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1.
Nat Commun ; 15(1): 3126, 2024 Apr 11.
Artigo em Inglês | MEDLINE | ID: mdl-38605047

RESUMO

Long reads that cover more variants per read raise opportunities for accurate haplotype construction, whereas the genotype errors of single nucleotide polymorphisms pose great computational challenges for haplotyping tools. Here we introduce KSNP, an efficient haplotype construction tool based on the de Bruijn graph (DBG). KSNP leverages the ability of DBG in handling high-throughput erroneous reads to tackle the challenges. Compared to other notable tools in this field, KSNP achieves at least 5-fold speedup while producing comparable haplotype results. The time required for assembling human haplotypes is reduced to nearly the data-in time.


Assuntos
Algoritmos , Polimorfismo de Nucleotídeo Único , Humanos , Haplótipos/genética , Análise de Sequência de DNA/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Software
2.
Artigo em Inglês | MEDLINE | ID: mdl-20431146

RESUMO

Multiclass cancer classification on microarray data has provided the feasibility of cancer diagnosis across all of the common malignancies in parallel. Using multiclass cancer feature selection approaches, it is now possible to identify genes relevant to a set of cancer types. However, besides identifying the relevant genes for the set of all cancer types, it is deemed to be more informative to biologists if the relevance of each gene to specific cancer or subset of cancer types could be revealed or pinpointed. In this paper, we introduce two new definitions of multiclass relevancy features, i.e., full class relevant (FCR) and partial class relevant (PCR) features. Particularly, FCR denotes genes that serve as candidate biomarkers for discriminating all cancer types. PCR, on the other hand, are genes that distinguish subsets of cancer types. Subsequently, a Markov blanket embedded memetic algorithm is proposed for the simultaneous identification of both FCR and PCR genes. Results obtained on commonly used synthetic and real-world microarray data sets show that the proposed approach converges to valid FCR and PCR genes that would assist biologists in their research work. The identification of both FCR and PCR genes is found to generate improvement in classification accuracy on many microarray data sets. Further comparison study to existing state-of-the-art feature selection algorithms also reveals the effectiveness and efficiency of the proposed approach.


Assuntos
Algoritmos , Biologia Computacional/métodos , Genes , Neoplasias/genética , Biomarcadores Tumorais , Simulação por Computador , Bases de Dados Genéticas , Humanos , Cadeias de Markov , Análise de Sequência com Séries de Oligonucleotídeos
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