Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)

MAJIQ-CLIN: A novel tool for the identification of Mendelian disease-causing variants from RNA-Seq data

The current diagnostic rate for patients with suspected Mendelian genetic disorders is only 25 to 58%, even though whole exome sequencing (WES) is part of the standard of care. One reason for the low diagnostic rate is that traditional WES analysis methods struggle to detect RNA splicing aberrations. It is estimated that 15-50% of human pathogenic variants alter splicing, with numerous splice-altering variants being causal for known Mendelian disorders. Developing reliable diagnostic tools to detect, quantify, prioritize, and visualize RNA splicing aberrations from patient RNA sequencing is therefore crucial. We present MAJIQ-CLIN, a method to address this need to augment clinical diagnostic using RNA-Seq and compare it to existing tools. We include the first systematic evaluation of the accuracy of such tools using synthetic data across several aberration types and transcript inclusion levels; we also evaluate accuracy on several datasets of biologically validated solved test cases. We show that MAJIQ-CLIN compares favorably to existing tools in both accuracy and efficiency, then use MAJIQ-CLIN to investigate several unsolved patient cases from the Undiagnosed Diseases Network.

Increased [18F]FDG uptake in the infarcted myocardial area displayed by combined PET/CMR correlates with snRNA-seq-detected inflammatory cell invasion

Combined [18F]FDG PET-cardiac MRI imaging (PET/CMR) is a useful tool to assess myocardial viability and cardiac function in patients with acute myocardial infarction (AMI). Here, we evaluated the prognostic value of PET/CMR in a porcine closed-chest reperfused AMI (rAMI) model. Late gadolinium enhancement by PET/CMR imaging displayed tracer uptake defect at the infarction site by 3 days after the rAMI in the majority of the animals (group Match, n = 28). Increased [18F]FDG uptake at the infarcted area (metabolism/contractility mismatch) with reduced tracer uptake in the remote viable myocardium (group Mismatch, n = 12) 3 days after rAMI was observed in the animals with larger infarct size and worse left ventricular ejection fraction (LVEF) (34 ± 8.7 vs 42.0 ± 5.2%), with lower LVEF also at the 1-month follow-up (35.8 ± 9.5 vs 43.0 ± 6.3%). Transcriptome analyses by bulk and single-nuclei RNA sequencing of the infarcted myocardium and border zones (n = 3 of each group, and 3 sham-operated controls) revealed a strong inflammatory response with infiltration of monocytes and macrophages in the infarcted and border areas in Mismatch animals. Our data indicate a high prognostic relevance of combined PET/MRI in the subacute phase of rAMI for subsequent impairment of heart function and underline the adverse effects of an excessive activation of the innate immune system in the initial phase after rAMI.

Splice_sim: a nucleotide conversion-enabled RNA-seq simulation and evaluation framework

Nucleotide conversion RNA sequencing techniques interrogate chemical RNA modifications in cellular transcripts, resulting in mismatch-containing reads. Biases in mapping the resulting reads to reference genomes remain poorly understood. We present splice_sim, a splice-aware RNA-seq simulation and evaluation pipeline that introduces user-defined nucleotide conversions at set frequencies, creates mixture models of converted and unconverted reads, and calculates mapping accuracies per genomic annotation. By simulating nucleotide conversion RNA-seq datasets under realistic experimental conditions, including metabolic RNA labeling and RNA bisulfite sequencing, we measure mapping accuracies of state-of-the-art spliced-read mappers for mouse and human transcripts and derive strategies to prevent biases in the data interpretation.

PxBLAT: an efficient python binding library for BLAT

BACKGROUND

With the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem.

RESULTS

PxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT.

CONCLUSIONS

PxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment.

BEERS2: RNA-Seq simulation through high fidelity in silico modeling

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully length messenger RNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in polymerase chain reaction (PCR) amplification, barcode read errors and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

In vivo CRISPR screening directly targeting testicular cells

CRISPR-Cas9 short guide RNA (sgRNA) library screening is a powerful approach to understand the molecular mechanisms of biological phenomena. However, its in vivo application is currently limited. Here, we developed our previously established in vitro revival screening method into an in vivo one to identify factors involved in spermatogenesis integrity by utilizing sperm capacitation as an indicator. By introducing an sgRNA library into testicular cells, we successfully pinpointed the retinal degeneration 3 (Rd3) gene as a significant factor in spermatogenesis. Single-cell RNA sequencing (scRNA-seq) analysis highlighted the high expression of Rd3 in round spermatids, and proteomics analysis indicated that Rd3 interacts with mitochondria. To search for cell-type-specific signaling pathways based on scRNA-seq and proteomics analyses, we developed a computational tool, Hub-Explorer. Through this, we discovered that Rd3 modulates oxidative stress by regulating mitochondrial distribution upon ciliogenesis induction. Collectively, our screening system provides a valuable in vivo approach to decipher molecular mechanisms in biological processes.

Alternative splicing of pre-mRNA modulates the immune response in Holstein cattle naturally infected with Mycobacterium avium subsp. paratuberculosis

Little is known about the role of alternative splicing (AS) in regulating gene expression in Mycobacteria-infected individuals in distinct stages of infection. Pre-mRNA AS consists of the removal of introns and the assembly of exons contained in eukaryotic genes. AS events can influence transcript stability or structure with important physiological consequences. Using RNA-Seq data from peripheral blood (PB) and ileocecal valve (ICV) samples collected from Holstein cattle with focal and diffuse paratuberculosis (PTB)-associated histopathological lesions in gut tissues and without lesions (controls), we detected differential AS profiles between the infected and control groups. Four of the identified AS events were experimentally validated by reverse transcription-digital droplet PCR (RT-ddPCR). AS events in several genes correlated with changes in gene expression. In the ICV of animals with diffuse lesions, for instance, alternatively spliced genes correlated with changes in the expression of genes involved in endocytosis, antigen processing and presentation, complement activation, and several inflammatory and autoimmune diseases in humans. Taken together, our results identified common mechanisms of AS involvement in the pathogenesis of PTB and human diseases and shed light on novel diagnostic and therapeutic interventions to control these diseases.

