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Related Concept Videos

Aliasing01:18

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Linear Approximation in Frequency Domain01:26

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Bandpass Sampling01:17

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
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IR Frequency Region: Fingerprint Region01:03

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Transcription Start Site Mapping Using Super-low Input Carrier-CAGE
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Fast and accurate site frequency spectrum estimation from low coverage sequence data.

Eunjung Han1, Janet S Sinsheimer2, John Novembre1

  • 1Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA, Department of Human Genetics and Biomathematics, University of California, Los Angeles, Los Angeles, CA 90095, USA and Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.

Bioinformatics (Oxford, England)
|November 1, 2014
PubMed
Summary
This summary is machine-generated.

A new score-limited dynamic programming (DP) algorithm accurately estimates the site frequency spectrum (SFS) from low-coverage sequencing data. This method is faster than previous approaches, enabling SFS analysis with large numbers of individuals.

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Area of Science:

  • Population genetics
  • Bioinformatics
  • Computational biology

Background:

  • The site frequency spectrum (SFS) is crucial for understanding population genetic processes from sequence variation.
  • Inferring the SFS from low-coverage sequencing data using genotype calls can introduce significant bias.
  • Existing methods using dynamic programming (DP) to estimate the SFS directly from sequencing data are computationally intensive, scaling quadratically with the number of samples.

Purpose of the Study:

  • To develop a computationally efficient algorithm for accurate SFS estimation from low-coverage sequencing data.
  • To overcome the quadratic time complexity of existing DP-based SFS inference methods.

Main Methods:

  • Propose a 'score-limited DP' algorithm to compute site allele frequency (SAF) likelihood.
  • The algorithm achieves linear time complexity with respect to the number of genomes.
  • Leverages the concentration of non-negligible SAF likelihood values in specific cells of the DP matrix.

Main Results:

  • The score-limited DP algorithm demonstrates comparable accuracy to the original DP algorithm.
  • The proposed algorithm significantly reduces computation time, making SFS estimation practical for large datasets.
  • Enables SFS analysis using low-coverage next-generation sequencing (NGS) data from numerous individuals.

Conclusions:

  • The score-limited DP algorithm provides a faster and accurate method for SFS estimation.
  • This advancement facilitates population genetic inferences from large-scale sequencing studies.
  • The program will be accessible via the Novembre lab website.