27 Search Results for "Kingsford, Carl"

Volume

LIPIcs, Volume 172

20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

WABI 2020, September 7-9, 2020, Pisa, Italy (Virtual Conference)

Editors: Carl Kingsford and Nadia Pisanti

Document
DOI: 10.4230/LIPIcs.WABI.2023.11
Revisiting the Complexity of and Algorithms for the Graph Traversal Edit Distance and Its Variants

Authors: Yutong Qiu, Yihang Shen, and Carl Kingsford

Published in: LIPIcs, Volume 273, 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023)

Abstract
The graph traversal edit distance (GTED), introduced by Ebrahimpour Boroojeny et al. (2018), is an elegant distance measure defined as the minimum edit distance between strings reconstructed from Eulerian trails in two edge-labeled graphs. GTED can be used to infer evolutionary relationships between species by comparing de Bruijn graphs directly without the computationally costly and error-prone process of genome assembly. Ebrahimpour Boroojeny et al. (2018) propose two ILP formulations for GTED and claim that GTED is polynomially solvable because the linear programming relaxation of one of the ILPs will always yield optimal integer solutions. The claim that GTED is polynomially solvable is contradictory to the complexity of existing string-to-graph matching problems. We resolve this conflict in complexity results by proving that GTED is NP-complete and showing that the ILPs proposed by Ebrahimpour Boroojeny et al. do not solve GTED but instead solve for a lower bound of GTED and are not solvable in polynomial time. In addition, we provide the first two, correct ILP formulations of GTED and evaluate their empirical efficiency. These results provide solid algorithmic foundations for comparing genome graphs and point to the direction of heuristics that estimate GTED efficiently.
Cite as

Yutong Qiu, Yihang Shen, and Carl Kingsford. Revisiting the Complexity of and Algorithms for the Graph Traversal Edit Distance and Its Variants. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 11:1-11:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{qiu_et_al:LIPIcs.WABI.2023.11,
  author =	{Qiu, Yutong and Shen, Yihang and Kingsford, Carl},
  title =	{{Revisiting the Complexity of and Algorithms for the Graph Traversal Edit Distance and Its Variants}},
  booktitle =	{23rd International Workshop on Algorithms in Bioinformatics (WABI 2023)},
  pages =	{11:1--11:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-294-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{273},
  editor =	{Belazzougui, Djamal and Ouangraoua, A\"{i}da},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2023.11},
  URN =		{urn:nbn:de:0030-drops-186374},
  doi =		{10.4230/LIPIcs.WABI.2023.11},
  annote =	{Keywords: Integer Linear Programming, Genome Graphs, Flow Network, Graph Comparison}
}
Document
DOI: 10.4230/LIPIcs.WABI.2023.23
Reinforcement Learning for Robotic Liquid Handler Planning

Authors: Mohsen Ferdosi, Yuejun Ge, and Carl Kingsford

Published in: LIPIcs, Volume 273, 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023)

Abstract
Robotic liquid handlers play a crucial role in automating laboratory tasks such as sample preparation, high-throughput screening, and assay development. Manually designing protocols takes significant effort, and can result in inefficient protocols and involve human error. We investigates the application of reinforcement learning to automate the protocol design process resulting in reduced human labor and further automation in liquid handling. We develop a reinforcement learning agent that can automatically output the step-by-step protocol based on the initial state of the deck, reagent types and volumes, and the desired state of the reagents after the protocol is finished. We show that finding the optimal protocol for solving a liquid handler instance is NP-complete, and we present a reinforcement learning algorithm that can solve the planning problem practically for cases with a deck of up to 20 × 20 wells and four different types of reagents. We design and implement an actor-critic approach, and we train our agent using the Impala algorithm. Our findings demonstrate that reinforcement learning can be used to automatically program liquid handler robotic arms, enabling more precise and efficient planning for the liquid handler and laboratory automation.
Cite as

Mohsen Ferdosi, Yuejun Ge, and Carl Kingsford. Reinforcement Learning for Robotic Liquid Handler Planning. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 23:1-23:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{ferdosi_et_al:LIPIcs.WABI.2023.23,
  author =	{Ferdosi, Mohsen and Ge, Yuejun and Kingsford, Carl},
  title =	{{Reinforcement Learning for Robotic Liquid Handler Planning}},
  booktitle =	{23rd International Workshop on Algorithms in Bioinformatics (WABI 2023)},
  pages =	{23:1--23:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-294-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{273},
  editor =	{Belazzougui, Djamal and Ouangraoua, A\"{i}da},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2023.23},
  URN =		{urn:nbn:de:0030-drops-186494},
  doi =		{10.4230/LIPIcs.WABI.2023.23},
  annote =	{Keywords: Liquid Handler, Reinforcement Learning, Planning}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.WABI.2020
LIPIcs, Volume 172, WABI 2020, Complete Volume

