An optimal procedure for gap closing in whole genome shotgun sequencing

Beigel, Richard; Alon, Noga; Kasif, Simon; Apaydın, Mehmet Serkan; Fortnow, Lance

doi:10.1145/369133.369152

Cited by 43 publications

(34 citation statements)

References 8 publications

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“…The problem of actively discovering various networks from connectivity queries has been much studied in [2,6,8,9,14,18]. In active learning of hidden networks, the object of the algorithm is to learn the network exactly.…”

Section: Past Workmentioning

confidence: 99%

Inferring Social Networks from Outbreaks

Angluin¹,

Aspnes²,

Reyzin³

2010

Lecture Notes in Computer Science

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Abstract. We consider the problem of inferring the most likely social network given connectivity constraints imposed by observations of outbreaks within the network. Given a set of vertices (or agents) V and constraints (or observations) Si ⊆ V we seek to find a minimum loglikelihood cost (or maximum likelihood) set of edges (or connections) E such that each Si induces a connected subgraph of (V, E). For the offline version of the problem, we prove an Ω(log(n)) hardness of approximation result for uniform cost networks and give an algorithm that almost matches this bound, even for arbitrary costs. Then we consider the online problem, where the constraints are satisfied as they arrive. We give an O(n log(n))-competitive algorithm for the arbitrary cost online problem, which has an Ω(n)-competitive lower bound. We look at the uniform cost case as well and give an O(n 2/3 log 2/3 (n))-competitive algorithm against an oblivious adversary, as well as an Ω( √ n)-competitive lower bound against an adaptive adversary. We examine cases when the underlying network graph is known to be a star or a path, and prove matching upper and lower bounds of Θ(log(n)) on the competitive ratio for them.

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Section: Past Workmentioning

confidence: 99%

Inferring Social Networks from Outbreaks

Angluin¹,

Aspnes²,

Reyzin³

2010

Lecture Notes in Computer Science

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“…Considerable effort, for example, Grebinski and Kucherov (1998), Beigel et al (2001), Alon et al (2004), Angluin and Chen (2004), and Alon and Asodi (2005), has been devoted to the case when the underlying reaction network is a graph, that is, chemicals react in pairs. Among them, Grebinski and Kucherov (1998), Beigel et al (2001) and Alon et al (2004) study the case when the underlying networks are Hamiltonian cycles or matchings, which have specific applications to genome sequencing.…”

Section: Q H (S) : Does S Include At Least One Edge Of H?mentioning

confidence: 99%

“…Among them, Grebinski and Kucherov (1998), Beigel et al (2001) and Alon et al (2004) study the case when the underlying networks are Hamiltonian cycles or matchings, which have specific applications to genome sequencing. In this application, DNA sequences are aligned according to the reactions that involve the two ends of pairs of DNA sequences in certain experimental settings.…”

Section: Q H (S) : Does S Include At Least One Edge Of H?mentioning

confidence: 99%

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Learning a Hidden Hypergraph

Angluin

Jiang

2005

Learning Theory

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We consider the problem of learning a hypergraph using edge-detecting queries. In this model, the learner may query whether a set of vertices induces an edge of the hidden hypergraph or not. We show that an r-uniform hypergraph with m edges and n vertices is learnable with O(2 4r m · poly(r, log n)) queries with high probability. The queries can be made in O(min(2 r (log m + r) 2 , (log m + r) 3 )) rounds. We also give an algorithm that learns an almost uniform hypergraph of dimension r using O(22 · poly(log n)) queries with high probability, where ∆ is the difference between the maximum and the minimum edge sizes. This upper bound matches our lower bound of Ω((2 ) for this class of hypergraphs in terms of dependence on m. The queries can also be made in O((1 + ∆) · min(2 r (log m + r) 2 , (log m + r) 3 )) rounds.

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“…Our extensive experience in finishing genomes had taught us the value of using all available information for scaffolding, not only the information contained in read pairs but also diverse types of independent mapping data. The laboratory procedures for ordering and orienting contigs are much more expensive than generating shotgun reads (Tettelin et al 1999;Beigel et al 2001), thus any additional information we can obtain electronically leads to considerable savings. We have developed a flexible scaffolder, Bambus, designed to accommodate large (including mammalian-sized) genomes and to use a variety of sources of linking information.…”

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confidence: 99%

Hierarchical Scaffolding With Bambus

Pop¹,

Kosack

Salzberg³

2004

Genome Res.

187

175

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The output of a genome assembler generally comprises a collection of contiguous DNA sequences (contigs) whose relative placement along the genome is not defined. A procedure called scaffolding is commonly used to order and orient these contigs using paired read information. This ordering of contigs is an essential step when finishing and analyzing the data from a whole-genome shotgun project. Most recent assemblers include a scaffolding module; however, users have little control over the scaffolding algorithm or the information produced. We thus developed a general-purpose scaffolder, called Bambus, which affords users significant flexibility in controlling the scaffolding parameters. Bambus was used recently to scaffold the low-coverage draft dog genome data. Most significantly, Bambus enables the use of linking data other than that inferred from mate-pair information. For example, the sequence of a completed genome can be used to guide the scaffolding of a related organism. We present several applications of Bambus: support for finishing, comparative genomics, analysis of the haplotype structure of genomes, and scaffolding of a mammalian genome at low coverage. Bambus is available as an open-source package from our Web site.Large-scale whole-genome shotgun sequencing (WGS) was successfully used in 1995 to sequence, for the first time, the complete genome of a free-living organism, Haemophilus influenzae (Fleischmann et al. 1995), a 1.83 million-base-pair (Mb) bacterium. Previously this technique-pioneered by Fred Sanger in 1982 (Sanger et al. 1982)-had only been used to sequence DNA molecules of at most 200 thousand base pairs (kb). Shotgun sequencing involves breaking up the DNA at random into a collection of small fragments, sequencing those fragments, then using a computer program, called an assembler, to piece back together the original molecule. For Haemophilus, the original assembly contained 140 contiguous pieces of DNA (contigs) which then were joined together through laboratory experiments. The success of the Haemophilus assembly largely hinged upon the use of a "double-ended shotgun" strategy, in which both ends of a collection of DNA fragments of known sizes (2 kb and 15-20 kb) were sequenced. The pairing of sequencing reads from the ends of each fragment allowed the researchers at The Institute for Genomic Research (TIGR) to order and orient the 140 contigs and then quickly fill in the gaps to produce the complete sequence of the genome. This procedure of ordering and orienting a collection of contigs, using paired read information, is called scaffolding (Roach et al. 1995) because it builds a virtual scaffold upon which a genome can be completed.The success of the Haemophilus sequencing project led TIGR to incorporate scaffolding into all other sequencing projects, using a computer program called "grouper". Other sequencing centers performed scaffolding in a largely manual fashion, and until recently "grouper" was the only stand-alone scaffolder in use. The successful use of WGS at Celera to assem...

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An optimal procedure for gap closing in whole genome shotgun sequencing

Cited by 43 publications

References 8 publications

Inferring Social Networks from Outbreaks

Inferring Social Networks from Outbreaks

Learning a Hidden Hypergraph

Hierarchical Scaffolding With Bambus

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