NAR Genomics and Bioinformatics最新文献

Biological nitrogen fixation is a fundamental biogeochemical process that transforms molecular nitrogen into biologically available nitrogen via diazotrophic microbes. Diazotrophs anaerobically fix nitrogen using the nitrogenase enzyme which is arranged in three different gene clusters: (i) molybdenum nitrogenase (nifHDK) is the most abundant, followed by it's alternatives, (ii) vanadium nitrogenase (vnfHDK) and (iii) iron nitrogenase (anfHDK). Multiple databases have been constructed as resources for diazotrophic 'omics analysis; however, an integrated database based on whole genome references does not exist. Here, we present NFixDB (Nitrogen Fixation DataBase), a comprehensive integrated whole genome based database for diazotrophs, which includes all nitrogenases (nifHDK, vnfHDK, anfHDK) and nitrogenase-like enzymes (e.g. nflHD) linked to ribosomal RNA operons (16S-5S-23S). NFixDB was computed using Hidden Markov Models (HMMs) against the entire whole genome based Genome Taxonomy Database (GTDB R214), providing searchable reference HMMs for all nitrogenase and nitrogenase-like genes, complete ribosomal RNA operons, both GTDB and NCBI/RefSeq taxonomy, and an SQL database for querying matches. We compared NFixDB to nifH databases from Buckley, Zehr, Mise and FunGene finding extensive evidence of nifH, in addition to vnfH and nflH. NFixDB contains >4000 verified nifHDK sequences contained on 50 unique phyla of bacteria and archaea. NFixDB provides the first comprehensive nitrogenase database available to researchers unlocking diazotrophic microbial potential.

Trans-splicing is a post-transcriptional processing event that joins exons from separate RNAs to produce a chimeric RNA. However, the detailed mechanism of trans-splicing remains poorly understood. Here, we characterize trans-spliced genes and provide insights into the mechanism of trans-splicing in the tunicate Ciona. Tunicates are the closest invertebrates to humans, and their genes frequently undergo trans-splicing. Our analysis revealed that, in genes that give rise to both trans-spliced and non-trans-spliced messenger RNAs, trans-splice acceptor sites were preferentially located at the first functional acceptor site, and their paired donor sites were weak in both Ciona and humans. Additionally, we found that Ciona trans-spliced genes had GU- and AU-rich 5' transcribed regions. Our data and findings not only are useful for Ciona research community, but may also aid in a better understanding of the trans-splicing mechanism, potentially advancing the development of gene therapy based on trans-splicing.

Population genomics has revolutionized our ability to study bacterial evolution by enabling data-driven discovery of the genetic architecture of trait variation. Genome-wide association studies (GWAS) have more recently become accompanied by genome-wide epistasis and co-selection (GWES) analysis, which offers a phenotype-free approach to generating hypotheses about selective processes that simultaneously impact multiple loci across the genome. However, existing GWES methods only consider associations between distant pairs of loci within the genome due to the strong impact of linkage-disequilibrium (LD) over short distances. Based on the general functional organisation of genomes it is nevertheless expected that majority of co-selection and epistasis will act within relatively short genomic proximity, on co-variation occurring within genes and their promoter regions, and within operons. Here, we introduce LDWeaver, which enables an exhaustive GWES across both short- and long-range LD, to disentangle likely neutral co-variation from selection. We demonstrate the ability of LDWeaver to efficiently generate hypotheses about co-selection using large genomic surveys of multiple major human bacterial pathogen species and validate several findings using functional annotation and phenotypic measurements. Our approach will facilitate the study of bacterial evolution in the light of rapidly expanding population genomic data.

In this computational study, we explore the folding of a particular sequence using various computational tools to produce two-dimensional structures, which are then transformed into three-dimensional structures. We then study the geometry, energetics and dynamics of these structures using full electron quantum-chemical and classical molecular dynamics calculations. Our study focuses on the SARS-CoV-2 RNA fragment GGaGGaGGuguugcaGG and its various structures, including a G-quadruplex and five different hairpins. We examine the impact of two types of counterions (K⁺ and Na⁺) and flanking nucleotides on their geometrical characteristics, relative stability and dynamic properties. Our results show that the G-quadruplex structure is the most stable among the constructed hairpins. We confirm its topological stability through molecular dynamics simulations. Furthermore, we observe that the nucleotide loop consisting of seven nucleotides is the most flexible part of the RNA fragment. Additionally, we find that RNA networks of intermolecular hydrogen bonds are highly sensitive to the surrounding environment. Our findings reveal the loss of 79 old hydrogen bonds and the formation of 91 new ones in the case when the G-quadruplex containing flanking nucleotides is additionally stabilized by Na⁺ counterions.