Authors: Carl Kingsford and Nadia Pisanti

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
LIPIcs, Volume 172, WABI 2020, Complete Volume
Cite as

20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 1-360, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Proceedings{kingsford_et_al:LIPIcs.WABI.2020,
  title =	{{LIPIcs, Volume 172, WABI 2020, Complete Volume}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{1--360},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020},
  URN =		{urn:nbn:de:0030-drops-127881},
  doi =		{10.4230/LIPIcs.WABI.2020},
  annote =	{Keywords: LIPIcs, Volume 172, WABI 2020, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.WABI.2020.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Carl Kingsford and Nadia Pisanti

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{kingsford_et_al:LIPIcs.WABI.2020.0,
  author =	{Kingsford, Carl and Pisanti, Nadia},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{0:i--0:x},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.0},
  URN =		{urn:nbn:de:0030-drops-127891},
  doi =		{10.4230/LIPIcs.WABI.2020.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.1
Approximate Search for Known Gene Clusters in New Genomes Using PQ-Trees

Authors: Galia R. Zimerman, Dina Svetlitsky, Meirav Zehavi, and Michal Ziv-Ukelson

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
We define a new problem in comparative genomics, denoted PQ-Tree Search, that takes as input a PQ-tree T representing the known gene orders of a gene cluster of interest, a gene-to-gene substitution scoring function h, integer parameters d_T and d_S, and a new genome S. The objective is to identify in S approximate new instances of the gene cluster that could vary from the known gene orders by genome rearrangements that are constrained by T, by gene substitutions that are governed by h, and by gene deletions and insertions that are bounded from above by d_T and d_S, respectively. We prove that the PQ-Tree Search problem is NP-hard and propose a parameterized algorithm that solves the optimization variant of PQ-Tree Search in O^*(2^{γ}) time, where γ is the maximum degree of a node in T and O^* is used to hide factors polynomial in the input size. The algorithm is implemented as a search tool, denoted PQFinder, and applied to search for instances of chromosomal gene clusters in plasmids, within a dataset of 1,487 prokaryotic genomes. We report on 29 chromosomal gene clusters that are rearranged in plasmids, where the rearrangements are guided by the corresponding PQ-tree. One of these results, coding for a heavy metal efflux pump, is further analysed to exemplify how PQFinder can be harnessed to reveal interesting new structural variants of known gene clusters.
Cite as

Galia R. Zimerman, Dina Svetlitsky, Meirav Zehavi, and Michal Ziv-Ukelson. Approximate Search for Known Gene Clusters in New Genomes Using PQ-Trees. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 1:1-1:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{zimerman_et_al:LIPIcs.WABI.2020.1,
  author =	{Zimerman, Galia R. and Svetlitsky, Dina and Zehavi, Meirav and Ziv-Ukelson, Michal},
  title =	{{Approximate Search for Known Gene Clusters in New Genomes Using PQ-Trees}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{1:1--1:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.1},
  URN =		{urn:nbn:de:0030-drops-127906},
  doi =		{10.4230/LIPIcs.WABI.2020.1},
  annote =	{Keywords: PQ-Tree, Gene Cluster, Efflux Pump}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.2
An Interpretable Classification Method for Predicting Drug Resistance in M. Tuberculosis

Authors: Hooman Zabeti, Nick Dexter, Amir Hosein Safari, Nafiseh Sedaghat, Maxwell Libbrecht, and Leonid Chindelevitch

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Motivation: The prediction of drug resistance and the identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Modern methods based on testing against a catalogue of previously identified mutations often yield poor predictive performance. On the other hand, machine learning techniques have demonstrated high predictive accuracy, but many of them lack interpretability to aid in identifying specific mutations which lead to resistance. We propose a novel technique, inspired by the group testing problem and Boolean compressed sensing, which yields highly accurate predictions and interpretable results at the same time. Results: We develop a modified version of the Boolean compressed sensing problem for identifying drug resistance, and implement its formulation as an integer linear program. This allows us to characterize the predictive accuracy of the technique and select an appropriate metric to optimize. A simple adaptation of the problem also allows us to quantify the sensitivity-specificity trade-off of our model under different regimes. We test the predictive accuracy of our approach on a variety of commonly used antibiotics in treating tuberculosis and find that it has accuracy comparable to that of standard machine learning models and points to several genes with previously identified association to drug resistance.
Cite as

