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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Incomplete Dominance01:43

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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Related Experiment Video

Updated: Jun 13, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Xiaoyu Wang1,2, Fuyi Li3, Yiwen Zhang4

  • 1Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.

Briefings in Bioinformatics
|September 14, 2024
PubMed
Summary
This summary is machine-generated.

This review explores deep learning models for predicting non-coding variant effects using bulk and single-cell sequencing data. It highlights advancements and limitations in understanding gene regulation and offers a guide for bioinformaticians.

Keywords:
deep learningmachine learningnon-coding variantsvariant effect prediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing advances gene regulatory process understanding.
  • Identifying variant effects is crucial for gene regulation mechanisms.
  • Non-coding variants, >90% of all variants, are increasingly studied.

Purpose of the Study:

  • To provide a comprehensive overview of non-coding variant models using bulk and single-cell sequencing data.
  • To review model-based interpretation and downstream tasks for variant effects.
  • To guide the bioinformatic community in advancing genetic variant effect research.

Main Methods:

  • Review of deep learning approaches for variant effect prediction.
  • Analysis of sequencing technologies for epigenetic profiling.
  • Examination of models based on bulk and single-cell sequencing data.

Main Results:

  • Deep learning models offer new insights into the global regulatory landscape.
  • Various approaches exist for discerning non-coding variant effects.
  • Current methods have limitations in variant effect prediction.

Conclusions:

  • Further advancements are needed in variant effect prediction.
  • This review offers a practical guide for the bioinformatic community.
  • Understanding non-coding variants is key to unraveling gene regulation.