IEICE TRANSACTIONS on Fundamentals
Efficient Methods for Determining DNA Probe Orders
Summary :
In STS-based mapping, it is necessary to obtain the correct order of probes in a DNA sequence from a given set of fragments or an equivalently a hybridization matrix A. It is well-known that the problem is formulated as the combinatorial problem of obtaining a permutation of A's columns so that the resulting matrix has a consecutive-one property. If the data (the hybridization matrix) is error free and includes enough information, then the above column order uniquely determines the correct order of the probes. Unfortunately this does not hold if the data include errors, and this has been a popular research target in computational biology. Even if there is no error, ambiguities in the probe order may still remain. This in fact happens because of the lack of some information regarding the data, but almost no further investigation has previously been made. In this paper, we define a measure of such imperfectness of the data as the minimum amount of the additional fragments that are needed to uniquely fix the probe order. Polynomial-time algorithms to compute such additional fragments of the minimum cost are presented. A computer simulation using genes of human chromosome 20 is also noted.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.5 pp.1292-1298
- Publication Date
- 2006/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e89-a.5.1292
- Type of Manuscript
- Special Section PAPER (Special Section on Discrete Mathematics and Its Applications)
- Category
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Hiro ITO, Kazuo IWAMA, Takeyuki TAMURA, "Efficient Methods for Determining DNA Probe Orders" in IEICE TRANSACTIONS on Fundamentals,
vol. E89-A, no. 5, pp. 1292-1298, May 2006, doi: 10.1093/ietfec/e89-a.5.1292.
Abstract: In STS-based mapping, it is necessary to obtain the correct order of probes in a DNA sequence from a given set of fragments or an equivalently a hybridization matrix A. It is well-known that the problem is formulated as the combinatorial problem of obtaining a permutation of A's columns so that the resulting matrix has a consecutive-one property. If the data (the hybridization matrix) is error free and includes enough information, then the above column order uniquely determines the correct order of the probes. Unfortunately this does not hold if the data include errors, and this has been a popular research target in computational biology. Even if there is no error, ambiguities in the probe order may still remain. This in fact happens because of the lack of some information regarding the data, but almost no further investigation has previously been made. In this paper, we define a measure of such imperfectness of the data as the minimum amount of the additional fragments that are needed to uniquely fix the probe order. Polynomial-time algorithms to compute such additional fragments of the minimum cost are presented. A computer simulation using genes of human chromosome 20 is also noted.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.5.1292/_p
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@ARTICLE{e89-a_5_1292,
author={Hiro ITO, Kazuo IWAMA, Takeyuki TAMURA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Efficient Methods for Determining DNA Probe Orders},
year={2006},
volume={E89-A},
number={5},
pages={1292-1298},
abstract={In STS-based mapping, it is necessary to obtain the correct order of probes in a DNA sequence from a given set of fragments or an equivalently a hybridization matrix A. It is well-known that the problem is formulated as the combinatorial problem of obtaining a permutation of A's columns so that the resulting matrix has a consecutive-one property. If the data (the hybridization matrix) is error free and includes enough information, then the above column order uniquely determines the correct order of the probes. Unfortunately this does not hold if the data include errors, and this has been a popular research target in computational biology. Even if there is no error, ambiguities in the probe order may still remain. This in fact happens because of the lack of some information regarding the data, but almost no further investigation has previously been made. In this paper, we define a measure of such imperfectness of the data as the minimum amount of the additional fragments that are needed to uniquely fix the probe order. Polynomial-time algorithms to compute such additional fragments of the minimum cost are presented. A computer simulation using genes of human chromosome 20 is also noted.},
keywords={},
doi={10.1093/ietfec/e89-a.5.1292},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Efficient Methods for Determining DNA Probe Orders
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1292
EP - 1298
AU - Hiro ITO
AU - Kazuo IWAMA
AU - Takeyuki TAMURA
PY - 2006
DO - 10.1093/ietfec/e89-a.5.1292
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2006
AB - In STS-based mapping, it is necessary to obtain the correct order of probes in a DNA sequence from a given set of fragments or an equivalently a hybridization matrix A. It is well-known that the problem is formulated as the combinatorial problem of obtaining a permutation of A's columns so that the resulting matrix has a consecutive-one property. If the data (the hybridization matrix) is error free and includes enough information, then the above column order uniquely determines the correct order of the probes. Unfortunately this does not hold if the data include errors, and this has been a popular research target in computational biology. Even if there is no error, ambiguities in the probe order may still remain. This in fact happens because of the lack of some information regarding the data, but almost no further investigation has previously been made. In this paper, we define a measure of such imperfectness of the data as the minimum amount of the additional fragments that are needed to uniquely fix the probe order. Polynomial-time algorithms to compute such additional fragments of the minimum cost are presented. A computer simulation using genes of human chromosome 20 is also noted.
ER -
English
