Standard RNA-seq Research Articles

Prime-seq, efficient and powerful bulk RNA sequencing

Cost-efficient library generation by early barcoding has been central in propelling single-cell RNA sequencing. Here, we optimize and validate prime-seq, an early barcoding bulk RNA-seq method. We show that it performs equivalently to TruSeq, a standard bulk RNA-seq method, but is fourfold more cost-efficient due to almost 50-fold cheaper library costs. We also validate a direct RNA isolation step, show that intronic reads are derived from RNA, and compare cost-efficiencies of available protocols. We conclude that prime-seq is currently one of the best options to set up an early barcoding bulk RNA-seq protocol from which many labs would profit.

New Clues to Prognostic Biomarkers of Four Hematological Malignancies

Globally, one out of every two reported cases of hematologic malignancies (HMs) results in death. Each year approximately 1.24 million cases of HMs are recorded, of which 58% become fatal. Early detection remains critical in the management and treatment of HMs. However, this is thwarted by the inadequate number of reliable biomarkers. In this study, we mined public databases for RNA-seq data on four common HMs intending to identify novel biomarkers that could serve as HM management and treatment targets. A standard RNA-seq analysis pipeline was strictly adhered to in identifying differentially expressed genes (DEGs) with DESeq2, limma+voom and edgeR. We further performed gene enrichment analysis, protein-protein interaction (PPI) network analysis, survival analysis and tumor immune infiltration level detection on the genes using G:Profiler, Cytoscape and STRING, GEPIA tool and TIMER, respectively. A total of 2,136 highly-ranked DEGs were identified in HM vs. non-HM samples. Gene ontology and pathway enrichment analyses revealed the DEGs to be mainly enriched in steroid biosynthesis (5.075×10-4), cholesterol biosynthesis (2.525×10-8), protein binding (3.308×10-18), catalytic activity (2.158×10-10) and biogenesis (5.929×10-8). The PPI network resulted in 60 hub genes which were verified with data from TCGA, MET500, CPTAC and GTEx projects. Survival analyses with clinical data from TCGA showed that high expression of SRSF1, SRSF6, UBE2Z and PCF11, and low expression of HECW2 were correlated with poor prognosis in HMs. In summary, our study unraveled essential genes that could serve as potential biomarkers for prognosis and may serve as drug targets for HM management.

A High-Throughput 3'-Tag RNA Sequencing for Large-Scale Time-Series Transcriptome Studies.

RNA sequencing (RNA-seq) has proven invaluable for exploring gene expression variation under complex environmental cues. However, the cost of standard RNA-seq (e.g., Illumina TruSeq or NEBNext) remains a barrier for high-throughput applications. 3'-Tag RNA-seq (3'-TagSeq) is a cost-effective solution that permits large-scale experiments. Unlike standard RNA-seq, which generates sequencing libraries for full-length mRNAs, 3'-TagSeq only generates a single fragment from the 3' end of each transcript (a tag read) and quantifies gene expression by tag abundance. Consequently, 3'-TagSeq requires lower sequencing depth (~5 million reads per sample) than standard RNA-seq (~30 million reads per sample), which reduces costs and allows increased technical and biological replication in experiments. Because 3'-TagSeq is considerably cheaper than standard RNA-seq while exhibiting comparable accuracy and reproducibility, researchers focusing on gene expression levels in large or extensive time-series experiments might find 3'-TagSeq to be superior to standard RNA-seq. In this chapter, we describe 3'-TagSeq sequencing library preparation and provide example bioinformatics and statistical analyses of gene expression data.

Chemical capping improves template switching and enhances sequencing of small RNAs.