Current methods of processing archaeological samples combined with advances in sequencing methods lead to disclosure of a large part of H. neanderthalensis and Denisovans genetic information. It is hardly surprising that the genome variability between modern humans, Denisovans and H. neanderthalensis is relatively limited. Genomic studies may provide insight on the metabolism of extinct human species or lineages. Detailed analysis of G-quadruplex sequences in H. neanderthalensis and Denisovans mitochondrial DNA showed us interesting features. Relatively similar patterns in mitochondrial DNA are found compared to modern humans, with one notable exception for H. neanderthalensis. An interesting difference between H. neanderthalensis and H. sapiens corresponds to a motif found in the D-loop region of mtDNA, which is responsible for mitochondrial DNA replication. This area is directly responsible for the number of mitochondria and consequently for the efficient energy metabolism of cell. H. neanderthalensis harbor a long uninterrupted run of guanines in this region, which may cause problems for replication, in contrast with H. sapiens, for which this run is generally shorter and interrupted. One may propose that the predominant H. sapiens motif provided a selective advantage for modern humans regarding mtDNA replication and function.

With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.

Nanopore sequencing technologies have enabled the direct detection of base modifications in DNA or RNA molecules. Despite these advancements, the tools for visualizing electrical current, essential for analyzing base modifications, are often lacking in clarity and compatibility with diverse nanopore pipelines. Here, we present Nanopore Current Events Magnifier (nanoCEM, https://github.com/lrslab/nanoCEM), a Python command-line tool designed to facilitate the identification of DNA/RNA modification sites through enhanced visualization and statistical analysis. Compatible with the four preprocessing methods including 'f5c resquiggle', 'f5c eventalign', 'Tombo' and 'move table', nanoCEM is applicable to RNA and DNA analysis across multiple flow cell types. By utilizing rescaling techniques and calculating various statistical features, nanoCEM provides more accurate and comparable visualization of current events, allowing researchers to effectively observe differences between samples and showcase the modified sites.

Chromatin-associated non-coding RNAs play important roles in various cellular processes by targeting genomic loci. Two types of genome-wide NGS experiments exist to detect such targets: 'one-to-al', which focuses on targets of a single RNA, and 'all-to-al', which captures targets of all RNAs in a sample. As with many NGS experiments, they are prone to biases and noise, so it becomes essential to detect 'peaks'-specific interactions of an RNA with genomic targets. Here, we present BaRDIC-Binomial RNA-DNA Interaction Caller-a tailored method to detect peaks in both types of RNA-DNA interaction data. BaRDIC is the first tool to simultaneously take into account the two most prominent biases in the data: chromatin heterogeneity and distance-dependent decay of interaction frequency. Since RNAs differ in their interaction preferences, BaRDIC adapts peak sizes according to the abundances and contact patterns of individual RNAs. These features enable BaRDIC to make more robust predictions than currently applied peak-calling algorithms and better handle the characteristic sparsity of all-to-all data. The BaRDIC package is freely available at https://github.com/dmitrymyl/BaRDIC.

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO₂ emissions low. The code is available at https://nf-co.re/sarek.

The prevalence of nucleic and peptide short sequences across organismal genomes and proteomes has not been thoroughly investigated. We examined 45 785 reference genomes and 21 871 reference proteomes, spanning archaea, bacteria, eukaryotes and viruses to calculate the rarity of short sequences in them. To capture this, we developed a metric of the rarity of each sequence in nature, the rarity index. We find that the frequency of certain dipeptides in rare oligopeptide sequences is hundreds of times lower than expected, which is not the case for any dinucleotides. We also generate predictive regression models that infer the rarity of nucleic and proteomic sequences across nature or within each domain of life and viruses separately. When examining each of the three domains of life and viruses separately, the R² performance of the model predicting rarity for 5-mer peptides from mono- and dipeptides ranged between 0.814 and 0.932. A separate model predicting rarity for 10-mer oligonucleotides from mono- and dinucleotides achieved R² performance between 0.408 and 0.606. Our results indicate that the mono- and dinucleotide composition of nucleic sequences and the mono- and dipeptide composition of peptide sequences can explain a significant proportion of the variance in their frequencies in nature.