Alternative RNA Splicing in the Retina: Insights and Perspectives

Alternative splicing is a fundamental and highly regulated post-transcriptional process that enhances transcriptome and proteome diversity. This process is particularly important in neuronal tissues, such as the retina, which exhibit some of the highest levels of differentially spliced genes in the body. Alternative splicing is regulated both temporally and spatially during neuronal development, can be cell-type-specific, and when altered can cause a number of pathologies, including retinal degeneration. Advancements in high-throughput sequencing technologies have facilitated investigations of the alternative splicing landscape of the retina in both healthy and disease states. Additionally, innovations in human stem cell engineering, specifically in the generation of 3D retinal organoids, which recapitulate many aspects of the in vivo retinal microenvironment, have aided studies of the role of alternative splicing in human retinal development and degeneration. Here we review these advances and discuss the ongoing development of strategies for the treatment of alternative splicing-related retinal disease.

Hyperglucagonaemia in diabetes: altered amino acid metabolism triggers mTORC1 activation, which drives glucagon production

AIM/HYPOTHESIS

Hyperglycaemia is associated with alpha cell dysfunction, leading to dysregulated glucagon secretion in type 1 and type 2 diabetes; however, the mechanisms involved are still elusive. The nutrient sensor mammalian target of rapamycin complex 1 (mTORC1) plays a major role in the maintenance of alpha cell mass and function. We studied the regulation of alpha cell mTORC1 by nutrients and its role in the development of hyperglucagonaemia in diabetes.

METHODS

Alpha cell mTORC1 activity was assessed by immunostaining for phosphorylation of its downstream target, the ribosomal protein S6, and glucagon, followed by confocal microscopy on pancreatic sections and flow cytometry on dispersed human and mouse islets and the alpha cell line, αTC1-6. Metabolomics and metabolic flux were studied by 13C glucose labelling in 2.8 or 16.7 mmol/l glucose followed by LC-MS analysis. To study the role of mTORC1 in mediating hyperglucagonaemia in diabetes, we generated an inducible alpha cell-specific Rptor knockout in the Akita mouse model of diabetes and tested the effects on glucose tolerance by IPGTT and on glucagon secretion.

RESULTS

mTORC1 activity was increased in alpha cells from diabetic Akita mice in parallel to the development of hyperglycaemia and hyperglucagonaemia (two- to eightfold increase). Acute exposure of mouse and human islets to amino acids stimulated alpha cell mTORC1 (3.5-fold increase), whereas high glucose concentrations inhibited mTORC1 (1.4-fold decrease). The mTORC1 response to glucose was abolished in human and mouse diabetic alpha cells following prolonged islet exposure to high glucose levels, resulting in sustained activation of mTORC1, along with increased glucagon secretion. Metabolomics and metabolic flux analysis showed that exposure to high glucose levels enhanced glycolysis, glucose oxidation and the synthesis of glucose-derived amino acids. In addition, chronic exposure to high glucose levels increased the expression of Slc7a2 and Slc38a4, which encode amino acid transporters, as well as the levels of branched-chain amino acids and methionine cycle metabolites (~1.3-fold increase for both). Finally, conditional Rptor knockout in alpha cells from adult diabetic mice inhibited mTORC1, thereby inhibiting glucagon secretion (~sixfold decrease) and improving diabetes, despite persistent insulin deficiency.

CONCLUSIONS/INTERPRETATION

Alpha cell exposure to hyperglycaemia enhances amino acid synthesis and transport, resulting in sustained activation of mTORC1, thereby increasing glucagon secretion. mTORC1 therefore plays a major role in mediating alpha cell dysfunction in diabetes.

DATA AVAILABILITY

All sequencing data are available from the Gene Expression Omnibus (GEO) repository (accession no. GSE154126; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE154126 ).

BEERS2: RNA-Seq simulation through high fidelity in silico modeling

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking, and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully-length mRNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM, or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in PCR amplification, barcode read errors, and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

A scoping review on deep learning for next-generation RNA-Seq. data analysis

In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional genomics scientists, particularly amongst clinical/biomedical research groups. The progressive enfoldment/adoption of NGS technologies has incited an abundance of next-generation transcriptomic data harbouring an opulence of new knowledge in public databases. Nevertheless, knowledge discovery from these next-generation RNA-Seq. data analysis necessitates extensive bioinformatics know-how besides elaborate data analysis software packages consistent with the type and context of data analysis. Several reliability and reproducibility concerns continue to impede RNA-Seq. data analysis. Characteristic challenges comprise of data quality, hardware and networking provisions, selection and prioritisation of data analysis tools, and yet significantly implementing of robust machine learning algorithms for maximised exploitation of these experimental transcriptomic data. Over the years, numerous machine learning algorithms have been implemented for improved transcriptomic data analysis executing predominantly shallow learning approaches. More recently, deep learning algorithms are becoming more mainstream, and enactment for next-generation RNA-Seq. data analysis could be revolutionary in the coming years in the biomedical domain. In this scoping review, we attempt to determine the existing literature's size and potential nature in deep learning and NGS RNA-Seq. data analysis. An analysis of the contemporary topics of next-generation RNA-Seq. data analysis based on deep learning algorithms is critically reviewed, emphasising open-source resources.

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis include many data types and file formats, and a wide variety of computational tools that can be applied alone or together as pipelines. This paper presents a review of the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. This review aims to address mainly the aspects involved in the differentially expressed gene (DEG) analysis from RNA sequencing data (RNA-seq), considering the computational methods. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.

