Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm

OMICs approaches and technologies for understanding low-high feed efficiency traits in chicken: implication to breeding

In poultry production, there has been a trend of continuous increase in cost of feed ingredients which represents the major proportion of the production costs. Feed costs can be reduced by improving feed efficiency traits which increase the possibility of using various indigestible feed sources and decrease the environmental impact of the enhanced poultry production. Therefore, feed efficiency has been used as one of the most important economic traits of selection in the breeding program of chickens. Recently, many OMICs experimental studies have been designed to characterize biological differences between the high and low feed efficiency chicken phenotypes. Biological complexity cannot be fully captured by main individual OMICs such as genomics, transcriptomics, proteomics and metabolomics. Therefore, researchers have combined multiple assays from the same set of samples to create multi-OMICs datasets. OMICs findings are crucial in improving existing approaches to poultry breeding. The current review aimed to highlight the components of feed efficiency and general OMICs approaches and technologies. Besides, individual and multi-OMICs based understanding of chicken feed efficiency traits and the application of the acquired knowledge in the chicken breeding program were addressed.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

All differential on the splicing front: Host alternative splicing alters the landscape of virus-host conflict

Alternative RNA splicing is a co-transcriptional process that richly increases proteome diversity, and is dynamically regulated based on cell species, lineage, and activation state. Virus infection in vertebrate hosts results in rapid host transcriptome-wide changes, and regulation of alternative splicing can direct a combinatorial effect on the host transcriptome. There has been a recent increase in genome-wide studies evaluating host alternative splicing during viral infection, which integrates well with prior knowledge on viral interactions with host splicing proteins. A critical challenge remains in linking how these individual events direct global changes, and whether alternative splicing is an overall favorable pathway for fending off or supporting viral infection. Here, we introduce the process of alternative splicing, discuss how to analyze splice regulation, and detail studies on genome-wide and splice factor changes during viral infection. We seek to highlight where the field can focus on moving forward, and how incorporation of a virus-host co-evolutionary perspective can benefit this burgeoning subject.

EventPointer 3.0: flexible and accurate splicing analysis that includes studying the differential usage of protein-domains

Alternative splicing (AS) plays a key role in cancer: all its hallmarks have been associated with different mechanisms of abnormal AS. The improvement of the human transcriptome annotation and the availability of fast and accurate software to estimate isoform concentrations has boosted the analysis of transcriptome profiling from RNA-seq. The statistical analysis of AS is a challenging problem not yet fully solved. We have included in EventPointer (EP), a Bioconductor package, a novel statistical method that can use the bootstrap of the pseudoaligners. We compared it with other state-of-the-art algorithms to analyze AS. Its performance is outstanding for shallow sequencing conditions. The statistical framework is very flexible since it is based on design and contrast matrices. EP now includes a convenient tool to find the primers to validate the discoveries using PCR. We also added a statistical module to study alteration in protein domain related to AS. Applying it to 9514 patients from TCGA and TARGET in 19 different tumor types resulted in two conclusions: i) aberrant alternative splicing alters the relative presence of Protein domains and, ii) the number of enriched domains is strongly correlated with the age of the patients.

On the identifiability of the isoform deconvolution problem: application to select the proper fragment length in an RNA-seq library

MOTIVATION

Isoform deconvolution is an NP-hard problem. The accuracy of the proposed solutions is far from perfect. At present, it is not known if gene structure and isoform concentration can be uniquely inferred given paired-end reads, and there is no objective method to select the fragment length to improve the number of identifiable genes. Different pieces of evidence suggest that the optimal fragment length is gene-dependent, stressing the need for a method that selects the fragment length according to a reasonable trade-off across all the genes in the whole genome.

RESULTS

A gene is considered to be identifiable if it is possible to get both the structure and concentration of its transcripts univocally. Here, we present a method to state the identifiability of this deconvolution problem. Assuming a given transcriptome and that the coverage is sufficient to interrogate all junction reads of the transcripts, this method states whether or not a gene is identifiable given the read length and fragment length distribution. Applying this method using different read and fragment length combinations, the optimal average fragment length for the human transcriptome is around 400-600 nt for coding genes and 150-200 nt for long non-coding RNAs. The optimal read length is the largest one that fits in the fragment length. It is also discussed the potential profit of combining several libraries to reconstruct the transcriptome. Combining two libraries of very different fragment lengths results in a significant improvement in gene identifiability.

