Comparative evaluation of isoform-level gene expression estimation algorithms for RNA-seq and exon-array platforms

Raw transcriptomics data to gene specific SSRs: a validated free bioinformatics workflow for biologists

Recent advances in next-generation sequencing technologies have paved the path for a considerable amount of sequencing data at a relatively low cost. This has revolutionized the genomics and transcriptomics studies. However, different challenges are now created in handling such data with available bioinformatics platforms both in assembly and downstream analysis performed in order to infer correct biological meaning. Though there are a handful of commercial software and tools for some of the procedures, cost of such tools has made them prohibitive for most research laboratories. While individual open-source or free software tools are available for most of the bioinformatics applications, those components usually operate standalone and are not combined for a user-friendly workflow. Therefore, beginners in bioinformatics might find analysis procedures starting from raw sequence data too complicated and time-consuming with the associated learning-curve. Here, we outline a procedure for de novo transcriptome assembly and Simple Sequence Repeats (SSR) primer design solely based on tools that are available online for free use. For validation of the developed workflow, we used Illumina HiSeq reads of different tissue samples of Santalum album (sandalwood), generated from a previous transcriptomics project. A portion of the designed primers were tested in the lab with relevant samples and all of them successfully amplified the targeted regions. The presented bioinformatics workflow can accurately assemble quality transcriptomes and develop gene specific SSRs. Beginner biologists and researchers in bioinformatics can easily utilize this workflow for research purposes.

Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

Platform-integrated mRNA isoform quantification

MOTIVATION

Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction.

RESULTS

In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString's nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available.

AVAILABILITY AND IMPLEMENTATION

Source code is available at: https://github.com/CompbioLabUcf/IntMTQ.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A large-scale comparative study of isoform expressions measured on four platforms

BACKGROUND

Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains.

RESULTS

In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 404 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis.

CONCLUSION

In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

Systematic Identification of Housekeeping Genes Possibly Used as References in Caenorhabditis elegans by Large-Scale Data Integration

For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in Caenorhabditis elegans was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses. As a result, thirteen housekeeping genes (rps-23, rps-26, rps-27, rps-16, rps-2, rps-4, rps-17, rpl-24.1, rpl-27, rpl-33, rpl-36, rpl-35, and rpl-15) with enhanced stability were comprehensively identified by using six popular normalization algorithms and RankAggreg method. Functional enrichment analysis revealed that these genes were significantly overrepresented in GO terms or KEGG pathways related to ribosomes. Validation analysis using recently published datasets revealed that the expressions of newly identified candidate reference genes were more stable than the commonly used reference genes. Based on the results, we recommended using rpl-33 and rps-26 as the optimal reference genes for microarray and rps-2 and rps-4 for RNA-sequencing data validation. More importantly, the most stable rps-23 should be a promising reference gene for both data types. This study, for the first time, successfully displays a large-scale microarray data driven genome-wide identification of stable reference genes for normalizing gene expression data and provides a potential guideline on the selection of universal internal reference genes in C. elegans, for quantitative gene expression analysis.

Detecting, Categorizing, and Correcting Coverage Anomalies of RNA-Seq Quantification

Because of incomplete reference transcriptomes, incomplete sequencing bias models, or other modeling defects, algorithms to infer isoform expression from RNA sequencing (RNA-seq) sometimes do not accurately model expression. We present a computational method to detect instances where a quantification algorithm could not completely explain the input reads. Our approach identifies regions where the read coverage significantly deviates from expectation. We call these regions "expression anomalies." We further present a method to attribute their cause to either the incompleteness of the reference transcriptome or algorithmic mistakes. We detect anomalies for 30 GEUVADIS and 16 Human Body Map samples. By correcting anomalies when possible, we reduce the number of falsely predicted instances of differential expression. Anomalies that cannot be corrected are suspected to indicate the existence of isoforms unannotated by the reference. We detected 88 common anomalies of this type and find that they tend to have a lower-than-expected coverage toward their 3' ends.

Modeling elastin-associated vasculopathy with patient induced pluripotent stem cells and tissue engineering

Elastin-associated vasculopathies are life-threatening conditions of blood vessel dysfunction. The extracellular matrix protein elastin endows the recoil and compliance required for physiologic arterial function, while disruption of function can lead to aberrant vascular smooth muscle cell proliferation manifesting through stenosis, aneurysm, or vessel dissection. Although research efforts have been informative, they remain incomplete as no viable therapies exist outside of a heart transplant. Induced pluripotent stem cell technology may be uniquely suited to address current obstacles as these present a replenishable supply of patient-specific material with which to study disease. The following review will cover the cutting edge in vascular smooth muscle cell modeling of elastin-associated vasculopathy, and aid in the development of human disease modeling and drug screening approaches to identify potential treatments. Vascular proliferative disease can affect up to 50% of the population throughout the world, making this a relevant and critical area of research for therapeutic development.

