Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap

Clinical validation of RNA sequencing for Mendelian disorder diagnostics

Despite rapid advancements in clinical sequencing, over half of diagnostic evaluations still lack definitive results. RNA sequencing (RNA-seq) has shown promise in research settings for bridging this gap by providing essential functional data for accurate interpretation of diagnostic sequencing results. However, despite advanced research pipelines, clinical translation of diagnostic RNA-seq has not yet been realized. We have developed and validated a clinical diagnostic RNA-seq test for individuals with suspected genetic disorders who have existing or concurrent comprehensive DNA diagnostic testing. This diagnostic RNA-seq test processes RNA samples from fibroblasts or blood and derives clinical interpretations based on the analytical detection of outliers in gene expressions and splicing patterns. The clinical validation involves 130 samples, including 90 negative and 40 positive samples. We developed provisional expression and splicing benchmarks using short-read and long-read RNA-seq data from the GM24385 lymphoblastoid sample produced by the Genome in a Bottle Consortium. For clinical validation, we first established reference ranges for each gene and junction based on expression distributions from our control data. We then evaluated the clinical performance of our outlier-based pipeline using 40 positive samples with previously identified diagnostic findings from the Undiagnosed Diseases Network project. Our study provides a paradigm and necessary resources for independent laboratories to validate a clinical RNA-seq test.

Fantastic genes and where to find them expressed in CHO

The transcriptome of Chinese hamster ovary (CHO) cells plays a crucial role in determining cellular characteristics that are essential for biopharmaceutical applications. RNA-sequencing has been extensively used to profile gene expression patterns, aiming to gain a better understanding of intracellular behavior and mechanisms. Individual datasets, however, do not provide a comprehensive overview and characterization of the CHO cell's transcriptome, such that the fundamental structure of the transcriptome remains unknown. Using 15 RNA-sequencing datasets, encompassing almost 300 samples of various experimental setups, conditions and cell lines, we explore and classify the protein-coding transcriptome of CHO cells. Differences in cell line lineages are found to be the primary source of variation in transcribed genes. By employing a novel approach, we identified the core transcriptome that is ubiquitously expressed in all cell lines and culture conditions, as well as genes that remain entirely non-expressed. Additionally, we identified a set of genes that may be active or inactive depending on different conditions, which are linked to biological processes including translation as well as immune and stress response. Lastly, by integrating chromatin states derived from histone modifications, we provided additional context on the epigenetic level that supports our protein-coding gene classification. Our study offers a comprehensive insight into the CHO cell transcriptome and lays the foundation for future research into cellular adaptation to changing conditions and the development of phenotypes.

RNA Sequencing Protocols for Short-Read Sequencing

RNA sequencing (RNA-seq) methodologies allow the discovery of novel variants and transcripts. These comprise three general steps: (1) capture of RNA species of interest, (2) conversion of RNA to complementary DNA (cDNA), and (3) modification of cDNA to fit the sequencing platform. Here we describe four different library preparation protocols for short-read sequencing: cDNA synthesis with poly(A) selection, library preparation with ribosomal depletion, and cDNA synthesis with SMART® (Switching Mechanism at 5' end of RNA Template) technology for low and Pico inputs.

Identification and evaluation of plasma exosome RNA biomarkers for non-invasive diagnosis of hepatocellular carcinoma using RNA-seq

BACKGROUND

Non-invasive diagnostic methods, including medical imaging techniques and blood biomarkers such as alpha-fetoprotein (AFP), have been crucial in detecting hepatocellular carcinoma (HCC). However, imaging techniques are only effective for tumor size larger than 2 cm. AFP measurement remains unsatisfactory due to high rate of misdiagnosis and underdiagnosis. Therefore, new reliable biomarkers and better non-invasive diagnostic approach are necessary for HCC identification.

METHODS

The differentially expressed genes were identified using multiple public RNA-seq data of liver tissues from healthy individuals and HCC patients including peritumoral and tumor tissues. The hub genes for HCC diagnosis were identified combining pathway enrichment analysis and protein-protein interaction network analysis. The performance of hub genes for non-invasive HCC diagnosis was analyzed in plasma of healthy individuals, HBV infected patients, and HCC patients based on exosomal RNA-seq data. A multi-layer perceptron (MLP) model based on exosomal hub genes was developed for non-invasive HCC diagnosis.

RESULTS

Through differential gene expression and pathway enrichment analysis on multiple public RNA-seq datasets, we first identified 30 dysregulated genes in HCC tissues. Protein-protein interaction analysis further narrowed down this list to 10 key genes: BRCA2, CDK1, MCM4, PLK1, DNA2, BLM, PCNA, POLD1, BRCA1 and FEN1. By further evaluation using additional public HCC tissue datasets, POLD1 and MCM4 were excluded from consideration as potential biomarkers due to their suboptimal performance. Notably, CDK1, FEN1, and PCNA gene were found to be significantly elevated in the plasma exosomes of HCC patients compared to non-HCC individuals, including those with HBV-infected hepatitis and healthy controls. The MLP model, based on three biomarkers, showed an area under the curve (AUC) of 0.85 and 0.84 in training and test dataset respectively, after adjusting for the covariates sex and age.

CONCLUSION

We identified three key genes, CDK1, FEN1, and PCNA, as exosomal biomarkers for non-invasive diagnosis of HCC. The MLP model utilizing three biomarkers showed good differentiation between non-HCC individuals and HCC patients, which exhibits promising potential as a non-invasive diagnostic tool for detecting HCC. Additional validation with a larger sample size is essential to thoroughly assess the reliability of the biomarkers and the model's performance.

Quantifying tumor specificity using Bayesian probabilistic modeling for drug and immunotherapeutic target discovery

In diseases such as cancer, the design of new therapeutic strategies requires extensive, costly, and unfortunately sometimes deadly testing to reveal life threatening off-target effects. We hypothesized that the disease specificity of targets can be systematically learned for all genes by jointly evaluating complementary molecular measurements of healthy tissues using a hierarchical Bayesian modeling approach. Our method, BayesTS, integrates protein and gene expression evidence and includes tunable parameters to moderate tissue essentiality. Applied to all protein coding genes, BayesTS outperforms alternative strategies to define therapeutic targets and nominates previously unknown targets while allowing for incorporation of new types of modalities. To expand target repertoires, we show that extension of BayesTS to splicing antigens and combinatorial target pairs results in more specific targets for therapy. We expect that BayesTS will facilitate improved target prioritization for oncology drug development, ultimately leading to the discovery of more effective and safer treatments.