Hooman Zabeti, Nick Dexter, Amir Hosein Safari, Nafiseh Sedaghat, Maxwell Libbrecht, and Leonid Chindelevitch. An Interpretable Classification Method for Predicting Drug Resistance in M. Tuberculosis. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 2:1-2:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{zabeti_et_al:LIPIcs.WABI.2020.2,
  author =	{Zabeti, Hooman and Dexter, Nick and Safari, Amir Hosein and Sedaghat, Nafiseh and Libbrecht, Maxwell and Chindelevitch, Leonid},
  title =	{{An Interpretable Classification Method for Predicting Drug Resistance in M. Tuberculosis}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{2:1--2:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.2},
  URN =		{urn:nbn:de:0030-drops-127911},
  doi =		{10.4230/LIPIcs.WABI.2020.2},
  annote =	{Keywords: Drug resistance, whole-genome sequencing, interpretable machine learning, integer linear programming, rule-based learning}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.3
Natural Family-Free Genomic Distance

Authors: Diego P. Rubert, Fábio V. Martinez, and Marília D. V. Braga

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
A classical problem in comparative genomics is to compute the rearrangement distance, that is the minimum number of large-scale rearrangements required to transform a given genome into another given genome. While the most traditional approaches in this area are family-based, i.e., require the classification of DNA fragments of both genomes into families, more recently an alternative model was proposed, which, instead of family classification, simply uses the pairwise similarities between DNA fragments of both genomes to compute their rearrangement distance. This model represents structural rearrangements by the generic double cut and join (DCJ) operation and is then called family-free DCJ distance. It computes the DCJ distance between the two genomes by searching for a matching of their genes based on the given pairwise similarities, therefore helping to find gene homologies. The drawback is that its computation is NP-hard. Another point is that the family-free DCJ distance must correspond to a maximal matching of the genes, due to the fact that unmatched genes are just ignored: maximizing the matching prevents the free lunch artifact of having empty or almost empty matchings giving the smaller distances. In this paper, besides DCJ operations, we allow content-modifying operations of insertions and deletions of DNA segments and propose a new and more general family-free genomic distance. In our model we use the pairwise similarities to assign weights to both matched and unmatched genes, so that an optimal solution does not necessarily maximize the matching. Our model then results in a natural family-free genomic distance, that takes into consideration all given genes and has a search space composed of matchings of any size. We provide an efficient ILP formulation to solve it, by extending the previous formulations for computing family-based genomic distances from Shao et al. (J. Comput. Biol., 2015) and Bohnenkämper et al. (Proc. of RECOMB, 2020). Our experiments show that the ILP can handle not only bacterial genomes, but also fungi and insects, or sets of chromosomes of mammals and plants. In a comparison study of six fruit fly genomes, we obtained accurate results.
Cite as

Diego P. Rubert, Fábio V. Martinez, and Marília D. V. Braga. Natural Family-Free Genomic Distance. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 3:1-3:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{rubert_et_al:LIPIcs.WABI.2020.3,
  author =	{Rubert, Diego P. and Martinez, F\'{a}bio V. and Braga, Mar{\'\i}lia D. V.},
  title =	{{Natural Family-Free Genomic Distance}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{3:1--3:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.3},
  URN =		{urn:nbn:de:0030-drops-127926},
  doi =		{10.4230/LIPIcs.WABI.2020.3},
  annote =	{Keywords: Comparative genomics, Genome rearrangement, DCJ-indel distance}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.4
Fast Lightweight Accurate Xenograft Sorting

