NAR Genomics and Bioinformatics最新文献

The ability of eukaryotic cells to orchestrate mechanical interactions from the subcellular to the organismal levels is mediated by their cytoskeleton. One of the key components of the eukaryotic cytoskeleton is actin, a highly conserved building block of the actin filaments, which interact with many other proteins and underlie diverse cell structures, necessary for organizing intracellular transport, phagocytosis and cell movement. Many organisms have evolved multiple actin variants, which share similar amino acid sequences but differ more dramatically at the gene level, including the presence and number of introns. In the current study, we show that the intron-containing and intronless actin genes are present in the poriferan Halisarca dujardini and that the encoded actins can perform different functions. These actins differ in the gene expression profiles, post-translational modifications, cellular, and subcellular localizations. The intronless actin genes of H. dujardini, HdA1/2/3, are products of recent duplications, exhibit low divergence between paralogs, and serve as the primary cytoskeletal actins. The divergent intron-containing actin gene, HdA6, is differentially expressed in a specific cell lineage and its expression is dependent on the state of cell aggregation, which indicates its unique functions in the morphogenetic processes of the sponge.

High concentration of lipoprotein(a) [Lp(a)], a lipoprotein with proatherogenic properties, is an important risk factor for cardiovascular disease. This concentration is mostly genetically determined by a complex interplay between the number of kringle IV type 2 repeats and Lp(a)-affecting variants. Besides Lp(a) plasma concentration, there is an unmet need to identify individuals most at risk based on their LPA genotype. We developed KILDA (KIv2 Length Determined from a kmer Analysis), a Nextflow pipeline, to identify the number of kringle IV type 2 repeats and Lp(a)-affecting variants directly from kmers generated from FASTQ files. The pipeline was tested on the 1000 Genomes Project (n = 2459) and results were equivalent to DRAGEN-LPA (R ²= 0.92). In silico datasets proved the robustness of KILDA's predictions under different scenarios of sequencing coverage and quality. In brief, KILDA is a robust, open-source, and free-to-use pipeline that can identify the number of kringle IV type 2 repeats and Lp(a)-associated variants even when inputting low-coverage libraries.

[This corrects the article DOI: 10.1093/nar/lqaf005.].

Transcription factors (TFs) are key regulators of cellular gene expression programs in health and disease. Here we set out to integrate genomic, transcriptomic, and phosphoproteomic data to characterize TF activity in lung adenocarcinoma patients. Using expression data from patient samples and genomic information on TF binding to super-enhancers, starting from a list of 1667 human TFs we calculated a patient-specific activity score and identified 34 with perturbed activity in the cancer samples, as evidenced by the expression of their direct targets. We then leveraged phosphoproteomic data on the same samples to identify phosphorylation events that modulate TF activity. This novel data integration approach to TF characterization led to the identification of ERG as a key regulator in lung adenocarcinoma whose activity strongly correlates with patient survival.

Single nucleotide polymorphisms (SNPs) can alter RNA structure by changing the proportions of existing conformations or leading to new conformations in the structural ensemble. Such structure-changing SNPs, or riboSNitches, have been associated with diseases in humans and climate adaptation in plants. While several computational tools are available for predicting whether an SNP is a riboSNitch, these tools were generally developed to analyze individual RNAs and are not optimized for genome-wide analyses. To fill this gap, we developed VariantFoldRNA, a flexible, containerized, and automated pipeline for genome-wide prediction of riboSNitches. Our pipeline automatically installs all dependencies, can be run locally or on high-performance clusters, and is modular, enabling the user to customize the analysis for the research question of interest. VariantFoldRNA can predict riboSNitches genome-wide at user-specified temperatures and splicing conditions, opening the door to novel analyses. The pipeline is an open-source command-line tool that is freely available at https://github.com/The-Bevilacqua-Lab/variantfoldrna.

