CLEVER: clique-enumerating variant finder

Marschall, Tobias; Costa, Ivan G.; Canzar, Stefan; Bauer, Markus; Klau, Gunnar W.; Schliep, Alexander; Schönhuth, Alexander

doi:10.1093/bioinformatics/bts566

Cited by 103 publications

(121 citation statements)

References 33 publications

(66 reference statements)

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“…Although a follow-up version GASVPro [33] has been published recently, the available software does not yet include all necessary preprocessing tools to process BAM files. Secondly, we also ran CLEVER [37] as one of the most recent and accurate variation detection tools. This method computes a probability for mappings arising from the same deletion, and builds a graph with the mappings as vertices and edges for pairs with Significantly high probability.…”

Section: Resultsmentioning

confidence: 99%

“…The leftmost column specifies the number of simulated overlapping deletions (no deletion, single deletion, or pair of overlapping deletions) and the predictions (no deletion, single deletion, pair of overlapping deletions, or three or more overlapping deletions). The remainder of the table shows the corresponding counts per tool (agglomerative clustering, GASV [22], GASV with option "maximal", and CLEVER [37]) for four different settings (coverage 20× and 60×, and scenario A and B, cf. Figure 6).…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…Then, a mapping with distance d is considered being stretched if d > D max , and the size of the putative deletion is expected to be between d - D max and d - D min . Instead of this discretization, other methods [36,37], such as ours, keep and make use of the information that the fragment length and thus the mapping distances can usually be approximated by a normal distribution with some mean μ . (Figure 1 shows a histogram of the fragment length distribution in the data set discussed in Section "Real data".)…”

Section: Introductionmentioning

confidence: 99%

“…[5,6,22,33,37,38], assign all given mappings to clusters, where each cluster corresponds to one deletion that is supported by all mappings in the cluster. Figure 2 shows an example for such a deletion cluster.…”

Section: Introductionmentioning

confidence: 99%

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Unraveling overlapping deletions by agglomerative clustering

Wittler

2013

BMC Genomics

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BackgroundStructural variations in human genomes, such as deletions, play an important role in cancer development. Next-Generation Sequencing technologies have been central in providing ways to detect such variations. Methods like paired-end mapping allow to simultaneously analyze data from several samples in order to, e.g., distinguish tumor from patient specific variations. However, it has been shown that, especially in this setting, there is a need to explicitly take overlapping deletions into consideration. Existing tools have only minor capabilities to call overlapping deletions, unable to unravel complex signals to obtain consistent predictions.ResultWe present a first approach specifically designed to cluster short-read paired-end data into possibly overlapping deletion predictions. The method does not make any assumptions on the composition of the data, such as the number of samples, heterogeneity, polyploidy, etc. Taking paired ends mapped to a reference genome as input, it iteratively merges mappings to clusters based on a similarity score that takes both the putative location and size of a deletion into account.ConclusionWe demonstrate that agglomerative clustering is suitable to predict deletions. Analyzing real data from three samples of a cancer patient, we found putatively overlapping deletions and observed that, as a side-effect, erroneous mappings are mostly identified as singleton clusters. An evaluation on simulated data shows, compared to other methods which can output overlapping clusters, high accuracy in separating overlapping from single deletions.

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Section: Resultsmentioning

confidence: 99%

Section: Supplementary Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unraveling overlapping deletions by agglomerative clustering

Wittler

2013

BMC Genomics

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“…A predominant example is the discovery of deletions of more than 200 base pairs (bp), which has been addressed by a large variety of approaches: examples are Breakdancer [2], VariationHunter [6], (MATE-)CLEVER [16,17], DELLY [22], GASV(-Pro) [24,25], see also the references therein, and again the above-mentioned reviews [1,20].…”

Section: Introductionmentioning

confidence: 99%