A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials

Translating RNA-seq into clinical diagnostics requires ensuring the reliability and cross-laboratory consistency of detecting clinically relevant subtle differential expressions, such as those between different disease subtypes or stages. As part of the Quartet project, we present an RNA-seq benchmarking study across 45 laboratories using the Quartet and MAQC reference samples spiked with ERCC controls. Based on multiple types of 'ground truth', we systematically assess the real-world RNA-seq performance and investigate the influencing factors involved in 26 experimental processes and 140 bioinformatics pipelines. Here we show greater inter-laboratory variations in detecting subtle differential expressions among the Quartet samples. Experimental factors including mRNA enrichment and strandedness, and each bioinformatics step, emerge as primary sources of variations in gene expression. We underscore the profound influence of experimental execution, and provide best practice recommendations for experimental designs, strategies for filtering low-expression genes, and the optimal gene annotation and analysis pipelines. In summary, this study lays the foundation for developing and quality control of RNA-seq for clinical diagnostic purposes.

Antennal transcriptome analysis reveals sensory receptors potentially associated with host detection in the livestock pest Lucilia cuprina

BACKGROUND

Lucilia cuprina (Wiedemann, 1830) (Diptera: Calliphoridae) is the main causative agent of flystrike of sheep in Australia and New Zealand. Female flies lay eggs in an open wound or natural orifice, and the developing larvae eat the host's tissues, a condition called myiasis. To improve our understanding of host-seeking behavior, we quantified gene expression in male and female antennae based on their behavior.

METHODS

A spatial olfactometer was used to evaluate the olfactory response of L. cuprina mated males and gravid females to fresh or rotting beef. Antennal RNA-Seq analysis was used to identify sensory receptors differentially expressed between groups.

RESULTS

Lucilia cuprina females were more attracted to rotten compared to fresh beef (> fivefold increase). However, males and some females did not respond to either type of beef. RNA-Seq analysis was performed on antennae dissected from attracted females, non-attracted females and males. Transcripts encoding sensory receptors from 11 gene families were identified above a threshold (≥ 5 transcript per million) including 49 ATP-binding cassette transporters (ABCs), two ammonium transporters (AMTs), 37 odorant receptors (ORs), 16 ionotropic receptors (IRs), 5 gustatory receptors (GRs), 22 odorant-binding proteins (OBPs), 9 CD36-sensory neuron membrane proteins (CD36/SNMPs), 4 chemosensory proteins (CSPs), 4 myeloid lipid-recognition (ML) and Niemann-Pick C2 disease proteins (ML/NPC2), 2 pickpocket receptors (PPKs) and 3 transient receptor potential channels (TRPs). Differential expression analyses identified sex-biased sensory receptors.

CONCLUSIONS

We identified sensory receptors that were differentially expressed between the antennae of both sexes and hence may be associated with host detection by female flies. The most promising for future investigations were as follows: an odorant receptor (LcupOR46) which is female-biased in L. cuprina and Cochliomyia hominivorax Coquerel, 1858; an ABC transporter (ABC G23.1) that was the sole sensory receptor upregulated in the antennae of females attracted to rotting beef compared to non-attracted females; a female-biased ammonia transporter (AMT_Rh50), which was previously associated with ammonium detection in Drosophila melanogaster Meigen, 1830. This is the first report suggesting a possible role for ABC transporters in L. cuprina olfaction and potentially in other insects.

De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide

Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.

Production of Ochratoxin A and Citrinin and the Expression of Their Biosynthetic Genes from Penicillium verrucosum in Liquid Culture

Penicillium verrucosum is a fungal pathogen capable of producing two mycotoxins of concern, ochratoxin A (OTA) and citrinin (CIT). The production profile of these two mycotoxins is not well understood but could help mitigate co-contamination in the food supply. As such, the production of OTA and CIT from P. verrucosum DAOMC 242724 was investigated under different growing conditions in liquid culture. We found that among the different liquid media chosen, liquid YES (yeast extract sucrose) medium induced the highest production of both OTA and CIT, when P. verrucosum DAOMC 242724 was cultured in stationary mode. Shake culture significantly reduced the amounts of OTA and CIT produced. Among all culture conditions tested, far greater amounts of CIT were produced compared to OTA. Consequently, upon transcriptomic data analysis, a statistically significant increase in the expression of CIT biosynthetic genes was easier to detect than the expression of OTA biosynthetic genes. Our study also revealed that the putative biosynthetic gene clusters of OTA and CIT in P. verrusocum DAOMC 242724 are likely distinct from each other. It appears that despite sharing a highly similar structure, the isocoumarin rings of OTA and CIT are each assembled by a specialized polyketide synthase enzyme. Our data identified a putative nonreducing polyketide synthase responsible for assembling the carbo-skeleton of CIT. In contrast, a highly reducing polyketide synthase appears to be involved in the biosynthesis of OTA.

Recommendations for detection, validation, and evaluation of RNA editing events in cardiovascular and neurological/neurodegenerative diseases

RNA editing, a common and potentially highly functional form of RNA modification, encompasses two different RNA modifications, namely adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. As inosines are interpreted as guanosines by the cellular machinery, both A-to-I and C-to-U editing change the nucleotide sequence of the RNA. Editing events in coding sequences have the potential to change the amino acid sequence of proteins, whereas editing events in noncoding RNAs can, for example, affect microRNA target binding. With advancing RNA sequencing technology, more RNA editing events are being discovered, studied, and reported. However, RNA editing events are still often overlooked or discarded as sequence read quality defects. With this position paper, we aim to provide guidelines and recommendations for the detection, validation, and follow-up experiments to study RNA editing, taking examples from the fields of cardiovascular and brain disease. We discuss all steps, from sample collection, storage, and preparation, to different strategies for RNA sequencing and editing-sensitive data analysis strategies, to validation and follow-up experiments, as well as potential pitfalls and gaps in the available technologies. This paper may be used as an experimental guideline for RNA editing studies in any disease context.

Dynamic profiles of lncRNAs reveal a functional natural antisense RNA that regulates the development of Schistosoma japonicum

Schistosomes are flatworm parasites that undergo a complex life cycle involving two hosts. The regulation of the parasite's developmental processes relies on both coding RNAs and non-coding RNAs. However, the roles of non-coding RNAs, including long non-coding RNAs (lncRNAs) in schistosomes remain largely unexplored. Here we conduct advanced RNA sequencing on male and female S. japonicum during their pairing and reproductive development, resulting in the identification of nearly 8,000 lncRNAs. This extensive dataset enables us to construct a comprehensive co-expression network of lncRNAs and mRNAs, shedding light on their interactions during the crucial reproductive stages within the mammalian host. Importantly, we have also revealed a specific lncRNA, LNC3385, which appears to play a critical role in the survival and reproduction of the parasite. These findings not only enhance our understanding of the dynamic nature of lncRNAs during the reproductive phase of schistosomes but also highlight LNC3385 as a potential therapeutic target for combating schistosomiasis.

