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Related Concept Videos

Aliasing01:18

Aliasing

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.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Bandpass Sampling01:17

Bandpass Sampling

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.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
Passive Filters01:27

Passive Filters

Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff frequency...
Upsampling01:22

Upsampling

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...
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in the 3500–3100 cm−1 range. Even though both O−H and N−H bonds vibrate at a similar...

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Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

The functional spectrum of low-frequency coding variation.

Gabor T Marth1, Fuli Yu, Amit R Indap

  • 1Department of Biology, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA. gabor.marth@bc.edu

Genome Biology
|September 16, 2011
PubMed
Summary
This summary is machine-generated.

This study reveals that rare coding variants, below 1% allele frequency, are more common in specific populations and likely impact protein function. This finding advances the understanding of human genetic variation.

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Rare coding variants are crucial but underrepresented in genetic databases.
  • Previous studies indicated enrichment of functional variants at 2-5% allele frequency, but data for variants below 1% was limited.

Purpose of the Study:

  • To investigate the frequency and characteristics of rare coding variants (<1% allele frequency).
  • To assess the population-specificity and functional impact of these variants.
  • To establish effective methods for analyzing deep-coverage exome data.

Main Methods:

  • Deep-coverage exon-capture sequencing data from nearly 700 samples (1000 Genomes Exon Pilot Project).
  • Development and application of robust informatics pipelines for data processing and analysis.
  • Analysis of exonic single nucleotide polymorphisms (SNPs) across approximately 1,000 human genes.

Main Results:

  • Discovered 12,758 exonic SNPs, with 70% being novel.
  • Identified that 74% of discovered variants had an allele frequency below 1%.
  • Confirmed that coding variants below 1% allele frequency exhibit increased population-specificity and are enriched for functional impact.

Conclusions:

  • This research significantly advances the detection and interpretation of low-frequency coding variation.
  • The study provides a clear technical framework for analyzing DNA capture data.
  • It elucidates the functional and population-specific properties of rare coding genetic variation.