Selection of Reference Genes for Expression Analysis in Chinese Medicinal Herb Huperzia serrata

Huperzine A (HupA) is a powerful and selective inhibitor of acetylcholinesterase. It has attracted widespread attention endangering the ultimate plant sources of Lycopodiaceae family. In this study, we used Huperzia serrata, extensively used in Traditional Chinese medicine (TCM), a slow growing vascular plant as the model plant of the Lycopodiaceae family to develop and validate the reference genes. We aim to use gene expression platform to understand the gene expression of different tissues and developmental stages of this medicinal herb. Eight candidate reference genes were selected based on RNA-seq data and evaluated with qRT-PCR. The expression of L/ODC and cytochrome P450s genes known for their involvement in lycopodium alkaloid biosynthesis, were also studied to validate the selected reference genes. The most stable genes were TBP, GAPDH, and their combination (TBP + GAPDH). We report for the first time the reference gene of H. serrata’s different tissues which would provide important insights into understanding their biological functions comparing other Lycopodiaceae plants and facilitate a good biopharming approach.

A junction coverage compatibility score to quantify the reliability of transcript abundance estimates and annotation catalogs

Comparison of observed exon–exon junction counts to those predicted from estimated transcript abundances can identify genes with misannotated or misquantified transcripts.

Most methods for statistical analysis of RNA-seq data take a matrix of abundance estimates for some type of genomic features as their input, and consequently the quality of any obtained results is directly dependent on the quality of these abundances. Here, we present the junction coverage compatibility score, which provides a way to evaluate the reliability of transcript-level abundance estimates and the accuracy of transcript annotation catalogs. It works by comparing the observed number of reads spanning each annotated splice junction in a genomic region to the predicted number of junction-spanning reads, inferred from the estimated transcript abundances and the genomic coordinates of the corresponding annotated transcripts. We show that although most genes show good agreement between the observed and predicted junction coverages, there is a small set of genes that do not. Genes with poor agreement are found regardless of the method used to estimate transcript abundances, and the corresponding transcript abundances should be treated with care in any downstream analyses.

Function, clinical application, and strategies of Pre-mRNA splicing in cancer

Pre-mRNA splicing is a fundamental process that plays a considerable role in generating protein diversity. Pre-mRNA splicing is also the key to the pathology of numerous diseases, especially cancers. In this review, we discuss how aberrant splicing isoforms precisely regulate three basic functional aspects in cancer: proliferation, metastasis and apoptosis. Importantly, clinical function of aberrant splicing isoforms is also discussed, in particular concerning drug resistance and radiosensitivity. Furthermore, this review discusses emerging strategies how to modulate pathologic aberrant splicing isoforms, which are attractive, novel therapeutic agents in cancer. Last we outline current and future directions of isoforms diagnostic methodologies reported so far in cancer. Thus, it is highlighting significance of aberrant splicing isoforms as markers for cancer and as targets for cancer therapy.

Transcriptome analysis of grapevine under salinity and identification of key genes responsible for salt tolerance

The negative effects of soil salinity towards grape yield depend upon salt concentration, cultivar type, developmental stage, and rootstock. Thompson Seedless variety of grape plant is considered moderately sensitive to salinity when grown upon its own root stock. In recent epoch, identification of key genes responsive to salinity offers hope to generate salinity-tolerant crop plants by their overexpression through genetic manipulation. In the present report, salt responsive transcriptome analysis of Thompson Seedless grape variety was done to identify vital genes involved in salinity tolerance which could be used further to generate salt liberal grape plant or other crop plants. Transcriptome libraries for control and 150-mM-NaCl-treated grape leaves were sequenced on Illumina platform where 714 genes were found to be differentially expressed. Gene ontology analysis indicated that under salinity conditions, the genes involved in metabolic process were highly enriched. Keto Encyclopedia of Genes and Genomes analysis revealed that, among the top 22 enriched pathways for the salt stress upregulated genes, the carbohydrate metabolism, signal transduction, energy metabolism, amino acid metabolism, biosynthesis of secondary metabolite, and lipid metabolism pathways possessed the largest number of transcripts. Key salinity-induced genes were selected and validated through qRT-PCR analysis which was comparable to RNA-seq results. Real-time PCR analysis also revealed that after 24 days of salinity, the expression of most of the selected key genes was highest. These salinity-induced genes will be characterized further in a model plant and also in Vitis vinifera through transgenic approach to disclose their role towards salt tolerance.

