arXiv subjects

Giovanni Manzini

Publications and source records attributed to Giovanni Manzini.

41 records · Page 3Linked to original sources

arXiv2016

From H&M to Gap for Lightweight BWT Merging

Recently, Holt and McMillan [Bionformatics 2014, ACM-BCB 2014] have proposed a simple and elegant algorithm to merge the Burrows-Wheeler transforms of a family of strings. In this paper we show that the H&M algorithm can be improved so that, in addition to merging the BWTs, it can also merge the Longest Common Prefix (LCP) arrays. The new algorithm, called Gap because of how it operates, has the same asymptotic cost as the H&M algorithm and requires additional space only for storing the LCP values.

cs.DS↗

arXiv2015

Relative Select

Motivated by the problem of storing coloured de Bruijn graphs, we show how, if we can already support fast select queries on one string, then we can store a little extra information and support fairly fast select queries on a similar string.

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arXiv2014

Reusing an FM-index

Intuitively, if two strings $S_1$ and $S_2$ are sufficiently similar and we already have an FM-index for $S_1$ then, by storing a little extra information, we should be able to reuse parts of that index in an FM-index for $S_2$. We formalize this intuition and show that it can lead to significant space savings in practice, as well as to some interesting theoretical problems.

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arXiv2014

Compressed Spaced Suffix Arrays

Spaced seeds are important tools for similarity search in bioinformatics, and using several seeds together often significantly improves their performance. With existing approaches, however, for each seed we keep a separate linear-size data structure, either a hash table or a spaced suffix array (SSA). In this paper we show how to compress SSAs relative to normal suffix arrays (SAs) and still support fast random access to them. We first prove a theoretical upper bound on the space needed to store an SSA when we already have the SA. We then present experiments indicating that our approach works even better in practice.

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arXiv2009

Lightweight Data Indexing and Compression in External Memory

In this paper we describe algorithms for computing the BWT and for building (compressed) indexes in external memory. The innovative feature of our algorithms is that they are lightweight in the sense that, for an input of size $n$, they use only ${n}$ bits of disk working space while all previous approaches use $\Th{n \log n}$ bits of disk working space. Moreover, our algorithms access disk data only via sequential scans, thus they take full advantage of modern disk features that make sequential disk accesses much faster than random accesses. We also present a scan-based algorithm for inverting the BWT that uses $\Th{n}$ bits of working space, and a lightweight {\em internal-memory} algorithm for computing the BWT which is the fastest in the literature when the available working space is $\os{n}$ bits. Finally, we prove {\em lower} bounds on the complexity of computing and inverting the BWT via sequential scans in terms of the classic product: internal-memory space $\times$ number of passes over the disk data.

cs.DS↗