Characterization Of Background Noise Research Articles

Characterization of background noise in MiSeq MPS data when sequencing human mitochondrial DNA from various sample sources and library preparation methods

Improved resolution of massively parallel sequencing (MPS) allows for the characterization of mitochondrial (mt) DNA heteroplasmy to levels previously unattainable with traditional sequencing approaches. An essential criterion for the reporting of heteroplasmy is the ability of the MPS method to distinguish minor sequence variants (MSVs) from system noise, or error. Therefore, an assessment of the background noise in the MPS method is desirable to identify the point at which reliable data can be reported. Substitution and sequence specific error (SSE) was evaluated for a variety of sample types and two library preparations. Substitution error rates ranged from 0.18 to 0.49 per 100 nucleotides with C positions generally having the highest rate of misincorporation. Comparison of error rates across sample types indicated a significant increase for samples with damaged DNA. The positions of error were varied across datasets (pairwise concordance 0–68%), but had greater consistency within the damaged samples (80–96%). The most commonly observed motif preceding error in forward reads was CCG, while GGT was most common in reverse reads, both consistent with previous findings. The findings illustrate that for datasets containing samples with damaged DNA, reporting thresholds for heteroplasmy may have to be modified and individual sites with error levels exceeding thresholds should be scrutinized. Collectively, the shifting error profiles observed across the various sample types and library preparation methods demonstrates the need for an assessment of error under these varying circumstances. Characterization of the applicable background noise will help to ensure that thresholds are reliably set for detection of true MSVs.

Characterization of background noise in capture-based targeted sequencing data

BackgroundTargeted deep sequencing is increasingly used to detect low-allelic fraction variants; it is therefore essential that errors that constitute baseline noise and impose a practical limit on detection are characterized. In the present study, we systematically evaluate the extent to which errors are incurred during specific steps of the capture-based targeted sequencing process.ResultsWe removed most sequencing artifacts by filtering out low-quality bases and then analyze the remaining background noise. By recognizing that plasma DNA is naturally fragmented to be of a size comparable to that of mono-nucleosomal DNA, we were able to identify and characterize errors that are specifically associated with acoustic shearing. Two-thirds of C:G > A:T errors and one quarter of C:G > G:C errors were attributed to the oxidation of guanine during acoustic shearing, and this was further validated by comparative experiments conducted under different shearing conditions. The acoustic shearing step also causes A > G and A > T substitutions localized to the end bases of sheared DNA fragments, indicating a probable association of these errors with DNA breakage. Finally, the hybrid selection step contributes to one-third of the remaining C:G > A:T and one-fifth of the C > T errors.ConclusionsThe results of this study provide a comprehensive summary of various errors incurred during targeted deep sequencing, and their underlying causes. This information will be invaluable to drive technical improvements in this sequencing method, and may increase the future usage of targeted deep sequencing methods for low-allelic fraction variant detection.

Adaptive V/UV Speech Detection Based on Characterization of Background Noise

The paper presents an adaptive system for Voiced/Unvoiced (V/UV) speech detection in the presence of background noise. Genetic algorithms were used to select the features that offer the best V/UV detection according to the output of a background Noise Classifier (NC) and a Signal-to-Noise Ratio Estimation (SNRE) system. The system was implemented, and the tests performed using the TIMIT speech corpus and its phonetic classification. The results were compared with a nonadaptive classification system and the V/UV detectors adopted by two important speech coding standards: the V/UV detection system in the ETSI ES 202 212 v1.1.2 and the speech classification in the Selectable Mode Vocoder (SMV) algorithm. In all cases the proposed adaptive V/UV classifier outperforms the traditional solutions giving an improvement of 25% in very noisy environments.