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Fast and accurate short read alignment with BurrowsWheeler transform
 BIOINFORMATICS, 2009, ADVANCE ACCESS
, 2009
"... Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hashtable based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to a ..."
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Cited by 2043 (22 self)
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Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hashtable based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for singleend reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented BWA, a new read alignment package that is based on backward search with BurrowsWheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20X faster than MAQ while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM format. Variant calling and other downstream analyses after the alignment can be achieved with the opensource SAMtools software package.
Ultrafast and memoryefficient alignment of short DNA sequences to the human genome. Genome biology
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Fast and accurate longread alignment with Burrows–Wheeler transform
, 2010
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Opportunistic Data Structures with Applications
, 2000
"... In this paper we address the issue of compressing and indexing data. We devise a data structure whose space occupancy is a function of the entropy of the underlying data set. We call the data structure opportunistic since its space occupancy is decreased when the input is compressible and this space ..."
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Cited by 290 (11 self)
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In this paper we address the issue of compressing and indexing data. We devise a data structure whose space occupancy is a function of the entropy of the underlying data set. We call the data structure opportunistic since its space occupancy is decreased when the input is compressible and this space reduction is achieved at no significant slowdown in the query performance. More precisely, its space occupancy is optimal in an informationcontent sense because a text T [1, u] is stored using O(H k (T )) + o(1) bits per input symbol in the worst case, where H k (T ) is the kth order empirical entropy of T (the bound holds for any fixed k). Given an arbitrary string P [1; p], the opportunistic data structure allows to search for the occ occurrences of P in T in O(p + occ log u) time (for any fixed > 0). If data are uncompressible we achieve the best space bound currently known [12]; on compressible data our solution improves the succinct suffix array of [12] and the classical suffix tree and suffix array data structures either in space or in query time or both.
Compressed fulltext indexes
 ACM COMPUTING SURVEYS
, 2007
"... Fulltext indexes provide fast substring search over large text collections. A serious problem of these indexes has traditionally been their space consumption. A recent trend is to develop indexes that exploit the compressibility of the text, so that their size is a function of the compressed text l ..."
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Cited by 267 (97 self)
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Fulltext indexes provide fast substring search over large text collections. A serious problem of these indexes has traditionally been their space consumption. A recent trend is to develop indexes that exploit the compressibility of the text, so that their size is a function of the compressed text length. This concept has evolved into selfindexes, which in addition contain enough information to reproduce any text portion, so they replace the text. The exciting possibility of an index that takes space close to that of the compressed text, replaces it, and in addition provides fast search over it, has triggered a wealth of activity and produced surprising results in a very short time, and radically changed the status of this area in less than five years. The most successful indexes nowadays are able to obtain almost optimal space and search time simultaneously. In this paper we present the main concepts underlying selfindexes. We explain the relationship between text entropy and regularities that show up in index structures and permit compressing them. Then we cover the most relevant selfindexes up to date, focusing on the essential aspects on how they exploit the text compressibility and how they solve efficiently various search problems. We aim at giving the theoretical background to understand and follow the developments in this area.
XMill: an Efficient Compressor for XML Data
, 1999
"... We describe a tool for compressing XML data, with applications in data exchange and archiving, which usually achieves about twice the compression ratio of gzip at roughly the same speed. The compressor, called XMill, incorporates and combines existing compressors in order to apply them to heterogene ..."
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Cited by 228 (0 self)
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We describe a tool for compressing XML data, with applications in data exchange and archiving, which usually achieves about twice the compression ratio of gzip at roughly the same speed. The compressor, called XMill, incorporates and combines existing compressors in order to apply them to heterogeneous XML data: it uses zlib, the library function for gzip, a collection of datatype specific compressors for simple data types, and, possibly, user defined compressors for application specific data types. 1 Introduction We have implemented a compressor/decompressor for XML data, to be used in data exchange and archiving, that achieves about twice the compression rate of generalpurpose compressors (gzip), at about the same speed. The tool can be downloaded from www.research.att.com/sw/tools/xmill/. XML is now being adopted by many organizations and industry groups, like the healthcare, banking, chemical, and telecommunications industries. The attraction in XML is that it is a selfdescribi...