RNA-Seq Analysis of an Antisense Sequence Optimized for Exon Skipping in Duchenne Patients Reveals No Off-Target Effect

Non-coding uridine-rich small nuclear RNAs (UsnRNAs) have emerged in recent years as effective tools for exon skipping for the treatment of Duchenne muscular dystrophy (DMD), a degenerative muscular genetic disorder. We recently showed the high capacity of a recombinant adeno-associated virus (rAAV)-U7snRNA vector to restore the reading frame of the DMD mRNA in the muscles of DMD dogs. We are now moving toward a phase I/II clinical trial with an rAAV-U7snRNA-E53, carrying an antisense sequence designed to hybridize exon 53 of the human DMD messenger. As observed for genome-editing tools, antisense sequences present a risk of off-target effects, reflecting partial hybridization onto unintended transcripts. To characterize the clinical antisense sequence, we studied its expression and explored the occurrence of its off-target effects in human in vitro models of skeletal muscle and liver. We presented a comprehensive methodology combining RNA sequencing and in silico filtering to analyze off-targets. We showed that U7snRNA-E53 induced the effective exon skipping of the DMD transcript without inducing the notable deregulation of transcripts in human cells, neither at gene expression nor at the mRNA splicing level. Altogether, these results suggest that the use of the rAAV-U7snRNA-E53 vector for exon skipping could be safe in eligible DMD patients.

A Simple and Universal System for Gene Manipulation in Aspergillus fumigatus: In Vitro-Assembled Cas9-Guide RNA Ribonucleoproteins Coupled with Microhomology Repair Templates

Tackling the multifactorial nature of virulence and antifungal drug resistance in A. fumigatus requires the mechanistic interrogation of a multitude of genes, sometimes across multiple genetic backgrounds. Classical fungal gene replacement systems can be laborious and time-consuming and, in wild-type isolates, are impeded by low rates of homologous recombination. Our simple and universal CRISPR-Cas9 system for gene manipulation generates efficient gene targeting across different genetic backgrounds of A. fumigatus. We anticipate that our system will simplify genome editing in A. fumigatus, allowing for the generation of single- and multigene knockout libraries. In addition, our system will facilitate the delineation of virulence factors and antifungal drug resistance genes in different genetic backgrounds of A. fumigatus.

CRISPR (clustered regularly interspaced short palindromic repeat)-Cas9 is a novel genome-editing system that has been successfully established in Aspergillus fumigatus. However, the current state of the technology relies heavily on DNA-based expression cassettes for delivering Cas9 and the guide RNA (gRNA) to the cell. Therefore, the power of the technology is limited to strains that are engineered to express Cas9 and gRNA. To overcome such limitations, we developed a simple and universal CRISPR-Cas9 system for gene deletion that works across different genetic backgrounds of A. fumigatus. The system employs in vitro assembly of dual Cas9 ribonucleoproteins (RNPs) for targeted gene deletion. Additionally, our CRISPR-Cas9 system utilizes 35 to 50 bp of flanking regions for mediating homologous recombination at Cas9 double-strand breaks (DSBs). As a proof of concept, we first tested our system in the ΔakuB (ΔakuB^ku80) laboratory strain and generated high rates (97%) of gene deletion using 2 µg of the repair template flanked by homology regions as short as 35 bp. Next, we inspected the portability of our system across other genetic backgrounds of A. fumigatus, namely, the wild-type strain Af293 and a clinical isolate, A. fumigatus DI15-102. In the Af293 strain, 2 µg of the repair template flanked by 35 and 50 bp of homology resulted in highly efficient gene deletion (46% and 74%, respectively) in comparison to classical gene replacement systems. Similar deletion efficiencies were also obtained in the clinical isolate DI15-102. Taken together, our data show that in vitro-assembled Cas9 RNPs coupled with microhomology repair templates are an efficient and universal system for gene manipulation in A. fumigatus.

IMPORTANCE Tackling the multifactorial nature of virulence and antifungal drug resistance in A. fumigatus requires the mechanistic interrogation of a multitude of genes, sometimes across multiple genetic backgrounds. Classical fungal gene replacement systems can be laborious and time-consuming and, in wild-type isolates, are impeded by low rates of homologous recombination. Our simple and universal CRISPR-Cas9 system for gene manipulation generates efficient gene targeting across different genetic backgrounds of A. fumigatus. We anticipate that our system will simplify genome editing in A. fumigatus, allowing for the generation of single- and multigene knockout libraries. In addition, our system will facilitate the delineation of virulence factors and antifungal drug resistance genes in different genetic backgrounds of A. fumigatus.

