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10.1038/s41598-020-74567-y http://scihub22266oqcxt.onion/10.1038/s41598-020-74567-y 33087762!7578822!33087762     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2020 ; 10 (1): 17925 Nephropedia Template TP {{tp|p=33087762|t=2020. Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction |pdf=|usr=}}{{33087762}} gab.com Text Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33087762 #FOAvip To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome. Twit Text FOAVip Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33087762 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33087762 #FOAvip English Wikipedia <ref>{{Cite pmid|33087762}}</ref> Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction #MMPMID33087762 Tong L; Wu PY; Phan JH; Hassazadeh HR; Tong W; Wang MD Sci Rep 2020[Oct]; 10 (1): 17925 PMID33087762show ga To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome. |*Gene Expression[MESH] |Data Analysis[MESH] |Datasets as Topic[MESH] |High-Throughput Nucleotide Sequencing/*methods[MESH] |Humans[MESH] |Microarray Analysis[MESH] |Neoplasms/*genetics[MESH] |Predictive Value of Tests[MESH] |Prognosis[MESH]Impact of RNA-seq data analysis algorithms on gene expression estimati(epmc).pdf DeepDyve Pubget Overpricing