Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Costa-Silva, Juliana; Domingues, Douglas Silva; Menotti, David; Hungría, Mariangela; Lopes, Fabrício Martins

doi:10.1016/j.csbj.2022.11.051

Cited by 11 publications

(3 citation statements)

References 157 publications

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“…Recently, Costa-Silva et al [33] , provided a broad overview of advances in the field of differential gene expression analysis using RNA-Seq data. This review highlighted the various computational methodologies used to examine these data.…”

Section: Introductionmentioning

confidence: 99%

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

Rosati,

Palmieri,

Brunelli

et al. 2024

Computational and Structural Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

Rosati,

Palmieri,

Brunelli

et al. 2024

Computational and Structural Biotechnology Journal

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“…One of the most common reasons for analyzing RNA-seq data is to identify DEGs for different groups or conditions [1] , [2] , [3] . Many studies have been reported on efficient and accurate means of identifying DEGs (e.g., [ 4 , 5 ]). Conventional differential expression (DE) analysis such as edgeR [6] and DESeq2 [7] typically consists of two steps (data normalization and DEG identification), and each R package has its own methods for each step [8] .…”

Section: Introductionmentioning

confidence: 99%

Enhanced clustering-based differential expression analysis method for RNA-seq data

Makino,

Shimizu,

Kadota

2024

MethodsX

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“…The traditional RNA-seq analysis approach, which relies on established tools such as edgeR (9), DESeq2 (10), and Cufflinks (11), primarily focuses on identifying differentially expressed genes (DEGs) through pairwise comparisons (12). However, for conditions like NAFLD, which involve a complex scoring system and a continuous range of histological variations, this approach has its limitations.…”

Section: Introductionmentioning

confidence: 99%

A gene-based clustering approach reveals QSOX1/IL1RAP as promising biomarkers for the severity of non-alcoholic fatty liver disease

Huang

Cai

et al. 2023

Preprint

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Background and Aims: Non-alcoholic fatty liver disease (NAFLD) is a progressive liver disease that ranges from simple steatosis to inflammation, fibrosis, and cirrhosis. To address the unmet need for new NAFLD biomarkers, we aimed to identify candidate biomarkers using publicly available RNA sequencing (RNA-seq) and proteomics data. Methods: A novel approach involving unsupervised gene clustering was performed using homogeneously processed and integrated RNA-seq data of 625 liver specimens to screen for NAFLD biomarkers, in combination with public proteomics data from healthy controls and NAFLD patients. Additionally, we validated the results in the NAFLD and healthy cohorts using enzyme-linked immunosorbent assay (ELISA) of plasma and immunohistochemical staining (IHC) of liver samples. Results: We generated a database (https://dreamapp.biomed.au.dk/NAFLD/) for exploring gene expression changes along NAFLD progression to facilitate the identification of genes and pathways involved in the disease's progression. Through cross-analysis of the gene and protein clusters, we identified 38 genes as potential biomarkers for NAFLD severity. Up-regulation of Quiescin sulfhydryl oxidase 1 (QSOX1) and down-regulation of Interleukin-1 receptor accessory protein (IL1RAP) were associated with increasing NAFLD severity in RNA-seq and proteomics data. Particularly, the QSOX1/IL1RAP ratio in plasma demonstrated effectiveness in diagnosing NAFLD, with an area under the receiver operating characteristic (AUROC) of up to 0.95 as quantified by proteomics profiling, and an AUROC of 0.82 with ELISA. Conclusions: We discovered a significant association between the levels of QSOX1/IL1RAP and NAFLD severity. Furthermore, the QSOX1/IL1RAP ratio shows promise as a non-invasive biomarker for diagnosing NAFLD and assessing its severity. (ChiCTR2300073940).

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Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Cited by 11 publications

References 157 publications

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

Enhanced clustering-based differential expression analysis method for RNA-seq data

A gene-based clustering approach reveals QSOX1/IL1RAP as promising biomarkers for the severity of non-alcoholic fatty liver disease

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