Authors: Jens Zentgraf and Sven Rahmann

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Motivation: With an increasing number of patient-derived xenograft (PDX) models being created and subsequently sequenced to study tumor heterogeneity and to guide therapy decisions, there is a similarly increasing need for methods to separate reads originating from the graft (human) tumor and reads originating from the host species' (mouse) surrounding tissue. Two kinds of methods are in use: On the one hand, alignment-based tools require that reads are mapped and aligned (by an external mapper/aligner) to the host and graft genomes separately first; the tool itself then processes the resulting alignments and quality metrics (typically BAM files) to assign each read or read pair. On the other hand, alignment-free tools work directly on the raw read data (typically FASTQ files). Recent studies compare different approaches and tools, with varying results. Results: We show that alignment-free methods for xenograft sorting are superior concerning CPU time usage and equivalent in accuracy. We improve upon the state of the art by presenting a fast lightweight approach based on three-way bucketed quotiented Cuckoo hashing. Our hash table requires memory comparable to an FM index typically used for read alignment and less than other alignment-free approaches. It allows extremely fast lookups and uses less CPU time than other alignment-free methods and alignment-based methods at similar accuracy.
Cite as

Jens Zentgraf and Sven Rahmann. Fast Lightweight Accurate Xenograft Sorting. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 4:1-4:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{zentgraf_et_al:LIPIcs.WABI.2020.4,
  author =	{Zentgraf, Jens and Rahmann, Sven},
  title =	{{Fast Lightweight Accurate Xenograft Sorting}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{4:1--4:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.4},
  URN =		{urn:nbn:de:0030-drops-127933},
  doi =		{10.4230/LIPIcs.WABI.2020.4},
  annote =	{Keywords: xenograft sorting, alignment-free method, Cuckoo hashing, k-mer}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.5
Phyolin: Identifying a Linear Perfect Phylogeny in Single-Cell DNA Sequencing Data of Tumors

Authors: Leah L. Weber and Mohammed El-Kebir

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Cancer arises from an evolutionary process where somatic mutations occur and eventually give rise to clonal expansions. Modeling this evolutionary process as a phylogeny is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. However, cancer phylogeny inference from single-cell DNA sequencing data of tumors is challenging due to limitations with sequencing technology and the complexity of the resulting problem. Therefore, as a first step some value might be obtained from correctly classifying the evolutionary process as either linear or branched. The biological implications of these two high-level patterns are different and understanding what cancer types and which patients have each of these trajectories could provide useful insight for both clinicians and researchers. Here, we introduce the Linear Perfect Phylogeny Flipping Problem as a means of testing a null model that the tree topology is linear and show that it is NP-hard. We develop Phyolin and, through both in silico experiments and real data application, show that it is an accurate, easy to use and a reasonably fast method for classifying an evolutionary trajectory as linear or branched.
Cite as

Leah L. Weber and Mohammed El-Kebir. Phyolin: Identifying a Linear Perfect Phylogeny in Single-Cell DNA Sequencing Data of Tumors. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 5:1-5:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{weber_et_al:LIPIcs.WABI.2020.5,
  author =	{Weber, Leah L. and El-Kebir, Mohammed},
  title =	{{Phyolin: Identifying a Linear Perfect Phylogeny in Single-Cell DNA Sequencing Data of Tumors}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{5:1--5:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.5},
  URN =		{urn:nbn:de:0030-drops-127946},
  doi =		{10.4230/LIPIcs.WABI.2020.5},
  annote =	{Keywords: Constraint programming, intra-tumor heterogeneity, combinatorial optimization}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.6
The Longest Run Subsequence Problem

Authors: Sven Schrinner, Manish Goel, Michael Wulfert, Philipp Spohr, Korbinian Schneeberger, and Gunnar W. Klau

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Genome assembly is one of the most important problems in computational genomics. Here, we suggest addressing the scaffolding phase, in which contigs need to be linked and ordered to obtain larger pseudo-chromosomes, by means of a second incomplete assembly of a related species. The idea is to use alignments of binned regions in one contig to find the most homologous contig in the other assembly. We show that ordering the contigs of the other assembly can be expressed by a new string problem, the longest run subsequence problem (LRS). We show that LRS is NP-hard and present reduction rules and two algorithmic approaches that, together, are able to solve large instances of LRS to provable optimality. In particular, they can solve realistic instances resulting from partial Arabidopsis thaliana assemblies in short computation time. Our source code and all data used in the experiments are freely available.
Cite as

Sven Schrinner, Manish Goel, Michael Wulfert, Philipp Spohr, Korbinian Schneeberger, and Gunnar W. Klau. The Longest Run Subsequence Problem. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 6:1-6:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{schrinner_et_al:LIPIcs.WABI.2020.6,
  author =	{Schrinner, Sven and Goel, Manish and Wulfert, Michael and Spohr, Philipp and Schneeberger, Korbinian and Klau, Gunnar W.},
  title =	{{The Longest Run Subsequence Problem}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{6:1--6:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.6},
  URN =		{urn:nbn:de:0030-drops-127951},
  doi =		{10.4230/LIPIcs.WABI.2020.6},
  annote =	{Keywords: alignments, assembly, string algorithm, longest subsequence}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.7
Linear Time Construction of Indexable Founder Block Graphs

