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A number of different high throughput technologies can be used to identify copy number variable sites, and the literature documents multiple effective algorithms. We focus here on the specific problem of detecting regions where variation in copy number is relatively common in the sample at hand. This problem encompasses the cases of copy number polymorphisms, related samples, technical replicates, and cancerous sub-populations from the same individual.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We present a segmentation method named generalized fused lasso (GFL) to reconstruct copy number variant regions. GFL is based on penalized estimation and is capable of processing multiple signals jointly. Our approach is computationally very attractive and leads to sensitivity and specificity levels comparable to those of state-of-the-art specialized methodologies. We illustrate its applicability with simulated and real data sets.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The flexibility of our framework makes it applicable to data obtained with a wide range of technology. Its versatility and speed make GFL particularly useful in the initial screening stages of large data sets.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-13-205","type":"journal-article","created":{"date-parts":[[2012,8,16]],"date-time":"2012-08-16T12:14:33Z","timestamp":1345119273000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Reconstructing DNA copy number by joint segmentation of multiple sequences"],"prefix":"10.1186","volume":"13","author":[{"given":"Zhongyang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth","family":"Lange","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chiara","family":"Sabatti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,8,16]]},"reference":[{"key":"5545_CR1","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1111\/j.0006-341X.2000.01088.x","volume":"56","author":"MA Newton","year":"2000","unstructured":"Newton MA, Lee Y: Inferring the location and effect of tumor suppressor genes by instability-selection modeling of allelic-loss data. 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