},
  url             = {https://doi.org/10.1016/j.tig.2014.07.001}
}