Unraveling overlapping deletions by agglomerative clustering

Wittler R (2013)
BMC Genomics 14(Suppl 1): S12.

OA correctedArticle.pdf
Journal Article | Original Article | Published | English
Background Structural 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. Result We 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. Conclusion We 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.
Publishing Year

Cite this

Wittler R. Unraveling overlapping deletions by agglomerative clustering. BMC Genomics. 2013;14(Suppl 1):S12.
Wittler, R. (2013). Unraveling overlapping deletions by agglomerative clustering. BMC Genomics, 14(Suppl 1), S12. doi:10.1186/1471-2164-14-S1-S12
Wittler, R. (2013). Unraveling overlapping deletions by agglomerative clustering. BMC Genomics 14, S12.
Wittler, R., 2013. Unraveling overlapping deletions by agglomerative clustering. BMC Genomics, 14(Suppl 1), p S12.
R. Wittler, “Unraveling overlapping deletions by agglomerative clustering”, BMC Genomics, vol. 14, 2013, pp. S12.
Wittler, R.: Unraveling overlapping deletions by agglomerative clustering. BMC Genomics. 14, S12 (2013).
Wittler, Roland. “Unraveling overlapping deletions by agglomerative clustering”. BMC Genomics 14.Suppl 1 (2013): S12.
Main File(s)
Access Level
OA Open Access
Last Uploaded
2013-01-29 13:48:53
Access Level
OA Open Access
Last Uploaded
2013-04-23 10:33:58

This data publication is cited in the following publications:
This publication cites the following data publications:

3 Citations in Europe PMC

Data provided by Europe PubMed Central.

MicroRNA-5p and -3p co-expression and cross-targeting in colon cancer cells.
Choo KB, Soon YL, Nguyen PN, Hiew MS, Huang CJ., J. Biomed. Sci. 21(), 2014
PMID: 25287248
Haploid to diploid alignment for variation calling assessment.
Makinen V, Rahkola J., BMC Bioinformatics 14 Suppl 15(), 2013
PMID: 24564537

40 References

Data provided by Europe PubMed Central.

Nonmuscle myosin IIA is associated with poor prognosis of esophageal squamous cancer.
Xia ZK, Yuan YC, Yin N, Yin BL, Tan ZP, Hu YR., Dis. Esophagus 25(5), 2012
PMID: 21951916
Identification of PP2A complexes and pathways involved in cell transformation.
Sablina AA, Hector M, Colpaert N, Hahn WC., Cancer Res. 70(24), 2010
PMID: 21159657
miR-196a downregulation increases the expression of type I and III collagens in keloid fibroblasts.
Kashiyama K, Mitsutake N, Matsuse M, Ogi T, Saenko VA, Ujifuku K, Utani A, Hirano A, Yamashita S., J. Invest. Dermatol. 132(6), 2012
PMID: 22358059
TGF-β-mediated downregulation of microRNA-196a contributes to the constitutive upregulated type I collagen expression in scleroderma dermal fibroblasts.
Honda N, Jinnin M, Kajihara I, Makino T, Makino K, Masuguchi S, Fukushima S, Okamoto Y, Hasegawa M, Fujimoto M, Ihn H., J. Immunol. 188(7), 2012
PMID: 22379029
Repression of versican expression by microRNA-143.
Wang X, Hu G, Zhou J., J. Biol. Chem. 285(30), 2010
PMID: 20489207
Widespread changes in protein synthesis induced by microRNAs.
Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N., Nature 455(7209), 2008
PMID: 18668040
MicroRNA dysregulation in colorectal cancer: a clinical perspective.
Dong Y, Wu WK, Wu CW, Sung JJ, Yu J, Ng SS., Br. J. Cancer 104(6), 2011
PMID: 21364594
MicroRNA networks in mouse lung organogenesis.
Dong J, Jiang G, Asmann YW, Tomaszek S, Jen J, Kislinger T, Wigle DA., PLoS ONE 5(5), 2010
PMID: 20520778
Identifying the target mRNAs of microRNAs in colorectal cancer.
Kim S, Choi M, Cho KH., Comput Biol Chem 33(1), 2009
PMID: 18723399
Identification of a panel of sensitive and specific DNA methylation markers for squamous cell lung cancer.
Anglim PP, Galler JS, Koss MN, Hagen JA, Turla S, Campan M, Weisenberger DJ, Laird PW, Siegmund KD, Laird-Offringa IA., Mol. Cancer 7(), 2008
PMID: 18616821
Sensitive and specific detection of early gastric cancer with DNA methylation analysis of gastric washes.
Watanabe Y, Kim HS, Castoro RJ, Chung W, Estecio MR, Kondo K, Guo Y, Ahmed SS, Toyota M, Itoh F, Suk KT, Cho MY, Shen L, Jelinek J, Issa JP., Gastroenterology 136(7), 2009
PMID: 19375421
CpG island hypermethylation in human astrocytomas.
Wu X, Rauch TA, Zhong X, Bennett WP, Latif F, Krex D, Pfeifer GP., Cancer Res. 70(7), 2010
PMID: 20233874
DNA methylation of colon mucosa in ulcerative colitis patients: correlation with inflammatory status.
Saito S, Kato J, Hiraoka S, Horii J, Suzuki H, Higashi R, Kaji E, Kondo Y, Yamamoto K., Inflamm. Bowel Dis. 17(9), 2011
PMID: 21830274


0 Marked Publications

Open Data PUB

Web of Science

View record in Web of Science®


PMID: 23369161
PubMed | Europe PMC

Search this title in

Google Scholar