A comprehensive assessment of exome capture methods for RNA sequencing of formalin-fixed and paraffin-embedded samples

RNA-Seq analysis of Formalin-Fixed and Paraffin-Embedded (FFPE) samples has emerged as a highly effective approach and is increasingly being used in clinical research and drug development. However, the processing and storage of FFPE samples are known to cause extensive degradation of RNAs, which limits the discovery of gene expression or gene fusion-based biomarkers using RNA sequencing, particularly methods reliant on Poly(A) enrichment. Recently, researchers have developed an exome targeted RNA-Seq methodology that utilizes biotinylated oligonucleotide probes to enrich RNA transcripts of interest, which could overcome these limitations. Nevertheless, the standardization of this experimental framework, including probe designs, sample multiplexing, sequencing read length, and bioinformatic pipelines, remains an essential requirement. In this study, we conducted a comprehensive comparison of three main commercially available exome capture kits and evaluated key experimental parameters, to provide the overview of the advantages and limitations associated with the selection of library preparation protocols and sequencing platforms. The results provide valuable insights into the best practices for obtaining high-quality data from FFPE samples.

Restrander: rapid orientation and artefact removal for long-read cDNA data

In transcriptomic analyses, it is helpful to keep track of the strand of the RNA molecules. However, the Oxford Nanopore long-read cDNA sequencing protocols generate reads that correspond to either the first or second-strand cDNA, therefore the strandedness of the initial transcript has to be inferred bioinformatically. Reverse transcription and PCR can also introduce artefacts which should be flagged in data pre-processing. Here we introduce Restrander, a lightning-fast and highly accurate tool for restranding and removing artefacts in long-read cDNA sequencing data. Thanks to its C++ implementation, Restrander was faster than Oxford Nanopore Technologies' existing tool Pychopper, and correctly restranded more reads due to its strategy of searching for polyA/T tails in addition to primer sequences from the reverse transcription and template-switch steps. We found that restranding improved the process of visualising and exploring data, and increased the number of novel isoforms discovered by bambu, particularly in regions where sense and anti-sense transcripts co-occur. The artefact detection implemented in Restrander quantifies reads lacking the correct 5' and 3' ends, a useful feature in quality control for library preparation. Restrander is pre-configured for all major cDNA protocols, and can be customised with user-defined primers. Restrander is available at https://github.com/mritchielab/restrander.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

Characterization of A-to-I Editing in Pigs under a Long-Term High-Energy Diet

Long-term high-energy intake has detrimental effects on pig health and elevates the risk of metabolic disease. RNA editing modifying RNA bases in a post-transcriptional process has been extensively studied for model animals. However, less evidence is available that RNA editing plays a role in the development of metabolic disorders. Here, we profiled the A-to-I editing in three tissues and six gut segments and characterized the functional aspect of editing sites in model pigs for metabolic disorders. We detected 64,367 non-redundant A-to-I editing sites across the pig genome, and 20.1% correlated with their located genes' expression. The largest number of A-to-I sites was found in the abdominal aorta with the highest editing levels. The significant difference in editing levels between high-energy induced and control pigs was detected in the abdominal aorta, testis, duodenum, ileum, colon, and cecum. We next focused on 6041 functional A-to-I sites that detected differences or specificity between treatments. We found functional A-to-I sites specifically involved in a tissue-specific manner. Two of them, located in gene SLA-DQB1 and near gene B4GALT5 were found to be shared by three tissues and six gut segments. Although we did not find them enriched in each of the gene features, in correlation analysis, we noticed that functional A-to-I sites were significantly enriched in gene 3'-UTRs. This result indicates, in general, A-to-I editing has the largest potential in the regulation of gene expression through changing the 3'-UTRs' sequence, which is functionally involved in pigs under a long-term high-energy diet. Our work provides valuable knowledge of A-to-I editing sites functionally involved in the development of the metabolic disorder.

Strategy for RNA-Seq Experimental Design and Data Analysis

Ribonucleic acids (RNAs) are fundamental molecules that control regulation and expression of the genome and therefore the function of a cell. Robust analysis and quantification of RNA transcripts hold critical importance in understanding cell function, altered phenotypes in different biological context, for understanding and targeting diseases. The development of RNA-sequencing (RNA-Seq) now provides opportunities to analyze the expression and function of RNA molecules at an unprecedented scale. However, the strategy for RNA-Seq experimental design and data analysis can substantially differ depending on the biological application. The design choice could also have significant impact for downstream results and interpretation of data. Here we describe key critical considerations required for RNA-Seq experimental design and also describe a step-by-step bioinformatics workflow detailing the different steps required for RNA-Seq data analysis. We believe this article will be a valuable guide for designing and analyzing RNA-Seq data to address a wide range of different biological questions.

Profiling of bacterial transcriptome from ultra-low input with MiniBac-seq

There is a lack of appropriate methods for preparing bacterial RNA-seq library with ultra-low amount of RNA. To address this issue, we developed miniBac-seq, a strand-specific method for high-quality library construction from sub-nanogram of total RNA, which is 100-fold lower than the current benchmark kit and dramatically reduces preparation cost ($28 + $15 × samples). We further demonstrated the high sensitivity of miniBac-seq via detecting more than 500 genes from amount of total RNA equivalent to that of a single bacterial cell. Finally, we profiled the transcriptome of growth-arrested bacteria in isogenic culture of Escherichia coli. This subpopulation of bacteria is generally low in abundance but is a potent reservoir of antibiotic persistence, and their gene expression has been largely unknown due to technical limitations. Using miniBac-seq, we identified potential molecular driver towards arrested growth as well as antibiotic tolerance. Our method thus expands the capacity to quantify bacterial transcriptome in situ, which is useful to the understanding of bacterial physiology and regulation in their native contexts.

High-throughput total RNA sequencing in single cells using VASA-seq

Most methods for single-cell transcriptome sequencing amplify the termini of polyadenylated transcripts, capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts and hinders alternative splicing analysis. We, therefore, developed VASA-seq to detect the total transcriptome in single cells, which is enabled by fragmenting and tailing all RNA molecules subsequent to cell lysis. The method is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to more than 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. Analyzing the dynamics of the total single-cell transcriptome, we discovered cell type markers, many based on non-coding RNA, and performed in vivo cell cycle analysis via detection of non-polyadenylated histone genes. RNA velocity characterization was improved, accurately retracing blood maturation trajectories. Moreover, our VASA-seq data provide a comprehensive analysis of alternative splicing during mammalian development, which highlighted substantial rearrangements during blood development and heart morphogenesis.

Pan-cancer identification of the relationship of metabolism-related differentially expressed transcription regulation with non-differentially expressed target genes via a gated recurrent unit network

The transcriptome describes the expression of all genes in a sample. Most studies have investigated the differential patterns or discrimination powers of transcript expression levels. In this study, we hypothesized that the quantitative correlations between the expression levels of transcription factors (TFs) and their regulated target genes (mRNAs) serve as a novel view of healthy status, and a disease sample exhibits a differential landscape (mqTrans) of transcription regulations compared with healthy status. We formulated quantitative transcription regulation relationships of metabolism-related genes as a multi-input multi-output regression model via a gated recurrent unit (GRU) network. The GRU model was trained using healthy blood transcriptomes and the expression levels of mRNAs were predicted by those of the TFs. The mqTrans feature of a gene was defined as the difference between its predicted and actual expression levels. A pan-cancer investigation of the differentially expressed mqTrans features was conducted between the early- and late-stage cancers in 26 cancer types of The Cancer Genome Atlas database. This study focused on the differentially expressed mqTrans features, that did not show differential expression in the actual expression levels. These genes could not be detected by conventional differential analysis. Such dark biomarkers are worthy of further wet-lab investigation. The experimental data also showed that the proposed mqTrans investigation improved the classification between early- and late-stage samples for some cancer types. Thus, the mqTrans features serve as a complementary view to transcriptomes, an OMIC type with mature high-throughput production technologies, and abundant public resources.