Severe infectious diseases remain the leading cause of death in children and young adults worldwide. Monogenic inborn errors of immunity (IEIs) are traditionally defined as a heterogeneous group of rare inborn genetic diseases affecting the functioning of the immune system. Greater awareness has led to the clinical definition of 485 monogenic IEIs and whole exome sequencing (WES) is becoming increasingly relevant for IEI genetic diagnosis. The current protocol for IEI genetic studies includes manual filtering of the list of genes obtained as a WES read-out providing a short list of candidate genes. This procedure is time-consuming and can produce mistakes due to human error in manual filtering. IEIVariantFilter is a new web-based bioinformatics tool to speed up and refine the genetic diagnosis of IEI patients oriented for users in the biomedical field without needing bioinformatics expertise. IEIVariantFilter prioritizes genetic variants based on ranges of zygosity, the quality of reads, the predicted variant effect, and genes related to immunity, considering a consanguineous hypothesis whenever necessary. IEIVariantFilter facilitates gene and variant list prioritization, speeding up the identification of candidate disease-causing variants for validation by experimental studies. The software improves the genetic diagnosis of patients, thereby facilitating precision medicine and fast and proper treatment.

Although an established model organism, Tetrahymena thermophila remains comparatively inaccessible to high throughput screens, and alternative bioinformatic approaches still rely on unconnected datasets and outdated algorithms. Here, we report a new approach to consolidating RNA-seq and microarray data based on a systematic exploration of parameters and computational controls, enabling us to infer functional gene associations from their co-expression patterns. To illustrate the power of this approach, we took advantage of new data regarding a previously studied pathway, the biogenesis of a secretory organelle called the mucocyst. Our untargeted clustering approach recovered over 80% of the genes that were previously verified to play a role in mucocyst biogenesis. Furthermore, we tested four new genes that we predicted to be mucocyst-associated based on their co-expression and found that knocking out each of them results in mucocyst secretion defects. We also found that our approach succeeds in clustering genes associated with several other cellular pathways that we evaluated based on prior literature. We present the Tetrahymena Gene Network Explorer (TGNE) as an interactive tool for genetic hypothesis generation and functional annotation in this organism and as a framework for building similar tools for other systems.

Epigenomes, characterized by patterns of different signatures such as chromatin accessibility, chromatin interactions, and DNA methylation, vary across cell types and play a pivotal role in regulating gene expression. By mapping these signatures, the underlying mechanisms of development and diseases can be uncovered. However, many canonical epigenetic methods focus on mapping only one signature. Simultaneous measurement of epigenetic signatures from the same cell or tissue provides significant benefits for research, especially when resources are limited, and precise analysis is essential. Here, we report a technique called Methyl-Micro-C (MMC), which simultaneously profiles chromatin accessibility, chromatin interactions, and DNA methylation in the same sample. MMC enhances the resolution of chromatin interactions and the coverage of CpGs by combining MNase-mediated fragmentation with enzymatic conversion. This technique allows for the profiling of three-dimensional epigenomes, capturing consistent chromatin accessibility, chromatin interactions, and DNA methylation signals in an efficient manner. It is also relatively straightforward, allowing researchers to implement and apply it easily.

T cells play a vital role in adaptive immunity by targeting pathogen-infected or cancerous cells, but predicting their specificity remains challenging. Encoding T-cell receptor (TCR) sequences into informative feature spaces is therefore crucial for advancing specificity prediction and downstream applications. For this, we developed a variational autoencoder (VAE)-based model trained on paired TCR α-β chain data, incorporating all six complementarity-determining regions. A semi-supervised 'two-stage VAE' framework, integrating cosine triplet loss and a classifier, was found to further refine peptide-specific latent representations, outperforming sequence-based methods in specificity prediction. Clustering analyses leveraging our VAE latent space were evaluated using K-means, agglomerative clustering, and a novel graph-based method. Agglomerative clustering achieved the most biologically relevant results, balancing cluster purity and retention despite noise in TCR specificity annotations. We extended these insights to evaluate TCR repertoire data. Across datasets, VAE-based models outperformed sequence-based methods, particularly in retention metrics, with notable improvements in the SARS-CoV-2 repertoire dataset. Moreover, the cancer repertoire analysis highlighted the generalizability of our approach, where the model displayed high performance despite minimal similarity between the training and test data. Collectively, these results demonstrate the potential of VAE-based latent representations to offer a robust framework for prediction, clustering, and repertoire analysis.

Single-cell sequencing technologies provide us with information at the level of individual cells. Combining single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. Integrating both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.