ReviewCancer informatics2014
Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays.
Review in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- A phenome-wide association study of CNVs genotyped from genome sequencing read depth in the UK Biobank.American journal of human genetics · 2026Article
- CNValidatron: accurate and efficient validation of PennCNV calls using computer vision.BMC bioinformatics · 2026Article
- The hidden Markov model and its applications in bioinformatics analysis.Genes & diseases · 2026Review
- CNValidatron: Accurate And Efficient Validation of PennCNV Calls Using Computer Vision.bioRxiv : the preprint server for biology · 2025Article
- CNPI: Rapid Analyses of Human Copy Number Data.Journal of molecular biology · 2025Article
- Detection of Copy Number Variations in Woori-Heukdon Populations with the Illumina PorcineSNP60 Bead-Chip Array.Animals : an open access journal from MDPI · 2025Article
- Dermokine mutations contribute to epithelial-mesenchymal transition and advanced melanoma through ERK/MAPK pathways.PloS one · 2023Article
- Progress in Methods for Copy Number Variation Profiling.International journal of molecular sciences · 2022Review
- Technologies for Pharmacogenomics: A Review.Genes · 2020Review
- Genome‑wide copy number analysis of circulating tumor cells in breast cancer patients with liver metastasis.Oncology reports · 2020Article
- SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences From Reference Genomes.Frontiers in genetics · 2020Article
- Population-wide copy number variation calling using variant call format files from 6,898 individuals.Genetic epidemiology · 2020Article
- Rare Inherited and De Novo CNVs Reveal Complex Contributions to ASD Risk in Multiplex Families.American journal of human genetics · 2016Article
- The Role of Constitutional Copy Number Variants in Breast Cancer.Microarrays (Basel, Switzerland) · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.
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Registered trials
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.