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Related Concept Videos

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and

Hélène Lopez-Maestre1,2, Lilia Brinza3, Camille Marchet4

  • 1Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France.

Nucleic Acids Research
|July 27, 2016
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Summary

This study introduces a novel method for identifying Single Nucleotide Polymorphisms (SNPs) using only RNA-seq data, eliminating the need for a reference genome. This approach is valuable for non-model species and association studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Population Genetics

Background:

  • Single Nucleotide Polymorphisms (SNPs) are crucial genetic markers for association studies.
  • Current SNP identification methods require a reference genome, limiting their application in non-model species.
  • Transcriptome sequencing (RNA-seq) offers a cost-effective alternative for identifying SNPs in transcribed regions.

Purpose of the Study:

  • To develop and validate a method for identifying, quantifying, and annotating SNPs using only RNA-seq data, without a reference genome.
  • To enable SNP analysis in non-model organisms and facilitate association studies with phenotypes of interest.

Main Methods:

  • A novel bioinformatics pipeline utilizing RNA-sequencing data.
  • SNP identification, quantification, and annotation without a reference genome.
  • Experimental validation using RNA-seq data from human and two non-model species.

Main Results:

  • The proposed method accurately identifies, quantifies, and annotates SNPs from RNA-seq data.
  • Demonstrated precision and recall comparable to reference-based methods using human data.
  • Successful experimental validation in two non-model species, highlighting its broad applicability.

Conclusions:

  • This reference-free method expands SNP discovery capabilities to any species, particularly non-model organisms.
  • Enables annotation of SNPs and prediction of their impact on protein sequences.
  • Facilitates the investigation of SNP associations with phenotypes of interest.