The role of Meteorin-like in skeletal development and bone fracture healing

Meteorin-like protein (Metrnl), homologous to the initially identified neurotrophic factor Meteorin, is a secreted, multifunctional protein. Here we used mouse models to investigate Metrnl's role in skeletal development and bone fracture healing. During development Metrnl was expressed in the perichondrium and primary ossification center. In neonates, single cell RNA-seq of diaphyseal bone demonstrated strongest expression of Metrnl transcript by osteoblasts. In vitro, Metrnl was osteoinductive, increasing osteoblast differentiation and mineralization in tissue culture models. In vivo, loss of Metrnl expression resulted in no change in skeletal metrics in utero, at birth, or during postnatal growth. Six-week-old Metrnl-null mice displayed similar body length, body weight, tibial length, femoral length, BV/TV, trabecular number, trabecular thickness, and cortical thickness as littermate controls. In 4-month-old mice, lack of Metrnl expression did not change structural stiffness, ultimate force, or energy to fracture of femora under 3-point-bending. Last, we investigated the role of Metrnl in bone fracture healing. Metrnl expression increased in response to tibial injury, however, loss of Metrnl expression did not affect the amount of bone deposited within the healing tissue nor did it change the structural parameters of healing tissue. This work identifies Metrnl as a dispensable molecule for skeletal development. However, the osteoinductive capabilities of Metrnl may be utilized to modulate osteoblast differentiation in cell-based orthopedic therapies.

Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

Read alignment is an important step in RNA-seq analysis as the result of alignment forms the basis for downstream analyses. However, recent studies have shown that published alignment tools have variable mapping sensitivity and do not necessarily align all the reads which should have been aligned, a problem we termed as the false-negative non-alignment problem. Here we present Scavenger, a python-based bioinformatics pipeline for recovering unaligned reads using a novel mechanism in which a putative alignment location is discovered based on sequence similarity between aligned and unaligned reads. We showed that Scavenger could recover unaligned reads in a range of simulated and real RNA-seq datasets, including single-cell RNA-seq data. We found that recovered reads tend to contain more genetic variants with respect to the reference genome compared to previously aligned reads, indicating that divergence between personal and reference genomes plays a role in the false-negative non-alignment problem. Even when the number of recovered reads is relatively small compared to the total number of reads, the addition of these recovered reads can impact downstream analyses, especially in terms of estimating the expression and differential expression of lowly expressed genes, such as pseudogenes.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

A Unified Computational Framework for a Robust, Reliable, and Reproducible Identification of Novel miRNAs From the RNA Sequencing Data

In eukaryotic cells, miRNAs regulate a plethora of cellular functionalities ranging from cellular metabolisms, and development to the regulation of biological networks and pathways, both under homeostatic and pathological states like cancer.Despite their immense importance as key regulators of cellular processes, accurate and reliable estimation of miRNAs using Next Generation Sequencing is challenging, largely due to the limited availability of robust computational tools/methods/pipelines. Here, we introduce miRPipe, an end-to-end computational framework for the identification, characterization, and expression estimation of small RNAs, including the known and novel miRNAs and previously annotated pi-RNAs from small-RNA sequencing profiles. Our workflow detects unique novel miRNAs by incorporating the sequence information of seed and non-seed regions, concomitant with clustering analysis. This approach allows reliable and reproducible detection of unique novel miRNAs and functionally same miRNAs (paralogues). We validated the performance of miRPipe with the available state-of-the-art pipelines using both synthetic datasets generated using the newly developed miRSim tool and three cancer datasets (Chronic Lymphocytic Leukemia, Lung cancer, and breast cancer). In the experiment over the synthetic dataset, miRPipe is observed to outperform the existing state-of-the-art pipelines (accuracy: 95.23% and F1-score: 94.17%). Analysis on all the three cancer datasets shows that miRPipe is able to extract more number of known dysregulated miRNAs or piRNAs from the datasets as compared to the existing pipelines.

Articular cartilage and synovium may be important sources of post-surgical synovial fluid inflammatory mediators

The primary source of synovial fluid inflammatory mediators is currently unknown and may include different tissues comprising the joint, including the synovium and articular cartilage. Prior work in a porcine model has demonstrated that anterior cruciate ligament (ACL) surgery leads to significant changes in early gene expression in the synovium and articular cartilage, which are the same whether concomitant ligament restoration is performed or not. In this study, 36 Yucatan minipigs underwent ACL surgery, and a custom multiplex assay was used to measure synovial fluid protein levels of MMP-1, MMP-2, MMP-3, MMP-7, MMP-9, MMP-12, MMP-13, IL-1α, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-18, GM-CSF, and TNFα in 18 animals at 1 and 4 weeks after surgery. Linear regressions were used to evaluate the relationships between synovial fluid protein levels and the previously reported gene expression levels in the articular cartilage and synovium from the same animal cohort. Synovial fluid levels of MMP-13 and IL-6 were significantly correlated with synovial gene expression (P=.003 and P<.001 respectively), while IL-1α levels were significantly correlated with articular cartilage gene expression (P=.037). The synovium may be an important source of MMP-13 and IL-6, and the articular cartilage may be an important source of IL-1α in post-surgical inflammation. In developing treatments for post-surgical inflammation, the synovium may therefore be a promising target for modulating inflammatory mediators such as MMP-13 and IL-6 in the synovial fluid.

Evaluating whole-genome expression differences in idiopathic and diabetic adhesive capsulitis

BACKGROUND

Diabetic patients have a greater incidence of adhesive capsulitis (AC) and a more protracted disease course than patients with idiopathic AC. The purpose of this study was to compare gene expression differences between AC with diabetes mellitus and AC without diabetes mellitus.

METHODS

Shoulder capsule samples were prospectively obtained from diabetic or nondiabetic patients who presented with shoulder dysfunction and underwent arthroscopy (N = 16). Shoulder samples of AC with and without diabetes (n = 8) were compared with normal shoulder samples with and without diabetes as the control group (n = 8). Shoulder capsule samples were subjected to whole-transcriptome RNA sequencing, and differential expression was analyzed with EdgeR. Only genes with a false discovery rate < 5% were included for further functional enrichment analysis.

RESULTS

The sample population had a mean age of 47 years (range, 24-62 years), and the mean hemoglobin A_1c level for nondiabetic and diabetic patients was 5.18% and 8.71%, respectively. RNA-sequencing analysis revealed that 66 genes were differentially expressed between diabetic patients and nondiabetic patients with AC whereas only 3 genes were differentially expressed when control patients with and without diabetes were compared. Furthermore, 286 genes were differentially expressed in idiopathic AC patients, and 61 genes were differentially expressed in diabetic AC patients. On gene clustering analysis, idiopathic AC was enriched with multiple structural and muscle-related pathways, such as muscle filament sliding, whereas diabetic AC included a greater number of hormonal and inflammatory signaling pathways, such as cellular response to corticotropin-releasing factor.