Authors: Veli Mäkinen, Bastien Cazaux, Massimo Equi, Tuukka Norri, and Alexandru I. Tomescu

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
We introduce a compact pangenome representation based on an optimal segmentation concept that aims to reconstruct founder sequences from a multiple sequence alignment (MSA). Such founder sequences have the feature that each row of the MSA is a recombination of the founders. Several linear time dynamic programming algorithms have been previously devised to optimize segmentations that induce founder blocks that then can be concatenated into a set of founder sequences. All possible concatenation orders can be expressed as a founder block graph. We observe a key property of such graphs: if the node labels (founder segments) do not repeat in the paths of the graph, such graphs can be indexed for efficient string matching. We call such graphs segment repeat-free founder block graphs. We give a linear time algorithm to construct a segment repeat-free founder block graph given an MSA. The algorithm combines techniques from the founder segmentation algorithms (Cazaux et al. SPIRE 2019) and fully-functional bidirectional Burrows-Wheeler index (Belazzougui and Cunial, CPM 2019). We derive a succinct index structure to support queries of arbitrary length in the paths of the graph. Experiments on an MSA of SARS-CoV-2 strains are reported. An MSA of size 410× 29811 is compacted in one minute into a segment repeat-free founder block graph of 3900 nodes and 4440 edges. The maximum length and total length of node labels is 12 and 34968, respectively. The index on the graph takes only 3% of the size of the MSA.
Cite as

Veli Mäkinen, Bastien Cazaux, Massimo Equi, Tuukka Norri, and Alexandru I. Tomescu. Linear Time Construction of Indexable Founder Block Graphs. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 7:1-7:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{makinen_et_al:LIPIcs.WABI.2020.7,
  author =	{M\"{a}kinen, Veli and Cazaux, Bastien and Equi, Massimo and Norri, Tuukka and Tomescu, Alexandru I.},
  title =	{{Linear Time Construction of Indexable Founder Block Graphs}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{7:1--7:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.7},
  URN =		{urn:nbn:de:0030-drops-127961},
  doi =		{10.4230/LIPIcs.WABI.2020.7},
  annote =	{Keywords: Pangenome indexing, founder reconstruction, multiple sequence alignment, compressed data structures, string matching}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.8
GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs

Authors: Vijini G. Mallawaarachchi, Anuradha S. Wickramarachchi, and Yu Lin

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Metagenomic sequencing allows us to study structure, diversity and ecology in microbial communities without the necessity of obtaining pure cultures. In many metagenomics studies, the reads obtained from metagenomics sequencing are first assembled into longer contigs and these contigs are then binned into clusters of contigs where contigs in a cluster are expected to come from the same species. As different species may share common sequences in their genomes, one assembled contig may belong to multiple species. However, existing tools for contig binning only support non-overlapped binning, i.e., each contig is assigned to at most one bin (species). In this paper, we introduce GraphBin2 which refines the binning results obtained from existing tools and, more importantly, is able to assign contigs to multiple bins. GraphBin2 uses the connectivity and coverage information from assembly graphs to adjust existing binning results on contigs and to infer contigs shared by multiple species. Experimental results on both simulated and real datasets demonstrate that GraphBin2 not only improves binning results of existing tools but also supports to assign contigs to multiple bins.
Cite as

Vijini G. Mallawaarachchi, Anuradha S. Wickramarachchi, and Yu Lin. GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 8:1-8:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{mallawaarachchi_et_al:LIPIcs.WABI.2020.8,
  author =	{Mallawaarachchi, Vijini G. and Wickramarachchi, Anuradha S. and Lin, Yu},
  title =	{{GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{8:1--8:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.8},
  URN =		{urn:nbn:de:0030-drops-127974},
  doi =		{10.4230/LIPIcs.WABI.2020.8},
  annote =	{Keywords: Metagenomics binning, contigs, assembly graphs, overlapped binning}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.9
Fast and Efficient Rmap Assembly Using the Bi-Labelled de Bruijn Graph

Authors: Kingshuk Mukherjee, Massimiliano Rossi, Leena Salmela, and Christina Boucher