Template-switching reverse transcription is widely used in RNA sequencing for low-input and low-quality samples, including RNA from single cells or formalin-fixed paraffin-embedded (FFPE) tissues. Previously, we identified the native eukaryotic mRNA 5′ cap as a key structural element for enhancing template switching efficiency. Here, we introduce CapTS-seq, a new strategy for sequencing small RNAs that combines chemical capping and template switching. We probed a variety of non-native synthetic cap structures and found that an unmethylated guanosine triphosphate cap led to the lowest bias and highest efficiency for template switching. Through cross-examination of different nucleotides at the cap position, our data provided unequivocal evidence that the 5′ cap acts as a template for the first nucleotide in reverse transcriptase-mediated post-templated addition to the emerging cDNA—a key feature to propel template switching. We deployed CapTS-seq for sequencing synthetic miRNAs, human total brain and liver FFPE RNA, and demonstrated that it consistently improves library quality for miRNAs in comparison with a gold standard template switching-based small RNA-seq kit.

Searchlight: automated bulk RNA-seq exploration and visualisation using dynamically generated R scripts

BackgroundOnce bulk RNA-seq data has been processed, i.e. aligned and then expression and differential tables generated, there remains the essential process where the biology is explored, visualized and interpreted. Without the use of a visualisation and interpretation pipeline this step can be time consuming and laborious, and is often completed using R. Though commercial visualisation and interpretation pipelines are comprehensive, freely available pipelines are currently more limited.ResultsHere we demonstrate Searchlight, a freely available bulk RNA-seq visualisation and interpretation pipeline. Searchlight provides: a comprehensive statistical and visual analysis, focusing on the global, pathway and single gene levels; compatibility with most differential experimental designs irrespective of organism or experimental complexity, via three workflows; reports; and support for downstream user modification of plots via user-friendly R-scripts and a Shiny app. We show that Searchlight offers greater automation than current best tools (VIPER and BioJupies). We demonstrate in a timed re-analysis study, that alongside a standard bulk RNA-seq processing pipeline, Searchlight can be used to complete bulk RNA-seq projects up to the point of manuscript quality figures, in under 3 h.ConclusionsCompared to a manual R based analysis or current best freely available pipelines (VIPER and BioJupies), Searchlight can reduce the time and effort needed to complete bulk RNA-seq projects to manuscript level. Searchlight is suitable for bioinformaticians, service providers and bench scientists. https://github.com/Searchlight2/Searchlight2.

Abstract 198: Quantifying miRNA activity in single cell clusters

Abstract Background: MicroRNAs are small noncoding RNAs that mediate gene regulation at the post-transcriptional level via multiple mechanisms such as mRNA degradation, translational inhibition, and mRNA stabilization. They are involved in several cellular processes from development to homeostasis, and their deregulation is implicated in several diseases, including cancer. Since miRNA lacks the polyA tail, the standard single cell RNAseq protocols do not capture miRNAs, thus severely limiting our understanding of miRNA functions at cellular resolution. To overcome this limitation, we develop a novel machine learning method to infer the miRNA activity in a sample given its RNAseq profile. Methods: We develop a model using XGBoost, to predict miRNA profile in a sample from its global mRNA profile. We train and test the model using cross validation in the CCLE collection, as well as a number of healthy and cancer human tissue data obtained from GTEx and TCGA. We quantify the method's performance as the correlation between actual and predicted miRNA expression values across the test samples. We validate our model in multiple single cell datasets where miRNA and mRNA profiles are available for the same cell types by assessing the model's ability to identify cell type specific miRNAs based on a model trained on independent bulk datasets. Results: First, we show in CCLE collection, and multiple TCGA tissues, that a model based on all genes was far more accurate than the model based only on known targets. Our mean cross validation model accuracy across 10 tissues having greater than 100 paired miRNA and mRNA samples (in terms of Spearman correlation between predicted and actual expression of a miRNA) is 0.45 (min 0.39 in Pancreas to a max of 0.51 in Brain). In comparison to the normal tissues in GTEx, in the malignant counterpart in TCGA, due to greater heterogeneity, therefore greater variability in gene expression, our model performs significantly better (average cross validation accuracy improvement of 0.19). We have validated our model in independent single cell data. Using a model trained in bulk tissue data, we predict microRNA expression levels in a single cell based on the single cell RNA and compare our predicted fold difference by a miRNA's expression between two cell types with the actual fold difference. We quantify the prediction accuracy as the correlation between the predicted and actual fold differences across all miRNAs. In a total of 4 cell type pair comparisons (different sets of kidney, brain, breast, and skin), our model achieves an average accuracy of 0.81 (ranging from 0.73 to 0.89), thus strongly validating our model. Our next step is to apply our model to study miRNA activities during T cell development, Pancreatic Ductal Adenocarcinoma, and Glioblastoma, in collaboration with experimentalists. Conclusions: Our method addresses a major bottleneck in studying miRNA activities at a cellular resolution and can be applied to any scRNA data to infer miRNA activity. Citation Format: Gulden Olgun, Vishaka Gopalan, Sridhar Hannenhalli. Quantifying miRNA activity in single cell clusters [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 198.

