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references.bib
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@article {astarix-1-preprint,
author = {Ivanov, Pesho and Bichsel, Benjamin and Mustafa, Harun and Kahles, Andr{\'e} and R{\"a}tsch, Gunnar and Vechev, Martin},
title = {AStarix: Fast and Optimal Sequence-to-Graph Alignment},
elocation-id = {2020.01.22.915496},
year = 2020,
doi = {10.1101/2020.01.22.915496},
publisher = {Cold Spring Harbor Laboratory},
abstract = {We present an algorithm for the optimal alignment of sequences to genome graphs. It works by phrasing the edit distance minimization task as finding a shortest path on an implicit alignment graph. To find a shortest path, we instantiate the A* paradigm with a novel domain-specific heuristic function that accounts for the upcoming subsequence in the query to be aligned, resulting in a provably optimal alignment algorithm called AStarix.Experimental evaluation of AStarix shows that it is 1{\textendash}2 orders of magnitude faster than state-of-the-art optimal algorithms on the task of aligning Illumina reads to reference genome graphs. Implementations and evaluations are available at https://github.com/eth-sri/astarix.Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2020/06/11/2020.01.22.915496},
eprint = {https://www.biorxiv.org/content/early/2020/06/11/2020.01.22.915496.full.pdf},
journal = {bioRxiv}
}
@InBook{astarix-1,
title = {AStarix: Fast and Optimal Sequence-to-Graph Alignment},
year = 2020,
author = {Ivanov, Pesho and Bichsel, Benjamin and Mustafa, Harun and
Kahles, André and Rätsch, Gunnar and Vechev, Martin},
booktitle = {Research in Computational Molecular Biology},
publisher = {Springer International Publishing},
isbn = 9783030452575,
pages = {104–119},
doi = {10.1007/978-3-030-45257-5_7},
url = {http://dx.doi.org/10.1007/978-3-030-45257-5_7},
ISSN = {1611-3349}
}
@InBook{astarix-2,
title = {Fast and Optimal Sequence-to-Graph Alignment Guided by Seeds},
year = 2022,
author = {Ivanov, Pesho and Bichsel, Benjamin and Vechev, Martin},
booktitle = {Research in Computational Molecular Biology},
publisher = {Springer International Publishing},
isbn = 9783031047497,
pages = {306–325},
doi = {10.1007/978-3-031-04749-7_22},
url = {http://dx.doi.org/10.1007/978-3-031-04749-7_22},
ISSN = {1611-3349}
}
@article {astarix-2-preprint,
author = {Ivanov, Pesho and Bichsel, Benjamin and Vechev, Martin},
title = {Fast and Optimal Sequence-to-Graph Alignment Guided by Seeds},
elocation-id = {2021.11.05.467453},
year = 2021,
doi = {10.1101/2021.11.05.467453},
publisher = {Cold Spring Harbor Laboratory},
abstract = {We present a novel A* seed heuristic enabling fast and optimal sequence-to-graph alignment, guaranteed to minimize the edit distance of the alignment assuming non-negative edit costs. We phrase optimal alignment as a shortest path problem and solve it by instantiating the A* algorithm with our novel seed heuristic. The key idea of the seed heuristic is to extract seeds from the read, locate them in the reference, mark preceding reference positions by crumbs, and use the crumbs to direct the A* search. We prove admissibility of the seed heuristic, thus guaranteeing alignment optimality. Our implementation extends the free and open source AStarix aligner and demonstrates that the seed heuristic outperforms all state-of-the-art optimal aligners including Graphaligner, Vargas, PaSGAL, and the prefix heuristic previously employed by AStarix. Specifically, we achieve a consistent speedup of \>60{\texttimes} on both short Illumina reads and long HiFi reads (up to 25kbp), on both the E. coli linear reference genome (lMbp) and the MHC variant graph (5Mbp). Our speedup is enabled by the seed heuristic consistently skipping \>99.99\% of the table cells that optimal aligners based on dynamic programming compute.Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2021/11/08/2021.11.05.467453},
eprint = {https://www.biorxiv.org/content/early/2021/11/08/2021.11.05.467453.full.pdf},
journal = {bioRxiv}
}
@article{suzuki-kasahara,
doi = {10.1186/s12859-018-2014-8},
