Expression Analysis Of RNA-seq Data Research Articles

CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data.

RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data's utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq's efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data.

Abstract PO5-24-11: An integrated approach for comprehensive molecular and tumor microenvironment characterization of invasive lobular carcinoma

Abstract Introduction: Although recent studies have shown that invasive lobular carcinoma (ILC) is associated with worse long-term outcomes compared to invasive breast cancer of no special type (NST), they are treated in a similar manner. Therefore, classification approaches to help understand the molecular characteristics associated with the different BC subtypes are needed to personalize therapy. Here, we describe an integrated analytical approach to comprehensively characterize ILC samples. Methods: We collected genomic and transcriptomic data for 16,087 ILC and NST samples, with clinical and pathological annotation, from 59 datasets and used an internally developed algorithm based on clustering performed after gene expression analysis to classify them into 5 intrinsic subtypes: Basal, Luminal A (LumA), Luminal B (LumB), HER2 high, and HER2 low. We performed gene set enrichment analyses and utilized the DESeq2 algorithm to perform differential expression analysis of RNA-seq data. We performed molecular grade subtyping using methods described by Antysheva et al. (Cancer Res., 2022). Samples that were listed as ILC or had an inactivated CDH1 gene by mutation, deletion or low expression (< −2.5 MAD after median scaling) were defined as having an ILC histomolecular subtype. Using methods described by Bagaev et. al. (Cancer Cell, 2021), 29 functional gene expression signatures were selected, and unsupervised dense Louvain clustering was performed to identify TME subtypes. The Mann-Whitney U test was used for statistical analysis. The tertiary lymphoid structures (TLS) gene signature was evaluated using methods described by Sautès-Fridman et al. (Nat. Rev. Cancer, 2019). Results: In the ILC samples (n=1,887), we identified LumA (46%), LumB (40%), Basal (8%,), HER2 high (3%), and HER2 low (3%) subtypes. In contrast, the NST samples were classified as LumB (41%), LumA (25%), Basal (19%), HER2 high (9%), and HER2 low (5%). Molecular grade subtyping revealed that 67% of ILC samples were low grade and 33% were high grade, compared to NST where 55% were high grade and 45% were low grade. Notably, among high-grade ILC, LumB (64%) and Basal (23%) subtypes were predominant. Key differentially expressed genes included downregulated CDH1 and upregulated MMP23B, IGF1, and TFAP2B across LumA and LumB ILC samples. Further analysis revealed downregulation of TACSTD2 and CDH3 in the Basal subtype for ILC samples compared to NST samples. Transcriptomic-based TME subtyping revealed distinct TME distributions across the BC subtypes and molecular grades for ILC samples (Table 1). In high-grade ILC LumB samples, 38% had a Desert (D; predictive of poor immunotherapy response) and 39% had an Immune-Enriched (IE; predictive of favorable immunotherapy response) TME. In low-grade ILC LumB samples we found that while 51% had a D TME, only 14% had an IE TME. We found an overrepresentation of the TLS signature [Solid TLS] in ILC compared to NST samples, and higher matrix remodeling and CAF signatures in LumA compared to the LumB subtype among low-grade ILC samples. Samples with high CD274 expression levels had higher percentages of T cells and M1 macrophages, and LumA samples had significantly higher (p < 0.001) CD274 expression compared to the other BC subtypes. Conclusion: These results provide in-depth molecular and tumor microenvironment characterization of ILC. Further optimization of this analytical approach could lead to the development of more effective therapeutic strategies. Table 1. ILC subtypes classification Citation Format: Jason Mouabbi, Konstantin Chernyshov, Oleg Baranov, Vladimir Kushnarev, Polina Turova, Anna Butusova, Sofia Menshikova, Jessica Brown, Nikita Kotlov, Patrick Clayton, Krystle Nomie, Nathan Fowler, Debu Tripathy. An integrated approach for comprehensive molecular and tumor microenvironment characterization of invasive lobular carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-24-11.

