DART: a fast and accurate RNA-seq mapper with a partitioning strategy

CropGene: a software package for the analysis of genomic and transcriptomic data of agricultural plants

Currently, the breeding of agricultural plants is increasingly based on the use of molecular biological data on genetic sequences, which makes it possible to significantly accelerate the breeding process, create new plant varieties through genomic editing. These data have a large volume, variety and require a large amount of resources, both labor and computing, to analyze the costs. Data analysis of such volume and complexity can be effective only when using modern bioinformatics methods, which include algorithms for identifying genes, predicting their function, and evaluating the effect of mutation on plant phenotype. Such an analysis has recently become impossible without the use of integrated software systems that solve problems of different levels by executing computational pipelines. The paper describes the CropGene software package developed for the comprehensive analysis of genomic and transcriptomic data of agricultural plants. CropGene includes several blocks of bioinformatic analysis, such as analysis of gene variations, assembly of genomes and transcriptomes, as well as annotation of genes and proteins. CropGene implements new methods for analyzing long non-coding RNAs, protein domains, searching and analyzing polymorphisms, and genome-wide association research. CropGene has a user-friendly interface and supports working with various types of data, which greatly simplifies its use for researchers who do not have deep knowledge in the field of bioinformatics. The paper provides examples of the use of CropGene for the analysis of agricultural organisms such as Solanum tuberosum and Zea mays. With CropGene, genetic markers have been identified that explain up to 50 % of the variability in seed color parameters; potential genes that may become promising material for producing potato varieties; more than 100 thousand new long non-coding RNAs. Orthogroups were also found, the domain structure of which shows a marked similarity with the domain architecture of characteristic secreted A2 phospholipases. Thus, CropGene is an important tool for scientists and practitioners working in the field of agrobiotechnology and plant genetics.

Transcriptome analysis reveals molecular targets of erythrocyte invasion phenotype diversity in natural Plasmodium falciparum isolates from Cameroon

Further understanding of the molecular mediators of alternative RBC invasion phenotypes in endemic malaria parasites will support malaria blood-stage vaccine or drug development. This study investigated the prevalence of sialic acid (SA)-dependent and SA-independent RBC invasion pathways in endemic Plasmodium falciparum parasites from Cameroon and compared the schizont stage transcriptomes in these two groups to uncover the wider repertoire of transcriptional variation associated with the use of alternative RBC invasion pathway phenotypes. A two-color flow cytometry-based invasion-inhibition assay against RBCs treated with neuraminidase, trypsin, and chymotrypsin and deep RNA sequencing of schizont stages harvested in the first ex vivo replication cycle in culture were employed in this investigation. RBC invasion phenotypes were determined for 63 isolates from asymptomatic children with uncomplicated malaria. Approximately 80% of the isolates invaded neuraminidase-treated but not chymotrypsin-treated RBCs, representing SA-independent pathways of RBC invasion. The schizont transcriptome profiles of 16 isolates with invasion phenotypes revealed a total of 5,136 gene transcripts, with 85% of isolates predicted at schizont stages. Two distinct transcriptome profile clusters belonging to SA-dependent and SA-independent parasites were obtained by data reduction with principal component analysis. Differential analysis of gene expression between the two clusters implicated, in addition to the well-characterized adhesins, the upregulation of genes encoding proteins mediating merozoite organelle discharges as well as several conserved, virulent, merozoite-associated, and exported proteins. The latter majority have been shown to have structural and physiological relevance to RBC surface remodeling and immune evasion in malaria and thus have potential as anti-invasion targets.

Machine learning on alignment features for parent-of-origin classification of simulated hybrid RNA-seq

BACKGROUND

Parent-of-origin allele-specific gene expression (ASE) can be detected in interspecies hybrids by virtue of RNA sequence variants between the parental haplotypes. ASE is detectable by differential expression analysis (DEA) applied to the counts of RNA-seq read pairs aligned to parental references, but aligners do not always choose the correct parental reference.

RESULTS

We used public data for species that are known to hybridize. We measured our ability to assign RNA-seq read pairs to their proper transcriptome or genome references. We tested software packages that assign each read pair to a reference position and found that they often favored the incorrect species reference. To address this problem, we introduce a post process that extracts alignment features and trains a random forest classifier to choose the better alignment. On each simulated hybrid dataset tested, our machine-learning post-processor achieved higher accuracy than the aligner by itself at choosing the correct parent-of-origin per RNA-seq read pair.

CONCLUSIONS

For the parent-of-origin classification of RNA-seq, machine learning can improve the accuracy of alignment-based methods. This approach could be useful for enhancing ASE detection in interspecies hybrids, though RNA-seq from real hybrids may present challenges not captured by our simulations. We believe this is the first application of machine learning to this problem domain.