AVAILABILITY AND IMPLEMENTATION

Code is available in GitHub (https://github.com/JFerrer-B/transcriptome-identifiability).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Study of Alternative Splicing Events in Human Induced Pluripotent Stem Cells From a Down's Syndrome Patient

Down's syndrome (DS) is one of the most commonly known disorders with multiple congenital disabilities. Besides severe cognitive impairment and intellectual disability, individuals with DS also exhibit additional phenotypes of variable penetrance and severity, with one or more comorbid conditions, including Alzheimer's disease, congenital heart disease, or leukemia. Various vital genes and regulatory networks had been studied to reveal the pathogenesis of the disease. Nevertheless, very few studies have examined alternative splicing. Alternative splicing (AS) is a regulatory mechanism of gene expression when making one multi-exon protein-coding gene produce more than one unique mature mRNA. We employed the GeneChip Human Transcriptome Array 2.0 (HTA 2.0) for the global gene analysis with hiPSCs from DS and healthy individuals. Examining differentially expressed genes (DEGs) in these groups and focusing on specific transcripts with AS, 466 up-regulated and 722 down-regulated genes with AS events were identified. These genes were significantly enriched in biological processes, such as cell adhesion, cardiac muscle contraction, and immune response, through gene ontology (GO) analysis of DEGs. Candidate genes, such as FN1 were further explored for potentially playing a key role in DS. This study provides important insights into the potential role that AS plays in DS.

Rapid Transient Transcriptional Adaptation to Hypergravity in Jurkat T Cells Revealed by Comparative Analysis of Microarray and RNA-Seq Data

Cellular responses to micro- and hypergravity are rapid and complex and appear within the first few seconds of exposure. Transcriptomic analyses are a valuable tool to analyze these genome-wide cellular alterations. For a better understanding of the cellular dynamics upon altered gravity exposure, it is important to compare different time points. However, since most of the experiments are designed as endpoint measurements, the combination of cross-experiment meta-studies is inevitable. Microarray and RNA-Seq analyses are two of the main methods to study transcriptomics. In the field of altered gravity research, both methods are frequently used. However, the generation of these data sets is difficult and time-consuming and therefore the number of available data sets in this research field is limited. In this study, we investigated the comparability of microarray and RNA-Seq data and applied the results to a comparison of the transcriptomics dynamics between the hypergravity conditions during two real flight platforms and a centrifuge experiment to identify temporal adaptation processes. We performed a comparative study on an Affymetrix HTA2.0 microarray and a paired-end RNA-Seq data set originating from the same Jurkat T cell RNA samples from a short-term hypergravity experiment. The overall agreeability was high, with better sensitivity of the RNA-Seq analysis. The microarray data set showed weaknesses on the level of single upregulated genes, likely due to its normalization approach. On an aggregated level of biotypes, chromosomal distribution, and gene sets, both technologies performed equally well. The microarray showed better performance on the detection of altered gravity-related splicing events. We found that all initially altered transcripts fully adapted after 15 min to hypergravity and concluded that the altered gene expression response to hypergravity is transient and fully reversible. Based on the combined multiple-platform meta-analysis, we could demonstrate rapid transcriptional adaptation to hypergravity, the differential expression of the ATPase subunits ATP6V1A and ATP6V1D, and the cluster of differentiation (CD) molecules CD1E, CD2AP, CD46, CD47, CD53, CD69, CD96, CD164, and CD226 in hypergravity. We could experimentally demonstrate that it is possible to develop methodological evidence for the meta-analysis of individual data.

Discovery of a transcriptomic core of genes shared in 8 primary retinoblastoma with a novel detection score analysis

PURPOSE

Expression microarrays are powerful technology that allows large-scale analysis of RNA profiles in a tissue; these platforms include underexploited detection scores outputs. We developed an algorithm using the detection score, to generate a detection profile of shared elements in retinoblastoma as well as to determine its transcriptomic size and structure.