url = {https://doi.org/10.1186/s12859-018-2014-8},
year = {2018},
month = feb,
publisher = {Springer Science and Business Media {LLC}},
volume = {19},
number = {S1},
author = {Hajime Suzuki and Masahiro Kasahara},
title = {Introducing difference recurrence relations for faster semi-global alignment of long sequences},
journal = {{BMC} Bioinformatics}
}
@article{wfa,
author = {Marco-Sola, Santiago and Moure, Juan Carlos and Moreto, Miquel and Espinosa, Antonio},
title = "{Fast gap-affine pairwise alignment using the wavefront algorithm}",
journal = {Bioinformatics},
volume = {37},
number = {4},
pages = {456-463},
year = {2020},
month = {09},
abstract = "{Pairwise alignment of sequences is a fundamental method in modern molecular biology, implemented within multiple bioinformatics tools and libraries. Current advances in sequencing technologies press for the development of faster pairwise alignment algorithms that can scale with increasing read lengths and production yields.In this article, we present the wavefront alignment algorithm (WFA), an exact gap-affine algorithm that takes advantage of homologous regions between the sequences to accelerate the alignment process. As opposed to traditional dynamic programming algorithms that run in quadratic time, the WFA runs in time O(ns), proportional to the read length n and the alignment score s, using O(s2) memory. Furthermore, our algorithm exhibits simple data dependencies that can be easily vectorized, even by the automatic features of modern compilers, for different architectures, without the need to adapt the code. We evaluate the performance of our algorithm, together with other state-of-the-art implementations. As a result, we demonstrate that the WFA runs 20–300× faster than other methods aligning short Illumina-like sequences, and 10–100× faster using long noisy reads like those produced by Oxford Nanopore Technologies.The WFA algorithm is implemented within the wavefront-aligner library, and it is publicly available at https://github.com/smarco/WFA.}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa777},
url = {https://doi.org/10.1093/bioinformatics/btaa777},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/37/4/456/39629847/btaa777.pdf},
}
@article{block-aligner,
author = {Liu, Daniel and Steinegger, Martin},
title = "{Block Aligner: an adaptive SIMD-accelerated aligner for sequences and position-specific scoring matrices}",
journal = {Bioinformatics},
pages = {btad487},
year = {2023},
month = {08},
issn = {1367-4811},
doi = {10.1093/bioinformatics/btad487},
url = {https://doi.org/10.1093/bioinformatics/btad487},
eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btad487/51032249/btad487.pdf},
}
@article{ultra-long-reads,
doi = {10.1038/nbt.4060},
url = {https://doi.org/10.1038/nbt.4060},
year = {2018},
month = jan,
publisher = {Springer Science and Business Media {LLC}},
volume = {36},
number = {4},
pages = {338--345},
author = {Miten Jain and Sergey Koren and Karen H Miga and Josh Quick and Arthur C Rand and Thomas A Sasani and John R Tyson and Andrew D Beggs and Alexander T Dilthey and Ian T Fiddes and Sunir Malla and Hannah Marriott and Tom Nieto and Justin O{\textquotesingle}Grady and Hugh E Olsen and Brent S Pedersen and Arang Rhie and Hollian Richardson and Aaron R Quinlan and Terrance P Snutch and Louise Tee and Benedict Paten and Adam M Phillippy and Jared T Simpson and Nicholas J Loman and Matthew Loose},
title = {Nanopore sequencing and assembly of a human genome with ultra-long reads},
journal = {Nature Biotechnology}
}
@article{four-russians-ed,
title = {A faster algorithm computing string edit distances},
journal = {Journal of Computer and System Sciences},
volume = {20},
number = {1},
pages = {18-31},
year = {1980},
issn = {0022-0000},
doi = {10.1016/0022-0000(80)90002-1},
url = {https://www.sciencedirect.com/science/article/pii/0022000080900021},
author = {William J. Masek and Michael S. Paterson},
