ArticleBioinformatics (Oxford, England)2011
A novel signal processing approach for the detection of copy number variations in the human genome.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Early Psychosocial Neglect Adversely Impacts Developmental Trajectories of Brain Oscillations and Their Interactions.Journal of cognitive neuroscience · 2015Trial
- Fuzzy methods for the detection of copy number variations in comparative genomic hybridization arrays.Saudi journal of biological sciences · 2020Article
- Noise cancellation using total variation for copy number variation detection.BMC bioinformatics · 2018Article
- A gene interaction network‑based method to measure the common and heterogeneous mechanisms of gynecological cancer.Molecular medicine reports · 2018Article
- Neuronal networks in the developing brain are adversely modulated by early psychosocial neglect.Journal of neurophysiology · 2017Article
- Resting and task-modulated high-frequency brain rhythms measured by scalp encephalography in infants with tuberous sclerosis complex.Journal of autism and developmental disorders · 2015Article
- Function of cancer associated genes revealed by modern univariate and multivariate association tests.PloS one · 2015Article
- Identification of under-detected periodicity in time-series microarray data by using empirical mode decomposition.PloS one · 2014Article
- Use of autocorrelation scanning in DNA copy number analysis.Bioinformatics (Oxford, England) · 2013Article
- Space-time adaptive processing for improved estimation of preictal seizure activity.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2012Article
- Estimation of correlations between copy-number variants in non-coding DNA.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2011Article
- Multiscale information for network characterization in epilepsy.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2011Article
- Optimization of Signal Decomposition Matched Filtering (SDMF) for Improved Detection of Copy-Number Variations.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number Variations.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
motivationHuman genomic variability occurs at different scales, from single nucleotide polymorphisms (SNPs) to large DNA segments. Copy number variations (CNVs) represent a significant part of our genetic heterogeneity and have also been associated with many diseases and disorders. Short, localized CNVs, which may play an important role in human disease, may be undetectable in noisy genomic data. Therefore, robust methodologies are needed for their detection. Furthermore, for meaningful identification of pathological CNVs, estimation of normal allelic aberrations is necessary.
resultsWe developed a signal processing-based methodology for sequence denoising followed by pattern matching, to increase SNR in genomic data and improve CNV detection. We applied this signal-decomposition-matched filtering (SDMF) methodology to 429 normal genomic sequences, and compared detected CNVs to those in the Database of Genomic Variants. SDMF successfully detected a significant number of previously identified CNVs with frequencies of occurrence ≥10%, as well as unreported short CNVs. Its performance was also compared to circular binary segmentation (CBS). through simulations. SDMF had a significantly lower false detection rate and was significantly faster than CBS, an important advantage for handling large datasets generated with high-resolution arrays. By focusing on improving SNR (instead of the robustness of the detection algorithm), SDMF is a very promising methodology for identifying CNVs at all genomic spatial scales. AVAILABILITY: The data are available at http://tcga-data.nci.nih.gov/tcga/ The software and list of analyzed sequence IDs are available at http://www.hsph.harvard.edu/~betensky/ A Matlab code for Empirical Mode Decomposition may be found at: http://www.clear.rice.edu/elec301/Projects02/empiricalMode/code.html CONTACT: caterina@mit.edu.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.