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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Updated: Mar 18, 2026

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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RNAScope-Ancestry: A Cross-Modality Framework for Inferring Genetic Ancestry from RNA-Seq with Application to MECA.

Rashi Verma1, Shivam Sharma2, Harriet Na Blankson1

  • 1Morehouse School of Medicine.

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|March 16, 2026
PubMed
Summary
This summary is machine-generated.

RNA sequencing (RNA-seq) can now determine genetic ancestry, complementing gene expression analysis. This new RNAScope-Ancestry pipeline offers a dual-purpose tool for population genetics research.

Keywords:
Cardiovascular DiseaseEthnicityGenetic AncestryRaceShort-Read RNA-Seq

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Traditional genetic ancestry inference requires specialized genotyping arrays or whole-genome sequencing.
  • RNA sequencing (RNA-seq) data has not been fully utilized for ancestry estimation.

Purpose of the Study:

  • To introduce RNAScope-Ancestry, a novel computational pipeline for simultaneous genetic ancestry and transcriptomic analysis using RNA-seq data.
  • To evaluate the pipeline's performance in estimating ancestry for diverse populations.

Main Methods:

  • Leveraged short-read RNA-seq data from 490 MECA participants and 1000 Genomes reference populations.
  • Performed variant calling, quality filtering, and principal component analysis.
  • Utilized 230 high-frequency SNPs and the Rye algorithm for ancestry estimation.

Main Results:

  • MECA participants' ancestry aligned with African and admixed populations, primarily West African.
  • Identified top 50 ancestry-associated transcripts by correlating with gene expression.
  • Validated reproducibility in 109 longitudinal samples.

Conclusions:

  • RNAScope-Ancestry provides a dual-purpose analysis of RNA-seq data for both ancestry and transcriptomics.
  • The open-source pipeline is generalizable and enhances the utility of existing RNA-seq datasets for population genetic studies.