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RNA-seq03:21

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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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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data.

Shun H Yip, Pak Chung Sham, Junwen Wang

    Briefings in Bioinformatics
    |February 27, 2018
    PubMed
    Summary

    Highly variable gene (HVG) discovery in single-cell RNA sequencing (scRNA-seq) requires larger sample sizes than differential gene expression (DEG) analysis for reproducible results. This study compares seven HVG methods, highlighting discrepancies and offering recommendations.

    Keywords:
    DEG analysishighly variable genescRNA-seqsingle-cell RNA seqsoftware

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Traditional RNA sequencing (RNA-seq) analyzes gene expression in bulk samples, masking cell-to-cell variations.
    • Single-cell RNA sequencing (scRNA-seq) enables gene expression analysis at the individual cell level.
    • Highly variable gene (HVG) discovery identifies genes driving cell-to-cell differences within homogeneous populations.

    Purpose of the Study:

    • To compare the performance and reproducibility of seven HVG detection methods across six popular bioinformatics software packages.
    • To assess the impact of sample size on the reliability of HVG analysis compared to differential gene expression (DEG) analysis.
    • To identify discrepancies and potential issues within current HVG analysis tools and provide recommendations for best practices.

    Main Methods:

    • Comparative analysis of seven HVG detection algorithms: BASiCS, Brennecke, scLVM, scran, scVEGs, and Seurat.
    • Evaluation of method reproducibility across varying sample sizes.
    • Benchmarking against established differential gene expression (DEG) analysis principles.

    Main Results:

    • Reproducibility in HVG analysis is more sensitive to sample size compared to DEG analysis.
    • Significant discrepancies were observed between the results generated by different HVG analysis methods.
    • The study identified potential limitations and areas for improvement in existing HVG detection software.

    Conclusions:

    • Larger sample sizes are crucial for robust and reproducible HVG identification in scRNA-seq data.
    • Careful consideration of method choice and potential discrepancies is necessary for accurate biological interpretation.
    • Recommendations are provided to guide researchers in selecting and applying HVG analysis tools effectively.