RNA splicing in human disease and in the clinic

Defects at the level of the pre-mRNA splicing process represent a major cause of human disease. Approximately 15-50% of all human disease mutations have been shown to alter functioning of basic and auxiliary splicing elements. These elements are required to ensure proper processing of pre-mRNA splicing molecules, with their disruption leading to misprocessing of the pre-mRNA molecule and disease. The splicing process is a complex process, with much still to be uncovered before we are able to accurately predict whether a reported genomic sequence variant (GV) represents a splicing-associated disease mutation or a harmless polymorphism. Furthermore, even when a mutation is correctly identified as affecting the splicing process, there still remains the difficulty of providing an exact evaluation of the potential impact on disease onset, severity and duration. In this review, we provide a brief overview of splicing diagnostic methodologies, from in silico bioinformatics approaches to wet lab in vitro and in vivo systems to evaluate splicing efficiencies. In particular, we provide an overview of how the latest developments in high-throughput sequencing can be applied to the clinic, and are already changing clinical approaches.

RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples

Background

RNA sequencing (RNA-seq) and microarrays are two transcriptomics techniques aimed at the quantification of transcribed genes and their isoforms. Here we compare the latest Affymetrix HTA 2.0 microarray with Illumina 2000 RNA-seq for the analysis of patient samples - normal lung epithelium tissue and squamous cell carcinoma lung tumours. Protein coding mRNAs and long non-coding RNAs (lncRNAs) were included in the study.

Results

Both platforms performed equally well for protein-coding RNAs, however the stochastic variability was higher for the sequencing data than for microarrays. This reduced the number of differentially expressed genes and genes with predictive potential for RNA-seq compared to microarray data. Analysis of this variability revealed a lack of reads for short and low abundant genes; lncRNAs, being shorter and less abundant RNAs, were found especially susceptible to this issue. A major difference between the two platforms was uncovered by analysis of alternatively spliced genes. Investigation of differential exon abundance showed insufficient reads for many exons and exon junctions in RNA-seq while the detection on the array platform was more stable. Nevertheless, we identified 207 genes which undergo alternative splicing and were consistently detected by both techniques.

Conclusions

Despite the fact that the results of gene expression analysis were highly consistent between Human Transcriptome Arrays and RNA-seq platforms, the analysis of alternative splicing produced discordant results. We concluded that modern microarrays can still outperform sequencing for standard analysis of gene expression in terms of reproducibility and cost.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3819-y) contains supplementary material, which is available to authorized users.

The Transcription Factor Nfatc2 Regulates β-Cell Proliferation and Genes Associated with Type 2 Diabetes in Mouse and Human Islets

Human genome-wide association studies (GWAS) have shown that genetic variation at >130 gene loci is associated with type 2 diabetes (T2D). We asked if the expression of the candidate T2D-associated genes within these loci is regulated by a common locus in pancreatic islets. Using an obese F2 mouse intercross segregating for T2D, we show that the expression of ~40% of the T2D-associated genes is linked to a broad region on mouse chromosome (Chr) 2. As all but 9 of these genes are not physically located on Chr 2, linkage to Chr 2 suggests a genomic factor(s) located on Chr 2 regulates their expression in trans. The transcription factor Nfatc2 is physically located on Chr 2 and its expression demonstrates cis linkage; i.e., its expression maps to itself. When conditioned on the expression of Nfatc2, linkage for the T2D-associated genes was greatly diminished, supporting Nfatc2 as a driver of their expression. Plasma insulin also showed linkage to the same broad region on Chr 2. Overexpression of a constitutively active (ca) form of Nfatc2 induced β-cell proliferation in mouse and human islets, and transcriptionally regulated more than half of the T2D-associated genes. Overexpression of either ca-Nfatc2 or ca-Nfatc1 in mouse islets enhanced insulin secretion, whereas only ca-Nfatc2 was able to promote β-cell proliferation, suggesting distinct molecular pathways mediating insulin secretion vs. β-cell proliferation are regulated by NFAT. Our results suggest that many of the T2D-associated genes are downstream transcriptional targets of NFAT, and may act coordinately in a pathway through which NFAT regulates β-cell proliferation in both mouse and human islets.

Genome-wide association studies (GWAS) and linkage studies provide a powerful way to establish a causal connection between a gene locus and a physiological or pathophysiological phenotype. We wondered if candidate genes associated with type 2 diabetes in human populations, in addition to being causal for the disease, could also be intermediate traits in a pathway leading to disease. In addition, we wished to know if there were any regulatory loci that could coordinately drive the expression of these genes in pancreatic islets and thus complete a pathway; i.e. Driver → GWAS candidate expression → type 2 diabetes. Using data from a mouse intercross between a diabetes-susceptible and a diabetes-resistant mouse strain, we found that the expression of ~40% of >130 candidate GWAS genes genetically mapped to a hot spot on mouse chromosome 2. Using a variety of statistical methods, we identified the transcription factor Nfatc2 as the candidate driver. Follow-up experiments showed that overexpression of Nfatc2 does indeed affect the expression of the GWAS genes and regulates β-cell proliferation and insulin secretion. The work shows that in addition to being causal, GWAS candidate genes can be intermediate traits in a pathway leading to disease. Model organisms can be used to explore these novel causal pathways.