Jorge Muñoz-Minjares

Confidence masks for genome DNA copy number variations in applications to HR-CGH array measurements

Abstract The array-comparative genomic hybridization (aCGH) and next generation sequence technologies enable cost-efficient high resolution detection of DNA copy number variations (CNVs). However, while the CNVs estimates provided by different methods are often inconsistent with each other, still a little can be found about the estimation errors. Based on our recent studies of the confidence limits for stepwise signals measured in noise, we develop an efficient algorithm for computing the confidence upper and lower boundary masks in order to guarantee an existence of genomic changes with required probability. We suggest combining these masks with estimates in order to give medical experts more information about true CNVs structures. Applications given for high-resolution CGH microarray measurements ensure that there is a probability that some changes predicted by an estimator may not exist.

Confidence limits for genome DNA copy number variations in HR-CGH array measurements

Abstract Estimation of the genome copy number variations (CNVs) measured using the high-resolution array-comparative genomic hybridization (HR-CGH) microarray is commonly provided in the presence of large Gaussian noise having white properties with different segmental variances. Medical experts must thus be highly concerned about the confidence limits for CNVs in order to make correct decisions about genomic changes. We carry out a probabilistic analysis of CNVs in HR-CGH microarray measurements and show that jitter in the breakpoints can be approximated with the discrete skew Laplace distribution. Using this distribution, we find the confidence upper and lower boundaries to guarantee an existence of genomic changes in the confidence interval of 99.73%. We suggest combining these boundaries with the estimates to give medical experts more information about actual CNVs. Experimental verification of the theory is provided by simulation and using real HR-CGH microarray-based measurements.

Approximate jitter probability in the breakpoints of genome copy number variations

The jitter probability is derived and analyzed in the breakpoints of the measured genome copy number variations (CNVs). It is supposed that measurements are provided using the high resolution array-comparative genomic hybridization (HR-CGH) microarray. We show that jitter is fundamentally inherent to the measured CNVs and that the jitter probability has the recently derived discrete skew Laplace distribution. No one estimator, even ideal, is able to provide the jitter-free breakpoints detection. The jitter region can be outlined for the required probability via measurements with known noise variances. Some simulation results are supplied.

Jitter representation in SCNA breakpoints using asymmetric exponential power distribution

Jitter is inherent to the breakpoints of measured genome somatic copy number alterations (SCNAs). Therefore, an analysis of jitter is required to reduce errors in the SCNA estimation. The high resolution technologies of hybridization are used to detect SCNAs. However, the SCNA measurements are accompanied with intensive noise that may cause errors and ambiguities in the breakpoint detection with low signal-to-noise rations (SNRs). In this paper, we show that the asymmetric exponential power distribution (AEPD) provides much better approximation to the jitter distribution than the earlier proposed discrete skew Laplace distribution. We use the AEPD to approximate the jitter distribution by finding the best fit for the measured SCNAs. The proposed approximation is tested experimentally by data generated with several values of the SNRs.

Matching Confidence Masks with Experts Annotations for Estimates of Chromosomal Copy Number Alterations

Structural aberrations (SAs), gains or losses in large segments of genomes, are associated with several genetic disorders. The SAs are commonly called the copy number alterations (CNAs) and their identification/classification is required to identify diseases. Many methods have been proposed to estimate the breakpoints and segmental constants in the CNAs with highest precision using the most powerful technologies of hybridization. However, locations and lengths of CNAs estimated using well-elaborated methods are often contradictory due to extensive variability of measurements and performance of the algorithms. Still much less attention is given to the estimation accuracy and it is difficult to select the best estimator. In this work, we propose to modify the confidence masks replacing the skew Laplace distribution with the asymmetric exponential power distribution (AEP) to approximate the jitter distribution in CNAs. Next, the estimates obtained using different algorithms are matched with the annotations made by experts employing the improved masks. Finally, we specify the match confidence probability of each CNAs detector algorithm respect the experts estimates.

Improving approximation of jitter probability in the breakpoints of simulated copy number alterations

We analyze and approximate the jitter distribution function in the breakpoints of the measured genome copy number alterations (CNAs). The CNAs measured using the high resolution technologies of hybridization are contaminated with an intensive noise that may cause uncertainty in the detected breakpoints and segments. We show that jitter is fundamentally inherent to the simulate CNAs and that the jitter probability represented with the discrete skew Laplace distribution is not accurate when the signal-to-noise ratio (SNR) is small. To approximate the jitter distribution with highest accuracy, we modify the skew Laplace distribution to have the SNR function dependent on the discrete departure from the breakpoint. We propose several approximating functions and test them by experimental data.

Enhancing Estimates of Breakpoints in Genome Copy Number Alteration using Confidence Masks

Chromosomal structural changes in human body known as copy number alteration (CNA) are often associated with diseases, such as various forms of cancer. Therefore, accurate estimation of breakpoints of the CNAs is important to understand the genetic basis of many diseases. The high‐resolution comparative genomic hybridization (HR‐ CGH) and single‐nucleotide polymorphism (SNP) technologies enable cost‐efficient and high‐throughput CNA detection. However, probing provided using these profiles gives data highly contaminated by intensive Gaussian noise having white properties. We observe the probabilistic properties of CNA in HR‐CGH and SNP measurements and show that jitter in the breakpoints can statistically be described with either the discrete skew Laplace distribution when the segmental signal‐to‐noise ratio (SNR) exceeds unity or modified Bessel function‐based approximation when SNR is <1. Based upon these approaches, the confidence masks can be developed and used to enhance the estimates of the CNAs for the given confidence probability by removing some unlikely existing breakpoints.

The Role of Optimal Detection of CNAs and Error Analysis Using Next Generation Sequencing

These characteristics have brought revolutionary advances to genetic field and resulted in the development of a wide variety of methods, which allowed researchers to ask virtually any question related to the genome, transcriptome, or epigenome of any organism. But in spite of advantages of the NGS technology, an important flaw inherent to earlier developed methods still remains. The NGS data are contaminated by intensive noise that requires using efficient methods of statistical signal processing and bioinformatics to eliminate undesirable information, so that NGS data could finally be used by researchers to make a clinical interpretation.