Recent years have seen the emergence of several “third-generation” sequencing platforms, each of which aims to address shortcomings of standard next-generation short-read sequencing by producing data that capture long-range information, thereby allowing us to access regions of the genome that are inaccessible with short-reads alone. These technologies either produce physically longer reads typically with higher error rates or instead capture long-range information at low error rates by virtue of read “barcodes” as in 10x Genomics’ Chromium platform. As with virtually all sequencing data, sequence alignment for third-generation sequencing data is the foundation on which all downstream analyses are based. Here we introduce a latent variable model for improving barcoded read alignment, thereby enabling improved downstream genotyping and phasing. We demonstrate the feasibility of this approach through developing EMerAld— or EMA for short— and testing it on the barcoded short-reads produced by 10x’s sequencing technologies. EMA not only produces more accurate alignments, but unlike other methods also assigns interpretable probabilities to the alignments it generates. We show that genotypes called from EMA’s alignments contain over 30% fewer false positives than those called from Lariat’s (the current 10x alignment tool), with a fewer number of false negatives, on datasets of NA12878 and NA24385 as compared to NIST GIAB gold standard variant calls. Moreover, we demonstrate that EMA is able to effectively resolve alignments in regions containing nearby homologous elements— a particularly challenging problem in read mapping— through the introduction of a novel statistical binning optimization framework, which allows us to find variants in the pharmacogenomically important CYP2D region that go undetected when using Lariat or BWA. Lastly, we show that EMA’s alignments improve phasing performance compared to Lariat’s in both NA12878 and NA24385, producing fewer switch/mismatch errors and larger phase blocks on average. EMA software and datasets used are available at http://ema.csail.mit.edu.

This paper presents an extension of research scope of the relationship between quality of service (QoS) and quality of experience (QoE), which is based on contribution of lower open system interconnection layers, such as physical and media access control, in overall QoS/QoE paradigm. Degradations that inevitably occur in transmission channel are an important reason for appearance of QoS distortion and therefore low values of video quality. Various channel quality indicators (CQIs) relating to domain of communication channel can be used for notification of different interferences in the channel. In order to extend the relationship between QoS and QoE to the transmission channel, the paper proposed the mathematical model that used CQI to estimate values of QoS indicators by using statistical analysis. The model was also expanded by objective video quality metrics in order to evaluate QoE. Verification of the model was checked by experimental method with consideration of Internet protocol television (IPTV) service delivery in Digital Subscriber Line network.