abstract = {The edit distance between two character strings can be defined as the minimum cost of a sequence of editing operations which transforms one string into the other. The operations we admit are deleting, inserting and replacing one symbol at a time, with possibly different costs for each of these operations. The problem of finding the longest common subsequence of two strings is a special case of the problem of computing edit distances. We describe an algorithm for computing the edit distance between two strings of length n and m, n ⪖ m, which requires O(n · max(1, mlog n)) steps whenever the costs of edit operations are integral multiples of a single positive real number and the alphabet for the strings is finite. These conditions are necessary for the algorithm to achieve the time bound.}
}
@Article{no-subquadratic-ed,
author = {Backurs, Arturs and Indyk, Piotr},
title = {Edit Distance Cannot Be Computed in Strongly Subquadratic
Time (Unless SETH is False)},
journal = {SIAM Journal on Computing},
year = 2018,
volume = 47,
number = 3,
month = {Jan},
pages = {1087–1097},
issn = {1095-7111},
doi = {10.1137/15m1053128},
url = {http://dx.doi.org/10.1137/15M1053128},
publisher = {Society for Industrial & Applied Mathematics (SIAM)}
}
@article{nw,
author = {Needleman, Saul B. and Wunsch, Christian D.},
title = {A general method applicable to the search for similarities
in the amino acid sequence of two proteins},
journal = {Journal of Molecular Biology},
year = 1970,
volume = 48,
number = 3,
month = {Mar},
pages = {443–453},
issn = {0022-2836},
doi = {10.1016/0022-2836(70)90057-4},
url = {http://dx.doi.org/10.1016/0022-2836(70)90057-4},
publisher = {Elsevier BV}
}
@article{sankoff,
author = {Sankoff, David},
title = {Matching Sequences under Deletion/Insertion Constraints},
journal = {Proceedings of the National Academy of Sciences},
year = 1972,
volume = 69,
number = 1,
month = {Jan},
pages = {4–6},
issn = {1091-6490},
doi = {10.1073/pnas.69.1.4},
url = {http://dx.doi.org/10.1073/pnas.69.1.4},
publisher = {Proceedings of the National Academy of Sciences}
}
@article{sw,
author = {Smith, T.F. and Waterman, M.S.},
title = {Identification of common molecular subsequences},
journal = {Journal of Molecular Biology},
year = 1981,
volume = 147,
number = 1,
month = {Mar},
pages = {195–197},
issn = {0022-2836},
doi = {10.1016/0022-2836(81)90087-5},
url = {http://dx.doi.org/10.1016/0022-2836(81)90087-5},
publisher = {Elsevier BV}
}
@article{gotoh,
author = {Gotoh, Osamu},
title = {An improved algorithm for matching biological sequences},
journal = {Journal of Molecular Biology},
year = 1982,
volume = 162,
number = 3,
month = {Dec},
pages = {705–708},
issn = {0022-2836},
doi = {10.1016/0022-2836(82)90398-9},
url = {http://dx.doi.org/10.1016/0022-2836(82)90398-9},
publisher = {Elsevier BV}
}
@article{waterman,
author = {Waterman, M.S and Smith, T.F and Beyer, W.A},
title = {Some biological sequence metrics},
journal = {Advances in Mathematics},
year = 1976,
volume = 20,
number = 3,
month = {Jun},
pages = {367–387},
issn = {0001-8708},
doi = {10.1016/0001-8708(76)90202-4},
url = {http://dx.doi.org/10.1016/0001-8708(76)90202-4},
publisher = {Elsevier BV}
}
@Article{sellers74algorithm,
author = {Sellers, Peter H},
title = {An algorithm for the distance between two finite sequences},
journal = {Journal of Combinatorial Theory, Series A},
year = 1974,
volume = 16,
number = 2,
month = mar,
pages = {253–258},
issn = {0097-3165},
doi = {10.1016/0097-3165(74)90050-8},
url = {http://dx.doi.org/10.1016/0097-3165(74)90050-8},
publisher = {Elsevier BV}
}
@article{sellers,
author = {Sellers, Peter H.},
title = {On the Theory and Computation of Evolutionary Distances},
journal = {SIAM Journal on Applied Mathematics},
year = 1974,
volume = 26,
number = 4,
month = {Jun},
pages = {787–793},
issn = {1095-712X},
doi = {10.1137/0126070},
url = {http://dx.doi.org/10.1137/0126070},
publisher = {Society for Industrial & Applied Mathematics (SIAM)}
}
@Article{altschul,
author = {Altschul, Stephen F. and Erickson, Bruce W.},
title = {Optimal sequence alignment using affine gap costs},
journal = {Bulletin of Mathematical Biology},
year = 1986,
volume = 48,
number = {5–6},
month = {Sep},
pages = {603–616},
issn = {1522-9602},