Simple linear work suffix array construction
, 2003
"... Abstract. Suffix trees and suffix arrays are widely used and largely interchangeable index structures on strings and sequences. Practitioners prefer suffix arrays due to their simplicity and space efficiency while theoreticians use suffix trees due to lineartime construction algorithms and more exp ..."
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Cited by 214 (6 self)
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Abstract. Suffix trees and suffix arrays are widely used and largely interchangeable index structures on strings and sequences. Practitioners prefer suffix arrays due to their simplicity and space efficiency while theoreticians use suffix trees due to lineartime construction algorithms and more explicit structure. We narrow this gap between theory and practice with a simple lineartime construction algorithm for suffix arrays. The simplicity is demonstrated with a C++ implementation of 50 effective lines of code. The algorithm is called DC3, which stems from the central underlying concept of difference cover. This view leads to a generalized algorithm, DC, that allows a spaceefficient implementation and, moreover, supports the choice of a space–time tradeoff. For any v ∈ [1, √ n], it runs in O(vn) time using O(n / √ v) space in addition to the input string and the suffix array. We also present variants of the algorithm for several parallel and hierarchical memory models of computation. The algorithms for BSP and EREWPRAM models are asymptotically faster than all previous suffix tree or array construction algorithms.
Indexing compressed text
, 2005
"... We design two compressed data structures for the fulltext indexing problem that support efficient substring searches using roughly the space required for storing the text in compressed form. Our first compressed data structure retrieves the occ occurrences of a pattern P[1, p] within a text T [1, ..."
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Cited by 173 (14 self)
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We design two compressed data structures for the fulltext indexing problem that support efficient substring searches using roughly the space required for storing the text in compressed form. Our first compressed data structure retrieves the occ occurrences of a pattern P[1, p] within a text T [1, n] inO(p + occ log 1+ɛ n) time for any chosen ɛ, 0<ɛ<1. This data structure uses at most 5nHk(T)+o(n) bits of storage, where Hk(T)isthekth order empirical entropy of T. The space usage is �(n) bits in the worst case and o(n) bits for compressible texts. This data structure exploits the relationship between suffix arrays and the Burrows–Wheeler Transform, and can be regarded as a compressed suffix array. Our second compressed data structure achieves O(p + occ) query time using O(nHk(T) log ɛ n)+ o(n) bits of storage for any chosen ɛ, 0<ɛ<1. Therefore, it provides optimal outputsensitive query time using o(n log n) bits in the worst case. This second data structure builds upon the first one and exploits the interplay between two compressors: the Burrows–Wheeler Transform and theLZ78 algorithm.
Compressed representations of sequences and fulltext indexes
 ACM Transactions on Algorithms
, 2007
"... Abstract. Given a sequence S = s1s2... sn of integers smaller than r = O(polylog(n)), we show how S can be represented using nH0(S) + o(n) bits, so that we can know any sq, as well as answer rank and select queries on S, in constant time. H0(S) is the zeroorder empirical entropy of S and nH0(S) pro ..."
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Cited by 162 (79 self)
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Abstract. Given a sequence S = s1s2... sn of integers smaller than r = O(polylog(n)), we show how S can be represented using nH0(S) + o(n) bits, so that we can know any sq, as well as answer rank and select queries on S, in constant time. H0(S) is the zeroorder empirical entropy of S and nH0(S) provides an Information Theoretic lower bound to the bit storage of any sequence S via a fixed encoding of its symbols. This extends previous results on binary sequences, and improves previous results on general sequences where those queries are answered in O(log r) time. For larger r, we can still represent S in nH0(S) + o(n log r) bits and answer queries in O(log r / log log n) time. Another contribution of this paper is to show how to combine our compressed representation of integer sequences with an existing compression boosting technique to design compressed fulltext indexes that scale well with the size of the input alphabet Σ. Namely, we design a variant of the FMindex that indexes a string T [1, n] within nHk(T) + o(n) bits of storage, where Hk(T) is the kth order empirical entropy of T. This space bound holds simultaneously for all k ≤ α log Σ  n, constant 0 < α < 1, and Σ  = O(polylog(n)). This index counts the occurrences of an arbitrary pattern P [1, p] as a substring of T in O(p) time; it locates each pattern occurrence in O(log 1+ε n) time, for any constant 0 < ε < 1; and it reports a text substring of length ℓ in O(ℓ + log 1+ε n) time.