Abstract 6596: BRMS1 downregulation alters the tumor microenvironment and induces immunotherapy resistance in lung adenocarcinoma

Abstract Background: Lung cancer is the leading cause of cancer-related deaths globally. During cancer progression, tumor cells interact with tumor-infiltrating immune cells (TIICs) creating an immunosuppressive microenvironment. Therefore, it is essential to delineate the communication dynamics between cancer cells and TIICs to identify novel pathways for therapeutic targeting. BRMS1 is a metastasis suppressor gene that is frequently downregulated in lung adenocarcinoma (LUAD). We, and others, have shown that loss of BRMS1 results in increased distant metastatic disease. Given the strong correlation between immune suppression, metastasis, and immunotherapy resistance we sought to elucidate if BRMS1 contributes to these processes. Specifically, we hypothesized that loss of BRMS1 in LUAD cells influences the immune cell composition, shaping an immunosuppressive microenvironment and decreasing tumor response to immunotherapy. Methods: To experimentally address our hypothesis, we generated KrasG12D P53fl/fl Brms1−/− mice inducing spontaneous tumors with Ad-Cre intratracheal inoculation. RNA-seq scRNA-seq on Brms1+/+ and Brms1−/− tumors, coupled with cell type annotations using mMCP-counter and DCATS were used to identify diverse TIIC subsets. To validate our observation, we performed immunofluorescence and flow cytometry to assess the number of TIICs. To explore the impact of Brms1 loss on the efficacy of anti-PD1 immunotherapy, a syngeneic CMT167 mouse model with BRMS1KD cells implanted in the flank was treated with an anti-PD1 antibody. Results: Differential gene expression analysis of RNA-seq data revealed a reduction in CCL9, CCL24, CLEC1B, PYCARD and other proinflammatory molecules in Brms1−/− tumors. Gene ontology and GSEA highlighted a diminished immune response signature in Brms1−/- tumors. In contrast, hallmark gene sets like IFNγ response, IL6 signaling, and inflammation are highly enriched in Brms1+/+ tumors. mMCP-counter suggested a decrease in CD8+ T cells in Brms1−/- tumors. Likewise, scRNA-seq data confirmed a significant decrease in cytotoxic CD8+ T cells in the Brms1−/− mice. Immunofluorescence and flow cytometric analysis showed a reduction in cytotoxic CD8+ T cells with low proliferative potential (Ki67+) in the Brms1−/− mice. Postmortem on our syngeneic mouse tumors after a 30-day anti-PD1 antibody treatment revealed a significantly larger mean tumor size in the BRMS1KD group compared to the WT group (942.4 mm³ vs. 184 mm³; p<0.023). Immunophenotyping on these tumors using flow cytometry also demonstrated reduced cytotoxic CD8+ T cells in the KD group post-treatment. Conclusion: Our data suggest that reduced BRMS1 expression in LUAD alters CD8+ T cell composition and primary tumor response to immunotherapy. Implications of our findings underscore the potential of BRMS1 as a therapeutic target in the context of immunotherapy-resistant LUAD. Citation Format: Manendra B. Lankadasari, Yuan Liu, Brooke Mastrogiacomo, Yingjie Zhu, Elizabeth G. Dunne, Cameron N. Fick, Choudhary Harsha, Jorge S. Reis-Filho, David R. Jones. BRMS1 downregulation alters the tumor microenvironment and induces immunotherapy resistance in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6596.

Genome-Wide Identification of Bilberry WRKY Transcription Factors: Go Wild and Duplicate.

WRKY transcription factor genes compose an important family of transcriptional regulators that are present in several plant species. According to previous studies, these genes can also perform important roles in bilberry (Vaccinium myrtillus L.) metabolism, making it essential to deepen our understanding of fruit ripening regulation and anthocyanin biosynthesis. In this context, the detailed characterization of these proteins will provide a comprehensive view of the functional features of VmWRKY genes in different plant organs and in response to different intensities of light. In this study, the investigation of the complete genome of the bilberry identified 76 VmWRKY genes that were evaluated and distributed in all twelve chromosomes. The proteins encoded by these genes were classified into four groups (I, II, III, and IV) based on their conserved domains and zinc finger domain types. Fifteen pairs of VmWRKY genes in segmental duplication and four pairs in tandem duplication were detected. A cis element analysis showed that all promoters of the VmWRKY genes contain at least one potential cis stress-response element. Differential expression analysis of RNA-seq data revealed that VmWRKY genes from bilberry show preferential or specific expression in samples. These findings provide an overview of the functional characterization of these proteins in bilberry.

Differential genomic and transcriptomic analysis of invasive lobular and ductal carcinomas.

