Ribosomes are the molecular machinery that catalyse all the fundamental steps involved in the translation of mRNAs into proteins. Given the complexity of this process, the efficiency of protein synthesis depends on a large number of factors among which ribosome drop-off (i.e. the premature detachment of the ribosome from the mRNA template) plays an important role. However, an in vitro quantification of the extent to which ribosome drop-off occurs is not trivial due to difficulties in obtaining the needed experimental evidence. In this work we focus on the study of ribosome drop-off in Saccharomyces cerevisiae by using 'Ribofilio', a novel software tool that relies on a high sensitive strategy to estimate the ribosome drop-off rate from ribosome profiling data. Our results show that ribosome drop-off events occur at a significant rate also when S. cerevisiae is cultured in standard conditions. In this context, we also identified a correlation between the ribosome drop-off rate and the genes length: the longer the gene, the lower the drop-off rate.

SARS-CoV-2 is the cause of the current worldwide pandemic of severe acute respiratory syndrome. The change of nucleotide composition of the SARS-CoV-2 genome is crucial for understanding the spread and transmission dynamics of the virus because viral nucleotide sequences are essential in identifying viral strains. Recent studies have shown that cytosine (C) to uracil (U) substitutions are overrepresented in SARS-CoV-2 genome sequences. These asymmetric substitutions between C and U indicate that traditional time-reversible substitution models cannot be applied to the evolution of SARS-CoV-2 sequences. Thus, we develop a new time-irreversible model of nucleotide substitutions to estimate the substitution rates in SARS-CoV-2 genomes. We investigated the number of nucleotide substitutions among the 7862 genomic sequences of SARS-CoV-2 registered in the Global Initiative on Sharing All Influenza Data (GISAID) that have been sampled from all over the world. Using the new method, the substitution rates in SARS-CoV-2 genomes were estimated. The C-to-U substitution rates of SARS-CoV-2 were estimated to be 1.95×10-3±4.88×10-4 per site per year, compared with 1.48×10-4±7.42×10-5 per site per year for all other types of substitutions.

Data analysis tools are continuously changed and improved over time. In order to test how these changes influence the comparability between analyses, the output of different workflow options of the nf-core/rnaseq pipeline were compared. Five different pipeline settings (STAR+Salmon, STAR+RSEM, STAR+featureCounts, HISAT2+featureCounts, pseudoaligner Salmon) were run on three datasets (human, Arabidopsis, zebrafish) containing spike-ins of the External RNA Control Consortium (ERCC). Fold change ratios and differential expression of genes and spike-ins were used for comparative analyses of the different tools and versions settings of the pipeline. An overlap of 85% for differential gene classification between pipelines could be shown. Genes interpreted with a bias were mostly those present at lower concentration. Also, the number of isoforms and exons per gene were determinants. Previous pipeline versions using featureCounts showed a higher sensitivity to detect one-isoform genes like ERCC. To ensure data comparability in long-term analysis series it would be recommendable to either stay with the pipeline version the series was initialized with or to run both versions during a transition time in order to ensure that the target genes are addressed the same way.

Recent advances in single-cell multi-omics technologies have provided unprecedented insights into regulatory processes. We introduce TREASMO, a versatile Python package designed to quantify and visualize transcriptional regulatory dynamics in single-cell multi-omics datasets. TREASMO has four modules, spanning data preparation, correlation quantification, downstream analysis and visualization, enabling comprehensive dataset exploration. By introducing a novel single-cell gene-peak correlation strength index, TREASMO facilitates accurate identification of regulatory changes at single-cell resolution. Validation on a hematopoietic stem and progenitor cell dataset showcases TREASMO's capacity in quantifying the gene-peak correlation strength at the single-cell level, identifying regulatory markers and discovering temporal regulatory patterns along the trajectory.

Genome annotation plays a crucial role in providing comprehensive catalog of genes and transcripts for a particular species. As research projects generate new transcriptome data worldwide, integrating this information into existing annotations becomes essential. However, most bioinformatics pipelines are limited in their ability to effectively and consistently update annotations using new RNA-seq data. Here we introduce TAGADA, an RNA-seq pipeline for Transcripts And Genes Assembly, Deconvolution, and Analysis. Given a genomic sequence, a reference annotation and RNA-seq reads, TAGADA enhances existing gene models by generating an improved annotation. It also computes expression values for both the reference and novel annotation, identifies long non-coding transcripts (lncRNAs), and provides a comprehensive quality control report. Developed using Nextflow DSL2, TAGADA offers user-friendly functionalities and ensures reproducibility across different computing platforms through its containerized environment. In this study, we demonstrate the efficacy of TAGADA using RNA-seq data from the GENE-SWiTCH project alongside chicken and pig genome annotations as references. Results indicate that TAGADA can substantially increase the number of annotated transcripts by approximately [Formula: see text] in these species. Furthermore, we illustrate how TAGADA can integrate Illumina NovaSeq short reads with PacBio Iso-Seq long reads, showcasing its versatility. TAGADA is available at github.com/FAANG/analysis-TAGADA.