Genome-wide profiling of long non-coding RNA of the rice blast fungus Magnaporthe oryzae during infection

Background

Long non-coding RNAs (lncRNAs) play essential roles in developmental processes and disease development at the transcriptional and post-transcriptional levels across diverse taxa. However, only few studies have profiled fungal lncRNAs in a genome-wide manner during host infection.

Results

Infection-associated lncRNAs were identified using lncRNA profiling over six stages of host infection (e.g., vegetative growth, pre-penetration, biotrophic, and necrotrophic stages) in the model pathogenic fungus, Magnaporthe oryzae. We identified 2,601 novel lncRNAs, including 1,286 antisense lncRNAs and 980 intergenic lncRNAs. Among the identified lncRNAs, 755 were expressed in a stage-specific manner and 560 were infection-specifically expressed lncRNAs (ISELs). To decipher the potential roles of lncRNAs during infection, we identified 365 protein-coding genes that were associated with 214 ISELs. Analysis of the predicted functions of these associated genes suggested that lncRNAs regulate pathogenesis-related genes, including xylanases and effectors.

Conclusions

The ISELs and their associated genes provide a comprehensive view of lncRNAs during fungal pathogen-plant interactions. This study expands new insights into the role of lncRNAs in the rice blast fungus, as well as other plant pathogenic fungi.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08380-4.

Systematic comparative analysis of strand-specific RNA-seq library preparation methods for low input samples

Despite the recent precipitous decline in the cost of genome sequencing, library preparation for RNA-seq is still laborious and expensive for applications such as high throughput screening. Limited availability of RNA generated by some experimental workflows poses an additional challenge and increases the cost of RNA library preparation. In a search for low cost, automation-compatible RNA library preparation kits that maintain strand specificity and are amenable to low input RNA quantities, we systematically tested two recent commercial technologies—Swift RNA and Swift Rapid RNA, presently offered by Integrated DNA Technologies (IDT) —alongside the Illumina TruSeq stranded mRNA, the de facto standard workflow for bulk transcriptomics. We used the Universal Human Reference RNA (UHRR) (composed of equal quantities of total RNA from 10 human cancer cell lines) to benchmark gene expression in these kits, at input quantities ranging between 10 to 500 ng. We found normalized read counts between all treatment groups to be in high agreement. Compared to the Illumina TruSeq stranded mRNA kit, both Swift RNA library kits offer shorter workflow times enabled by their patented Adaptase technology. We also found the Swift RNA kit to produce the fewest number of differentially expressed genes and pathways directly attributable to input mRNA amount.

how_are_we_stranded_here: quick determination of RNA-Seq strandedness

Background

Quality control checks are the first step in RNA-Sequencing analysis, which enable the identification of common issues that occur in the sequenced reads. Checks for sequence quality, contamination, and complexity are commonplace, and allow users to implement steps downstream which can account for these issues. Strand-specificity of reads is frequently overlooked and is often unavailable even in published data, yet when unknown or incorrectly specified can have detrimental effects on the reproducibility and accuracy of downstream analyses.

Results

To address these issues, we developed how_are_we_stranded_here, a Python library that helps to quickly infer strandedness of paired-end RNA-Sequencing data. Testing on both simulated and real RNA-Sequencing reads showed that it correctly measures strandedness, and measures outside the normal range may indicate sample contamination.

Conclusions

how_are_we_stranded_here is fast and user friendly, making it easy to implement in quality control pipelines prior to analysing RNA-Sequencing data. how_are_we_stranded_here is freely available at https://github.com/betsig/how_are_we_stranded_here.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04572-7.

Sequences to Differences in Gene Expression: Analysis of RNA-Seq Data

RNA-Seq is now a routinely employed assay to measure gene expression. As the technique matured over the last decade, so have dedicated analytic tools. In this chapter, we first describe the mainstream as well as the most up-to-date protocols and their implications on downstream analysis. We then detail the steps entailing RNA-Seq analysis in three main stages: (i) preprocessing and data preparation, (ii) upstream processing, and (iii) high-level analyses. We review the most recent and relevant tools as one workflow following a stepwise order. The chapter further encompasses in-depth features of these tools. Details of the required code are made available throughout the chapter, as well as of the underlying statistics. We illustrate these steps with analysis of publicly available RNA-Seq data.

Heliorhodopsin Evolution Is Driven by Photosensory Promiscuity in Monoderms

Rhodopsins are light-activated proteins displaying an enormous versatility of function as cation/anion pumps or sensing environmental stimuli and are widely distributed across all domains of life. Even with wide sequence divergence and uncertain evolutionary linkages between microbial (type 1) and animal (type 2) rhodopsins, the membrane orientation of the core structural scaffold of both was presumed universal. This was recently amended through the discovery of heliorhodopsins (HeRs; type 3), that, in contrast to known rhodopsins, display an inverted membrane topology and yet retain similarities in sequence, structure, and the light-activated response. While no ion-pumping activity has been demonstrated for HeRs and multiple crystal structures are available, fundamental questions regarding their cellular and ecological function or even their taxonomic distribution remain unresolved. Here, we investigated HeR function and distribution using genomic/metagenomic data with protein domain fusions, contextual genomic information, and gene coexpression analysis with strand-specific metatranscriptomics. We bring to resolution the debated monoderm/diderm occurrence patterns and show that HeRs are restricted to monoderms. Moreover, we provide compelling evidence that HeRs are a novel type of sensory rhodopsins linked to histidine kinases and other two-component system genes across phyla. In addition, we also describe two novel putative signal-transducing domains fused to some HeRs. We posit that HeRs likely function as generalized light-dependent switches involved in the mitigation of light-induced oxidative stress and metabolic circuitry regulation. Their role as sensory rhodopsins is corroborated by their photocycle dynamics and their presence/function in monoderms is likely connected to the higher sensitivity of these organisms to light-induced damage.