579 Background: Invasive lobular carcinoma (ILC) is more aggressive than hormone receptor (HR)-positive invasive ductal carcinoma (IDC). However, in practice, ILC and IDC are often treated in a similar fashion with endocrine therapy and chemotherapy. Identifying novel biomarkers, genetic alterations, transcriptomic features, and tumor microenvironment (TME) variations could initiate the development of personalized treatment plans for patients with ILC. Methods: We collected ILC and luminal (non-basal/non-HER2) IDC samples from four datasets (TCGA, METABRIC, RATHER PMC4700448, and UQCCR PMC31263747) and performed differential expression and gene set enrichment analyses, revealing novel genomic, transcriptomic, and TME differences. Using methods from Bagaev et al., we quantified the activity of 29 functional gene expression signatures with single sample gene set enrichment analysis before clustering the samples into five TME subtypes; statistical significance was measured with the Mann-Whitney U test. Differential expression analysis of RNA-Seq data was completed using DESeq2. Further, we analyzed the frequency of specific biomarkers to identify potential therapeutic options. Mutations and biomarker enrichment were assessed using the chi-squared test. Results: We analyzed 1,735 samples (1,442 luminal IDCs and 293 ILCs). CDH1 mutations were more prevalent in ILC samples (56%) compared to IDC samples (6%). Of the 44% of ILC samples with wild-type CDH1, 90% had low CDH1 expression. Inference models showed differences in transcription factors expression between ILC and IDC. ILC had significantly higher expression of TFAP2B, SOCS2, NOSTRIN, THBS4, SCUBE2, and GDF9 and lower expression of CDCA4, PSMG1, LMOD1, and SLC7A5 (adj p < 0.0001 for all genes). Analysis of the TME showed that 44% of ILC samples were immune enriched with high PDL1, CTLA4, and LAG3 expression. In comparison, approximately 30% of ILC samples contained enhanced vascularization and expressed high VEGFA, PDGFRA, and PDGFRB. Finally, compared to luminal IDC, ILC tend to have a statistically significant higher TROP2 expression, similar to that seen in basal subtype. Conclusions: ILC and IDC expressed distinct genomic alterations, gene expression, transcriptomic features, TMEs, and biomarkers. These differences can be used as a blueprint to tailor ILC phenotype-specific interventional clinical trials.

FunHoP analysis reveals upregulation of mitochondrial genes in prostate cancer.

Mitochondrial activity in cancer cells has been central to cancer research since Otto Warburg first published his thesis on the topic in 1956. Although Warburg proposed that oxidative phosphorylation in the tricarboxylic acid (TCA) cycle was perturbed in cancer, later research has shown that oxidative phosphorylation is activated in most cancers, including prostate cancer (PCa). However, more detailed knowledge on mitochondrial metabolism and metabolic pathways in cancers is still lacking. In this study we expand our previously developed method for analyzing functional homologous proteins (FunHoP), which can provide a more detailed view of metabolic pathways. FunHoP uses results from differential expression analysis of RNA-Seq data to improve pathway analysis. By adding information on subcellular localization based on experimental data and computational predictions we can use FunHoP to differentiate between mitochondrial and non-mitochondrial processes in cancerous and normal prostate cell lines. Our results show that mitochondrial pathways are upregulated in PCa and that splitting metabolic pathways into mitochondrial and non-mitochondrial counterparts using FunHoP adds to the interpretation of the metabolic properties of PCa cells.

Fused inverse-normal method for integrated differential expression analysis of RNA-seq data

BackgroundUse of next-generation sequencing technologies to transcriptomics (RNA-seq) for gene expression profiling has found widespread application in studying different biological conditions including cancers. However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a p-value combination method for meta-analysis of multiple independent but related RNA-seq studies that accounts for sample size of a study and direction of expression of genes in individual studies.ResultsThe proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene RAD51 in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as TCF7L2 and MAPT as important upstream regulators in GBM.ConclusionsThe proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease.

RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis.

RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. With RNfuzzyApp, we provide a user-friendly, web-based R shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, fully automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, cluster overlap analysis, Mfuzz loop computations, as well as cluster enrichments. RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its assignment of orthologs, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis

RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. With RNfuzzyApp, we provide a user-friendly, web-based R shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, fully automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, cluster overlap analysis, Mfuzz loop computations, as well as cluster enrichments. RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its assignment of orthologs, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis.

