A Unified Approach for Reconstructing Ancient Gene Clusters

Stoye, Jens; Wittler, Roland

doi:10.1109/tcbb.2008.135

Cited by 30 publications

(29 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To detect all conflicts, it is sufficient to find the minimal conflicting sets, a general notion that was used to deal with inconsistency in reconstructing ancestral genomes [26,27]. …”

Section: Methodsmentioning

confidence: 99%

“…Remark: General, but exponential time, techniques were described in [26,27], for the different problem of reconstructing ancestral genomes and gene clusters, but they can be applied for deletion clusters. As far as we know, ad-hoc approaches for the specific case of deletion clusters have not been investigated.…”

Section: Appendix A: Statements Of Algorithmic and Combinatorial Optimentioning

confidence: 99%

See 1 more Smart Citation

Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes

Wittler

Chauve

2011

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundStructural variations in human genomes, such as insertions, deletion, or rearrangements, play an important role in cancer development. Next-Generation Sequencing technologies have been central in providing ways to detect such variations. Most existing methods however are limited to the analysis of a single genome, and it is only recently that the comparison of closely related genomes has been considered. In particular, a few recent works considered the analysis of data sets obtained by sequencing both tumor and healthy tissues of the same cancer patient. In that context, the goal is to detect variations that are specific to exactly one of the genomes, for example to differentiate between patient-specific and tumor-specific variations. This is a difficult task, especially when facing the additional challenge of the possible contamination of healthy tissues by tumor cells and conversely.ResultsIn the current work, we analyzed a data set of paired-end short-reads, obtained by sequencing tumor tissues and healthy tissues, both from the same cancer patient. Based on a combinatorial notion of conflict between deletions, we show that in the tumor data, more deletions are predicted than there could actually be in a diploid genome. In contrast, the predictions for the data from normal tissues are almost conflict-free. We designed and applied a method, specific to the analysis of such pooled and contaminated data sets, to detect potential tumor-specific deletions. Our method takes the deletion calls from both data sets and assigns reads from the mixed tumor/normal data to the normal one with the goal to minimize the number of reads that need to be discarded to obtain a set of conflict-free deletion clusters. We observed that, on the specific data set we analyze, only a very small fraction of the reads needs to be discarded to obtain a set of consistent deletions.ConclusionsWe present a framework based on a rigorous definition of consistency between deletions and the assumption that the tumor sample also contains normal cells. A combined analysis of both data sets based on this model allowed a consistent explanation of almost all data, providing a detailed picture of candidate patient- and tumor-specific deletions.

show abstract

“…To detect all conflicts, it is sufficient to find the minimal conflicting sets, a general notion that was used to deal with inconsistency in reconstructing ancestral genomes [26,27]. …”

Section: Methodsmentioning

confidence: 99%

Section: Appendix A: Statements Of Algorithmic and Combinatorial Optimentioning

confidence: 99%

Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes

Wittler

Chauve

2011

BMC Bioinformatics

View full text Add to dashboard Cite

show abstract

“…The notion of Minimal Conflicting Set was introduced to handle non-C1P binary matrices and false positives in [3] and [28]. If a binary matrix M does not have the C1P, a Minimal Conflicting Set (MCS) is a submatrix M ′ of M composed of a subset of the rows of M such that M ′ does not have the C1P, but every proper subset of rows of M ′ has the C1P.…”

Section: Such Ordering Of the Markers Define Chromosomal Segments Calmentioning

confidence: 99%

“…For example, the algorithm of Stoye and Wittler [28] described in Section 2 is a satisfiability oracle based algorithm -it uses a polynomial-time oracle to decide if a given submatrix has the C1P, but it doesn't use this structure any further. Once it has found the complete list C of MCS, it will proceed to check all MC1PS sets as candidate conflicting sets before terminating.…”

Section: Theorem 4 [15]mentioning

confidence: 99%

Minimal Conflicting Sets for the Consecutive Ones Property in Ancestral Genome Reconstruction

Chauve

Haus

Stephen

et al. 2009

Comparative Genomics

View full text Add to dashboard Cite

Abstract. A binary matrix has the Consecutive Ones Property (C1P) if its columns can be ordered in such a way that all 1's on each row are consecutive. A Minimal Conflicting Set is a set of rows that does not have the C1P, but every proper subset has the C1P. Such submatrices have been considered in comparative genomics applications, but very little is known about their combinatorial structure and efficient algorithms to compute them. We first describe an algorithm that detects rows that belong to Minimal Conflicting Sets. This algorithm has a polynomial time complexity when the number of 1s in each row of the considered matrix is bounded by a constant. Next, we show that the problem of computing all Minimal Conflicting Sets can be reduced to the joint generation of all minimal true clauses and maximal false clauses for some monotone boolean function. We use these methods on simulated data related to ancestral genome reconstruction to show that computing Minimal Conflicting Set is useful in discriminating between true positive and false positive ancestral syntenies. We also study a dataset of yeast genomes and address the reliability of an ancestral genome proposal of the Saccahromycetaceae yeasts.Draft, do not distribute.

show abstract

“…Recent research in comparative genomics has proved MCS to be of particular interest. Indeed, Bergeron et al [2] and Stoye et al [25] have shown how to compute parsimonious evolution scenarios of gene clusters by ranking rows according to their Conflicting Index (CI), i.e., the number of MCSR involving a row. In both papers, the problems of efficiently computing the CI of a row and of generating all the MCS of a matrix problems were explicitly raised.…”

Section: Introductionmentioning

confidence: 99%

A Polynomial-Time Algorithm for Finding a Minimal Conflicting Set Containing a Given Row

Blin¹,

Rizzi

2011

Computer Science – Theory and Applications

View full text Add to dashboard Cite

show abstract

A Unified Approach for Reconstructing Ancient Gene Clusters

Cited by 30 publications

References 23 publications

Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes

Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes

Minimal Conflicting Sets for the Consecutive Ones Property in Ancestral Genome Reconstruction

A Polynomial-Time Algorithm for Finding a Minimal Conflicting Set Containing a Given Row

Contact Info

Product

Resources

About