45,085 research outputs found
Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data
There is a high prevalence of coronary artery disease (CAD) in patients with left bundle branch block (LBBB); however there are many other causes for this electrocardiographic abnormality. Non-invasive assessment of these patients remains difficult, and all commonly used modalities exhibit several drawbacks. This often leads to these patients undergoing invasive coronary angiography which may not have been necessary. In this review, we examine the uses and limitations of commonly performed non-invasive tests for diagnosis of CAD in patients with LBBB
Performance of an Echo Canceller and Channel Estimator for On-Channel Repeaters in DVB-T/H Networks
This paper investigates the design and performance of an FIR echo canceller for on-channel repeaters in DVB-T/H network within the framework of the PLUTO project. The possible
approaches for echo cancellation are briefly reviewed and the main guidelines for the design of such systems are presented. The main system parameters are discussed. The performance of an FIR echo canceller based on an open loop feedforward approach for channel estimation is tested for different radio channel conditions and for different number of taps of the FIR filter. It is shown that a minimum number of taps is recommended to achieve a certain mean rejection ratio or isolation depending on the type of channel. The expected degradation in performance due to the use of fixed point rather than floating point arithmetic in hardware implementation is presented for different number of bits. Channel estimation based on training sequences is investigated. The performance of Maximum Length Sequences and Constant Amplitude Zero Autocorrelation (CAZAC) Sequences is compared for different channels. Recommendations are given for training sequence type, length and
level for DVB-T/H on-channel repeater deployment
A Simple Data-Adaptive Probabilistic Variant Calling Model
Background: Several sources of noise obfuscate the identification of single
nucleotide variation (SNV) in next generation sequencing data. For instance,
errors may be introduced during library construction and sequencing steps. In
addition, the reference genome and the algorithms used for the alignment of the
reads are further critical factors determining the efficacy of variant calling
methods. It is crucial to account for these factors in individual sequencing
experiments.
Results: We introduce a simple data-adaptive model for variant calling. This
model automatically adjusts to specific factors such as alignment errors. To
achieve this, several characteristics are sampled from sites with low mismatch
rates, and these are used to estimate empirical log-likelihoods. These
likelihoods are then combined to a score that typically gives rise to a mixture
distribution. From these we determine a decision threshold to separate
potentially variant sites from the noisy background.
Conclusions: In simulations we show that our simple proposed model is
competitive with frequently used much more complex SNV calling algorithms in
terms of sensitivity and specificity. It performs specifically well in cases
with low allele frequencies. The application to next-generation sequencing data
reveals stark differences of the score distributions indicating a strong
influence of data specific sources of noise. The proposed model is specifically
designed to adjust to these differences.Comment: 19 pages, 6 figure
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