RNA sequencing (RNA-seq) is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species. With RNfuzzyApp, we provide a user-friendly, web-based R shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, fully automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, cluster overlap analysis, Mfuzz loop computations, as well as cluster enrichments. RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

Abstract PS4-25: Comprehensive genomic analysis reveals molecular correlates of response to immune checkpoint inhibitors (ICI) in metastatic triple-negative breast cancer (mTNBC)

Abstract Background: Genomic mechanisms associated with response to ICI in mTNBC are largely unknown. The aim of this work is to assess the genomic and immune profiles of mTNBC samples collected from patients (pts) treated with ICI. Methods: We identified 31 women with mTNBC treated with ICI (pembrolizumab, n=6, NCT02447003; atezolizumab, n=4, NCT01375842; nivolumab + cabozantinib, n = 6, NCT03316586; pembrolizumab + eribulin, n=8, NCT02513472; atezolizumab + nab-paclitaxel, n=7, NCT01633970) who had tumor tissue or blood available for sequencing obtained before and after ICI. Clinical benefit (CB), here defined as any objective response or stable disease (SD) for > 24 weeks, was observed in 20 pts (65%). An extraordinary responder was defined as having CB ≥ 2 yrs; 5 pts were considered extraordinary responders (range 26-60months). Whole exome sequencing (WES) was done on each tumor and on germline DNA from blood (23 pts had successful WES performed on samples collected before ICI; 5 of these had WES on samples taken after disease progression). RNA sequencing (RNAseq) was successfully performed in 18 of the tumors with WES performed on samples before ICI; and 3 of these had RNAseq on samples taken after disease progression. 18 pts had tumors assessed by multiplex immunofluorescence (mIF) panels encompassing CD4, CD8, PD-1, PD-L1, and cytokeratin on samples collected before ICI. WES, deep targeted panel and low coverage whole genome sequencing were performed on serially collected plasma samples from 22 pts to evaluate tumor fraction and specific mutations. The association between biomarkers and clinical benefit to ICI was assessed. Results: 21 of 31 pts (67%) had received ≥1 prior lines of systemic therapy in the metastatic setting before starting ICI. Among the most frequently mutated genes at baseline are: TP53 (57%); PIK3CA (18%); DNAH5, MYH8 (both 13%); KMT2C, AKT1, LAMA2 (all 9%). Pts with CB had a higher tumor mutational burden (TMB) than pts with no CB (p=0.018). Differential expression analysis of RNAseq data revealed an upregulation of several immune-related genes in pts with CB, indicating increased immune infiltration in that group. Gene set enrichment analysis of this expression data using hallmark and canonical pathway gene sets from MSigDB (nominal p-val < 0.05) showed that, compared to samples from pts without CB, extraordinary responders had elevated transcriptional signatures of several cancer-related pathways associated with cell survival, proliferation and metabolism, as well as genes associated with increased immune infiltration and upregulation of inflammatory response programs. The mIF showed that the tumor microenvironment (TME) of pts with CB were enriched in Cytokeratin-negative/PD-L1-positive cells compared to those without CB (p=0.014). Expression of CD4, CD8 and PD-1 was not significantly different between pts with and without CB. Genomic analysis of circulating tumor DNA, and tumor evolutionary analysis for pts with both pre- and post-ICI samples (acquired resistance) will be presented. Conclusions: Clinical benefit to ICI in mTNBC was associated with upregulation of immune-related pathways, enrichment of non-tumoral PD-L1-positive cells in TME, and high TMB. Citation Format: Romualdo Barroso-Sousa, Juliet Forman, Zachary T. Weber, Katherine Collier, Katrina Z. Kao, Edward T. Richardson, III, Tanya Keenan, Ofir Cohen, Michael P. Manos, Ryan C. Brennick, Patrick Ott, F. Steve Hodi, Deborah A. Dillon, Nancy U. Lin, Eliezer E. Van Allen, Scott Rodig, Eric P. Winer, Elizabeth A. Mittendorf, Catherine J. Wu, Daniel Stover, Nikhil Wagle, Sachet Shukla, Sara Tolaney. Comprehensive genomic analysis reveals molecular correlates of response to immune checkpoint inhibitors (ICI) in metastatic triple-negative breast cancer (mTNBC) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-25.

Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data

BackgroundThe correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking.ResultsWe compare methods developed for single-cell and bulk RNA-seq, and specifically for microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, power, and correct identification of differentially abundant genera. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing.ConclusionsThe multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

Comparison of differential accessibility analysis strategies for ATAC-seq data

ATAC-seq is widely used to measure chromatin accessibility and identify open chromatin regions (OCRs). OCRs usually indicate active regulatory elements in the genome and are directly associated with the gene regulatory network. The identification of differential accessibility regions (DARs) between different biological conditions is critical in determining the differential activity of regulatory elements. Differential analysis of ATAC-seq shares many similarities with differential expression analysis of RNA-seq data. However, the distribution of ATAC-seq signal intensity is different from that of RNA-seq data, and higher sensitivity is required for DARs identification. Many different tools can be used to perform differential analysis of ATAC-seq data, but a comprehensive comparison and benchmarking of these methods is still lacking. Here, we used simulated datasets to systematically measure the sensitivity and specificity of six different methods. We further discussed the statistical and signal density cut-offs in the differential analysis of ATAC-seq by applying them to real data. Batch effects are very common in high-throughput sequencing experiments. We illustrated that batch-effect correction can dramatically improve sensitivity in the differential analysis of ATAC-seq data. Finally, we developed a user-friendly package, BeCorrect, to perform batch effect correction and visualization of corrected ATAC-seq signals in a genome browser.

What Genes Can Tell: A Closer Look at Vestibular Schwannoma.

Comprehensive molecular profiling of radioresistant and cystic vestibular schwannoma (VS) subtypes. Our study utilized whole-exome sequencing (WES), RNA-sequencing (RNAseq), and correlated clinical data from 12 samples (2 samples of solid sporadic subtype, 8 with cystic changes, and 2 previously irradiated). Academic medical center. Patients diagnosed with VS who required surgical treatment. Inclusion: Cystic and radioresistant tumors matched to age and tumor volume, with solid sporadic VS samples as control; Exclusion: NF-2 patients. WES using custom probes for copy number analysis. A modified version of the Agilent Human Whole Exome sequencing hybrid capture system was used to process samples. Recurrent variants were identified and compared between groups. Leukocyte-derived DNA was utilized as internal control to reduce false-positives. Analysis of genetic landscape of VS subtypes (naive solid VS, cystic VS, and previously irradiated VS) by performing deep next-generation sequencing. WES data achieved a mean coverage of 202X and RNAseq generated an average of 74 million total reads. As a group, 25% of samples had 22q loss. Somatic analysis identified previously reported genes and multiple novel mutations across samples. Differential expression analysis of RNAseq data found significantly mutated genes such as COL6A3, CLMP, ART4, Lumican that were shared by both cystic VS and irradiated VS, but not seen in sporadic VS. Using WES we were able to demonstrate that cystic and irradiated samples are subtypes of VS with an increased mutation burden and a unique genetic fingerprint. We identified differences between the genomic and molecular profile of cystic VS and radioresistant VS. Our results help advance the understanding of the pathophysiology of these tumor subtypes and suggest possible molecular targets for novel treatment strategies.

Differential Gene Expression Analysis of RNA-Seq Data for Detecting Internal Targets of Antimicrobial Peptides

The field of antimicrobial peptides (AMPs) has been facing the long-standing question of whether AMPs only attack the cell membranes or also interact with intracellular targets. It is intrinsically impossible to decouple the targets because any AMP should translocate through the cell membrane to reach the cytoplasmic space, which inevitably affects the cell membrane. To tackle this question, we took a novel approach by utilizing a tunable, internal gene expression system to induce the production of the human antimicrobial peptide LL37 inside Escherichia coli cells.

Stability of methods for differential expression analysis of RNA-seq data

BackgroundAs RNA-seq becomes the assay of choice for measuring gene expression levels, differential expression analysis has received extensive attentions of researchers. To date, for the evaluation of DE methods, most attention has been paid on validity. Yet another important aspect of DE methods, stability, is overlooked and has not been studied to the best of our knowledge.ResultsIn this study, we empirically show the need of assessing stability of DE methods and propose a stability metric, called Area Under the Correlation curve (AUCOR), that generates the perturbed datasets by a mixture distribution and combines the information of similarities between sets of selected features from these perturbed datasets and the original dataset.ConclusionEmpirical results support that AUCOR can effectively rank the DE methods in terms of stability for given RNA-seq datasets. In addition, we explore how biological or technical factors from experiments and data analysis affect the stability of DE methods. AUCOR is implemented in the open-source R package AUCOR, with source code freely available at https://github.com/linbingqing/stableDE.

