Safely Filling Gaps with Partial Solutions Common to All Solutions

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Gap filling has emerged as a natural sub-problem of many de novo genome assembly projects. The gap filling problem generally asks for an s-t path in an assembly graph whose length matches the gap length estimate. Several methods have addressed it, but only few have focused on strategies for dealing with multiple gap filling solutions and for guaranteeing reliable results. Such strategies include reporting only unique solutions, or exhaustively enumerating all filling solutions and heuristically creating their consensus. Our main contribution is a new method for reliable gap filling: filling gaps with those sub-paths common to all gap filling solutions. We call these partial solutions safe, following the framework of (Tomescu and Medvedev, RECOMB 2016). We give an efficient safe algorithm running in O(dm) time and space, where d is the gap length estimate and m is the number of edges of the assembly graph. To show the benefits of this method, we implemented this algorithm for the problem of filling gaps in scaffolds. Our experimental results on bacterial and on conservative human assemblies show that, on average, our method can retrieve over 73 percent more safe and correct bases as compared to previous methods, with a similar precision.

Original languageEnglish
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume16
Issue number2
Pages (from-to)617-626
Number of pages10
ISSN1545-5963
DOIs
Publication statusPublished - 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 113 Computer and information sciences

Cite this

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title = "Safely Filling Gaps with Partial Solutions Common to All Solutions",
abstract = "Gap filling has emerged as a natural sub-problem of many de novo genome assembly projects. The gap filling problem generally asks for an s-t path in an assembly graph whose length matches the gap length estimate. Several methods have addressed it, but only few have focused on strategies for dealing with multiple gap filling solutions and for guaranteeing reliable results. Such strategies include reporting only unique solutions, or exhaustively enumerating all filling solutions and heuristically creating their consensus. Our main contribution is a new method for reliable gap filling: filling gaps with those sub-paths common to all gap filling solutions. We call these partial solutions safe, following the framework of (Tomescu and Medvedev, RECOMB 2016). We give an efficient safe algorithm running in O(dm) time and space, where d is the gap length estimate and m is the number of edges of the assembly graph. To show the benefits of this method, we implemented this algorithm for the problem of filling gaps in scaffolds. Our experimental results on bacterial and on conservative human assemblies show that, on average, our method can retrieve over 73 percent more safe and correct bases as compared to previous methods, with a similar precision.",
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author = "Leena Salmela and Tomescu, {Alexandru I.}",
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Safely Filling Gaps with Partial Solutions Common to All Solutions. / Salmela, Leena; Tomescu, Alexandru I.

In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 16, No. 2, 2019, p. 617-626.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Safely Filling Gaps with Partial Solutions Common to All Solutions

AU - Salmela, Leena

AU - Tomescu, Alexandru I.

PY - 2019

Y1 - 2019

N2 - Gap filling has emerged as a natural sub-problem of many de novo genome assembly projects. The gap filling problem generally asks for an s-t path in an assembly graph whose length matches the gap length estimate. Several methods have addressed it, but only few have focused on strategies for dealing with multiple gap filling solutions and for guaranteeing reliable results. Such strategies include reporting only unique solutions, or exhaustively enumerating all filling solutions and heuristically creating their consensus. Our main contribution is a new method for reliable gap filling: filling gaps with those sub-paths common to all gap filling solutions. We call these partial solutions safe, following the framework of (Tomescu and Medvedev, RECOMB 2016). We give an efficient safe algorithm running in O(dm) time and space, where d is the gap length estimate and m is the number of edges of the assembly graph. To show the benefits of this method, we implemented this algorithm for the problem of filling gaps in scaffolds. Our experimental results on bacterial and on conservative human assemblies show that, on average, our method can retrieve over 73 percent more safe and correct bases as compared to previous methods, with a similar precision.

AB - Gap filling has emerged as a natural sub-problem of many de novo genome assembly projects. The gap filling problem generally asks for an s-t path in an assembly graph whose length matches the gap length estimate. Several methods have addressed it, but only few have focused on strategies for dealing with multiple gap filling solutions and for guaranteeing reliable results. Such strategies include reporting only unique solutions, or exhaustively enumerating all filling solutions and heuristically creating their consensus. Our main contribution is a new method for reliable gap filling: filling gaps with those sub-paths common to all gap filling solutions. We call these partial solutions safe, following the framework of (Tomescu and Medvedev, RECOMB 2016). We give an efficient safe algorithm running in O(dm) time and space, where d is the gap length estimate and m is the number of edges of the assembly graph. To show the benefits of this method, we implemented this algorithm for the problem of filling gaps in scaffolds. Our experimental results on bacterial and on conservative human assemblies show that, on average, our method can retrieve over 73 percent more safe and correct bases as compared to previous methods, with a similar precision.

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