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

Exploring Molecular Pathways in Refractive Errors Associated with Inherited Retinal Dystrophies

The term inherited retinal dystrophies (IRDs) refers to a diverse range of conditions characterized by retinal dysfunction, and mostly deterioration, leading to a gradual decay of the visual function and eventually to total vision loss. IRDs have a global impact on about 1 in every 3000 to 4000 individuals. However, the prevalence statistics might differ significantly depending on the exact type of dystrophy and the demographic being examined. The cellular pathophysiology and genetic foundation of IRDs have been extensively studied, however, knowledge regarding associated refractive errors remain limited. This review aims to clarify the cellular and molecular processes that underlie refractive errors in IRDs. We did a thorough search of the current literature (Pubmed, accession Feb 2024), selecting works describing phenotypic differences among genes-related to IRDs, particularly in relation to refractive errors. First, we summarize the wide range of IRDs and their genetic causes, describing the genes and biological pathways connected to the etiology of the disease. We then explore the complex relationship between refractive errors and retinal dysfunction, including how the impairment of the vision-related mechanisms in the retina can affect ocular biometry and optical characteristics. New data about the involvement of aberrant signaling pathways, photoreceptor degeneration, and dysfunctional retinal pigment epithelium (RPE) in the development of refractive errors in IRDs have been examined. We also discuss the therapeutic implications of refractive defects in individuals with IRD, including possible approaches to treating visual impairments. In addition, we address the value of using cutting-edge imaging methods and animal models to examine refractive errors linked to IRDs and suggest future lines of inquiry for identifying new targets for treatment. In summary, this study presents an integrated understanding of the cellular and molecular mechanisms underlying refractive errors in IRDs. It illuminates the intricacies of ocular phenotypes in these conditions and offers a tool for understanding mechanisms underlying isolated refractive errors, besides the IRD-related forms.

Best practices for germline variant and DNA methylation analysis of second- and third-generation sequencing data

This comprehensive review provides insights and suggested strategies for the analysis of germline variants using second- and third-generation sequencing technologies (SGS and TGS). It addresses the critical stages of data processing, starting from alignment and preprocessing to quality control, variant calling, and the removal of artifacts. The document emphasized the importance of meticulous data handling, highlighting advanced methodologies for annotating variants and identifying structural variations and methylated DNA sites. Special attention is given to the inspection of problematic variants, a step that is crucial for ensuring the accuracy of the analysis, particularly in clinical settings where genetic diagnostics can inform patient care. Additionally, the document covers the use of various bioinformatics tools and software that enhance the precision and reliability of these analyses. It outlines best practices for the annotation of variants, including considerations for problematic genetic alterations such as those in the human leukocyte antigen region, runs of homozygosity, and mitochondrial DNA alterations. The document also explores the complexities associated with identifying structural variants and copy number variations, underscoring the challenges posed by these large-scale genomic alterations. The objective is to offer a comprehensive framework for researchers and clinicians, ensuring that genetic analyses conducted with SGS and TGS are both accurate and reproducible. By following these best practices, the document aims to increase the diagnostic accuracy for hereditary diseases, facilitating early diagnosis, prevention, and personalized treatment strategies. This review serves as a valuable resource for both novices and experts in the field, providing insights into the latest advancements and methodologies in genetic analysis. It also aims to encourage the adoption of these practices in diverse research and clinical contexts, promoting consistency and reliability across studies.

Proteomics identifies multiple retinitis pigmentosa associated proteins involved in retinal degeneration in a mouse model bearing a Pde6b mutation

Retinitis pigmentosa (RP) is a progressive and degenerative retinal disease resulting in severe vision loss. RP have been extensively studied for pathogenetic mechanisms and treatments. Yet there is little information about alterations of RP associated proteins in phosphodiesterase 6 beta (Pde6b) mutated model. To explore the roles of RP causing proteins, we performed a label free quantitative mass spectrometry based proteomic analysis in rd10 mouse retinas. 3737 proteins were identified at the degenerative time points in rd10 mice. 222 and 289 differentially expressed proteins (DEPs) (fold change, FC > 2, p < 0.05) were detected at 5 and 8 weeks. Based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, visual perception and phototransduction were severely affected. The downregulated DEPs were significantly enriched in cilium assembly and protein localization. 25 decreased DEPs causing autosomal recessive/dominant retinitis pigmentosa were visualized by heatmaps. Protein-protein interaction network represented 13 DEPs interacted directly with Pde6b protein. 25 DEPs causing RP were involved in phototransduction, visual perception, response to stimulus, protein localization and cilium assembly pathways. The significantly reduced expressions of DEPs were further validated by quantitative reverse transcription polymerase chain reaction (qPCR), Western blots (WB) and immunohistochemistry (IHC). This study revealed the molecular mechanisms underlying early and late stage of RP, as well as changes of RP-causing proteins.