CONCLUSIONS

Whole-transcriptome expression profiles demonstrate a fundamentally different underlying pathophysiology when comparing diabetic AC with idiopathic AC, suggesting that these conditions are distinct clinical entities. The new genes expressed explain the differences in the disease course and suggest new therapeutic targets that may lead to different treatment paradigms in these 2 subsets.

Discovery of clinically relevant fusions in pediatric cancer

Background

Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions.

Results

Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information.

Conclusions

The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08094-z.

Identification and in silico characterization of structural and functional impacts of genetic variants in milk protein genes in the Zebu breeds Guzerat and Gyr

Whole genome sequencing of bovine breeds has allowed identification of genetic variants in milk protein genes. However, functional repercussion of such variants at a molecular level has seldom been investigated. Here, the results of a multistep Bioinformatic analysis for functional characterization of recently identified genetic variants in Brazilian Gyr and Guzerat breeds is described, including predicted effects on the following: (i) evolutionary conserved nucleotide positions/regions; (ii) protein function, stability, and interactions; (iii) splicing, branching, and miRNA binding sites; (iv) promoters and transcription factor binding sites; and (v) collocation with QTL. Seventy-one genetic variants were identified in the caseins (CSN1S1, CSN2, CSN1S2, and CSN3), LALBA, LGB, and LTF genes. Eleven potentially regulatory variants and two missense mutations were identified. LALBA Ile60Val was predicted to affect protein stability and flexibility, by reducing the number the disulfide bonds established. LTF Thr546Asn is predicted to generate steric clashes, which could mildly affect iron coordination. In addition, LALBA Ile60Val and LTF Thr546Asn affect exonic splicing enhancers and silencers. Consequently, both mutations have the potential of affecting immune response at individual level, not only in the mammary gland. Although laborious, this multistep procedure for classifying variants allowed the identification of potentially functional variants for milk protein genes.

ASimulatoR: splice-aware RNA-Seq data simulation

SUMMARY

A plethora of tools exist for RNA-Seq data analysis with a focus on alternative splicing (AS). However, appropriate data for their comparative evaluation is missing. The R package ASimulatoR simulates gold standard RNA-Seq datasets with fine-grained control over the distribution of AS events, which allow for evaluating alternative splicing tools, e.g. to study the effect of sequencing depth on the performance of AS event detection.

AVAILABILITY AND IMPLEMENTATION

ASimulatoR is freely available at https://github.com/biomedbigdata/ASimulatoR as an R package under GPL-3 license.

CAMPAREE: a robust and configurable RNA expression simulator

Background

The accurate interpretation of RNA-Seq data presents a moving target as scientists continue to introduce new experimental techniques and analysis algorithms. Simulated datasets are an invaluable tool to accurately assess the performance of RNA-Seq analysis methods. However, existing RNA-Seq simulators focus on modeling the technical biases and artifacts of sequencing, rather than on simulating the original RNA samples. A first step in simulating RNA-Seq is to simulate RNA.

Results

To fill this need, we developed the Configurable And Modular Program Allowing RNA Expression Emulation (CAMPAREE), a simulator using empirical data to simulate diploid RNA samples at the level of individual molecules. We demonstrated CAMPAREE’s use for generating idealized coverage plots from real data, and for adding the ability to generate allele-specific data to existing RNA-Seq simulators that do not natively support this feature.

Conclusions

Separating input sample modeling from library preparation/sequencing offers added flexibility for both users and developers to mix-and-match different sample and sequencing simulators to suit their specific needs. Furthermore, the ability to maintain sample and sequencing simulators independently provides greater agility to incorporate new biological findings about transcriptomics and new developments in sequencing technologies. Additionally, by simulating at the level of individual molecules, CAMPAREE has the potential to model molecules transcribed from the same genes as a heterogeneous population of transcripts with different states of degradation and processing (splicing, editing, etc.). CAMPAREE was developed in Python, is open source, and freely available at https://github.com/itmat/CAMPAREE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07934-2.

New evaluation methods of read mapping by 17 aligners on simulated and empirical NGS data: an updated comparison of DNA- and RNA-Seq data from Illumina and Ion Torrent technologies

During the last (15) years, improved omics sequencing technologies have expanded the scale and resolution of various biological applications, generating high-throughput datasets that require carefully chosen software tools to be processed. Therefore, following the sequencing development, bioinformatics researchers have been challenged to implement alignment algorithms for next-generation sequencing reads. However, nowadays selection of aligners based on genome characteristics is poorly studied, so our benchmarking study extended the “state of art” comparing 17 different aligners. The chosen tools were assessed on empirical human DNA- and RNA-Seq data, as well as on simulated datasets in human and mouse, evaluating a set of parameters previously not considered in such kind of benchmarks. As expected, we found that each tool was the best in specific conditions. For Ion Torrent single-end RNA-Seq samples, the most suitable aligners were CLC and BWA-MEM, which reached the best results in terms of efficiency, accuracy, duplication rate, saturation profile and running time. About Illumina paired-end osteomyelitis transcriptomics data, instead, the best performer algorithm, together with the already cited CLC, resulted Novoalign, which excelled in accuracy and saturation analyses. Segemehl and DNASTAR performed the best on both DNA-Seq data, with Segemehl particularly suitable for exome data. In conclusion, our study could guide users in the selection of a suitable aligner based on genome and transcriptome characteristics. However, several other aspects, emerged from our work, should be considered in the evolution of alignment research area, such as the involvement of artificial intelligence to support cloud computing and mapping to multiple genomes.

MOCCASIN: a method for correcting for known and unknown confounders in RNA splicing analysis

The effects of confounding factors on gene expression analysis have been extensively studied following the introduction of high-throughput microarrays and subsequently RNA sequencing. In contrast, there is a lack of equivalent analysis and tools for RNA splicing. Here we first assess the effect of confounders on both expression and splicing quantifications in two large public RNA-Seq datasets (TARGET, ENCODE). We show quantification of splicing variations are affected at least as much as those of gene expression, revealing unwanted sources of variations in both datasets. Next, we develop MOCCASIN, a method to correct the effect of both known and unknown confounders on RNA splicing quantification and demonstrate MOCCASIN's effectiveness on both synthetic and real data. Code, synthetic and corrected datasets are all made available as resources.