Abstract 2705: Spatial transcriptomics - a valuable tool to visualize compound effect in precision cut cancer tissue slices (PCCTS)

Abstract The recent advances in immunotherapies, such as immune checkpoint modulators, bispecific antibodies, and adoptive T-cell transfer, opens new opportunities for the treatment of cancer. Having this broad spectrum of new therapeutic agents available, the demand for predictive and robust preclinical models to minimize translational failures in immuno-oncology is increasing. Indivumed has successfully implemented the model of Precision Cut Cancer Tissue Slices (PCCTS) derived from viable human tumor tissue to serve as a platform for testing different applications such as chemotherapeutic agents, small molecules and antibodies. In this study, we investigated the effects of OKT3® (Muromonab), a therapeutic antibody against CD3, on PCCTS from a patient diagnosed with non-small cell lung cancer (NSCLC) in respect of gene expression changes using Spatial Transcriptomics. Spatial Transcriptomics (10x Genomics) is a technique to spatially resolve whole RNA-seq information. Tissue sections were prepared using a Krumdieck Tissue slicer and fresh tissue sections were incubated for 24 h with 10 µg/ml OKT3 followed by performing the Visium Spatial Gene Expression Workflow from 10x Genomics. For this purpose, a glass slide containing spatially barcoded capture probes were used to bind mRNAs of a tissue section. Following cDNA synthesis and library preparation, the libraries were sequenced using standard RNA-seq technology. Specific sequence barcodes allowed to assign gene expression data to the histological positions in the tissue. For visualization of gene expression patterns, the software Space Ranger and Loupe Browser were used. This method enabled the identification of cellular subpopulations in the spatial context before and after treatment with OKT3, whereas molecular identities and positional information of diverse cells would have been lost upon transcriptome analysis of bulk samples. Our data showed significant differences between untreated and OKT3 treated tissue slices especially in the microenvironment that encompasses inflammatory cells, extracellular matrix, and stromal cells interacting with tumor cells for cancer growth and progression. The combination of the PCCTS platform with Spatial Transcriptomics has been shown to be most valuable for the understanding of compound effects. The location of cells and their changes in gene expression patterns are important to understand tissue functionality and corresponding pathological changes. Citation Format: Nicole Kerstedt, Nicole Grabinski, Kristina Bernoth, Monika Schoeppler, Bita Motamedi-Baniassad, Jana Krueger, Moiken Petersen, Hartmut Juhl, Kerstin A. David. Spatial transcriptomics - a valuable tool to visualize compound effect in precision cut cancer tissue slices (PCCTS) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2705.

Direct Immunodetection of Global A-to-I RNA Editing Activity with a Chemiluminescent Bioassay.

Adenosine-to-inosine (A-to-I) editing is a conserved eukaryotic RNA modification that contributes to development, immune response, and overall cellular function. Here, we utilize Endonuclease V (EndoV), which binds specifically to inosine in RNA, to develop an EndoV-linked immunosorbency assay (EndoVLISA) as a rapid, plate-based chemiluminescent method for measuring global A-to-I editing signatures in cellular RNA. We first optimize and validate our assay with chemically synthesized oligonucleotides. We then demonstrate rapid detection of inosine content in treated cell lines, demonstrating equivalent performance against current standard RNA-seq approaches. Lastly, we deploy our EndoVLISA for profiling differential A-to-I RNA editing signatures in normal and diseased human tissue, illustrating the utility of our platform as a diagnostic bioassay. Together, the EndoVLISA method is cost-effective, straightforward, and utilizes common laboratory equipment, offering a highly accessible new approach for studying A-to-I editing. Moreover, the multi-well plate format makes this the first assay amenable for direct high-throughput quantification of A-to-I editing for applications in disease detection and drug development.