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Genome wide optical maps are high resolution restriction maps that give a unique numeric representation to a genome. They are produced by assembling hundreds of thousands of single molecule optical maps, which are called Rmaps. Unfortunately, there exists very few choices for assembling Rmap data. There exists only one publicly-available non-proprietary method for assembly and one proprietary method that is available via an executable. Furthermore, the publicly-available method, by Valouev et al. (2006), follows the overlap-layout-consensus (OLC) paradigm, and therefore, is unable to scale for relatively large genomes. The algorithm behind the proprietary method, Bionano Genomics' Solve, is largely unknown. In this paper, we extend the definition of bi-labels in the paired de Bruijn graph to the context of optical mapping data, and present the first de Bruijn graph based method for Rmap assembly. We implement our approach, which we refer to as rmapper, and compare its performance against the assembler of Valouev et al. (2006) and Solve by Bionano Genomics on data from three genomes - E. coli, human, and climbing perch fish (Anabas Testudineus). Our method was the only one able to successfully run on all three genomes. The method of Valouev et al. (2006) only successfully ran on E. coli and Bionano Solve successfully ran on E. coli and human but not on the fish genome. Moreover, on the human genome rmapper was at least 130 times faster than Bionano Solve, used five times less memory and produced the highest genome fraction with zero mis-assemblies.
Cite as

Kingshuk Mukherjee, Massimiliano Rossi, Leena Salmela, and Christina Boucher. Fast and Efficient Rmap Assembly Using the Bi-Labelled de Bruijn Graph. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 9:1-9:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{mukherjee_et_al:LIPIcs.WABI.2020.9,
  author =	{Mukherjee, Kingshuk and Rossi, Massimiliano and Salmela, Leena and Boucher, Christina},
  title =	{{Fast and Efficient Rmap Assembly Using the Bi-Labelled de Bruijn Graph}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{9:1--9:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.9},
  URN =		{urn:nbn:de:0030-drops-127982},
  doi =		{10.4230/LIPIcs.WABI.2020.9},
  annote =	{Keywords: optical maps, de Bruijn graph, assembly}
}
Document
DOI: 10.4230/LIPIcs.WABI.2020.10
Economic Genome Assembly from Low Coverage Illumina and Nanopore Data

Authors: Thomas Gatter, Sarah von Löhneysen, Polina Drozdova, Tom Hartmann, and Peter F. Stadler

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Ongoing developments in genome sequencing have caused a fundamental paradigm shift in the field in recent years. With ever lower sequencing costs, projects are no longer limited by available raw data, but rather by computational demands. The high complexity of eukaryotic genomes in concordance with increasing data sizes creates unique demands on methods to assemble full genomes. We describe a new approach to assemble genomes from a combination of low-coverage short and long reads. LazyB starts from a bipartite overlap graph between long reads and restrictively filtered short-read unitigs, which are then reduced to a long-read overlap graph G. Instead of the more conventional approach of removing tips, bubbles, and other local features, LazyB stepwisely extracts subgraphs whose global properties approach a disjoint union of paths. First, a consistently oriented subgraph is extracted, which in a second step is reduced to a directed acyclic graph. In the next step, properties of proper interval graphs are used to extract contigs as maximum weight paths. These are translated into genomic sequences only in the final step. A prototype implementation of LazyB, entirely written in python, not only yields significantly more accurate assemblies of the yeast and fruit fly genomes compared to state-of-the-art pipelines but also requires much less computational effort. Our findings demonstrate a new low-cost method that enables the assembly of even large genomes with low computational effort.
Cite as

Thomas Gatter, Sarah von Löhneysen, Polina Drozdova, Tom Hartmann, and Peter F. Stadler. Economic Genome Assembly from Low Coverage Illumina and Nanopore Data. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 10:1-10:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{gatter_et_al:LIPIcs.WABI.2020.10,
  author =	{Gatter, Thomas and von L\"{o}hneysen, Sarah and Drozdova, Polina and Hartmann, Tom and Stadler, Peter F.},
  title =	{{Economic Genome Assembly from Low Coverage Illumina and Nanopore Data}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{10:1--10:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.10},
  URN =		{urn:nbn:de:0030-drops-127991},
  doi =		{10.4230/LIPIcs.WABI.2020.10},
  annote =	{Keywords: Nanopore sequencing, Illumina sequencing, genome assembly, spanning tree, unitigs, anchors}
}
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