Comparative evaluation of full-length isoform quantification from RNA-Seq

Background

Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short.

Results

Here we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systematic comparative analyses of isoform quantification accuracy and its impact on differential expression analysis. Genome, transcriptome and pseudo alignment-based methods are included; and a simple approach is included as a baseline control.

Conclusions

Salmon, kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform dramatically better than the simple approach. We determine the structural parameters with the greatest impact on quantification accuracy to be length and sequence compression complexity and not so much the number of isoforms. The effect of incomplete annotation on performance is also investigated. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification and isoform level DE should still be employed selectively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04198-1.

Cell-autonomous retinoic acid receptor signaling has stage-specific effects on mouse enteric nervous system

Retinoic acid (RA) signaling is essential for enteric nervous system (ENS) development, since vitamin A deficiency or mutations in RA signaling profoundly reduce bowel colonization by ENS precursors. These RA effects could occur because of RA activity within the ENS lineage or via RA activity in other cell types. To define cell-autonomous roles for retinoid signaling within the ENS lineage at distinct developmental time points, we activated a potent floxed dominant-negative RA receptor α (RarαDN) in the ENS using diverse CRE recombinase–expressing mouse lines. This strategy enabled us to block RA signaling at premigratory, migratory, and postmigratory stages for ENS precursors. We found that cell-autonomous loss of RA receptor (RAR) signaling dramatically affected ENS development. CRE activation of RarαDN expression at premigratory or migratory stages caused severe intestinal aganglionosis, but at later stages, RarαDN induced a broad range of phenotypes including hypoganglionosis, submucosal plexus loss, and abnormal neural differentiation. RNA sequencing highlighted distinct RA-regulated gene sets at different developmental stages. These studies show complicated context-dependent RA-mediated regulation of ENS development.

MetaFusion: A high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates

MOTIVATION

Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function.

RESULTS

MetaFusion is a flexible meta-calling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format (CFF). Calls are annotated, merged using graph clustering, filtered, and ranked to provide a final output of high confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases, and is provided with a benchmarking toolkit to calibrate new callers.

AVAILABILITY

MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Shared PPARα/γ Target Genes Regulate Brown Adipocyte Thermogenic Function

Brown adipose tissue (BAT) generates heat to maintain body temperature and suppress obesity. Agonists for nuclear receptors PPARα and PPARγ both affect brown adipocyte function, yet the interplay between these factors in BAT is uncertain. Here, we report that PPARα shares most genomic binding sites with PPARγ, and these common binding sites are more related to BAT function than PPARγ-selective sites without PPARα. Integrating PPARα and PPARγ genomic occupancy with cold-responsive BAT transcriptomes identifies a subset of 16 genes with potential relevance to BAT function. Among these, we focused on the lysosomal protease cathepsin Z (CTSZ) and showed it is necessary for mitochondrial respiration in both mouse and human brown adipocytes. Thus, CTSZ is a shared PPARα/γ target gene in BAT and a regulator of brown adipocyte thermogenic function.

CAR-T cell-mediated depletion of immunosuppressive tumor-associated macrophages promotes endogenous antitumor immunity and augments adoptive immunotherapy

The immunosuppressive tumor microenvironment (TME) represents a major barrier for effective immunotherapy. Tumor-associated macrophages (TAMs) are highly heterogeneous and plastic cell components of the TME which can either promote tumor progression (M2-like) or boost antitumor immunity (M1-like). Here, we demonstrate that a subset of TAMs that express folate receptor β (FRβ) possess an immunosuppressive M2-like profile. In syngeneic tumor mouse models, chimeric antigen receptor (CAR)-T cell-mediated selective elimination of FRβ⁺ TAMs in the TME results in an enrichment of pro-inflammatory monocytes, an influx of endogenous tumor-specific CD8⁺ T cells, delayed tumor progression, and prolonged survival. Preconditioning of the TME with FRβ-specific CAR-T cells also improves the effectiveness of tumor-directed anti-mesothelin CAR-T cells, while simultaneous co-administration of both CAR products does not. These results highlight the pro-tumor role of FRβ⁺ TAMs in the TME and the therapeutic implications of TAM-depleting agents as preparative adjuncts to conventional immunotherapies that directly target tumor antigens.

The BMP signaling gradient is interpreted through concentration thresholds in dorsal-ventral axial patterning

Bone Morphogenetic Protein (BMP) patterns the dorsal–ventral (DV) embryonic axis in all vertebrates, but it is unknown how cells along the DV axis interpret and translate the gradient of BMP signaling into differential gene activation that will give rise to distinct cell fates. To determine the mechanism of BMP morphogen interpretation in the zebrafish gastrula, we identified 57 genes that are directly activated by BMP signaling. By using Seurat analysis of single-cell RNA sequencing (scRNA-seq) data, we found that these genes are expressed in at least 3 distinct DV domains of the embryo. We distinguished between 3 models of BMP signal interpretation in which cells activate distinct gene expression through interpretation of thresholds of (1) the BMP signaling gradient slope; (2) the BMP signal duration; or (3) the level of BMP signal activation. We tested these 3 models using quantitative measurements of phosphorylated Smad5 (pSmad5) and by examining the spatial relationship between BMP signaling and activation of different target genes at single-cell resolution across the embryo. We found that BMP signaling gradient slope or BMP exposure duration did not account for the differential target gene expression domains. Instead, we show that cells respond to 3 distinct levels of BMP signaling activity to activate and position target gene expression. Together, we demonstrate that distinct pSmad5 threshold levels activate spatially distinct target genes to pattern the DV axis.

This study tested three models of how a BMP morphogen gradient is translated into differential gene activation that specifies distinct cell fates, finding that BMP signal concentration thresholds, not gradient shape or signal duration, position three distinct gene activation domains.