IMPORTANCE Heliorhodopsins are enigmatic, novel rhodopsins with a membrane orientation that is opposite to all known rhodopsins. However, their cellular and ecological functions are unknown, and even their taxonomic distribution remains a subject of debate. We provide evidence that HeRs are a novel type of sensory rhodopsins linked to histidine kinases and other two-component system genes across phyla boundaries. In support of this, we also identify two novel putative signal transducing domains in HeRs that are fused with them. We also observe linkages of HeRs to genes involved in mitigation of light-induced oxidative stress and increased carbon and nitrogen metabolism. Finally, we synthesize these findings into a framework that connects HeRs with the cellular response to light in monoderms, activating light-induced oxidative stress defenses along with carbon/nitrogen metabolic circuitries. These findings are consistent with the evolutionary, taxonomic, structural, and genomic data available so far.

Genome sequencing of turmeric provides evolutionary insights into its medicinal properties

Curcuma longa, or turmeric, is traditionally known for its immense medicinal properties and has diverse therapeutic applications. However, the absence of a reference genome sequence is a limiting factor in understanding the genomic basis of the origin of its medicinal properties. In this study, we present the draft genome sequence of C. longa, belonging to Zingiberaceae plant family, constructed using 10x Genomics linked reads and Oxford Nanopore long reads. For comprehensive gene set prediction and for insights into its gene expression, transcriptome sequencing of leaf tissue was also performed. The draft genome assembly had a size of 1.02 Gbp with ~70% repetitive sequences, and contained 50,401 coding gene sequences. The phylogenetic position of C. longa was resolved through a comprehensive genome-wide analysis including 16 other plant species. Using 5,388 orthogroups, the comparative evolutionary analysis performed across 17 species including C. longa revealed evolution in genes associated with secondary metabolism, plant phytohormones signaling, and various biotic and abiotic stress tolerance responses. These mechanisms are crucial for perennial and rhizomatous plants such as C. longa for defense and environmental stress tolerance via production of secondary metabolites, which are associated with the wide range of medicinal properties in C. longa.

Peroxisome Proliferator-Activated Receptor γ, but Not α or G-Protein Coupled Estrogen Receptor Drives Functioning of Postnatal Boar Testis-Next Generation Sequencing Analysis

Simple Summary

As of now, the Next Generation Sequencing (NGS) analysis has not been utilized to identify biological processes and signaling pathways that are regulated in the boar postnatal testes. Our prior studies revealed that the peroxisome proliferator-activated receptor (PPAR) and G-protein coupled estrogen receptor (GPER) were significant for the morpho-functional status of testicular cells. Here, the pharmacological blockage of PPARα, PPARγ or GPER was performed in ex vivo immature boar testes. The NGS results showed 382 transcripts with an altered expression. The blockage by the PPARγ antagonist markedly affected biological processes such as: drug metabolism (genes: Ctsh, Duox2, Atp1b1, Acss2, Pkd2, Aldh2, Hbb, Sdhd, Cox3, Nd4, Nd5, Cytb, Cbr1, and Pid1), adhesion (genes: Plpp3, Anxa1, Atp1b1, S100a8, Cd93, Ephb4, Vsir, Cldn11, Gpc4, Fermt3, Dusp26, Sox9, and Cdh5) and tube development (genes: Ctsh, Mmp14, Dll4, Anxa1, Ephb4, Pkd2, Angptl4, Robo4, Sox9, Hikeshi, Ing2, Loc100738836, and Rarres2), as well as the Notch signaling pathway. This was not the case for the PPARα or GPER antagonists. Our observations suggested that PPARγ may be the principal player in the management of the development and function of boar testes during the early postnatal window. Moreover, due to a highly similar porcine gene expression pattern to human homologues genes, our results can be used to understand both animal and human testes physiology and to predict or treat pathological processes.

Abstract

Porcine tissue gene expression is highly similar to the expression of homologous genes in humans. Based on this fact, the studies on porcine tissues can be employed to understand human physiology and to predict or treat diseases. Our prior studies clearly showed that there was a regulatory partnership of the peroxisome proliferator-activated receptor (PPAR) and the G-protein coupled membrane estrogen receptor (GPER) that relied upon the tumorigenesis of human and mouse testicular interstitial cells, as well as the PPAR-estrogen related receptor and GPER–xenoestrogen relationships which affected the functional status of immature boar testes. The main objective of this study was to identify the biological processes and signaling pathways governed by PPARα, PPARγ and GPER in the immature testes of seven-day-old boars after pharmacological receptor ligand treatment. Boar testicular tissues were cultured in an organotypic system with the respective PPARα, PPARγ or GPER antagonists. To evaluate the effect of the individual receptor deprivation in testicular tissue on global gene expression, Next Generation Sequencing was performed. Bioinformatic analysis revealed 382 transcripts with altered expression. While tissues treated with PPARα or GPER antagonists showed little significance in the enrichment analysis, the antagonists challenged with the PPARγ antagonist displayed significant alterations in biological processes such as: drug metabolism, adhesion and tubule development. Diverse disruption in the Notch signaling pathway was also observed. The findings of our study proposed that neither PPARα nor GPER, but PPARγ alone seemed to be the main player in the regulation of boar testes functioning during early the postnatal developmental window.

No Expression Divergence despite Transcriptional Interference between Nested Protein-Coding Genes in Mammals

Nested protein-coding genes accumulated throughout metazoan evolution, with early analyses of human and Drosophila microarray data indicating that this phenomenon was simply due to the presence of large introns. However, a recent study employing RNA-seq data uncovered evidence of transcriptional interference driving rapid expression divergence between Drosophila nested genes, illustrating that accurate expression estimation of overlapping genes can enhance detection of their relationships. Hence, here I apply an analogous approach to strand-specific RNA-seq data from human and mouse to revisit the role of transcriptional interference in the evolution of mammalian nested genes. A genomic survey reveals that whereas mammalian nested genes indeed accrued over evolutionary time, they are retained at lower frequencies than in Drosophila. Though several properties of mammalian nested genes align with observations in Drosophila and with expectations under transcriptional interference, contrary to both, their expression divergence is not statistically different from that between unnested genes, and also does not increase after nesting. Together, these results support the hypothesis that lower selection efficiencies limit rates of gene expression evolution in mammals, leading to their reliance on immediate eradication of deleterious nested genes to avoid transcriptional interference.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data

The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph (MOG), a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally testable hypotheses about candidate genes.

Role of mammalian long non-coding RNAs in normal and neuro oncological disorders

Long non-coding RNAs (lncRNAs) are expressed at lower levels than protein-coding genes but have a crucial role in gene regulation. LncRNA is distinct, they are being transcribed using RNA polymerase II, and their functionality depends on subcellular localization. Depending on their niche, they specifically interact with DNA, RNA, and proteins and modify chromatin function, regulate transcription at various stages, forms nuclear condensation bodies and nucleolar organization. lncRNAs may also change the stability and translation of cytoplasmic mRNAs and hamper signaling pathways. Thus, lncRNAs affect the physio-pathological states and lead to the development of various disorders, immune responses, and cancer. To date, ~40% of lncRNAs have been reported in the nervous system (NS) and are involved in the early development/differentiation of the NS to synaptogenesis. LncRNA expression patterns in the most common adult and pediatric tumor suggest them as potential biomarkers and provide a rationale for targeting them pharmaceutically. Here, we discuss the mechanisms of lncRNA synthesis, localization, and functions in transcriptional, post-transcriptional, and other forms of gene regulation, methods of lncRNA identification, and their potential therapeutic applications in neuro oncological disorders as explained by molecular mechanisms in other malignant disorders.