Benchmarking of five NGS mapping tools for the reference alignment of bacterial outer membrane vesicles-associated small RNAs

Advances in small RNAs (sRNAs)-related studies have posed a challenge for NGS-related bioinformatics, especially regarding the correct mapping of sRNAs. Depending on the algorithms and scoring matrices on which they are based, aligners are influenced by the characteristics of the dataset and the reference genome. These influences have been studied mainly in eukaryotes and to some extent in prokaryotes. However, in bacteria, the selection of aligners depending on sRNA-seq data associated with outer membrane vesicles (OMVs) and the features of the corresponding bacterial reference genome has not yet been investigated. We selected five aligners: BBmap, Bowtie2, BWA, Minimap2 and Segemehl, known for their generally good performance, to test them in mapping OMV-associated sRNAs from Aliivibrio fischeri to the bacterial reference genome. Significant differences in the performance of the five aligners were observed, resulting in differential recognition of OMV-associated sRNA biotypes in A. fischeri. Our results suggest that aligner(s) should not be arbitrarily selected for this task, which is often done, as this can be detrimental to the biological interpretation of NGS analysis results. Since each aligner has specific advantages and disadvantages, these need to be considered depending on the characteristics of the input OMV sRNAs dataset and the corresponding bacterial reference genome to improve the detection of existing, biologically important OMV sRNAs. Until we learn more about these dependencies, we recommend using at least two, preferably three, aligners that have good metrics for the given dataset/bacterial reference genome. The overlapping results should be considered trustworthy, yet their differences should not be dismissed lightly, but treated carefully in order not to overlook any biologically important OMV sRNA. This can be achieved by applying the intersect-then-combine approach. For the mapping of OMV-associated sRNAs of A. fischeri to the reference genome organized into two circular chromosomes and one circular plasmid, containing copies of sequences with rRNA- and tRNA-related features and no copies of sequences with protein-encoding features, if the aligners are used with their default parameters, we advise avoiding Segemehl, and recommend using the intersect-then-combine approach with BBmap, BWA and Minimap2 to improve the potential for discovery of biologically important OMV-associated sRNAs.

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

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

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.

Pharmacogenomic and Statistical Analysis

Genetic variants can alter response to drugs and other therapeutic interventions. The study of this phenomenon, called pharmacogenomics, is similar in many ways to other types of genetic studies but has distinct methodological and statistical considerations. Genetic variants involved in the processing of exogenous compounds exhibit great diversity and complexity, and the phenotypes studied in pharmacogenomics are also more complex than typical genetic studies. In this chapter, we review basic concepts in pharmacogenomic study designs, data generation techniques, statistical analysis approaches, and commonly used methods and briefly discuss the ultimate translation of findings to clinical care.

Epitranscriptome Analysis of Oxidative Stressed Retinal Epithelial Cells Depicted a Possible RNA Editing Landscape of Retinal Degeneration

Oxidative stress represents one of the principal causes of inherited retinal dystrophies, with many related molecular mechanisms still unknown. We investigated the posttranscriptional RNA editing landscape of human retinal pigment epithelium cells (RPE) exposed to the oxidant agent N-retinylidene-N-retinyl ethanolamine (A2E) for 1 h, 2 h, 3 h and 6 h. Using a transcriptomic approach, refined with a specific multialgorithm pipeline, 62,880 already annotated and de novo RNA editing sites within about 3000 genes were identified among all samples. Approximately 19% of these RNA editing sites were found within 3' UTR, including sites common to all time points that were predicted to change the binding capacity of 359 miRNAs towards 9654 target genes. A2E exposure also determined significant gene expression differences in deaminase family ADAR, APOBEC and ADAT members, involved in canonical and tRNA editing events. On GO and KEGG enrichment analyses, genes that showed different RNA editing levels are mainly involved in pathways strongly linked to a possible neovascularization of retinal tissue, with induced apoptosis mediated by the ECM and surface protein altered signaling. Collectively, this work demonstrated dynamic RNA editome profiles in RPE cells for the first time and shed more light on new mechanisms at the basis of retinal degeneration.

A novel role for vaping in mitochondrial gene dysregulation and inflammation fundamental to disease development

We constructed and analyzed the whole transcriptome in leukocytes of healthy adult vapers (with/without a history of smoking), ‘exclusive’ cigarette smokers, and controls (non-users of any tobacco products). Furthermore, we performed single-gene validation of expression data, and biochemical validation of vaping/smoking status by plasma cotinine measurement. Computational modeling, combining primary analysis (age- and sex-adjusted limmaVoom) and sensitivity analysis (cumulative e-liquid- and pack-year modeling), revealed that ‘current’ vaping, but not ‘past’ smoking, is significantly associated with gene dysregulation in vapers. Comparative analysis of the gene networks and canonical pathways dysregulated in vapers and smokers showed strikingly similar patterns in the two groups, although the extent of transcriptomic changes was more pronounced in smokers than vapers. Of significance is the preferential targeting of mitochondrial genes in both vapers and smokers, concurrent with impaired functional networks, which drive mitochondrial DNA-related disorders. Equally significant is the dysregulation of immune response genes in vapers and smokers, modulated by upstream cytokines, including members of the interleukin and interferon family, which play a crucial role in inflammation. Our findings accord with the growing evidence on the central role of mitochondria as signaling organelles involved in immunity and inflammatory response, which are fundamental to disease development.