Finding Recurrent Copy Number Alteration Regions: A Review of Methods

Rueda, Oscar M.; Dı́az-Uriarte, Ramón

doi:10.2174/157489310790596402

Cited by 38 publications

(42 citation statements)

References 75 publications

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“…For example, six possible scenarios of recurrent regions are described in the work by Rueda and Diaz-Uriarte [37]. Our simulation only covers the first two.…”

Section: Resultsmentioning

confidence: 99%

Multisample aCGH Data Analysis via Total Variation and Spectral Regularization

Zhou

Yang

Wan

et al. 2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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DNA copy number variation (CNV) accounts for a large proportion of genetic variation. One commonly used approach to detecting CNVs is array-based comparative genomic hybridization (aCGH). Although many methods have been proposed to analyze aCGH data, it is not clear how to combine information from multiple samples to improve CNV detection. In this paper, we propose to use a matrix to approximate the multisample aCGH data and minimize the total variation of each sample as well as the nuclear norm of the whole matrix. In this way, we can make use of the smoothness property of each sample and the correlation among multiple samples simultaneously in a convex optimization framework. We also developed an efficient and scalable algorithm to handle large-scale data. Experiments demonstrate that the proposed method outperforms the state-of-the-art techniques under a wide range of scenarios and it is capable of processing large data sets with millions of probes.

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“…For example, six possible scenarios of recurrent regions are described in the work by Rueda and Diaz-Uriarte [37]. Our simulation only covers the first two.…”

Section: Resultsmentioning

confidence: 99%

Multisample aCGH Data Analysis via Total Variation and Spectral Regularization

Zhou

Yang

Wan

et al. 2013

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…MSA can be viewed as an improvement over STAC, where it extends the notions of frequency and footprint statistics using original intensity ratio data instead of segmented data [8]. We also tried a comparison to RJaCGH [2], which uses a non-homogenous Hidden Markov Model fitted via the Reversible-Jump Markov Chain Monte Carlo method to estimate the probability that a region has copy number alterations; the method also allows the identification of minimal common regions of copy number changes among multiple individuals.…”

Section: Resultsmentioning

confidence: 99%

“…Previous methods developed to identify recurrent CNV regions (see [8] for a review) were primarily developed for aCGH data and hence did not incorporate confidence scores. For example, a previously published method, STAC [9], uses two statistics to identify recurrent CNV regions.…”

Section: Introductionmentioning

confidence: 99%

Amino acid classification based spectrum kernel fusion for protein subnuclear localization

Mei

Fei

2010

BMC Bioinformatics

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BackgroundPrediction of protein localization in subnuclear organelles is more challenging than general protein subcelluar localization. There are only three computational models for protein subnuclear localization thus far, to the best of our knowledge. Two models were based on protein primary sequence only. The first model assumed homogeneous amino acid substitution pattern across all protein sequence residue sites and used BLOSUM62 to encode k-mer of protein sequence. Ensemble of SVM based on different k-mers drew the final conclusion, achieving 50% overall accuracy. The simplified assumption did not exploit protein sequence profile and ignored the fact of heterogeneous amino acid substitution patterns across sites. The second model derived the PsePSSM feature representation from protein sequence by simply averaging the profile PSSM and combined the PseAA feature representation to construct a kNN ensemble classifier Nuc-PLoc, achieving 67.4% overall accuracy. The two models based on protein primary sequence only both achieved relatively poor predictive performance. The third model required that GO annotations be available, thus restricting the model's applicability.MethodsIn this paper, we only use the amino acid information of protein sequence without any other information to design a widely-applicable model for protein subnuclear localization. We use K-spectrum kernel to exploit the contextual information around an amino acid and the conserved motif information. Besides expanding window size, we adopt various amino acid classification approaches to capture diverse aspects of amino acid physiochemical properties. Each amino acid classification generates a series of spectrum kernels based on different window size. Thus, (I) window expansion can capture more contextual information and cover size-varying motifs; (II) various amino acid classifications can exploit multi-aspect biological information from the protein sequence. Finally, we combine all the spectrum kernels by simple addition into one single kernel called SpectrumKernel+ for protein subnuclear localization.ResultsWe conduct the performance evaluation experiments on two benchmark datasets: Lei and Nuc-PLoc. Experimental results show that SpectrumKernel+ achieves substantial performance improvement against the previous model Nuc-PLoc, with overall accuracy 83.47% against 67.4%; and 71.23% against 50% of Lei SVM Ensemble, against 66.50% of Lei GO SVM Ensemble.ConclusionThe method SpectrumKernel+ can exploit rich amino acid information of protein sequence by embedding into implicit size-varying motifs the multi-aspect amino acid physiochemical properties captured by amino acid classification approaches. The kernels derived from diverse amino acid classification approaches and different sizes of k-mer are summed together for data integration. Experiments show that the method SpectrumKernel+ significantly outperforms the existing models for protein subnuclear localization.

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“…Through of HMMs analysis, used as a probabilistic model to determine an unknown sequence of states based upon a sequence of observations [37][38][39], we defined Minimal Common Regions (MCRs) of recurring DNA gains and losses. The HMM algorithm is available at the ADaCGH server of Asterias (http://adacgh.bioinfo.cnio.es/).…”

Section: Determination Of Cnasmentioning

confidence: 99%

Circulating Free DNA in Serum and Genomic Alterations of Lung Cancer Patients and High Risk Individuals Exposed to Environmental Pollution

Marta¹,

T²,

Ulises³

et al. 2017

Biomark J

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The implementation of early-detection technologies and prognostic biomarkers are imperative, specifically in cities highly exposed to environmental carcinogens, such as PAHs and Arsenic. This study proposed to evaluate cfDNA levels and determine CNAs in serum cfDNA in LC and PNL patients. The study enrolled a total of 107 people, from the Metropolitan (N=51) and Antofagasta (N=56) regions, with medium or high LC risk. The cfDNA was isolated from serum aliquots using Qiagen QIAamp DNA mini kit. HMMs analysis in cfDNA, was used as a probabilistic model to determine an unknown sequence and defined Minimal Common Regions (MCRs) of recurring DNA gains and losses. This study shows that cfDNA levels increased during malignancy of LC but not significantly during premalignant lesions or pre-invasive stages. The cfDNA could be used as a complementary tool to prognosis and evolution of LC, with high precision (specificity of 87.3% and PNV of 76.4%). Additionally, the cfDNA carry some chromosomal aberrations related with the lung tumorigenesis process that could be used to confirm a suspected case of LC. Within these alterations our results suggest the loss of specific chromosomic fragments, contain genes related with inflammation response (SERPING1), humoral immune response (IGCK), innate immunity and epithelial integrity (CTNN1), tumor suppressor (MAP2K4, DMTF1 and ABCB4), and response to oxidative stress (COX10). In conclusion, these biomarkers in cfDNA, could have a great potential as screening methods in population with high risk for LC.

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Finding Recurrent Copy Number Alteration Regions: A Review of Methods

Cited by 38 publications

References 75 publications

Multisample aCGH Data Analysis via Total Variation and Spectral Regularization

Multisample aCGH Data Analysis via Total Variation and Spectral Regularization

Amino acid classification based spectrum kernel fusion for protein subnuclear localization

Circulating Free DNA in Serum and Genomic Alterations of Lung Cancer Patients and High Risk Individuals Exposed to Environmental Pollution

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