GeneTRACE-reconstruction of gene content of ancestral species
نویسندگان
چکیده
منابع مشابه
GeneTRACE - Reconstruction of Gene Content of Ancestral Species
While current computational methods allow the reconstruction of individual ancestral protein sequences, reconstruction of complete gene content of ancestral species is not yet an established task. In this paper, we describe GENETRACE, an efficient linear-time algorithm that allows the reconstruction of evolutionary history of individual protein families as well as the complete gene content of a...
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Ancestral sequence reconstruction is a well-known problem in molecular evolution. The problem presented in this study is inspired by sequence reconstruction, but instead of leaf-associated sequences we consider only their lengths. We call this problem ancestral gene length reconstruction. It is a problem of finding an optimal labeling which minimizes the total length's sum of the edges, where b...
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در این پایان نامه تاثیر دو نوع تست جزیی نگر و کلی نگر بر به یادسپاری محتوا ارزیابی شده که نتایج نشان دهندهکارایی تستهای کلی نگر بیشتر از سایر آزمونها است
15 صفحه اولReconstructing ancestral gene content by coevolution.
Inferring the gene content of ancestral genomes is a fundamental challenge in molecular evolution. Due to the statistical nature of this problem, ancestral genomes inferred by the maximum likelihood (ML) or the maximum-parsimony (MP) methods are prone to considerable error rates. In general, these errors are difficult to abolish by using longer genomic sequences or by analyzing more taxa. This ...
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Ancestral reconstruction is the extrapolation back in time from measured characteristics of individuals (or populations) to their common ancestors. It is an important application of phylogenetics, the reconstruction and study of the evolutionary relationships among individuals, populations, or species to their ancestors. In the context of biology, ancestral reconstruction can be used to recover...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2003
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btg174