A General Framework for Dynamic Succinct and Compressed Data Structures

Klitzke, Patrick; Nicholson, Patrick K.

doi:10.1137/1.9781611974317.14

Cited by 8 publications

(6 citation statements)

References 27 publications

(60 reference statements)

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“…2014; Klitzke and Nicholson, 2016;Cordova and Navarro, 2016;Álvarez García et al, 2019). In our work, we replace L and W with the compressed, dynamic bit vector implementations of Prezza (2017).…”

Section: From Static To Dynamicmentioning

confidence: 99%

Succinct dynamic de Bruijn graphs

et al. 2021

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Motivation The de Bruijn graph is one of the fundamental data structures for analysis of high throughput sequencing data. In order to be applicable to population-scale studies, it is essential to build and store the graph in a space- and time- efficient manner. In addition, due to the ever-changing nature of population studies, it has become essential to update the graph after construction e.g. add and remove nodes and edges. Although there has been substantial effort on making the construction and storage of the graph efficient, there is a limited amount of work in building the graph in an efficient and mutable manner. Hence, most space efficient data structures require complete reconstruction of the graph in order to add or remove edges or nodes. Results In this paper we present DynamicBOSS, a succinct representation of the de Bruijn graph that allows for an unlimited number of additions and deletions of nodes and edges. We compare our method with other competing methods and demonstrate that DynamicBOSS is the only method that supports both addition and deletion and is applicable to very large samples (e.g. greater than 15 billion k-mers). Competing dynamic methods e.g., FDBG (Crawford et al., 2018) cannot be constructed on large scale datasets, or cannot support both addition and deletion e.g., BiFrost (Holley and Melsted, 2019). Availability DynamicBOSS is publicly available at https://github.com/baharpan/dynboss. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: From Static To Dynamicmentioning

confidence: 99%

Succinct dynamic de Bruijn graphs

et al. 2021

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“…Recently, many dynamic, compressed bit vectors have been proposed (Mäkinen and Navarro, 2006;Grossi et al, 2013;Navarro and Nekrich, 2014; Klitzke and Nicholson, 2016;Cordova and Navarro, 2016;Álvarez García et al, 2019). In our work, we replace L and W with the compressed, dynamic bit vector implementations of Prezza (2017).…”

Section: From Static To Dynamicmentioning

confidence: 99%

Succinct Dynamic de Bruijn Graphs

Alipanahi

Kuhnle

Puglisi

et al. 2020

Preprint

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Motivation:The de Bruijn graph is one of the fundamental data structures for analysis of high throughput sequencing data. In order to be applicable to population-scale studies, it is essential to build and store the graph in a space-and time-efficient manner. In addition, due to the ever-changing nature of population studies, it has become essential to update the graph after construction e.g. add and remove nodes and edges. Although there has been substantial effort on making the construction and storage of the graph efficient, there is a limited amount of work in building the graph in an efficient and mutable manner. Hence, most space efficient data structures require complete reconstruction of the graph in order to add or remove edges or nodes. Results: In this paper we present DynamicBOSS, a succinct representation of the de Bruijn graph that allows for an unlimited number of additions and deletions of nodes and edges. We compare our method with other competing methods and demonstrate that DynamicBOSS is the only method that supports both addition and deletion and is applicable to very large samples (e.g. greater than 15 billion k-mers). Competing dynamic methods e.g., FDBG (Crawford et al., 2018) cannot be constructed on large scale datasets, or cannot support both addition and deletion e.g., BiFrost

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“…Dynamic compact data structures are in their infancy, and to date mostly of theoretical interest [18,21,22,27,28].…”

Section: Introductionmentioning

confidence: 99%

Compact Dynamic Rewritable (CDRW) Arrays

Poyias

Puglisi

Raman

2017

2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX)

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In this paper we consider the problem of compactly representing a rewritable array of bit-strings. The operations supported are: create(N, k), which creates a new array of size N , where each entry is of size at most k bits and equal to 0; set(i, v), which sets A[i] to v, provided that v is at most k bits long and get ( On the word RAM model with word size w, for n < 2 w and k ≤ w, we give practical solutions based on compact hashing that achieve O(1/ ) expected time for get and set and use (1 + )S + O(N ) bits, for any constant > 0. Experimental evaluation of our (preliminary, only somewhat optimized) implementations shows excellent performance in terms of both space and time, particularly when heuristics are added to our base algorithms.

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A General Framework for Dynamic Succinct and Compressed Data Structures

Cited by 8 publications

References 27 publications

Succinct dynamic de Bruijn graphs

Succinct dynamic de Bruijn graphs

Succinct Dynamic de Bruijn Graphs

Compact Dynamic Rewritable (CDRW) Arrays

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