Postnatal maturation of calcium signaling in islets of Langerhans from neonatal mice

Pancreatic islet cells develop mature physiological responses to glucose and other fuels postnatally. In this study, we used fluorescence imaging techniques to measure changes in intracellular calcium ([Ca²⁺]_i) to compare islets isolated from mice on postnatal days 0, 4, and 12 with islets from adult CD-1 mice. In addition, we used publicly available RNA-sequencing data to compare expression levels of key genes in β-cell physiology with [Ca²⁺]_i data across these ages. We show that islets isolated from mice on postnatal day 0 displayed elevated [Ca²⁺]_i in basal glucose (≤4 mM) but lower [Ca²⁺]_i responses to stimulation by 12-20 mM glucose compared to adult. Neonatal islets displayed more adult-like [Ca²⁺]_i in basal glucose by day 4 but continued to show lower [Ca²⁺]_i responses to 16 and 20 mM glucose stimulation up to at least day 12. A right shift in glucose sensing (EC₅₀) correlated with lower fragment-per-kilobase-of-transcript-per-million-reads-mapped (FPKM) of Slc2a2 (glut2) and Actn3 and increased FPKM for Galk1 and Nupr1. Differences in [Ca²⁺]_i responses to additional stimuli were also observed. Calcium levels in the endoplasmic reticulum were elevated on day 0 but became adult-like by day 4, which corresponded with reduced expression in Atp2a2 (SERCA2) and novel K+-channel Ktd17, increased expression of Pml, Wfs1, Thada, and Herpud1, and basal [Ca²⁺]_i maturing to adult levels. Ion-channel activity also matured rapidly, but RNA sequencing data mining did not yield strong leads. In conclusion, the maturation of islet [Ca²⁺]_i signaling is complex and multifaceted; several possible gene targets were identified that may participate in this process.

Single-cell transcriptomics of human islet ontogeny defines the molecular basis of β-cell dedifferentiation in T2D

OBJECTIVE

Dedifferentiation of pancreatic β-cells may reduce islet function in type 2 diabetes (T2D). However, the prevalence, plasticity and functional consequences of this cellular state remain unknown.

METHODS

We employed single-cell RNAseq to detail the maturation program of α- and β-cells during human ontogeny. We also compared islets from non-diabetic and T2D individuals.

RESULTS

Both α- and β-cells mature in part by repressing non-endocrine genes; however, α-cells retain hallmarks of an immature state, while β-cells attain a full β-cell specific gene expression program. In islets from T2D donors, both α- and β-cells have a less mature expression profile, de-repressing the juvenile genetic program and exocrine genes and increasing expression of exocytosis, inflammation and stress response signalling pathways. These changes are consistent with the increased proportion of β-cells displaying suboptimal function observed in T2D islets.

CONCLUSIONS

These findings provide new insights into the molecular program underlying islet cell maturation during human ontogeny and the loss of transcriptomic maturity that occurs in islets of type 2 diabetics.

Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis

RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.

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.

RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads

Background

Obtaining data from single-cell transcriptomic sequencing allows for the investigation of cell-specific gene expression patterns, which could not be addressed a few years ago. With the advancement of droplet-based protocols the number of studied cells continues to increase rapidly. This establishes the need for software tools for efficient processing of the produced large-scale datasets. We address this need by presenting RainDrop for fast gene-cell count matrix computation from single-cell RNA-seq data produced by 10x Genomics Chromium technology.

Results

RainDrop can process single-cell transcriptomic datasets consisting of 784 million reads sequenced from around 8.000 cells in less than 40 minutes on a standard workstation. It significantly outperforms the established Cell Ranger pipeline and the recently introduced Alevin tool in terms of runtime by a maximal (average) speedup of 30.4 (22.6) and 3.5 (2.4), respectively, while keeping high agreements of the generated results.

Conclusions

RainDrop is a software tool for highly efficient processing of large-scale droplet-based single-cell RNA-seq datasets on standard workstations written in C++. It is available at https://gitlab.rlp.net/stnieble/raindrop.

Enabling Precision Oncology Through Precision Diagnostics

Genomic testing enables clinical management to be tailored to individual cancer patients based on the molecular alterations present within cancer cells. Genomic sequencing results can be applied to detect and classify cancer, predict prognosis, and target therapies. Next-generation sequencing has revolutionized the field of cancer genomics by enabling rapid and cost-effective sequencing of large portions of the genome. With this technology, precision oncology is quickly becoming a realized paradigm for managing the treatment of cancer patients. However, many challenges must be overcome to efficiently implement the transition of next-generation sequencing from research applications to routine clinical practice, including using specimens commonly available in the clinical setting; determining how to process, store, and manage large amounts of sequencing data; determining how to interpret and prioritize molecular findings; and coordinating health professionals from multiple disciplines.

Mobile genomics: tools and techniques for tackling transposons

Next-generation sequencing approaches have fundamentally changed the types of questions that can be asked about gene function and regulation. With the goal of approaching truly genome-wide quantifications of all the interaction partners and downstream effects of particular genes, these quantitative assays have allowed for an unprecedented level of detail in exploring biological interactions. However, many challenges remain in our ability to accurately describe and quantify the interactions that take place in those hard to reach and extremely repetitive regions of our genome comprised mostly of transposable elements (TEs). Tools dedicated to TE-derived sequences have lagged behind, making the inclusion of these sequences in genome-wide analyses difficult. Recent improvements, both computational and experimental, allow for the better inclusion of TE sequences in genomic assays and a renewed appreciation for the importance of TE biology. This review will discuss the recent improvements that have been made in the computational analysis of TE-derived sequences as well as the areas where such analysis still proves difficult. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.