METHODS

We analyzed eight briefly cultured primary retinoblastomas with the Human transcriptome array 2.0 (HTA2.0). Transcripts and genes detection scores were determined using the Detection Above Background algorithm (DABG). We used unsupervised and supervised computational tools to analyze detected and undetected elements; WebGestalt was used to explore functions encoded by genes in relevant clusters and performed experimental validation.

RESULTS

We found a core cluster with 7,513 genes detected and shared by all samples, 4,321 genes in a cluster that was commonly absent, and 7,681 genes variably detected across the samples accounting for tumor heterogeneity. Relevant pathways identified in the core cluster relate to cell cycle, RNA transport, and DNA replication. We performed a kinome analysis of the core cluster and found 4 potential therapeutic kinase targets. Through analysis of the variably detected genes, we discovered 123 differentially expressed transcripts between bilateral and unilateral cases.

CONCLUSIONS

This novel analytical approach allowed determining the retinoblastoma transcriptomic size, a shared active transcriptomic core among the samples, potential therapeutic target kinases shared by all samples, transcripts related to inter tumor heterogeneity, and to determine transcriptomic profiles without the need of control tissues. This approach is useful to analyze other cancer or tissue types.

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.

Effects of High-Dose Ionizing Radiation in Human Gene Expression: A Meta-Analysis

The use of high-dose Ionizing Radiation (IR) is currently one of the most common modalities in treatment of many types of cancer. The objective of this work was to investigate the effects of high-dose ionizing radiation on healthy human tissue, utilizing quantitative analysis of gene expression. To this end, publicly available transcriptomics datasets from human samples irradiated with a high dose of radiation and non-irradiated (control) ones were selected, and gene expression was determined using RNA-Seq data analysis. Raw data from these studies were subjected to quality control and trimming. Mapping of RNA-Seq reads was performed by the partial selective alignment method, and differential gene expression analysis was conducted. Subsequently, a meta-analysis was performed to select differentially expressed genes across datasets. Based on the differentially expressed genes discovered by meta-analysis, we constructed a protein-to-protein interaction network, and we identified biological pathways and processes related to high-dose IR effects. Our findings suggest that cell cycle arrest is activated, supported by our top down-regulated genes associated with cell cycle activation. DNA repair genes are down-regulated in their majority. However, several genes implicated in the nucleotide excision repair pathway are upregulated. Nevertheless, apoptotic mechanisms seem to be activated probably due to severe high-dose-induced complex DNA damage. The significant upregulation of CDKN1A, as a downstream gene of TP53, further validates programmed cell death. Finally, down-regulation of TIMELESS, signifies a correlation between IR response and circadian rhythm. Nonetheless, high-dose IR exposure effects regarding normal tissue (radiation toxicity) and its possible long-term outcomes should be studied to a greater extend.

Technical differences between sequencing and microarray platforms impact transcriptomic subtyping of colorectal cancer

Gene expression profiling has increasing relevance in the molecular screening of patients with colorectal cancer (CRC). We investigated potential platform-specific effects on transcriptomic subtyping according to established frameworks by comparisons of expression profiles from RNA sequencing and exon-resolution microarrays in 126 primary microsatellite stable CRCs. There was a strong platform correspondence in global gene expression levels, albeit with systematic technical bias likely attributed to few sequencing reads covering short (<2000 nucleotides) and/or lowly expressed genes (<1 FPKM), as well as over-saturation of highly expressed genes on microarrays. Classification concordances according to both the consensus molecular subtypes and CRC intrinsic subtypes (CRIS) were also strong, but with disproportionate subtype distributions between platforms caused by frequent disagreements in adherence to sample classification thresholds. Subtypes defined largely by genes expressed at low levels, including the CRIS-D subtype and the estimated level of tumor-infiltrating cytotoxic lymphocytes, had a weaker correspondence in classification metrics between platforms. In conclusion, even subtle differences between platforms suggest that clinical translation of transcriptomic CRC subtyping frameworks is dependent on assay standardization, and systematic technical biases reinforce the need for careful selection of classifier genes.

Integration of CLIP experiments of RNA-binding proteins: a novel approach to predict context-dependent splicing factors from transcriptomic data

BACKGROUND

Splicing is a genetic process that has important implications in several diseases including cancer. Deciphering the complex rules of splicing regulation is crucial to understand and treat splicing-related diseases. Splicing factors and other RNA-binding proteins (RBPs) play a key role in the regulation of splicing. The specific binding sites of an RBP can be measured using CLIP experiments. However, to unveil which RBPs regulate a condition, it is necessary to have a priori hypotheses, as a single CLIP experiment targets a single protein.

