Graph-Based Representations for Supporting Genome Data Analysis and Visualization: Opportunities and Challenges

被引：5

作者：

Carletti, Vincenzo ^{[1
]}

Foggia, Pasquale ^{[1
]}

Garrison, Erik ^{[2
]}

Greco, Luca ^{[1
]}

Ritrovato, Pierluigi ^{[1
]}

Vento, Mario ^{[1
]}

机构：

[1] Univ Salerno, DIEM, Fisciano, Italy

[2] Univ Calif Santa Cruz, Genom Inst, Santa Cruz, CA 95064 USA

来源：

GRAPH-BASED REPRESENTATIONS IN PATTERN RECOGNITION, GBRPR 2019 | 2019年 / 11510卷

关键词：

Graph representation of genomic data; Sequence graphs; De Bruijn graphs; Genome graphs; ALGORITHMS; ALIGNMENT;

D O I：

10.1007/978-3-030-20081-7_23

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Genetics has known an extraordinary development in the last years, with a reduction of several orders of magnitude in the costs and the times required to obtain the sequence of nucleotides corresponding to a whole genome, leading to the availability of huge amounts of genomic data. While these data are essentially very long strings, several graphbased representations have been introduced to perform efficiently some operations on a single genome or on a set of related genomes. In this paper we will review the most important types of genetic graphs, together with the algorithmic challenges and open issues related to their use.

引用

页码：237 / 246

页数：10

共 19 条

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[2]

[Anonymous], 2018, BIORXIV

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