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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
Gene Flow02:39

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.
Genetic Drift03:33

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
Genetic Screens02:46

Genetic Screens

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 result in visible changes...

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Related Experiment Video

Updated: May 19, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.

Aishwarya Budhkar1, Juhyung Ha1, Qianqian Song2

  • 1Department of Computer Science, Indiana University Bloomington, Bloomington, IN, USA.

Computational and Structural Biotechnology Journal
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

SpaGene, a deep learning framework, integrates single-cell RNA sequencing and spatial transcriptomics data to reveal tissue biology. This method accurately imputes gene expression, enhancing understanding of cellular interactions and disease progression in tissues.

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Last Updated: May 19, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) offers high-resolution gene expression but lacks spatial context.
  • Spatial transcriptomics provides spatial resolution but has limited transcriptomics coverage.
  • Integrating both data types is crucial for a comprehensive understanding of tissue architecture and function.

Purpose of the Study:

  • To introduce SpaGene, a novel deep learning framework for integrating scRNA-seq and spatial transcriptomics data.
  • To impute missing gene expression data in spatial transcriptomics datasets using scRNA-seq information.
  • To enhance the understanding of tissue biology, cellular interactions, and disease progression.

Main Methods:

  • SpaGene utilizes a deep learning architecture comprising two encoder-decoder pairs, two translators, and two discriminators.
  • The framework is designed to effectively impute gene expression within spatial transcriptomics datasets.
  • Performance was benchmarked against six representative methods using a controlled gene-holdout evaluation protocol across diverse datasets.

Main Results:

  • SpaGene demonstrated superior performance compared to baseline methods, improving average Pearson correlation coefficient and cell-wise structural similarity index.
  • The model significantly reduced root mean squared error, indicating more accurate recovery of held-out spatial gene expression.
  • Application to lung tumor tissue revealed spatial immune cell enrichment at tumor boundaries and improved detection of microenvironment-driven pathways.

Conclusions:

  • SpaGene effectively integrates scRNA-seq and spatial transcriptomics data, providing accurate gene expression imputation.
  • The framework offers valuable insights into tissue spatial patterns, immune cell distribution, and tumor-immune interactions.
  • These findings support further biological validation and advance the study of complex biological systems.