Lymphoma Microenvironment Signature Predicts Clinical Outcomes in Diffuse Large B-Cell Lymphoma Patients

Background: Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, whose response to standard chemotherapy is variable. Stratification of DLBCL into GCB (germinal center B-cell)- or ABC (activated B-cell)-like type, so-called cell-of-origin (COO) classification, has been performed based on gene expression signatures and/or immunohistochemistry. Since COO classification may not necessarily retain prognostic power, a more accurate risk stratification model is warranted.Purpose: The goal of this study is to identify a gene expression signature(s) that accurately predicts clinical outcomes of DLBCL patients.Hypothesis: During the process of normal B-cell development in germinal center (GC), a variety of microenvironment cells, including T cells, fibroblasts, dendritic cells and macrophages, coordinately regulate clonal selection and proliferation of B-cells. Therefore, we hypothesized that the differences in the contexts of lymphoma microenvironment could affect clinical outcomes of DLBCL.Methods: Standard genome-wide gene expression analysis, including microarray and RNA-seq, can only measure abundant transcripts derived from lymphoma cells. In contrast, cells constituting lymphoma microenvironment are minor fractions in lymphoma tissues, and transcripts derived from these cells are expressed at low levels. To quantitatively measure those lowly-expressed transcripts, we employed the nCounter system, which enables an accurate measurement of rare and highly-fragmented transcripts from formalin-fixed paraffin-embedded (FFPE) tissues.Results: We first examined expression levels of 1900 genes related to immune-, cancer- and kinase-pathways using tissues from 30 DLBCL cases. We then identified a set of genes that were differentially expressed between cases exhibiting good prognosis (alive > 2 years without relapse) and those with poor prognosis (disease progression < 1 year). Strikingly, differentially-expressed genes were not B cell-related genes, but those related to microenvironment cells, including T cells, fibroblasts and macrophages. Furthermore, high levels of microenvironment-related genes dictated favorable prognosis. To identify subtypes of microenvironment cells, we next performed single-cell q-PCR assays (C1 system) and a multi-color immunofluorescence analysis of DLBCL tissues (Mantra system). We found that presence of GC microenvironment components such as follicular helper T cells, CD11c+ macrophages and FGFR1+ stromal cells was an accurate predictor for favorable prognosis. We defined the GC microenvironment signature based on the expression levels of ICOS, CD11c and FGFR1 mRNA using nCounter and established a novel scoring system. We then validated the system using an independent DLBCL cohort consisting of 170 cases. Our scoring system enabled precise prognostic stratification among patients treated with R-CHOP independently of IPI status and traditional COO-based prognostic models (Figure 1). Finally, to explore potential correlation between the microenvironment signature and mutational status of DLBCL cells, we performed target capture sequencing of 100 DLBCL cases using a clinically-validated sequencing platform (OncoPanel). Our interim analysis did not reveal a correlation between specific lymphoma mutations and the microenvironment signature, suggesting that GC microenvironment signature, which we have identified, predicts clinical outcomes independently from mutational status of DLBCL cells.Conclusions: We identified a GC microenvironment signature that accurately dictates clinical outcomes and established a novel nCounter-based scoring system, which may inform prognostic stratification and the selection of chemotherapeutic regimen of DLBCL. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

RNA-Seq Revealed an Integration of Multiple Resistance Functions for Predicting Response to the “7+3” Induction Chemotherapy of De Novo Acute Myeloid Leukemia