Alternative splicing analysis benchmark with DICAST

Alternative splicing is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools have been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools, which arguably provide the most detailed insights into the alternative splicing process. DICAST offers a modular and extensible framework for analysing alternative splicing integrating eleven splice-aware mapping and eight event detection tools. We benchmark all tools extensively on simulated as well as whole blood RNA-seq data. STAR and HISAT2 demonstrated the best balance between performance and run time. The performance of event detection tools varies widely with no tool outperforming all others. DICAST allows researchers to employ a consensus approach to consider the most successful tools jointly for robust event detection. Furthermore, we propose the first reporting standard to unify existing formats and to guide future tool development.

WAX INDUCER 1 Regulates β-Diketone Biosynthesis by Mediating Expression of the Cer-cqu Gene Cluster in Barley

Plant surface properties are crucial determinants of resilience to abiotic and biotic stresses. The outer layer of the plant cuticle consists of chemically diverse epicuticular waxes. The WAX INDUCER1/SHINE subfamily of APETALA2/ETHYLENE RESPONSIVE FACTORS regulates cuticle properties in plants. In this study, four barley genes homologous to the Arabidopsis thaliana AtWIN1 gene were mutated using RNA-guided Cas9 endonuclease. Mutations in one of them, the HvWIN1 gene, caused a recessive glossy sheath phenotype associated with β-diketone deficiency. A complementation test for win1 knockout (KO) and cer-x mutants showed that Cer-X and WIN1 are allelic variants of the same genomic locus. A comparison of the transcriptome from leaf sheaths of win1 KO and wild-type plants revealed a specific and strong downregulation of a large gene cluster residing at the previously known Cer-cqu locus. Our findings allowed us to postulate that the WIN1 transcription factor in barley is a master mediator of the β-diketone biosynthesis pathway acting through developmental stage- and organ-specific transactivation of the Cer-cqu gene cluster.

The Original Form of C4-Photosynthetic Phosphoenolpyruvate Carboxylase Is Retained in Pooids but Lost in Rice

Poaceae is the most prominent monocot family that contains the primary cereal crops wheat, rice, and maize. These cereal species exhibit physiological diversity, such as different photosynthetic systems and environmental stress tolerance. Phosphoenolpyruvate carboxylase (PEPC) in Poaceae is encoded by a small multigene family and plays a central role in C4-photosynthesis and dicarboxylic acid metabolism. Here, to better understand the molecular basis of the cereal species diversity, we analyzed the PEPC gene family in wheat together with other grass species. We could designate seven plant-type and one bacterial-type grass PEPC groups, ppc1a, ppc1b, ppc2a, ppc2b, ppc3, ppc4, ppcC4, and ppc-b, respectively, among which ppc1b is an uncharacterized type of PEPC. Evolutionary inference revealed that these PEPCs were derived from five types of ancient PEPCs (ppc1, ppc2, ppc3, ppc4, and ppc-b) in three chromosomal blocks of the ancestral Poaceae genome. C4-photosynthetic PEPC (ppcC4 ) had evolved from ppc1b, which seemed to be arisen by a chromosomal duplication event. We observed that ppc1b was lost in many Oryza species but preserved in Pooideae after natural selection. In silico analysis of cereal RNA-Seq data highlighted the preferential expression of ppc1b in upper ground organs, selective up-regulation of ppc1b under osmotic stress conditions, and nitrogen response of ppc1b. Characterization of wheat ppc1b showed high levels of gene expression in young leaves, transcriptional responses under nitrogen and abiotic stress, and the presence of a Dof1 binding site, similar to ppcC4 in maize. Our results indicate the evolving status of Poaceae PEPCs and suggest the functional association of ppc1-derivatives with adaptation to environmental changes.

Features of Activity of the Phenylpropanoid Biosynthesis Pathway in Melanin-Accumulating Barley Grains