Interoperable RNA-Seq analysis in the cloud

RNA-Sequencing (RNA-Seq) is currently the leading technology for genome-wide transcript quantification. Mapping the raw reads to transcript and gene level counts can be achieved by different aligners. Here we report an in-depth comparison of transcript quantification methods. Our goal is the specific use of cost-efficient RNA-Seq analysis for deployment in a cloud infrastructure composed of interacting microservices. The individual modules cover file transfer into the cloud and APIs to handle the cloud alignment jobs. We next demonstrate how newly generated RNA-Seq data can be placed in the context of thousands of previously published datasets in near real time. With in-depth benchmarks, we identify suitable gene count quantification methods to facilitate cost-effective, accurate, and cloud-based RNA-Seq analysis service. Pseudo-alignment algorithms such as kallisto and Salmon combine high read quality estimation with cost efficient runtime performance. HISAT2 is the fastest of the classical aligners with good alignment quality. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Proteolysis and cartilage development are activated in the synovium after surgical induction of post traumatic osteoarthritis

Anterior cruciate ligament (ACL) transection surgery in the minipig induces post-traumatic osteoarthritis (PTOA) in a pattern similar to that seen in human patients after ACL injury. Prior studies have reported the presence of cartilage matrix-degrading proteases, such as Matrix metalloproteinase-1 (MMP-1) and A disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS-4), in the synovial fluid of injured or arthritic joints; however, the tissue origin of these proteases is unknown. The objective of this study was to identify transcriptional processes activated in the synovium after surgical induction of PTOA with ACL transection, and to determine if processes associated with proteolysis were enriched in the synovium after ACL transection. Unilateral ACL transection was performed in adolescent Yucatan minipigs and synovium samples were collected at 1, 5, 9, and 14 days post-injury. Transcriptome-wide gene expression levels were determined using bulk RNA-Sequencing in the surgical animals and control animals with healthy knees. The greatest number of transcripts with significant changes was observed 1 day after injury. These changes were primarily associated with cellular proliferation, consistent with measurements of increased cellularity of the synovium at the two-week time point. At five to 14 days, the expression of transcripts relating to proteolysis and cartilage development was significantly enriched. While protease inhibitor-encoding transcripts (TIMP2, TIMP3) represented the largest fraction of protease-associated transcripts in the uninjured synovium, protease-encoding transcripts (including MMP1, MMP2, ADAMTS4) predominated after surgery. Cartilage development-associated transcripts that are typically not expressed by synovial cells, such as ACAN and COMP, were enriched in the synovium following ACL-transection. The upregulation in both catabolic processes (proteolysis) and anabolic processes (cartilage development) suggests that the synovium plays a complex, balancing role in the early response to PTOA induction.

iREAD: a tool for intron retention detection from RNA-seq data

BACKGROUND

Intron retention (IR) has been traditionally overlooked as 'noise' and received negligible attention in the field of gene expression analysis. In recent years, IR has become an emerging field for interrogating transcriptomes because it has been recognized to carry out important biological functions such as gene expression regulation and it has been found to be associated with complex diseases such as cancers. However, methods for detecting IR today are limited. Thus, there is a need to develop novel methods to improve IR detection.

RESULTS

Here we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input a BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR events by analyzing the features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. iREAD provides significant added value in detecting IR compared with output from IRFinder with a higher AUC on all datasets tested. Both methods showed low false positive rates and high false negative rates in different regimes, indicating that use together is generally beneficial. The output from iREAD can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. The software is freely available at https://github.com/genemine/iread.

CONCLUSION

Being complementary to existing tools, iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions. Intron retention analysis provides a complementary approach for understanding transcriptome.

Retina Development in Vertebrates: Systems Biology Approaches to Understanding Genetic Programs: On the Contribution of Next-Generation Sequencing Methods to the Characterization of the Regulatory Networks Controlling Vertebrate Eye Development

The ontogeny of the vertebrate retina has been a topic of interest to developmental biologists and human geneticists for many decades. Understanding the unfolding of the genetic program that transforms a field of progenitors cells into a functionally complex and multi-layered sensory organ is a formidable challenge. Although classical genetic studies succeeded in identifying the key regulators of retina specification, understanding the architecture of their gene network and predicting their behavior are still a distant hope. The emergence of next-generation sequencing platforms revolutionized the field unlocking the access to genome-wide datasets. Emerging techniques such as RNA-seq, ChIP-seq, ATAC-seq, or single cell RNA-seq are used to characterize eye developmental programs. These studies provide valuable information on the transcriptional and cis-regulatory profiles of precursors and differentiated cells, outlining the trajectories that connect each intermediate state. Here, recent systems biology efforts are reviewed to understand the genetic programs shaping the vertebrate retina.

Detecting sequence variants in clinically important protozoan parasites

Second and third generation sequencing methods are crucial for population genetic studies, and variant detection is a popular approach for exploiting this sequence data. While mini- and microsatellites are historically useful markers for studying important Protozoa such as Toxoplasma and Plasmodium spp., detecting non-repetitive variants such as those found in genes can be fundamental to investigating a pathogen's biology. These variants, namely single nucleotide polymorphisms and insertions and deletions, can help elucidate the genetic basis of an organism's pathogenicity, identify selective pressures, and resolve phylogenetic relationships. They also have the added benefit of possessing a comparatively low mutation rate, which contributes to their stability. However, there is a plethora of variant analysis tools with nuanced pipelines and conflicting recommendations for best practise, which can be confounding. This lack of standardisation means that variant analysis requires careful parameter optimisation, an understanding of its limitations, and the availability of high quality data. This review explores the value of variant detection when applied to non-model organisms such as clinically important protozoan pathogens. The limitations of current methods are discussed, including special considerations that require the end-users' attention to ensure that the results generated are reproducible, and the biological conclusions drawn are valid.

AAV-Mediated Gene Augmentation Therapy Restores Critical Functions in Mutant PRPF31+/- iPSC-Derived RPE Cells

Retinitis pigmentosa (RP) is the most common form of inherited vision loss and is characterized by degeneration of retinal photoreceptor cells and the retinal pigment epithelium (RPE). Mutations in pre-mRNA processing factor 31 (PRPF31) cause dominant RP via haploinsufficiency with incomplete penetrance. There is good evidence that the diverse severity of this disease is a result of differing levels of expression of the wild-type allele among patients. Thus, we hypothesize that PRPF31-related RP will be amenable to treatment by adeno-associated virus (AAV)-mediated gene augmentation therapy. To test this hypothesis, we used induced pluripotent stem cells (iPSCs) with mutations in PRPF31 and differentiated them into RPE cells. The mutant PRPF31 iPSC-RPE cells recapitulate the cellular phenotype associated with the PRPF31 pathology, including defective cell structure, diminished phagocytic function, defects in ciliogenesis, and compromised barrier function. Treatment of the mutant PRPF31 iPSC-RPE cells with AAV-PRPF31 restored normal phagocytosis and cilia formation, and it partially restored structure and barrier function. These results suggest that AAV-based gene therapy targeting RPE cells holds therapeutic promise for patients with PRPF31-related RP.

