ARMOR: An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

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Bioinformatics-based Screening of Key Genes for Saponin Metabolism in Quinoa

10.21203/rs.3.rs-139481/v1 ◽

2021 ◽

Author(s):

Chengang Guo ◽

Zhimin wei ◽

Wei Lyu ◽

Yanlou Geng

Keyword(s):

Differentially Expressed Genes ◽

Principal Component ◽

Enrichment Analysis ◽

Hierarchical Cluster ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Differential Analysis ◽

Rna Seq ◽

Protein Coding ◽

Key Genes

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.

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Differential analysis of RNA-seq incorporating quantification uncertainty

Nature Methods ◽

10.1038/nmeth.4324 ◽

2017 ◽

Vol 14 (7) ◽

pp. 687-690 ◽

Cited By ~ 519

Author(s):

Harold Pimentel ◽

Nicolas L Bray ◽

Suzette Puente ◽

Páll Melsted ◽

Lior Pachter

Keyword(s):

Differential Analysis ◽

Rna Seq

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SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data

PLoS ONE ◽

10.1371/journal.pone.0157022 ◽

2016 ◽

Vol 11 (6) ◽

pp. e0157022 ◽

Cited By ~ 188

Author(s):

Hugo Varet ◽

Loraine Brillet-Guéguen ◽

Jean-Yves Coppée ◽

Marie-Agnès Dillies

Keyword(s):

Differential Analysis ◽

Rna Seq

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SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence

Nucleic Acids Research ◽

10.1093/nar/gkw655 ◽

2016 ◽

pp. gkw655 ◽

Cited By ~ 22

Author(s):

Hélène Lopez-Maestre ◽

Lilia Brinza ◽

Camille Marchet ◽

Janice Kielbassa ◽

Sylvère Bastien ◽

...

Keyword(s):

Protein Sequence ◽

Reference Genome ◽

Differential Analysis ◽

Rna Seq ◽

Snp Calling

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EmpiReS: Differential Analysis of Gene Expression and Alternative Splicing

10.1101/2020.08.23.234237 ◽

2020 ◽

Author(s):

Gergely Csaba ◽

Evi Berchtold ◽

Armin Hadziahmetovic ◽

Markus Gruber ◽

Constantin Ammar ◽

...

Keyword(s):

Alternative Splicing ◽

High Reliability ◽

Simulated Data ◽

Absolute Quantification ◽

Differential Analysis ◽

Rna Seq ◽

Error Distributions ◽

Alternatively Spliced ◽

Replicate Measurements ◽

Recall Level

ABSTRACTWhile absolute quantification is challenging in high-throughput measurements, changes of features between conditions can often be determined with high precision. Therefore, analysis of fold changes is the standard method, but often, a doubly differential analysis of changes of changes is required. Differential alternative splicing is an example of a doubly differential analysis, i.e. fold changes between conditions for different isoforms of a gene. EmpiRe is a quantitative approach for various kinds of omics data based on fold changes for appropriate features of biological objects. Empirical error distributions for these fold changes are estimated from Replicate measurements and used to quantify feature fold changes and their directions. We assess the performance of EmpiRe to detect differentially expressed genes applied to RNA-Seq using simulated data. It achieved higher precision than established tools at nearly the same recall level. Furthermore, we assess the detection of alternatively Spliced genes via changes of isoform fold changes (EmpiReS) on distribution-free simulations and experimentally validated splicing events. EmpiReS achieves the best precision-recall values for simulations based on different biological datasets. We propose EmpiRe(S) as a general, quantitative and fast approach with high reliability and an excellent trade-off between sensitivity and precision in (doubly) differential analyses.

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Deep Learning Based Tumor Type Classification Using Gene Expression Data

10.1101/364323 ◽

2018 ◽

Cited By ~ 4

Author(s):

Boyu Lyu ◽

Anamul Haque

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Tumor Type ◽

Differential Analysis ◽

Rna Seq ◽

Heat Map ◽

Standard Practices ◽

Tumor Types ◽

Pan Cancer

ABSTRACTDifferential analysis occupies the most significant portion of the standard practices of RNA-Seq analysis. However, the conventional method is matching the tumor samples to the normal samples, which are both from the same tumor type. The output using such method would fail in differentiating tumor types because it lacks the knowledge from other tumor types. Pan-Cancer Atlas provides us with abundant information on 33 prevalent tumor types which could be used as prior knowledge to generate tumor-specific biomarkers. In this paper, we embedded the high dimensional RNA-Seq data into 2-D images and used a convolutional neural network to make classification of the 33 tumor types. The final accuracy we got was 95.59%, higher than another paper applying GA/KNN method on the same dataset. Based on the idea of Guided Grad Cam, as to each class, we generated significance heat-map for all the genes. By doing functional analysis on the genes with high intensities in the heat-maps, we validated that these top genes are related to tumor-specific pathways, and some of them have already been used as biomarkers, which proved the effectiveness of our method. As far as we know, we are the first to apply convolutional neural network on Pan-Cancer Atlas for classification, and we are also the first to match the significance of classification with the importance of genes. Our experiment results show that our method has a good performance and could also apply in other genomics data.

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Differential analysis of RNA-Seq incorporating quantification uncertainty

10.1101/058164 ◽

2016 ◽

Cited By ~ 35

Author(s):

Harold Pimentel ◽

Bray Nicolas L. ◽

Suzette Puente ◽

Páll Melsted ◽

Lior Pachter

Keyword(s):

Differential Analysis ◽

Rna Seq ◽

Linear Modeling ◽

Accurate Analysis ◽

Response Error ◽

Shiny App ◽

Novel Method

We describe a novel method for the differential analysis of RNA-Seq data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance. The method is implemented in an interactive shiny app called sleuth that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of RNA-Seq experiments.

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