Barley (Hordeum vulgare L.) grain pigmentation is caused by two types of phenolic compounds: anthocyanins (which are flavonoids) give a blue or purple color, and melanins (which are products of enzymatic oxidation and polymerization of phenolic compounds) give a black or brown color. Genes Ant1 and Ant2 determine the synthesis of purple anthocyanins in the grain pericarp, whereas melanins are formed under the control of the Blp1 gene in hulls and pericarp tissues. Unlike anthocyanin synthesis, melanin synthesis is poorly understood. The objective of the current work was to reveal features of the phenylpropanoid biosynthesis pathway functioning in melanin-accumulating barley grains. For this purpose, comparative transcriptomic and metabolomic analyses of three barley near-isogenic lines accumulating anthocyanins, melanins, or both in the grain, were performed. A comparative analysis of mRNA libraries constructed for three stages of spike development (booting, late milk, and early dough) showed transcriptional activation of genes encoding enzymes of the general phenylpropanoid pathway in all the lines regardless of pigmentation; however, as the spike matured, unique transcriptomic patterns associated with melanin and anthocyanin synthesis stood out. Secondary activation of transcription of the genes encoding enzymes of the general phenylpropanoid pathway together with genes of monolignol synthesis was revealed in the line accumulating only melanin. This pattern differs from the one observed in the anthocyanin-accumulating lines, where - together with the genes of general phenylpropanoid and monolignol synthesis pathways - flavonoid biosynthesis genes were found to be upregulated, with earlier activation of these genes in the line accumulating both types of pigments. These transcriptomic shifts may underlie the observed differences in concentrations of phenylpropanoid metabolites analyzed in the grain at a late developmental stage by high-performance liquid chromatography. Both melanin-accumulating lines showed an increased total level of benzoic acids. By contrast, anthocyanin-accumulating lines showed higher concentrations of flavonoids and p-coumaric and ferulic acids. A possible negative effect of melanogenesis on the total flavonoid content and a positive influence on the anthocyanin content were noted in the line accumulating both types of pigments. As a conclusion, redirection of metabolic fluxes in the phenylpropanoid biosynthesis pathway occurs when melanin is synthesized.

Approaches in Gene Coexpression Analysis in Eukaryotes

Simple Summary

Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it.

Abstract

Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.

Technology dictates algorithms: recent developments in read alignment

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

Long noncoding RNAs are involved in multiple immunological pathways in response to vaccination

Understanding the mechanisms of vaccine-elicited protection contributes to the development of new vaccines. The emerging field of systems vaccinology provides detailed information on host responses to vaccination and has been successfully applied to study the molecular mechanisms of several vaccines. Long noncoding RNAs (lncRNAs) are crucially involved in multiple biological processes, but their role in vaccine-induced immunity has not been explored. We performed an analysis of over 2,000 blood transcriptome samples from 17 vaccine cohorts to identify lncRNAs potentially involved with antibody responses to influenza and yellow fever vaccines. We have created an online database where all results from this analysis can be accessed easily. We found that lncRNAs participate in distinct immunological pathways related to vaccine-elicited responses. Among them, we showed that the expression of lncRNA FAM30A was high in B cells and correlates with the expression of immunoglobulin genes located in its genomic vicinity. We also identified altered expression of these lncRNAs in RNA-sequencing (RNA-seq) data from a cohort of children following immunization with intranasal live attenuated influenza vaccine, suggesting a common role across several diverse vaccines. Taken together, these findings provide evidence that lncRNAs have a significant impact on immune responses induced by vaccination.

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

Cytotoxicity and Transcriptomic Analysis of Silver Nanoparticles in Mouse Embryonic Fibroblast Cells

The rapid development of nanotechnology has led to the use of silver nanoparticles (AgNPs) in biomedical applications, including antibacterial, antiviral, anti-inflammatory, and anticancer therapies. The molecular mechanism of AgNPs-induced cytotoxicity has not been studied thoroughly using a combination of cellular assays and RNA sequencing (RNA-Seq) analysis. In this study, we prepared AgNPs using myricetin, an anti-oxidant polyphenol, and studied their effects on NIH3T3 mouse embryonic fibroblasts as an in vitro model system to explore the potential biomedical applications of AgNPs. AgNPs induced loss of cell viability and cell proliferation in a dose-dependent manner, as evident by increased leakage of lactate dehydrogenase (LDH) from cells. Reactive oxygen species (ROS) were a potential source of cytotoxicity. AgNPs also incrementally increased oxidative stress and the level of malondialdehyde, depleted glutathione and superoxide dismutase, reduced mitochondrial membrane potential and adenosine triphosphate (ATP), and caused DNA damage by increasing the level of 8-hydroxy-2′-deoxyguanosine and the expressions of the p53 and p21 genes in NIH3T3 cells. Thus, activation of oxidative stress may be crucial for NIH3T3 cytotoxicity. Interestingly, gene ontology (GO) term analysis revealed alterations in epigenetics-related biological processes including nucleosome assembly and DNA methylation due to AgNPs exposure. This study is the first demonstration that AgNPs can alter bulk histone gene expression. Therefore, our genome-scale study suggests that the apoptosis observed in NIH3T3 cells treated with AgNPs is mediated by the repression of genes required for cell survival and the aberrant enhancement of nucleosome assembly components to induce apoptosis.