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Efficient Compression and Indexing for Highly Repetitive DNA Sequence Collections | IEEE Journals & Magazine | IEEE Xplore

Efficient Compression and Indexing for Highly Repetitive DNA Sequence Collections


Abstract:

In this paper, we focus upon the important problem of indexing and searching highly repetitive DNA sequence collections. Given a collection \mathcal {G} of t sequence...Show More

Abstract:

In this paper, we focus upon the important problem of indexing and searching highly repetitive DNA sequence collections. Given a collection \mathcal {G} of t sequences \mathcal {S}_{i} of length n each, we can represent \mathcal {G} succinctly in 2n\mathcal {H}_{k}(\mathcal {T}) + \mathcal {O}(n^{\prime }\ {\log \log n}) + o(q n^{\prime }) + o(tn) bits using \mathcal {O}(t n^{2} + q n^{\prime }) time, where \mathcal {H}_{k}(\mathcal {T}) is the kth-order empirical entropy of the sequence \mathcal {T} \in \mathcal {G} that is used as the reference sequence, n^{\prime } is the total number of variations between \mathcal {T} and the sequences in \mathcal {G}, and q is a small fixed constant. We can restore any length {len} substring \mathcal {S}[ {sp}, \dots, {sp} + {len}-1] of \mathcal {S} \in \mathcal {G} in \mathcal {O}\bigl (n_{s}^{\prime } + {len}(\log n)^{2} / {\log \log n}\bigr) time and report all positions where P occurs in \mathcal {G} in \mathcal {O}\bigl (m \cdot t + {occ} \cdot t \cdot (\log n)^{2}/\log \log n \bigr) time. In addition, we propose a dynamic programming method to find the variations between \mathcal {T} and the sequences in \mathcal {G} in a space-efficient way, with which we can build succinct structures to enable efficient search. For highly repetitive sequences, experimental results on the tested data demonstrate that the proposed method has significant advantages in space usage and retrieval time over the current state-of-the-art methods. The source code is available online.
Published in: IEEE/ACM Transactions on Computational Biology and Bioinformatics ( Volume: 18, Issue: 6, 01 Nov.-Dec. 2021)
Page(s): 2394 - 2408
Date of Publication: 22 January 2020

ISSN Information:

PubMed ID: 31985436

Funding Agency:


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