Background:The “7+3” (cytarabine 100-200mg/M2 for 7 days; idarubicin 12mg/M2 for 3 days) regimen of chemotherapy is a standard induction therapy of acute myeloid leukemia (AML). While about 70% of patients achieve complete remission (CR) with the regimen, outcome of the remaining patients remains typically poor. In addition to cytogenetic and molecular aberrations, several independent cellular functions have been linked to chemoresistance, such as stem cell activities and oxidative phosphorylation (OXPHOS), suggesting chemoresistance a multifactorial mechanism. However, a comprehensive investigation of resistance functions and their interactions in the development and prediction of chemoresistance remain unexplored.Materials and Methods:We reviewed medical records of 146 patients with de novo AML receiving the “7+3” chemotherapy enrolled at the Taichung Veterans General Hospital (TCVGH). Thirty-six patients were selected for further analysis; 18 were referred to the CR group and the other 18 to the non-CR group. RNAs were extracted from pre-treatment bone marrow samples and subject to Illumina RNA-Seq. Sequencing reads were analyzed by a standard RNA-Seq pipeline and tested for differential expression between non-CR and CR groups. We employed Gene Set Enrichment Analysis (GSEA) to comprehensively screen for chemoresistance-associated cellular functions (represented by curated molecular signatures). A gene set scoring method was applied to measure per-sample activity of each selected function. Based on a logistic regression scheme, we merged several resistance functions into a prediction model by a weighted sum of the gene set scores. Power of these resistance functions in independently and jointly predicting chemoresistance was assessed by the area under receiver operating characteristic curve (AUC).Results:RNA-Seq of bone marrow samples of 36 de novo AML yielded an average throughput of 21.5 million aligned paired-end reads. We used GSEA to test the associations between ~8,500 curated molecular signatures and the global expressional changes between non-CR and CR patients. A total of 173 resistance functions were identified (adjusted P <0.01). Of note, about half of these functions were related to stemness (P=0.002), Myc targets/cell cycle (P ≤0.002), and mTOR signaling/OXPHOS (P <0.001). While the three function clusters were all associated with chemoresistance, of note, they barely share common component genes (Figure 1A), indicating the molecular independence of these functions. Hierarchical clustering divided the non-CR samples into four groups: Myc/cell cycle alone (22.2%), stemness (16.7%), Myc targets/cell cycle + mTOR signaling/OXPHOS (11.1%), and others (Figure 1A). Among them, stemness achieved the best prediction performance for chemoresistance (AUC=0.75 and accuracy=72.2%; Figure 1B). However, it suffered from a high false-negative rate (sensitivity=55.6%). We used a logistic regression model to merge the three resistance functions into a prediction model (per-patient score = 1.28×stemness + 0.81×Myc/cell cycle - 0.15×mTOR/OXPHOS). Remarkably, the integration of functions rescued the sensitivity back to 72.2% and achieved a superior performance (accuracy=80.6% and AUC=0.80; P for AUC=0.014; Figure 1B). We confirmed its prediction power for chemoresponse by an independent cohort published by Farge et al. (n=21; AUC=0.71 and P=0.02). We also evaluated the tri-function model in predicting overall survival by our (TCVGH) and two independent cohorts (Metzeler et al. and The Cancer Genome Atlas (TCGA)). Patients predicted to be chemorefractory indeed exhibited inferior overall survival (log-rank test P=0.003, <0.001, and =0.060, respectively; Figure 1C). For easy clinical implementation, we developed a 10-gene scoring system that significantly mimicked the tri-function score (correlation=0.96 and P <0.001).Conclusion:In the present study, by a high-throughput profiling and systematic analysis, we explored the landscape of resistance functions of the “7+3” chemotherapy. Our data showed the benefit of integrating multiple independent functions in predicting treatment response. Based on the findings, we proposed a tri-function prediction model together with a 10-gene scoring system. The accurate prediction model enables early decision of alternative induction regimens or clinical trials for de novo AML. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Equity Is Key to Build Back Better after COVID-19: Prioritize Noncommunicable Diseases and Kidney Health.

