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Linearization of ancestral multichromosomal genomes
- Source :
- BMC Bioinformatics, BMC Bioinformatics, 2012, 13 (Suppl 19), pp.S11. ⟨10.1186/1471-2105-13-S19-S11⟩, BMC Bioinformatics (13), . (2012), BMC Bioinformatics, BioMed Central, 2012, 13 (Suppl 19), pp.S11. ⟨10.1186/1471-2105-13-S19-S11⟩
- Publication Year :
- 2012
- Publisher :
- HAL CCSD, 2012.
-
Abstract
- Background Recovering the structure of ancestral genomes can be formalized in terms of properties of binary matrices such as the Consecutive-Ones Property (C1P). The Linearization Problem asks to extract, from a given binary matrix, a maximum weight subset of rows that satisfies such a property. This problem is in general intractable, and in particular if the ancestral genome is expected to contain only linear chromosomes or a unique circular chromosome. In the present work, we consider a relaxation of this problem, which allows ancestral genomes that can contain several chromosomes, each either linear or circular. Result We show that, when restricted to binary matrices of degree two, which correspond to adjacencies, the genomic characters used in most ancestral genome reconstruction methods, this relaxed version of the Linearization Problem is polynomially solvable using a reduction to a matching problem. This result holds in the more general case where columns have bounded multiplicity, which models possibly duplicated ancestral genes. We also prove that for matrices with rows of degrees 2 and 3, without multiplicity and without weights on the rows, the problem is NP-complete, thus tracing sharp tractability boundaries. Conclusion As it happened for the breakpoint median problem, also used in ancestral genome reconstruction, relaxing the definition of a genome turns an intractable problem into a tractable one. The relaxation is adapted to some biological contexts, such as bacterial genomes with several replicons, possibly partially assembled. Algorithms can also be used as heuristics for hard variants. More generally, this work opens a way to better understand linearization results for ancestral genome structure inference.
- Subjects :
- Ancestral Genome Reconstruction, Linearization, Algorithms, Computational Complexity
Matching (graph theory)
Computational complexity theory
Bioinformatics
0206 medical engineering
02 engineering and technology
Bacterial genome size
génome extra chromosomique
Biology
Biochemistry
Genome
Chromosomes
Reduction (complexity)
Combinatorics
Evolution, Molecular
03 medical and health sciences
Structural Biology
Linearization
[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]
Logical matrix
Molecular Biology
030304 developmental biology
Genetics
0303 health sciences
Applied Mathematics
génome
matrice
Genomics
Quantitative Biology::Genomics
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Computer Science Applications
Proceedings
reconstitution
Bio-informatique
Relaxation (approximation)
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
chromosome linéaire
020602 bioinformatics
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 14712105
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics, BMC Bioinformatics, 2012, 13 (Suppl 19), pp.S11. ⟨10.1186/1471-2105-13-S19-S11⟩, BMC Bioinformatics (13), . (2012), BMC Bioinformatics, BioMed Central, 2012, 13 (Suppl 19), pp.S11. ⟨10.1186/1471-2105-13-S19-S11⟩
- Accession number :
- edsair.doi.dedup.....7b591922201e3f8f47c734e7b39b5c00