SegAnnDB: Interactive Web-based Genomic Segmentation

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2014 Feb 5

PMID 24493034

Citations 6

Authors

Toby D Hocking

Valentina Boeva

Guillem Rigaill

Gudrun Schleiermacher

Isabelle Janoueix-Lerosey

Olivier Delattre

Wilfrid Richer

Franck Bourdeaut

Miyuki Suguro

Masao Seto

Francis Bach

Jean-Philippe Vert

Affiliations

Soon will be listed here.

Abstract

Motivation: DNA copy number profiles characterize regions of chromosome gains, losses and breakpoints in tumor genomes. Although many models have been proposed to detect these alterations, it is not clear which model is appropriate before visual inspection the signal, noise and models for a particular profile.

Results: We propose SegAnnDB, a Web-based computer vision system for genomic segmentation: first, visually inspect the profiles and manually annotate altered regions, then SegAnnDB determines the precise alteration locations using a mathematical model of the data and annotations. SegAnnDB facilitates collaboration between biologists and bioinformaticians, and uses the University of California, Santa Cruz genome browser to visualize copy number alterations alongside known genes.

Availability And Implementation: The breakpoints project on INRIA GForge hosts the source code, an Amazon Machine Image can be launched and a demonstration Web site is http://bioviz.rocq.inria.fr.

Citing Articles

On optimal multiple changepoint algorithms for large data.

Maidstone R, Hocking T, Rigaill G, Fearnhead P Stat Comput. 2020; 27(2):519-533.

PMID: 32355427 PMC: 7175693. DOI: 10.1007/s11222-016-9636-3.

Meta-mining of copy number profiles of high-risk neuroblastoma tumors.

Depuydt P, Koster J, Boeva V, Hocking T, Speleman F, Schleiermacher G Sci Data. 2018; 5:180240.

PMID: 30375995 PMC: 6207068. DOI: 10.1038/sdata.2018.240.

Genomic Amplifications and Distal 6q Loss: Novel Markers for Poor Survival in High-risk Neuroblastoma Patients.

Depuydt P, Boeva V, Hocking T, Cannoodt R, Ambros I, Ambros P J Natl Cancer Inst. 2018; 110(10):1084-1093.

PMID: 29514301 PMC: 6186524. DOI: 10.1093/jnci/djy022.

Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.

Hocking T, Goerner-Potvin P, Morin A, Shao X, Pastinen T, Bourque G Bioinformatics. 2016; 33(4):491-499.

PMID: 27797775 PMC: 5408812. DOI: 10.1093/bioinformatics/btw672.

Development and analysis of patient-derived xenograft mouse models in intravascular large B-cell lymphoma.

Shimada K, Shimada S, Sugimoto K, Nakatochi M, Suguro M, Hirakawa A Leukemia. 2016; 30(7):1568-79.

PMID: 27001523 DOI: 10.1038/leu.2016.67.

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