<h3>ABSTRACT</h3> Among the major classes of RNAs in the cell, tRNAs remain the most difficult to characterize via deep sequencing approaches, as tRNA secondary structure and nucleotide modifications can both interfere with cDNA synthesis by commonly-used reverse transcriptases (RTs). Here, we benchmark a recently-developed RNA cloning protocol, termed Ordered Two-Template Relay (OTTR), to characterize intact tRNAs and tRNA fragments in budding yeast and in mouse tissues. We find that OTTR robustly captures full-length tRNAs in budding yeast and in mouse testis, with relatively low levels of premature termination at known barriers – 1-methylguanine, N2,N2-dimethylguanine, and 1-methyladenine – to typical reverse transcriptases. Moreover, these and several other nucleotide modifications leave misincorporation signatures in OTTR datasets which enables their detection without any additional protocol steps. Turning to analysis of small RNAs such as tRNA cleavage products, we compare OTTR with several standard small RNA-Seq protocols, finding that OTTR provides the most accurate picture of tRNA fragment levels by comparison to “ground truth” Northern blots. Applying this protocol to mature mouse spermatozoa, our data dramatically alter our understanding of the small RNA cargo of mature mammalian sperm, revealing a far more complex population of tRFs – including both 5’ and 3’ tRNA halves derived from the majority of tRNAs – than previously appreciated. Taken together, our data confirm the superior performance of OTTR to commercial protocols in analysis of tRNA fragments, and force a reappraisal of potential epigenetic functions of the sperm small RNA payload.

ASimulatoR: splice-aware RNA-Seq data simulation.

A plethora of tools exist for RNA-Seq data analysis with a focus on alternative splicing (AS). However, appropriate data for their comparative evaluation is missing. The R package ASimulatoR simulates gold standard RNA-Seq datasets with fine-grained control over the distribution of AS events, which allow for evaluating alternative splicing tools, e.g. to study the effect of sequencing depth on the performance of AS event detection. ASimulatoR is freely available at https://github.com/biomedbigdata/ASimulatoR as an R package under GPL-3 license.

MicroExonator enables systematic discovery and quantification of microexons across mouse embryonic development

BackgroundMicroexons, exons that are ≤ 30 nucleotides, are a highly conserved and dynamically regulated set of cassette exons. They have key roles in nervous system development and function, as evidenced by recent results demonstrating the impact of microexons on behaviour and cognition. However, microexons are often overlooked due to the difficulty of detecting them using standard RNA-seq aligners.ResultsHere, we present MicroExonator, a novel pipeline for reproducible de novo discovery and quantification of microexons. We process 289 RNA-seq datasets from eighteen mouse tissues corresponding to nine embryonic and postnatal stages, providing the most comprehensive survey of microexons available for mice. We detect 2984 microexons, 332 of which are differentially spliced throughout mouse embryonic brain development, including 29 that are not present in mouse transcript annotation databases. Unsupervised clustering of microexons based on their inclusion patterns segregates brain tissues by developmental time, and further analysis suggests a key function for microexons in axon growth and synapse formation. Finally, we analyse single-cell RNA-seq data from the mouse visual cortex, and for the first time, we report differential inclusion between neuronal subpopulations, suggesting that some microexons could be cell type-specific.ConclusionsMicroExonator facilitates the investigation of microexons in transcriptome studies, particularly when analysing large volumes of data. As a proof of principle, we use MicroExonator to analyse a large collection of both mouse bulk and single-cell RNA-seq datasets. The analyses enabled the discovery of previously uncharacterized microexons, and our study provides a comprehensive microexon inclusion catalogue during mouse development.

Computational analysis of alternative polyadenylation from standard RNA-seq and single-cell RNA-seq data.

Alternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation. Since conventional APA profiling methods have not been widely adopted, global APA studies are very limited. In this chapter, we summarize current computational methods for analyzing APA in standard RNA-seq and scRNA-seq data and describe two state-of-the-art bioinformatic algorithms DaPars and scDaPars in detail. The bioinformatic pipelines for both DaPars and scDaPars are presented and the application of both algorithms are highlighted.

Phospho-RNAseq Profiling of Extracellular mRNAs and lncRNAs.