CREB deletion increases resilience to stress and downregulates inflammatory gene expression in the hippocampus

The transcription factor CREB (cyclic AMP response element (CRE)-binding protein) is implicated in the pathophysiology and treatment of depression. Structural and functional studies in both animals and humans suggest that abnormalities of the hippocampus may play a role in depression. CREB regulates thousands of genes, yet to date, only a handful that mediate depression or antidepressant response have been identified as relevant CREB targets. In order to comprehensively identify genes regulated by CREB in the hippocampus, we employed translating ribosome affinity purification (TRAP) to detect actively translating mRNAs in wild type and CREB-deficient mice. Using Creb^loxP/loxP; Rosa^LSL-GFP-L10a mice, we conducted whole genome sequencing to identify transcripts only in cells that lack CREB, as introduction of Cre-recombinase simultaneously deleted CREB and expressed GFP-tagged L10a ribosomes that enabled TRAP. We identified over 200 downregulated genes predominantly associated with inflammation and the immune system, including toll-like receptor 1 (TLR1). To determine if baseline disruption in gene expression in the hippocampus of CREB-deficient mice can modulate behavior, we used unpredictable chronic mild stress (UCMS) to produce a set of behavioral alterations with strong validity for depression. We found that CREB-deficient mice demonstrated resilience to the physiological effects of UCMS and also showed changes in affective behaviors specifically in the presence of stress. TLR1 expression was increased following UCMS in control but not in CREB-deficient mice. The results suggest that CREB-mediated regulation of immune system and inflammatory factors may provide additional targets for the treatment of depression.

Genomic architecture of Shh-dependent cochlear morphogenesis

The mammalian cochlea develops from a ventral outgrowth of the otic vesicle in response to Shh signaling. Mouse embryos lacking Shh or its essential signal transduction components display cochlear agenesis; however, a detailed understanding of the transcriptional network mediating this process is unclear. Here, we describe an integrated genomic approach to identify Shh-dependent genes and associated regulatory sequences that promote cochlear duct morphogenesis. A comparative transcriptome analysis of otic vesicles from mouse mutants exhibiting loss (Smo^ecko ) and gain (Shh-P1) of Shh signaling reveal a set of Shh-responsive genes partitioned into four expression categories in the ventral half of the otic vesicle. This target gene classification scheme provides novel insight into several unanticipated roles for Shh, including priming the cochlear epithelium for subsequent sensory development. We also mapped regions of open chromatin in the inner ear by ATAC-seq that, in combination with Gli2 ChIP-seq, identified inner ear enhancers in the vicinity of Shh-responsive genes. These datasets are useful entry points for deciphering Shh-dependent regulatory mechanisms involved in cochlear duct morphogenesis and establishment of its constituent cell types.

Circadian control of lung inflammation in influenza infection

Influenza is a leading cause of respiratory mortality and morbidity. While inflammation is essential for fighting infection, a balance of anti-viral defense and host tolerance is necessary for recovery. Circadian rhythms have been shown to modulate inflammation. However, the importance of diurnal variability in the timing of influenza infection is not well understood. Here we demonstrate that endogenous rhythms affect survival in influenza infection. Circadian control of influenza infection is mediated by enhanced inflammation as proven by increased cellularity in bronchoalveolar lavage (BAL), pulmonary transcriptomic profile and histology and is not attributable to viral burden. Better survival is associated with a time dependent preponderance of NK and NKT cells and lower proportion of inflammatory monocytes in the lung. Further, using a series of genetic mouse mutants, we elucidate cellular mechanisms underlying circadian gating of influenza infection.

Improved indel detection in DNA and RNA via realignment with ABRA2

MOTIVATION

Genomic variant detection from next-generation sequencing has become established as an extremely important component of research and clinical diagnoses in both cancer and Mendelian disorders. Insertions and deletions (indels) are a common source of variation and can frequently impact functionality, thus making their detection vitally important. While substantial effort has gone into detecting indels from DNA, there is still opportunity for improvement. Further, detection of indels from RNA-Seq data has largely been an afterthought and offers another critical area for variant detection.

RESULTS

We present here ABRA2, a redesign of the original ABRA implementation that offers support for realignment of both RNA and DNA short reads. The process results in improved accuracy and scalability including support for human whole genomes. Results demonstrate substantial improvement in indel detection for a variety of data types, including those that were not previously supported by ABRA. Further, ABRA2 results in broad improvements to variant calling accuracy across a wide range of post-processing workflows including whole genomes, targeted exomes and transcriptome sequencing.

AVAILABILITY AND IMPLEMENTATION

ABRA2 is implemented in a combination of Java and C/C++ and is freely available to all from: https://github.com/mozack/abra2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RNA-seq in Skeletal Biology

PURPOSE OF REVIEW

The goal of this paper is to review state-of-the-art transcriptome profiling methods and their recent applications in the field of skeletal biology.

RECENT FINDINGS

Next-generation sequencing of mRNA (RNA-seq) methods have been established and routinely used in skeletal biology research. RNA-seq has led to the identification of novel genes and transcription factors involved in skeletal development and disease, through its application in small and large animal models, as well as human tissue and cells. With the availability of advanced techniques such as single-cell RNA-seq, novel cell types in skeletal tissues are being identified. As the sequencing technologies are rapidly evolving, the exciting discoveries supported by transcriptomics will continue to emerge and improve our understanding of the biology of the skeleton.

Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell barcode whitelisting. Alevin's approach to UMI deduplication considers transcript-level constraints on the molecules from which UMIs may have arisen and accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads and improves the accuracy of gene abundance estimates. Alevin is considerably faster, typically eight times, than existing gene quantification approaches, while also using less memory.