doi = {10.1007/bf02462326},
url = {http://dx.doi.org/10.1007/BF02462326},
publisher = {Springer Science and Business Media LLC}
}
@Article{hirschberg75,
author = {Hirschberg, D. S.},
title = {A linear space algorithm for computing maximal common
subsequences},
journal = {Communications of the ACM},
year = 1975,
volume = 18,
number = 6,
month = {Jun},
pages = {341–343},
issn = {1557-7317},
doi = {10.1145/360825.360861},
url = {http://dx.doi.org/10.1145/360825.360861},
publisher = {Association for Computing Machinery (ACM)}
}
@Article{ukkonen83,
author = {Ukkonen, Esko},
title = {On approximate string matching},
journal = {Lecture Notes in Computer Science},
year = 1983,
pages = {487–495},
issn = {1611-3349},
doi = {10.1007/3-540-12689-9_129},
url = {http://dx.doi.org/10.1007/3-540-12689-9_129},
ISBN = 9783540386827,
publisher = {Springer Berlin Heidelberg}
}
@Article{ukkonen85,
author = {Ukkonen, Esko},
title = {Algorithms for approximate string matching},
journal = {Information and Control},
year = 1985,
volume = 64,
number = {1–3},
month = {Jan},
pages = {100–118},
issn = {0019-9958},
doi = {10.1016/s0019-9958(85)80046-2},
url = {http://dx.doi.org/10.1016/S0019-9958(85)80046-2},
publisher = {Elsevier BV}
}
@Article{ukkonen85-patterns,
author = {Ukkonen, Esko},
title = {Finding approximate patterns in strings},
journal = {Journal of Algorithms},
year = 1985,
volume = 6,
number = 1,
month = {Mar},
pages = {132–137},
issn = {0196-6774},
doi = {10.1016/0196-6774(85)90023-9},
url = {http://dx.doi.org/10.1016/0196-6774(85)90023-9},
publisher = {Elsevier BV}
}
@Article{myers88,
author = {Myers, Gene and Miller, Webb},
title = {Optimal alignments in linear space},
journal = {Bioinformatics},
year = 1988,
volume = 4,
number = 1,
pages = {11–17},
issn = {1460-2059},
doi = {10.1093/bioinformatics/4.1.11},
url = {http://dx.doi.org/10.1093/bioinformatics/4.1.11},
publisher = {Oxford University Press (OUP)}
}
@Article{smith81,
author = {Smith, T. F. and Waterman, M. S. and Fitch, W. M.},
title = {Comparative biosequence metrics},
journal = {Journal of Molecular Evolution},
year = 1981,
volume = 18,
number = 1,
month = {Jan},
pages = {38–46},
issn = {1432-1432},
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url = {http://dx.doi.org/10.1007/BF01733210},
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JOURNAL = {{Discrete Mathematics \& Theoretical Computer Science}},
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MONTH = Jun,
KEYWORDS = {Computer Science - Discrete Mathematics ; Computer Science - Data Structures and Algorithms ; Mathematics - Combinatorics ; Mathematics - Number Theory},
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author = {Stentz, Anthony},
title = {The Focussed D* Algorithm for Real-Time Replanning},
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isbn = {1558603638},
publisher = {Morgan Kaufmann Publishers Inc.},
address = {San Francisco, CA, USA},
abstract = {Finding the lowest-cost path through a graph is central to many problems including route planning for a mobile robot If arc costs change during the traverse then the remainder of the path may need to be replanned. This is the case for a sensor-equipped mobile robot with imperfect information about its environment. As the robot acquires additional information via its sensors it can revise its plan to reduce the total cost of the traverse. If the prior information is grossly incomplete the robot may discover useful information in every piece of sensor data. During replanning, the robot must either wait for the new path to be computed or move in the wrong direction therefore rapid replanning is essential The D* algorithm (Dynamic A*) plans optimal traverses ID real-time by incrementally repairing paths to the robots state as new information is discovered. This paper describes an extension to D* that focusses the repairs to significantly reduce the total time required for the initial path calculation and subsequent replanning operations. This extension completes the development of the D* algorithm as a full generalizatin of A* for dynamic environments where arc costs can change during the traverse of the solution path.},
