eprintid: 10157443 rev_number: 9 eprint_status: archive userid: 699 dir: disk0/10/15/74/43 datestamp: 2022-10-18 16:32:07 lastmod: 2022-10-18 16:32:07 status_changed: 2022-10-18 16:32:07 type: article metadata_visibility: show sword_depositor: 699 creators_name: Lalani, Zubair creators_name: Chu, Gillian creators_name: Hsu, Silas creators_name: Kagawa, Shaw creators_name: Xiang, Michael creators_name: Zaccaria, Simone creators_name: El-Kebir, Mohammed title: CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data ispublished: inpress divisions: C10 divisions: G99 divisions: B02 divisions: UCL divisions: D19 note: © 2022 Lalani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. abstract: Copy-number aberrations (CNAs) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and, thus, a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments with the same copy-number states that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of CNAs. For this reason, most pan-cancer research studies rely on manual procedures of quality control and anomaly correction. To improve copy-number segmentation, we introduce CNAViz, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAViz allows the user to obtain more accurate segmentation relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel single-cell DNA sequencing data from the same samples, we show that by using CNAViz, our user was able to obtain more accurate segmentation and improved accuracy in downstream copy-number calling. date: 2022-10-13 date_type: published publisher: Public Library of Science (PLoS) official_url: https://doi.org/10.1371/journal.pcbi.1010614 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1982286 doi: 10.1371/journal.pcbi.1010614 medium: Print-Electronic pii: PCOMPBIOL-D-22-01002 lyricists_name: Zaccaria, Simone lyricists_id: SZACC24 actors_name: Zaccaria, Simone actors_id: SZACC24 actors_role: owner funding_acknowledgements: 2046488 [Division of Computing and Communication Foundations]; M917 [Rosetrees Trust]; 1850502 [Division of Computing and Communication Foundations]; 1746047 [National Science Foundation]; [Cancer Center at Illinois] full_text_status: public publication: PLoS Computational Biology volume: 18 number: 10 article_number: e1010614 event_location: United States citation: Lalani, Zubair; Chu, Gillian; Hsu, Silas; Kagawa, Shaw; Xiang, Michael; Zaccaria, Simone; El-Kebir, Mohammed; (2022) CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data. PLoS Computational Biology , 18 (10) , Article e1010614. 10.1371/journal.pcbi.1010614 <https://doi.org/10.1371/journal.pcbi.1010614>. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10157443/2/Zaccaria_journal.pcbi.1010614.pdf