Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

Li, Tong; Wu, Po-Yen; Phan, John H.; Hassazadeh, Hamid R; Tong, Weida; Wang, May D.

doi:10.1038/s41598-020-74567-y

Cited by 22 publications

(13 citation statements)

References 62 publications

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“…1. However, the choice of bioinformatic methods for transcriptomics analysis has not reached an unequivocal consensus yet, and the poor concordance between differential expression results across methods remains a challenge [44][45][46][47] . For example, using either raw read counts or normalized expression values (e.g., TPM/CPM), or simply choosing different statistical analysis packages can lead to substantially different results from the same dataset.…”

Section: Scoring Differential Mrna Expression In Pd Within Unique Tra...mentioning

confidence: 99%

Druggable transcriptomic pathways revealed in Parkinson’s patient-derived midbrain neurons

Hurk

Lau

Marchetto

et al. 2022

npj Parkinsons Dis.

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Complex genetic predispositions accelerate the chronic degeneration of midbrain substantia nigra neurons in Parkinson’s disease (PD). Deciphering the human molecular makeup of PD pathophysiology can guide the discovery of therapeutics to slow the disease progression. However, insights from human postmortem brain studies only portray the latter stages of PD, and there is a lack of data surrounding molecular events preceding the neuronal loss in patients. We address this gap by identifying the gene dysregulation of live midbrain neurons reprogrammed in vitro from the skin cells of 42 individuals, including sporadic and familial PD patients and matched healthy controls. To minimize bias resulting from neuronal reprogramming and RNA-seq methods, we developed an analysis pipeline integrating PD transcriptomes from different RNA-seq datasets (unsorted and sorted bulk vs. single-cell and Patch-seq) and reprogramming strategies (induced pluripotency vs. direct conversion). This PD cohort’s transcriptome is enriched for human genes associated with known clinical phenotypes of PD, regulation of locomotion, bradykinesia and rigidity. Dysregulated gene expression emerges strongest in pathways underlying synaptic transmission, metabolism, intracellular trafficking, neural morphogenesis and cellular stress/immune responses. We confirmed a synaptic impairment with patch-clamping and identified pesticides and endoplasmic reticulum stressors as the most significant gene-chemical interactions in PD. Subsequently, we associated the PD transcriptomic profile with candidate pharmaceuticals in a large database and a registry of current clinical trials. This study highlights human transcriptomic pathways that can be targeted therapeutically before the irreversible neuronal loss. Furthermore, it demonstrates the preclinical relevance of unbiased large transcriptomic assays of reprogrammed patient neurons.

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Section: Scoring Differential Mrna Expression In Pd Within Unique Tra...mentioning

confidence: 99%

Druggable transcriptomic pathways revealed in Parkinson’s patient-derived midbrain neurons

Hurk

Lau

Marchetto

et al. 2022

npj Parkinsons Dis.

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“…A study evaluating the performance of different read aligners and the impact on downstream analysis revealed results could differ between Salmon and STAR using an RNA-Seq dataset of control and cold-acclimated conditions—for example, the percentage of mapped reads and significantly differentially expressed genes [33]. More broadly, these findings support observations that workflow architecture can impact results [34].…”

Section: Resultsmentioning

confidence: 70%

Latch Verified Bulk-RNA Seq toolkit: a cloud-based suite of workflows for bulk RNA-seq quality control, analysis, and functional enrichment

Le¹,

Steenwyk

Manske³

et al. 2022

Preprint

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Background Analysis of high-throughput bulk RNA-sequencing (RNA-seq) data reveals changes in gene expression between diverse conditions. Many tools have emerged to quality control RNA-seq reads, quantify expression levels, conduct functional enrichment among differentially expressed genes, or identify differential RNA splicing. However, unified toolkits for conducting these analyses are lacking. Moreover, existing software does not use cloud-based platforms that provide the necessary storage and computational resources to process RNA-seq data or intuitive graphical interfaces for easy use by experimental and computational scientists. Results To address these challenges, we introduce the Latch Verified Bulk RNA-Seq (LVBRS) toolkit, a flexible suite of programs packaged into a single workflow coupled with a graphical user interface for conducting quality control, transcript quantification, differential splicing, differential expression analysis, and functional enrichment analyses. For functional enrichment, the LVBRS toolkit supports three databases - Gene Ontology, KEGG Pathway, and Molecular Signatures database - capturing diverse functional information. We demonstrate the utility of the LVBRS toolkit by reanalyzing a publicly available dataset examining the impact of severe and mild models of hypoxia - induced by Cobalt (II) Chloride (CoCl2) and oxyquinoline treatment, respectively - on a human colon adenocarcinoma cell line. Our analyses reveal CoCl2 treatment results in more differentially expressed genes, recapitulating previously reported results that CoCl2 models more severe hypoxia. Moreover, including alternative splicing and functional enrichment analysis using a greater breadth of functional databases revealed additional biological insights - such as greater alternative splicing in the CoCl2 condition and differentially expressed DNA repair pathways. These results demonstrate the LVBRS toolkit's efficacy in facilitating biological insights from bulk RNA-seq data. Conclusions The LVBRS toolkit offers a robust unified framework for processing and analyzing Bulk RNA-Seq experiments. The easy-to-use graphical user interface will enable diverse scientists to conduct high-throughput bulk RNA-Seq analysis efficiently. Our aim is that the LVBRS toolkit will help streamline bulk RNA-seq workflows and facilitate deriving biologically meaningful insights from bulk RNA-seq data. The source code is freely available under the MIT license and hosted on the LatchBio Console (https://console.latch.bio/se/bulk-rnaseq), complete with documentation (https://latch.wiki/bulk-rna-seq-end-to-end).

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“…18 As a follow-up study, we further investigated the impact of the joint effects of RNA-seq pipelines on gene expression estimation and the downstream prediction of disease outcomes. 19 First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to evaluate each pipeline's performance on gene expression estimation quantitatively. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two realworld cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset).…”

Section: Akihiko Hirose Phd National Institute Of Health Sciences Japanmentioning

confidence: 99%

Emerging technologies and their impact on regulatory science

Anklam

Bahl

Ball

et al. 2021

Exp Biol Med (Maywood)

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There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools.

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Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

Cited by 22 publications

References 62 publications

Druggable transcriptomic pathways revealed in Parkinson’s patient-derived midbrain neurons

Druggable transcriptomic pathways revealed in Parkinson’s patient-derived midbrain neurons

Latch Verified Bulk-RNA Seq toolkit: a cloud-based suite of workflows for bulk RNA-seq quality control, analysis, and functional enrichment

Emerging technologies and their impact on regulatory science

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