scPipe is a flexible R/Bioconductor package originally developed to analyse platform-independent single-cell RNA-Seq data. To expand its preprocessing capability to accommodate new single-cell technologies, we further developed scPipe to handle single-cell ATAC-Seq and multi-modal (RNA-Seq and ATAC-Seq) data. After executing multiple data cleaning steps to remove duplicated reads, low abundance features and cells of poor quality, a SingleCellExperiment object is created that contains a sparse count matrix with features of interest in the rows and cells in the columns. Quality control information (e.g. counts per cell, features per cell, total number of fragments, fraction of fragments per peak) and any relevant feature annotations are stored as metadata. We demonstrate that scPipe can efficiently identify 'true' cells and provides flexibility for the user to fine-tune the quality control thresholds using various feature and cell-based metrics collected during data preprocessing. Researchers can then take advantage of various downstream single-cell tools available in Bioconductor for further analysis of scATAC-Seq data such as dimensionality reduction, clustering, motif enrichment, differential accessibility and cis-regulatory network analysis. The scPipe package enables a complete beginning-to-end pipeline for single-cell ATAC-Seq and RNA-Seq data analysis in R.

Mitochondrial diseases are the result of pathogenic variants in genes involved in the diverse functions of the mitochondrion. A comprehensive list of mitochondrial genes is needed to improve gene prioritization in the diagnosis of mitochondrial diseases and development of therapeutics that modulate mitochondrial function. MitoCarta is an experimentally derived catalog of proteins localized to mitochondria. We sought to expand this list of mitochondrial proteins to identify proteins that may not be localized to the mitochondria yet perform important mitochondrial functions. We used a computational approach to assign statistical significance to the overlap between STRING database gene network neighborhoods and MitoCarta proteins. Using a data-driven stringent significance threshold, 2059 proteins that were not located in MitoCarta were identified, which we termed mitochondrial proximal (MitoProximal) proteins. We identified all of the oxidative phosphorylation complex subunits and 90% of 149 genes that contain confirmed oxidative phosphorylation disease causal variants, lending validation to our methodology. Among the MitoProximal proteins, 134 are annotated to be localized to mitochondria but are not in the MitoCarta 3.0 database. We extend MitoCarta nearly 3-fold, generating a more comprehensive list of mitochondrial genes, a resource to facilitate the identification of pathogenic variants in mitochondrial and metabolic diseases.

Given the current status of coronavirus disease 2019 (COVID-19) as a global pandemic, it is of high priority to gain a deeper understanding of the disease's development and how the virus impacts its host. Adenosine (A)-to-Inosine (I) RNA editing is a post-transcriptional modification, catalyzed by the ADAR family of enzymes, that can be considered part of the inherent cellular defense mechanism as it affects the innate immune response in a complex manner. It was previously reported that various viruses could interact with the host's ADAR enzymes, resulting in epigenetic changes both to the virus and the host. Here, we analyze RNA-seq of nasopharyngeal swab specimens as well as whole-blood samples of COVID-19 infected individuals and show a significant elevation in the global RNA editing activity in COVID-19 compared to healthy controls. We also detect specific coding sites that exhibit higher editing activity. We further show that the increment in editing activity during the disease is temporary and returns to baseline shortly after the symptomatic period. These significant epigenetic changes may contribute to the immune system response and affect adverse outcomes seen in post-viral cases.

Homing endonuclease genes (HEGs) are ubiquitous selfish elements that generate targeted double-stranded DNA breaks, facilitating the recombination of the HEG DNA sequence into the break site and contributing to the evolutionary dynamics of HEG-encoding genomes. Bacteriophages (phages) are well-documented to carry HEGs, with the paramount characterization of HEGs being focused on those encoded by coliphage T4. Recently, it has been observed that the highly sampled vibriophage, ICP1, is similarly enriched with HEGs distinct from T4’s. Here, we examined the HEGs encoded by ICP1 and diverse phages, proposing HEG-driven mechanisms that contribute to phage evolution. Relative to ICP1 and T4, we found a variable distribution of HEGs across phages, with HEGs frequently encoded proximal to or within essential genes. We identified large regions (> 10kb) of high nucleotide identity flanked by HEGs, deemed HEG islands, which we hypothesize to be mobilized by the activity of flanking HEGs. Finally, we found examples of domain swapping between phage-encoded HEGs and genes encoded by other phages and phage satellites. We anticipate that HEGs have a larger impact on the evolutionary trajectory of phages than previously appreciated and that future work investigating the role of HEGs in phage evolution will continue to highlight these observations.

Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics datato identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.