Algorithm Engineering in Bioinformatics

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1547

Special Issue Editors


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Guest Editor
DEETC, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa e INESC-ID, R. Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
Interests: algorithms and data structures; data science and bioinformatics

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Guest Editor
DEI, Instituto Superior Técnico, Universidade de Lisboa e INESC-ID, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
Interests: algorithm engineering

Special Issue Information

Dear Colleagues,

The production of large-scale data, such as those resulting from the analysis of bacterial populations in industry, and the complexity of analysis procedures in bioinformatics and computational biology, as well as the reproducibility of results, have posed numerous challenges to computer engineering in general and algorithm engineering in particular. The volume of data, their properties, and the inherent optimization problems have led to the extension and development of new, efficient algorithms and data structures for indexing, compressing, and succinctly representing sequences, trees, and graphs; aligning sequences and trees; the determination and inference of patterns; and information visualization. Furthermore, in real applications in the field of bioinformatics, where input data are often biased and where the hardware used may differ from that in theoretical models, the results are not always what is initially expected. In this context, algorithm-engineering-based approaches are fundamental to transfer ideas and theoretical results, to design and analyze, to experimentally evaluate, and to implement algorithms in libraries and applications in the scope of bioinformatics and computational biology.

Dr. Cátia Vaz
Dr. Alexandre P. Francisco
Guest Editors

Manuscript Submission Information

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Keywords

  • genome scale algorithm engineering
  • combinatorial optimization in bioinformatics
  • sequence and genome analysis
  • network science for computational biology
  • machine learning and artificial intelligence in bioinformatics

Published Papers (1 paper)

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21 pages, 563 KiB  
Article
Computing RF Tree Distance over Succinct Representations
by António Pedro Branco, Cátia Vaz and Alexandre P. Francisco
Algorithms 2024, 17(1), 15; https://doi.org/10.3390/a17010015 - 28 Dec 2023
Viewed by 1318
Abstract
There are several tools available to infer phylogenetic trees, which depict the evolutionary relationships among biological entities such as viral and bacterial strains in infectious outbreaks or cancerous cells in tumor progression trees. These tools rely on several inference methods available to produce [...] Read more.
There are several tools available to infer phylogenetic trees, which depict the evolutionary relationships among biological entities such as viral and bacterial strains in infectious outbreaks or cancerous cells in tumor progression trees. These tools rely on several inference methods available to produce phylogenetic trees, with resulting trees not being unique. Thus, methods for comparing phylogenies that are capable of revealing where two phylogenetic trees agree or differ are required. An approach is then proposed to compute a similarity or dissimilarity measure between trees, with the Robinson–Foulds distance being one of the most used, and which can be computed in linear time and space. Nevertheless, given the large and increasing volume of phylogenetic data, phylogenetic trees are becoming very large with hundreds of thousands of leaves. In this context, space requirements become an issue both while computing tree distances and while storing trees. We propose then an efficient implementation of the Robinson–Foulds distance over tree succinct representations. Our implementation also generalizes the Robinson–Foulds distances to labelled phylogenetic trees, i.e., trees containing labels on all nodes, instead of only on leaves. Experimental results show that we are able to still achieve linear time while requiring less space. Our implementation in C++ is available as an open-source tool. Full article
(This article belongs to the Special Issue Algorithm Engineering in Bioinformatics)
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