Extracellular RNAs (exRNAs) in biofluids have attracted great interest as potential biomarkers. Although extracellular microRNAs in blood plasma are extensively characterized, extracellular messenger RNA (mRNA) and long noncoding RNA (lncRNA) studies are limited. We have recently reported that human plasma contains fragmented mRNAs and lncRNAs that are missed by standard small RNA-seq protocols due to lack of 5'phosphate or presence of 3'phosphate. Here, we describe a modified protocol for preparation of small RNA libraries for next generation sequencing called "phospho-RNA-seq." This protocol has been optimized for use with low-input exRNA-containing samples, such as plasma or serum, and has modifications introduced to capture extracellular RNA with varied 5'and 3'ends.

FlexiMAP: a regression-based method for discovering differential alternative polyadenylation events in standard RNA-seq data.

MotivationWe present flexible Modeling of Alternative PolyAdenylation (flexiMAP), a new beta-regression-based method implemented in R, for discovering differential alternative polyadenylation events in standard RNA-seq data.ResultsWe show, using both simulated and real data, that flexiMAP exhibits a good balance between specificity and sensitivity and compares favourably to existing methods, especially at low fold changes. In addition, the tests on simulated data reveal some hitherto unrecognized caveats of existing methods. Importantly, flexiMAP allows modeling of multiple known covariates that often confound the results of RNA-seq data analysis.Availability and implementationThe flexiMAP R package is available at: https://github.com/kszkop/flexiMAP. Scripts and data to reproduce the analysis in this paper are available at: https://doi.org/10.5281/zenodo.3689788.Supplementary information Supplementary data are available at Bioinformatics online.

TagSeq for gene expression in non-model plants: A pilot study at the Santa Rita Experimental Range NEON core site.

PremiseTagSeq is a cost‐effective approach for gene expression studies requiring a large number of samples. To date, TagSeq studies in plants have been limited to those with a high‐quality reference genome. We tested the suitability of reference transcriptomes for TagSeq in non‐model plants, as part of a study of natural gene expression variation at the Santa Rita Experimental Range National Ecological Observatory Network (NEON) core site.MethodsTissue for TagSeq was sampled from multiple individuals of four species (Bouteloua aristidoides and Eragrostis lehmanniana [Poaceae], Tidestromia lanuginosa [Amaranthaceae], and Parkinsonia florida [Fabaceae]) at two locations on three dates (56 samples total). One sample per species was used to create a reference transcriptome via standard RNA‐seq. TagSeq performance was assessed by recovery of reference loci, specificity of tag alignments, and variation among samples.ResultsA high fraction of tags aligned to each reference and mapped uniquely. Expression patterns were quantifiable for tens of thousands of loci, which revealed consistent spatial differentiation in expression for all species.DiscussionTagSeq using de novo reference transcriptomes was an effective approach to quantifying gene expression in this study. Tags were highly locus specific and generated biologically informative profiles for four non‐model plant species.

Coordinated analysis of exon and intron data reveals novel differential gene expression changes

RNA-Seq expression analysis currently relies primarily upon exon expression data. The recognized role of introns during translation, and the presence of substantial RNA-Seq counts attributable to introns, provide the rationale for the simultaneous consideration of both exon and intron data. We describe here a method for the coordinated analysis of exon and intron data by investigating their relationship within individual genes and across samples, while taking into account changes in both variability and expression level. This coordinated analysis of exon and intron data offers strong evidence for significant differences that distinguish the profiles of the exon-only expression data from the combined exon and intron data. One advantage of our proposed method, called matched change characterization for exons and introns (MEI), is its straightforward applicability to existing archived data using small modifications to standard RNA-Seq pipelines. Using MEI, we demonstrate that when data are examined for changes in variability across control and case conditions, novel differential changes can be detected. Notably, when MEI criteria were employed in the analysis of an archived data set involving polyarthritic subjects, the number of differentially expressed genes was expanded by sevenfold. More importantly, the observed changes in exon and intron variability with statistically significant false discovery rates could be traced to specific immune pathway gene networks. The application of MEI analysis provides a strategy for incorporating the significance of exon and intron variability and further developing the role of using both exons and intron sequencing counts in studies of gene regulatory processes.