booktitle = {Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2},
pages = {1652–1659},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {IJCAI'95}
}
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author = {Koenig, Sven and Likhachev, Maxim},
title = {Adaptive A},
journal = {Proceedings of the fourth international joint conference on
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publisher = {ACM Press}
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author = {Koenig, Sven and Likhachev, Maxim},
title = {Real-time adaptive A*},
journal = {Proceedings of the fifth international joint conference on
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@inproceedings{generalized-adaptive-astar,
author = {Sun, Xiaoxun and Koenig, Sven and Yeoh, William},
title = {Generalized Adaptive A*},
year = {2008},
isbn = {9780981738109},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
abstract = {Agents often have to solve series of similar search problems. Adaptive A* is a recent incremental heuristic search algorithm that solves series of similar search problems faster than A* because it updates the h-values using information from previous searches. It basically transforms consistent h-values into more informed consistent h-values. This allows it to find shortest paths in state spaces where the action costs can increase over time since consistent h-values remain consistent after action cost increases. However, it is not guaranteed to find shortest paths in state spaces where the action costs can decrease over time because consistent h-values do not necessarily remain consistent after action cost decreases. Thus, the h-values need to get corrected after action cost decreases. In this paper, we show how to do that, resulting in Generalized Adaptive A* (GAA*) that finds shortest paths in state spaces where the action costs can increase or decrease over time. Our experiments demonstrate that Generalized Adaptive A* outperforms breadth-first search, A* and D* Lite for moving-target search, where D* Lite is an alternative state-of-the-art incremental heuristic search algorithm that finds shortest paths in state spaces where the action costs can increase or decrease over time.},
booktitle = {Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1},
pages = {469–476},
numpages = {8},
keywords = {D* Lite, heuristic search, shortest paths, A*, incremental search, moving-target search},
location = {Estoril, Portugal},
series = {AAMAS '08}
}
@inproceedings{dstar-lite,
author = {Koenig, Sven and Likhachev, Maxim},
title = {D*lite},
year = {2002},
isbn = {0262511290},
publisher = {American Association for Artificial Intelligence},
address = {USA},
abstract = {Incremental heuristic search methods use heuristics to focus their search and reuse information from previous searches to find solutions to series of similar search tasks much faster than is possible by solving each search task from scratch. In this paper, we apply Lifelong Planning A* to robot navigation in unknown terrain, including goal-directed navigation in unknown terrain and mapping of unknown terrain. The resulting D* Lite algorithm is easy to understand and analyze. It implements the same behavior as Stentz' Focussed Dynamic A* but is algorithmically different. We prove properties about D* Lite and demonstrate experimentally the advantages of combining incremental and heuristic search for the applications studied. We believe that these results provide a strong foundation for further research on fast replanning methods in artificial intelligence and robotics.},
booktitle = {Eighteenth National Conference on Artificial Intelligence},
pages = {476–483},
numpages = {8},
location = {Edmonton, Alberta, Canada}
}
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publisher = {Public Library of Science (PLoS)}
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title = {The complete sequence of a human genome},
journal = {Science},
year = 2022,
volume = 376,
number = 6588,
month = {Apr},
pages = {44–53},