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PMID: 7497129 Published · ppublish English Journal Article Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

Toward simplifying and accurately formulating fragment assembly.

Myers EW

Abstract

The fragment assembly problem is that of reconstructing a DNA sequence from a collection of randomly sampled fragments. Traditionally, the objective of this problem has been to produce the shortest string that contains all the fragments as substrings, but in the case of repetitive target sequences this objective produces answers that are overcompressed. In this paper, the problem is reformulated as one of finding a maximum-likelihood reconstruction with respect to the two-sided Kolmogorov-Smirnov statistic, and it is argued that this is a better formulation of the problem. Next the fragment assembly problem is recast in graph-theoretic terms as one of finding a noncyclic subgraph with certain properties and the objectives of being shortest or maximally likely are also recast in this framework. Finally, a series of graph reduction transformations are given that dramatically reduce the size of the graph to be explored in practical instances of the problem. This reduction is very important as the underlying problems are NP-hard. In practice, the transformed problems are so small that simple branch-and-bound algorithms successfully solve them, thus permitting auxiliary experimental information to be taken into account in the form of overlap, orientation, and distance constraints.

MeSH Terms
Base Sequence DNA/chemistry Mathematics Models, Statistical Oligodeoxyribonucleotides Probability Reproducibility of Results
Chemicals
Oligodeoxyribonucleotides DNA
Authors & Affiliations
1 authors, click to expand affiliations / ORCID
Myers E W
Department of Computer Science, University of Arizona, Tucson 85721, USA.
Article Info
Journal
Journal of computational biology : a journal of computational molecular cell biology
Abbr.
J Comput Biol
ISSN
1066-5277
Published
1995-00-00
Pages
275-90
Language
English
Region
United States
NLM ID
9433358
Subset
IM
Grants
NLM NIH HHS · LM-04960 · United States
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