A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3' or 5' termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

Functional long non-coding and circular RNAs in zebrafish

The utility of model organisms to understand the function of a novel transcript/genes has allowed us to delineate their molecular mechanisms in maintaining cellular homeostasis. Organisms such as zebrafish have contributed a lot in the field of developmental and disease biology. Attributable to advancement and deep transcriptomics, many new transcript isoforms and non-coding RNAs such as long noncoding RNA (lncRNA) and circular RNAs (circRNAs) have been identified and cataloged in multiple databases and many more are yet to be identified. Various methods and tools have been utilized to identify lncRNAs/circRNAs in zebrafish using deep sequencing of transcriptomes as templates. Functional analysis of a few candidates such as tie1-AS, ECAL1 and CDR1as in zebrafish provides a prospective outline to approach other known or novel lncRNA/circRNA. New genetic alteration tools like TALENS and CRISPRs have helped in probing for the molecular function of lncRNA/circRNA in zebrafish. Further latest improvements in experimental and computational techniques offer the identification of lncRNA/circRNA counterparts in humans and zebrafish thereby allowing easy modeling and analysis of function at cellular level.

Investigation of the putative role of antisense transcripts as regulators of sense transcripts by correlation analysis of sense-antisense pairs in colorectal cancers

Antisense transcription occurs widely more expected than when it was first identified in bacteria in the 1980s. However, the functional relevance of antisense transcripts in transcription remains controversial. Here, we investigated the putative role of antisense transcripts in regulating their corresponding sense transcripts by analyzing changes in correlative relationships between sense-antisense pairs under tumor and normal conditions. A total of 3469 sense-antisense gene pairs (SAGPs) downloaded from BioMart mapped to a list of sense and antisense genes in RNA-seq data derived from 80 paired colorectal cancer (CRC) samples were analyzed. As a result, cancer-related genes were significantly enriched in the significantly correlated SAGPs (SCPs). Differentially expressed genes estimated between normal and tumor conditions were also significantly more enriched in SCPs than in non-SCPs. Interestingly, using differential correlation analysis, we found that tumor samples had a significantly larger density of genes with higher correlation coefficients than normal samples, as verified by various cancer transcriptomes from The Cancer Genome Atlas (TCGA). Moreover, we found that the magnitude of the correlation between SAGPs could distinguish poor prognostic CRCs from good prognostic CRCs, showing that correlation coefficients between the SAGPs of CRCs with a poor prognosis were significantly stronger than CRCs with a good prognosis. Consistent with this finding, the Cox proportion hazards model showed that the survival rates were significantly different between patients with high and low expression of genes in the SCPs. All these results strongly support the idea that antisense transcripts are important regulators of their corresponding sense transcripts.

Non-Coding RNA Signatures of B-Cell Acute Lymphoblastic Leukemia

Non-coding RNAs (ncRNAs) comprise a diverse class of non-protein coding transcripts that regulate critical cellular processes associated with cancer. Advances in RNA-sequencing (RNA-Seq) have led to the characterization of non-coding RNA expression across different types of human cancers. Through comprehensive RNA-Seq profiling, a growing number of studies demonstrate that ncRNAs, including long non-coding RNA (lncRNAs) and microRNAs (miRNA), play central roles in progenitor B-cell acute lymphoblastic leukemia (B-ALL) pathogenesis. Furthermore, due to their central roles in cellular homeostasis and their potential as biomarkers, the study of ncRNAs continues to provide new insight into the molecular mechanisms of B-ALL. This article reviews the ncRNA signatures reported for all B-ALL subtypes, focusing on technological developments in transcriptome profiling and recently discovered examples of ncRNAs with biologic and therapeutic relevance in B-ALL.

Intron retention and its impact on gene expression and protein diversity: A review and a practical guide

Intron retention (IR) occurs when a complete and unspliced intron remains in mature mRNA. An increasing body of literature has demonstrated a major role for IR in numerous biological functions, including several that impact human health and disease. Although experimental technologies used to study other forms of mRNA splicing can also be used to investigate IR, a specialized downstream computational analysis is optimal for IR discovery and analysis. Here we provide a review of IR and its biological implications, as well as a practical guide for how to detect and analyze it. Several methods, including long read third generation direct RNA sequencing, are described. We have developed an R package, FakIR, to facilitate the execution of the bioinformatic tasks recommended in this review and a tutorial on how to fit them to users aims. Additionally, we provide guidelines and experimental protocols to validate IR discovery and to evaluate the potential impact of IR on gene expression and protein output. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Splicing Regulation/Alternative Splicing RNA Methods > RNA Analyses in vitro and In Silico.

Multi-omics profiling highlights lipid metabolism alterations in pigs fed low-dose antibiotics

Background

In order to study the relations of hepatocellular functions, weight gain and metabolic imbalance caused by low-dose antibiotics (LDA) via epigenetic regulation of gene transcription, 32 weaned piglets were employed as animal models and randomly allocated into two groups with diets supplemented with 0 or LDA (chlorotetracycline and virginiamycin).

Results

During the 4 weeks of the experiment, LDA showed a clear growth-promoting effect, which was exemplified by the significantly elevated body weight and average daily gain. Promoter methylome profiling using liquid hybridization capture-based bisulfite sequencing (LHC-BS) indicated that most of the 745 differential methylation regions (DMRs) were hypermethylated in the LDA group. Several DMRs were significantly enriched in genes related with fatty acids metabolic pathways, such as FABP1 and PCK1. In addition, 71 differentially expressed genes (DEGs) were obtained by strand-specific transcriptome analysis of liver tissues, including ALOX15, CXCL10 and NNMT, which are three key DEGs that function in lipid metabolism and immunity and which had highly elevated expression in the LDA group. In accordance with these molecular changes, the lipidome analyses of serum by LC-MS identified 38 significantly differential lipids, most of which were downregulated in the LDA group.

Conclusions

Our results indicate that LDA could induce epigenetic and transcriptional changes of key genes and lead to enhanced efficiency of lipid metabolism in the liver.

Fusion-Bloom: fusion detection in assembled transcriptomes

Summary

Presence or absence of gene fusions is one of the most important diagnostic markers in many cancer types. Consequently, fusion detection methods using various genomics data types, such as RNA sequencing (RNA-seq) are valuable tools for research and clinical applications. While information-rich RNA-seq data have proven to be instrumental in discovery of a number of hallmark fusion events, bioinformatics tools to detect fusions still have room for improvement. Here, we present Fusion-Bloom, a fusion detection method that leverages recent developments in de novo transcriptome assembly and assembly-based structural variant calling technologies (RNA-Bloom and PAVFinder, respectively). We benchmarked Fusion-Bloom against the performance of five other state-of-the-art fusion detection tools using multiple datasets. Overall, we observed Fusion-Bloom to display a good balance between detection sensitivity and specificity. We expect the tool to find applications in translational research and clinical genomics pipelines.

