We are analyzing https://link.springer.com/article/10.1186/1471-2105-12-480.

Title:
GC-Content Normalization for RNA-Seq Data | BMC Bioinformatics
Description:
Background Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof. Results We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. Our methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and p-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq. Conclusions Our within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.
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Keywords {🔍}

normalization, gccontent, expression, counts, bias, rnaseq, read, data, article, gene, pubmed, lanes, dataset, yeast, figure, genes, count, differential, google, scholar, library, methods, preparation, maqc, effects, procedures, betweenlane, withinlane, error, analysis, effect, central, sequencing, results, genome, lane, length, fullquantile, type, dependence, based, uhr, rna, foldchange, brain, panel, protocol, number, genelevel, datasets,

Topics {✒️}

uhr/brain expression log-fold-change open access article unlike rna-seq p-values gc-normalized log-fold-change confounds fold-change estimation rna-seq estimated fold-change high-throughput dna sequencing strand-specific rna-seq libraries expression fold-change estimation high-throughput sequencing assays article download pdf spline-smoothed enrichment factors rna-seq p-values tend protein-nucleic acid interactions expression fold-change estimates perform gene-level lrt major technology-related artifacts full-quantile normalization procedure log-fold-change estimates perform gene-level tests turbo dna-free kit base-level read counts true log-fold-change rna-seq data generated analyzing rna-seq data previous gene-level normalization proposed full-quantile normalization human rna-seq data high gc-content tend regressing bin-level counts regressing gene-level counts gc-content effect differs gene-level read counts rna-seq normalization procedures scaling exon-level counts high throughput sequencing high-throughput studies lane-specific systematic biases benchmarking rna-seq datasets uhr/brain fold-change stranded rna-seq reads rna-seq data suggest gc-poor fragments tend gc-content normalization method gene gc-content values rna-seq read counts lane gc-content normalization evaluate normalization methods gc-content effect varies rna-seq p-values

Schema {🗺️}

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         Microarrays
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         Computer Appl. in Life Sciences
         Algorithms
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