ALTERED EXPRESSION OF GENES INVOLVED IN JAK-STAT, ADIPOCYTOKINE AND TOLL-LIKE RECEPTOR SIGNALING PATHWAYS ARE DETECTED IN BLASTOCOEL FLUID CONDITIONED MEDIA FROM EUPLOID EMBRYOS WITH POSITIVE IMPLANTATION OUTCOMES

Altered gene expression leading to modified expressions of pathways in preimplantation embryos may be a contributing factor in successful embryo implantation outcome. Identification of genes in day-5 blastocysts that are associated with successful uterine implantations would provide reproductive specialists with an additional measure of embryo quality when selecting the most viable embryo for transfer. This study evaluated global gene expression using RNASeq in blastocoel fluid-conditioned media from euploid IVF-generated embryos resulting in successful implantations versus those that failed to successfully implant. Retrospective analysis of day-5 euploid blastocoel fluid gene expression in seven (three successful; four unsuccessful) euploid embryos. Blastocoel fluid-conditioned media was obtained following biopsy of ICSI-generated day-5 blastocysts. RNA extractions, library preparation (SMART-Seq Stranded kit) and Illumina NextSeq500 sequencing were performed at the USC Functional Genomics Core. Standard RNASeq workflow was conducted: 1) removal of adaptor sequences 2) alignment to human Gh38 genome with STAR, and 3) FeatureCount to obtain raw gene count number for each sample. Differential gene expression analysis (DGE) of raw counts was performed using DESeq2 applying negative binomial generalized linear models and taking advantage of the package to generate more accurate shrunken log fold changes and variance stabilizing estimators for data with considerations regarding low counts and high dispersion. Significance was defined as a false-discovery rate (FDR) p-value <0.05 to adjust for multiple testing. Pathway analysis was performed using GAGE, mapping genes to KEGG pathways. After quality control 21,321 genes were included in the DGE analysis and 36 (FDRp<0.05) were identified as DE (6 up-regulated / 30 down-regulated). Pathway analysis revealed altered expression patterns of genes encoding proteins in the adipocytokine, Toll-like receptor and JAK-STAT signaling pathways between blastocoel fluid from embryos associated with successful versus unsuccessful implantation outcomes. Specifically, gene products of these pathways were up-regulated in blastocoel fluid from embryos associated with successful implantation. We identified differential gene expression in blastocoel fluid from euploid embryos associated with successful versus unsuccessful implantation. JAK-STAT pathway members were identified in this study, which is supported by a previous reports that members of this pathway are expressed during blastocyst stage development. Detection of these gene products in blastocoel fluid may represent apoptotic remnants of cells that were selectively eliminated from the developing embryo. One possibility is that the JAK-STAT pathway activates apoptotic gene expression in specific cells within the embryo as a means of embryo self-correction. Future research aims to clarify the roles of identified genes and consider bi-directionally dysregulated genes within a given pathway in the preimplantation embryo.

TIF-Seq2 disentangles overlapping isoforms in complex human transcriptomes.

Eukaryotic transcriptomes are complex, involving thousands of overlapping transcripts. The interleaved nature of the transcriptomes limits our ability to identify regulatory regions, and in some cases can lead to misinterpretation of gene expression. To improve the understanding of the overlapping transcriptomes, we have developed an optimized method, TIF-Seq2, able to sequence simultaneously the 5′ and 3′ ends of individual RNA molecules at single-nucleotide resolution. We investigated the transcriptome of a well characterized human cell line (K562) and identified thousands of unannotated transcript isoforms. By focusing on transcripts which are challenging to be investigated with RNA-Seq, we accurately defined boundaries of lowly expressed unannotated and read-through transcripts putatively encoding fusion genes. We validated our results by targeted long-read sequencing and standard RNA-Seq for chronic myeloid leukaemia patient samples. Taking the advantage of TIF-Seq2, we explored transcription regulation among overlapping units and investigated their crosstalk. We show that most overlapping upstream transcripts use poly(A) sites within the first 2 kb of the downstream transcription units. Our work shows that, by paring the 5′ and 3′ end of each RNA, TIF-Seq2 can improve the annotation of complex genomes, facilitate accurate assignment of promoters to genes and easily identify transcriptionally fused genes.