PD65-02 IDENTIFICATION OF COORDINATED TRANSCRIPTION FACTOR GROUPS ASSOCIATED WITH ANDROGEN DEPRIVATION THERAPY RESPONSE AND METASTATIC PROGRESSION IN PROSTATE CANCER

You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology IV1 Apr 2018PD65-02 IDENTIFICATION OF COORDINATED TRANSCRIPTION FACTOR GROUPS ASSOCIATED WITH ANDROGEN DEPRIVATION THERAPY RESPONSE AND METASTATIC PROGRESSION IN PROSTATE CANCER Callan Brownfield, Nitya Sharma, Kathry Pellegrini, Veronique Ouellet, Michael Feeman, Adeboye Osunkoya, Fred Saad, Carlos Moreno, and John Petros Callan BrownfieldCallan Brownfield More articles by this author , Nitya SharmaNitya Sharma More articles by this author , Kathry PellegriniKathry Pellegrini More articles by this author , Veronique OuelletVeronique Ouellet More articles by this author , Michael FeemanMichael Feeman More articles by this author , Adeboye OsunkoyaAdeboye Osunkoya More articles by this author , Fred SaadFred Saad More articles by this author , Carlos MorenoCarlos Moreno More articles by this author , and John PetrosJohn Petros More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.2986AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The primary cause of mortality from prostate cancer is the progression to metastatic disease in patients with CRPC, but it is still unclear what transcriptional relationships are shared between responses to ADT and metastasis. Thus, we aimed to find concerted transcription factor relationships that characterize the differential transcriptional response of patients that received ADT, as well as the progression from localized prostate cancer to metastatic disease. METHODS To investigate differential responses to ADT, We generated whole transcriptome gene expression data from 20 patient-matched formalin-fixed prostate needle core biopsies, taken before initiation of ADT, and corresponding formalin-fixed radical prostatectomy samples, acquired after ADT, and leveraged a large dataset (n > 1200) of multiple publicly available cohorts of primary and metastatic tumors, to reverse engineer transcriptional networks. We combined these analyses to uncover groups of putative transcription factor regulators that were unique to patients demonstrating a strong transcriptional response to ADT, as well as to patients with metastatic prostate cancer. Here we identify transcription factor coordinated groups (TFCGs), such as AR with CEBP factors, that are significantly associated with both a strong response to ADT, and metastatic lesions, as compared to primary tumors. Our analysis leverages multiple datatypes and independent datasets to find common TFCGs, that may serve as putative regulators of prostate cancer aggressiveness. We performed differential expression analysis of RNA-Seq data derived from 40 matched Bx and RPs from 20 patients obtained before and after ADT. We identified 190 significantly differentially expressed genes with a fold change greater than or equal to 2 (FDR < 0.05). RESULTS As expected, Ingenuity Pathway analysis (IPA) identified chemical agents related to AR-signaling and activation, such as dihydrotestosterone and the AR agonist metribilone (R1881) as being inhibited. Additionally, targets of U0126 (6 (1,4-diamino-2,3- dicyano-1,4 bis[2-aminophenylthio] butadiene), an inhibitor of MEK1 in the MAPK signaling pathway, were also downregulated by IPA analysis, suggesting MAPK activation. Additionally, beta-estradiol (estrogen) and PDGF were both predicted to be activated. Subtyping of the Post-ADT samples again revealed three groups, consistent with the clustering of significantly differentially expressed genes. Strikingly, we observed a subtype shift from the Pre-ADT Bxs that predominantly expressed PCS2 genes, to the PCS1 strong responders, or the PCS3 weak responders. Integrating the inherent biological characteristics of each subtype, these data suggest a possible ADT-induced selection from a PCS2 AR-dependent, ADT-sensitive tumor to either a more aggressive PCS1 or more benign PCS3 tumor. Furthermore, the observation that the strong responders exhibited expression of PCS1 genes implies that these samples may potentially become ADT resistant. CONCLUSIONS Our analysis utilizes a novel dataset that interrogates causal effects of ADT alone in matched samples, and integrates these data with a large cohort of primary and metastatic tumors to predict common mechanisms important to resistance to therapy and progression to metastatic disease. This hypothesis-driving study sheds light on important coordinated transcription factor activities that may serve as critical upstream regulators of prostate cancer aggressiveness, progression, metastasis, and development of androgen-resistance. Using this unbiased approach, we have identified transcriptional regulators associated with differential ADT responses and metastatic disease progression. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e1225 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Callan Brownfield More articles by this author Nitya Sharma More articles by this author Kathry Pellegrini More articles by this author Veronique Ouellet More articles by this author Michael Feeman More articles by this author Adeboye Osunkoya More articles by this author Fred Saad More articles by this author Carlos Moreno More articles by this author John Petros More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data