RESULTS

In this work, we present a novel methodology to predict context-specific splicing factors from transcriptomic data. For this, we systematically collect, integrate and analyze more than 900 CLIP experiments stored in four CLIP databases: POSTAR2, CLIPdb, DoRiNA and StarBase. The analysis of these experiments shows the strong coherence between the binding sites of RBPs of similar families. Augmenting this information with expression changes, we are able to correctly predict the splicing factors that regulate splicing in two gold-standard experiments in which specific splicing factors are knocked-down.

CONCLUSIONS

The methodology presented in this study allows the prediction of active splicing factors in either cancer or any other condition by only using the information of transcript expression. This approach opens a wide range of possible studies to understand the splicing regulation of different conditions. A tutorial with the source code and databases is available at https://gitlab.com/fcarazo.m/sfprediction .

Circular RNAs and RNA Splice Variants as Biomarkers for Prognosis and Therapeutic Response in the Liquid Biopsies of Lung Cancer Patients

Lung cancer, including non-small cell lung carcinoma (NSCLC), is the most frequently diagnosed cancer. It is also the leading cause of cancer-related mortality worldwide because of its late diagnosis and its resistance to therapies. Therefore, the identification of biomarkers for early diagnosis, prognosis, and monitoring of therapeutic response is urgently needed. Liquid biopsies, especially blood, are considered as promising tools to detect and quantify circulating cancer biomarkers. Cell-free circulating tumor DNA has been extensively studied. Recently, the possibility to detect and quantify RNAs in tumor biopsies, notably circulating cell-free RNAs, has gained great attention. RNA alternative splicing contributes to the proteome diversity through the biogenesis of several mRNA splice variants from the same pre-mRNA. Circular RNA (circRNA) is a new class of RNAs resulting from pre-mRNA back splicing. Owing to the development of high-throughput transcriptomic analyses, numerous RNA splice variants and, more recently, circRNAs have been identified and found to be differentially expressed in tumor patients compared to healthy controls. The contribution of some of these RNA splice variants and circRNAs to tumor progression, dissemination, or drug response has been clearly demonstrated in preclinical models. In this review, we discuss the potential of circRNAs and mRNA splice variants as candidate biomarkers for the prognosis and the therapeutic response of NSCLC in liquid biopsies.

Aberrant expression of alternative splicing variants in multiple sclerosis - A systematic review

OBJECTIVE

Alternative splicing is an important form of RNA processing that affects nearly all human genes. The differential expression of specific transcript and protein isoforms holds the potential of novel biomarkers for complex diseases. In this systematic review, we compiled the existing literature on aberrant alternative splicing events in multiple sclerosis (MS).

METHODS

A systematic literature search in the PubMed database was carried out and supplemented by screening the reference lists of the identified articles. We selected only MS-related original research studies which compared the levels of different isoforms of human protein-coding genes. A narrative synthesis of the research findings was conducted. Additionally, we performed a case-control analysis using high-density transcriptome microarray data to reevaluate the genes that were examined in the reviewed studies.

RESULTS

A total of 160 records were screened. Of those, 36 studies from the last two decades were included. Most commonly, peripheral blood samples were analyzed (32 studies), and PCR-based techniques were usually employed (27 studies) for measuring the expression of selected genes. Two studies used an exploratory genome-wide approach. Overall, 27 alternatively spliced genes were investigated. Nine of these genes appeared in at least two studies (CD40, CFLAR, FOXP3, IFNAR2, IL7R, MOG, PTPRC, SP140 and TNFRSF1A). The microarray data analysis confirmed differential alternative pre-mRNA splicing for 19 genes.

CONCLUSIONS

An altered RNA processing of genes mediating immune signaling pathways has been repeatedly implicated in MS. The analysis of individual exon-level expression patterns is stimulated by the advancement of transcriptome profiling technologies. In particular, the examination of genes encoded in MS-associated genetic regions may provide important insights into the pathogenesis of the disease and help to identify new biomarkers.