Availability and implementation

Fusion-Bloom is implemented as a UNIX Make utility, available at https://github.com/bcgsc/pavfinder and released under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Advancing brain barriers RNA sequencing: guidelines from experimental design to publication

BACKGROUND

RNA sequencing (RNA-Seq) in its varied forms has become an indispensable tool for analyzing differential gene expression and thus characterization of specific tissues. Aiming to understand the brain barriers genetic signature, RNA seq has also been introduced in brain barriers research. This has led to availability of both, bulk and single-cell RNA-Seq datasets over the last few years. If appropriately performed, the RNA-Seq studies provide powerful datasets that allow for significant deepening of knowledge on the molecular mechanisms that establish the brain barriers. However, RNA-Seq studies comprise complex workflows that require to consider many options and variables before, during and after the proper sequencing process.

MAIN BODY

In the current manuscript, we build on the interdisciplinary experience of the European PhD Training Network BtRAIN ( https://www.btrain-2020.eu/ ) where bioinformaticians and brain barriers researchers collaborated to analyze and establish RNA-Seq datasets on vertebrate brain barriers. The obstacles BtRAIN has identified in this process have been integrated into the present manuscript. It provides guidelines along the entire workflow of brain barriers RNA-Seq studies starting from the overall experimental design to interpretation of results. Focusing on the vertebrate endothelial blood-brain barrier (BBB) and epithelial blood-cerebrospinal-fluid barrier (BCSFB) of the choroid plexus, we provide a step-by-step description of the workflow, highlighting the decisions to be made at each step of the workflow and explaining the strengths and weaknesses of individual choices made. Finally, we propose recommendations for accurate data interpretation and on the information to be included into a publication to ensure appropriate accessibility of the data and reproducibility of the observations by the scientific community.

CONCLUSION

Next generation transcriptomic profiling of the brain barriers provides a novel resource for understanding the development, function and pathology of these barrier cells, which is essential for understanding CNS homeostasis and disease. Continuous advancement and sophistication of RNA-Seq will require interdisciplinary approaches between brain barrier researchers and bioinformaticians as successfully performed in BtRAIN. The present guidelines are built on the BtRAIN interdisciplinary experience and aim to facilitate collaboration of brain barriers researchers with bioinformaticians to advance RNA-Seq study design in the brain barriers community.

Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols

In recent years, RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only dependent on its expression level and gene length, but also the sequencing depth. To normalize these dependencies, RPKM (reads per kilobase of transcript per million reads mapped) and TPM (transcripts per million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects. However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions are very close across compared samples. Nevertheless, the sequenced RNA repertoires may differ significantly under different experimental conditions and/or across sequencing protocols; thus, the proportion of gene expression is not directly comparable in such cases. In this review, we illustrate typical scenarios in which RPKM and TPM are misused, unintentionally, and hope to raise scientists' awareness of this issue when comparing them across samples or different sequencing protocols.

Systematic analysis of 1298 RNA-Seq samples and construction of a comprehensive soybean (Glycine max) expression atlas

Soybean (Glycine max [L.] Merr.) is a major crop in animal feed and human nutrition, mainly for its rich protein and oil contents. The remarkable rise in soybean transcriptome studies over the past 5 years generated an enormous amount of RNA-seq data, encompassing various tissues, developmental conditions and genotypes. In this study, we have collected data from 1298 publicly available soybean transcriptome samples, processed the raw sequencing reads and mapped them to the soybean reference genome in a systematic fashion. We found that 94% of the annotated genes (52 737/56 044) had detectable expression in at least one sample. Unsupervised clustering revealed three major groups, comprising samples from aerial, underground and seed/seed-related parts. We found 452 genes with uniform and constant expression levels, supporting their roles as housekeeping genes. On the other hand, 1349 genes showed heavily biased expression patterns towards particular tissues. A transcript-level analysis revealed that 95% (70 963 of 74 490) of the assembled transcripts have intron chains exactly matching those from known transcripts, whereas 3256 assembled transcripts represent potentially novel splicing isoforms. The dataset compiled here constitute a new resource for the community, which can be downloaded or accessed through a user-friendly web interface at http://venanciogroup.uenf.br/resources/. This comprehensive transcriptome atlas will likely accelerate research on soybean genetics and genomics.

UNAGI: an automated pipeline for nanopore full-length cDNA sequencing uncovers novel transcripts and isoforms in yeast

Sequencing the entire RNA molecule leads to a better understanding of the transcriptome architecture. SMARTer (Switching Mechanism at 5'-End of RNA Template) is a technology aimed at generating full-length cDNA from low amounts of mRNA for sequencing by short-read sequencers such as those from Illumina. However, short read sequencing such as Illumina technology includes fragmentation that results in bias and information loss. Here, we built a pipeline, UNAGI or UNAnnotated Gene Identifier, to process long reads obtained with nanopore sequencing and compared this pipeline with the standard Illumina pipeline by studying the Saccharomyces cerevisiae transcriptome in full-length cDNA samples generated from two different biological samples: haploid and diploid cells. Additionally, we processed the long reads with another long read tool, FLAIR. Our strand-aware method revealed significant differential gene expression that was masked in Illumina data by antisense transcripts. Our pipeline, UNAGI, outperformed the Illumina pipeline and FLAIR in transcript reconstruction (sensitivity and specificity of 80% and 40% vs. 18% and 34% and 79% and 32%, respectively). Moreover, UNAGI discovered 3877 unannotated transcripts including 1282 intergenic transcripts while the Illumina pipeline discovered only 238 unannotated transcripts. For isoforms profiling, UNAGI also outperformed the Illumina pipeline and FLAIR in terms of sensitivity (91% vs. 82% and 63%, respectively). But the low accuracy of nanopore sequencing led to a closer gap in terms of specificity with Illumina pipeline (70% vs. 63%) and to a huge gap with FLAIR (70% vs 0.02%).

A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine

Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.

Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

Background

RNA sequencing data is providing abundant information about the levels of dysregulation of genes in various tumors. These data, as well as data based on older microarray technologies have enabled the identification of many genes which are upregulated in clear cell renal cell carcinoma (ccRCC) compared to matched normal tissue. Here we use RNA sequencing data in order to construct a panel of highly overexpressed genes in ccRCC so as to evaluate their RNA levels in whole blood and determine any diagnostic potential of these levels for renal cell carcinoma patients.

Methods

A bioinformatics analysis with Python was performed using TCGA, GEO and other databases to identify genes which are upregulated in ccRCC while being absent in the blood of healthy individuals. Quantitative Real Time PCR (RT-qPCR) was subsequently used to measure the levels of candidate genes in whole blood (PAX gene) of 16 ccRCC patients versus 11 healthy individuals. PCR results were processed in qBase and GraphPadPrism and statistics was done with Mann-Whitney U test.