BackgroundIn differential expression analysis of RNA-sequencing (RNA-seq) read count data for two sample groups, it is known that highly expressed genes (or longer genes) are more likely to be differentially expressed which is called read count bias (or gene length bias). This bias had great effect on the downstream Gene Ontology over-representation analysis. However, such a bias has not been systematically analyzed for different replicate types of RNA-seq data.ResultsWe show that the dispersion coefficient of a gene in the negative binomial modeling of read counts is the critical determinant of the read count bias (and gene length bias) by mathematical inference and tests for a number of simulated and real RNA-seq datasets. We demonstrate that the read count bias is mostly confined to data with small gene dispersions (e.g., technical replicates and some of genetically identical replicates such as cell lines or inbred animals), and many biological replicate data from unrelated samples do not suffer from such a bias except for genes with some small counts. It is also shown that the sample-permuting GSEA method yields a considerable number of false positives caused by the read count bias, while the preranked method does not.ConclusionWe showed the small gene variance (similarly, dispersion) is the main cause of read count bias (and gene length bias) for the first time and analyzed the read count bias for different replicate types of RNA-seq data and its effect on gene-set enrichment analysis.

Whole transcriptome profiling of the human hippocampus suggests an involvement of the KIBRA rs17070145 polymorphism in differential activation of the MAPK signaling pathway

The rs17070145-T variant of the WWC1 gene, coding for the KIBRA protein, has been associated with both increased episodic memory performance and lowered risk for late onset Alzheimer's disease, although the mechanism behind this protective effect has not been completely elucidated. To achieve a better understanding of the pathways modulated by rs17070145 and its associated functional variant(s), we used laser capture microdissection (LCM) and RNA-sequencing to investigate the effect of rs17070145 genotypes on whole transcriptome expression in the human hippocampus (HP) of 22 neuropathologically normal individuals, with a specific focus on the dentate gyrus (DG) and at the pyramidal cells (PC) of CA1 and CA3 sub-regions. Differential expression analysis of RNA-seq data within the HP based on the rs17070145 genotype revealed an overexpression of genes involved in the MAPK signaling pathway, potentially driven by the T/T genotype. The most important contribution comes from genes dysregulated within the DG region. Other genes significantly dysregulated, and not involved in the MAPK pathway (Adj P < 0.01 and Fold Change > |1.00|) were: RSPO4 (HP); ARC, DUSP5, DNAJB5, EGR4, PPP1R15A, WBP11P1, EGR1, GADD45B (DG); CH25H, HSPA1A, HSPA1B, TNFSF9, and NPAS4 (PC). Several evidences suggested that the MAPK signaling pathway is linked with memory and learning processes. In non-neuronal cells, the KIBRA protein is phosphorylated by ERK1/2 (involved in the MAPK signaling) in cells as well as in vitro. Several of the other dysregulated genes are involved in memory and learning processes, as well as in Alzheimer's Disease. In conclusion, our results suggest that the effect of the WWC1 rs17070145 polymorphism on memory performance and Alzheimer's disease might be due to a differential regulation of the MAPK signaling, a key pathway involved in memory and learning processes.

A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data.

Next-generation sequencing technologies have made RNA sequencing (RNA-seq) a popular choice for measuring gene expression level. To reduce the noise of gene expression measures and compare them between several conditions or samples, normalization is an essential step to adjust for varying sample sequencing depths and other unwanted technical effects. In this paper, we develop a novel global scaling normalization method by employing the available knowledge of housekeeping genes. We formulate the problem from the hypothesis testing perspective and find an optimal scaling factor that minimizes the deviation between the empirical and the nominal type I error. Applying our approach to various simulation studies and real examples, we demonstrate that it is more accurate and robust than the state-of-the-art alternatives in detecting differentially expression genes.