Results

While most analyzed genes were either undetectable or did not show any dysregulated expression, two genes, CDK18 and CCND1, were paradoxically downregulated in the blood of ccRCC patients compared to healthy controls. Furthermore, LOX showed a tendency towards upregulation in metastatic ccRCC samples compared to non-metastatic.

Conclusions

This analysis illustrates the difficulty of detecting tumor regulated genes in blood and the possible influence of interference from expression in blood cells even for genes conditionally absent in normal blood. Testing in plasma samples indicated that tumor specific mRNAs were not detectable. While CDK18, CCND1 and LOX mRNAs might carry biomarker potential, this would require validation in an independent, larger patient cohort.

RNA-seq and ChIP-seq as Complementary Approaches for Comprehension of Plant Transcriptional Regulatory Mechanism

The availability of data produced from various sequencing platforms offer the possibility to answer complex questions in plant research. However, drawbacks can arise when there are gaps in the information generated, and complementary platforms are essential to obtain more comprehensive data sets relating to specific biological process, such as responses to environmental perturbations in plant systems. The investigation of transcriptional regulation raises different challenges, particularly in associating differentially expressed transcription factors with their downstream responsive genes. In this paper, we discuss the integration of transcriptional factor studies through RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation sequencing (ChIP-seq). We show how the data from ChIP-seq can strengthen information generated from RNA-seq in elucidating gene regulatory mechanisms. In particular, we discuss how integration of ChIP-seq and RNA-seq data can help to unravel transcriptional regulatory networks. This review discusses recent advances in methods for studying transcriptional regulation using these two methods. It also provides guidelines for making choices in selecting specific protocols in RNA-seq pipelines for genome-wide analysis to achieve more detailed characterization of specific transcription regulatory pathways via ChIP-seq.

Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples

BACKGROUND

Long non-coding RNAs (lncRNAs) are emerging as important regulators of various biological processes. While many studies have exploited public resources such as RNA sequencing (RNA-Seq) data in The Cancer Genome Atlas to study lncRNAs in cancer, it is crucial to choose the optimal method for accurate expression quantification.

RESULTS

In this study, we compared the performance of pseudoalignment methods Kallisto and Salmon, alignment-based transcript quantification method RSEM, and alignment-based gene quantification methods HTSeq and featureCounts, in combination with read aligners STAR, Subread, and HISAT2, in lncRNA quantification, by applying them to both un-stranded and stranded RNA-Seq datasets. Full transcriptome annotation, including protein-coding and non-coding RNAs, greatly improves the specificity of lncRNA expression quantification. Pseudoalignment methods and RSEM outperform HTSeq and featureCounts for lncRNA quantification at both sample- and gene-level comparison, regardless of RNA-Seq protocol type, choice of aligners, and transcriptome annotation. Pseudoalignment methods and RSEM detect more lncRNAs and correlate highly with simulated ground truth. On the contrary, HTSeq and featureCounts often underestimate lncRNA expression. Antisense lncRNAs are poorly quantified by alignment-based gene quantification methods, which can be improved using stranded protocols and pseudoalignment methods.

CONCLUSIONS

Considering the consistency with ground truth and computational resources, pseudoalignment methods Kallisto or Salmon in combination with full transcriptome annotation is our recommended strategy for RNA-Seq analysis for lncRNAs.

Identifying inaccuracies in gene expression estimates from unstranded RNA-seq data

RNA-seq methods are widely utilized for transcriptomic profiling of biological samples. However, there are known caveats of this technology which can skew the gene expression estimates. Specifically, if the library preparation protocol does not retain RNA strand information then some genes can be erroneously quantitated. Although strand-specific protocols have been established, a significant portion of RNA-seq data is generated in non-strand-specific manner. We used a comprehensive stranded RNA-seq dataset of 15 blood cell types to identify genes for which expression would be erroneously estimated if strand information was not available. We found that about 10% of all genes and 2.5% of protein coding genes have a two-fold or higher difference in estimated expression when strand information of the reads was ignored. We used parameters of read alignments of these genes to construct a machine learning model that can identify which genes in an unstranded dataset might have incorrect expression estimates and which ones do not. We also show that differential expression analysis of genes with biased expression estimates in unstranded read data can be recovered by limiting the reads considered to those which span exonic boundaries. The resulting approach is implemented as a package available at https://github.com/mikpom/uslcount.

Comparative evaluation of RNA-Seq library preparation methods for strand-specificity and low input

Library preparation is a key step in sequencing. For RNA sequencing there are advantages to both strand specificity and working with minute starting material, yet until recently there was no kit available enabling both. The Illumina TruSeq stranded mRNA Sample Preparation kit (TruSeq) requires abundant starting material while the Takara Bio SMART-Seq v4 Ultra Low Input RNA kit (V4) sacrifices strand specificity. The SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Pico) by Takara Bio claims to overcome these limitations. Comparative evaluation of these kits is important for selecting the appropriate protocol. We compared the three kits in a realistic differential expression analysis. We prepared and sequenced samples from two experimental conditions of biological interest with each of the three kits. We report differences between the kits at the level of differential gene expression; for example, the Pico kit results in 55% fewer differentially expressed genes than TruSeq. Nevertheless, the agreement of the observed enriched pathways suggests that comparable functional results can be obtained. In summary we conclude that the Pico kit sufficiently reproduces the results of the other kits at the level of pathway analysis while providing a combination of options that is not available in the other kits.

Transcriptome analysis of strawberry (Fragaria × ananassa) fruits under osmotic stresses and identification of genes related to ascorbic acid pathway

Strawberry (Fragaria ananassa Duch.) is an economically important fruit with a high demand owing to its good taste and medicinal properties. However, its cultivation is affected by various biotic and abiotic stresses. Plants exhibit several intrinsic mechanisms to deal with stresses. In the case of strawberry, the mechanisms highlighting the response against these stresses remain to be elucidated, which has hampered the efforts to develop and cultivate strawberry plants with high yield and quality. Although a virtual reference genome of F. ananassa has recently been published, there is still a lack of information on the expression of genes in response to various stresses. Therefore, to provide molecular information for further studies with strawberry plants, we present the reference transcriptome dataset of F. ananassa, assembled and annotated from deep RNA-Seq data of fruits cultivated under salinity and drought stresses. We also systematically arranged a series of transcripts differentially expressed during these stresses, with an emphasis on genes related to the accumulation of ascorbic acid (AsA). Ascorbic acid is the most potent antioxidant present in these fruits and highly considered during biofortification. A comparison of the expression profile of these genes by RT-qPCR with the content of AsA in the fruits verified a tight regulation and balance between the expression of genes, from biosynthesis, degradation and recycling pathways, resulting in the reduced content of AsA in fruits under these stresses. These results provide a useful repertoire of genes for metabolic engineering, thereby improving the tolerance to stresses.