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BIOLOGY (1491 journals)            First | 1 2 3 4 5 6 7 8 | Last

Showing 1401 - 1600 of 1720 Journals sorted by number of followers
Hydrobiology     Open Access   (Followers: 39)
Sustainability and Climate Change     Full-text available via subscription   (Followers: 32)
Climate Change Ecology     Open Access   (Followers: 30)
F&S Science : Official journal of the American Society for Reproductive Medicine     Open Access   (Followers: 27)
Gut Microbiome     Open Access   (Followers: 26)
Biomaterials Advances     Full-text available via subscription   (Followers: 25)
Zoonotic Diseases     Open Access   (Followers: 24)
Anatomia     Open Access   (Followers: 15)
Phytopathology Research     Open Access   (Followers: 13)
International Turfgrass Society Research Journal     Free   (Followers: 13)
Arthropod Systematics & Phylogeny     Open Access   (Followers: 13)
Journal of Medical and Life Science     Open Access   (Followers: 13)
Forensic Genomics     Full-text available via subscription   (Followers: 12)
Alfarama Journal of Basic & Applied Sciences     Open Access   (Followers: 12)
PNAS Nexus     Open Access   (Followers: 11)
Molekul     Open Access   (Followers: 10)
Arctic     Open Access   (Followers: 9)
Ecological Solutions and Evidence     Open Access   (Followers: 9)
View     Open Access   (Followers: 9)
Advanced Membranes     Open Access   (Followers: 9)
Chem     Hybrid Journal   (Followers: 8)
Giant     Open Access   (Followers: 7)
STAR Protocols     Open Access   (Followers: 7)
Sensors and Actuators Reports     Open Access   (Followers: 7)
Animal Microbiome     Open Access   (Followers: 7)
Carbon Capture Science & Technology     Open Access   (Followers: 7)
Microplastics     Open Access   (Followers: 7)
Proceedings of the Vertebrate Pest Conference     Open Access   (Followers: 6)
Research     Open Access   (Followers: 6)
Molecular Biomedicine     Open Access   (Followers: 6)
Molecular Data Science     Full-text available via subscription   (Followers: 6)
Brain Science Advances     Open Access   (Followers: 6)
Abasyn Journal of Life Sciences     Open Access   (Followers: 5)
Cell Reports Medicine     Open Access   (Followers: 5)
Journal of Photochemistry and Photobiology     Open Access   (Followers: 5)
Current Research in Virological Science     Open Access   (Followers: 5)
Proceedings of the Indian National Science Academy     Full-text available via subscription   (Followers: 5)
Small Structures     Hybrid Journal   (Followers: 4)
Nano Select     Open Access   (Followers: 4)
The Lancet Microbe     Open Access   (Followers: 4)
Ecosystem Health and Sustainability     Open Access   (Followers: 4)
Gravitational and Space Research     Open Access   (Followers: 4)
Bioinformatics Advances : Journal of the International Society for Computational Biology     Open Access   (Followers: 4)
Science Talks     Full-text available via subscription   (Followers: 4)
Biosis : Biological Systems     Open Access   (Followers: 4)
Zitteliana     Open Access   (Followers: 3)
Bioeduscience     Open Access   (Followers: 3)
Aggregate     Open Access   (Followers: 3)
Current Research in Structural Biology     Open Access   (Followers: 3)
Environmental DNA     Open Access   (Followers: 3)
Cell Genomics     Full-text available via subscription   (Followers: 3)
Cell Reports Methods     Open Access   (Followers: 3)
Heilpflanzen     Hybrid Journal   (Followers: 3)
Peer Community Journal     Open Access   (Followers: 3)
Bioeduca : Journal of Biology Education     Open Access   (Followers: 3)
Journal of Plant Biology & Soil Health     Open Access   (Followers: 2)
Biogeographia : The Journal of Integrative Biogeography     Open Access   (Followers: 2)
Phenomics     Hybrid Journal   (Followers: 2)
All Life     Open Access   (Followers: 2)
Organs-on-a-Chip     Open Access   (Followers: 2)
International Journal of Bioelectromagnetism     Open Access   (Followers: 2)
Analytical Science Advances     Open Access   (Followers: 2)
Matrix Biology Plus     Open Access   (Followers: 2)
Contact (CTC)     Open Access   (Followers: 2)
Reproduction and Breeding     Open Access   (Followers: 2)
Advances in Biomarker Sciences and Technology     Open Access   (Followers: 2)
Journal of Life Science and Biomedicine     Open Access   (Followers: 2)
Current Research in Parasitology & Vector-Borne Diseases     Open Access   (Followers: 2)
JID Innovations     Open Access   (Followers: 2)
Medicine in Omics     Open Access   (Followers: 2)
Frontiers in Network Physiology     Open Access   (Followers: 2)
Biodiversity Observations     Open Access   (Followers: 2)
International Science and Technology Journal of Namibia     Open Access   (Followers: 2)
Journal of Metabolomics & Systems Biology     Open Access   (Followers: 1)
Bioethica     Open Access   (Followers: 1)
iBOL Barcode Bulletin     Open Access   (Followers: 1)
Canadian Journal of Bioethics     Open Access   (Followers: 1)
VITIS : Journal of Grapevine Research     Open Access   (Followers: 1)
Bionature     Open Access   (Followers: 1)
Journal of Bio-X Research     Open Access   (Followers: 1)
NAR Genomics and Bioinformatics     Open Access   (Followers: 1)
Nova Biologica Reperta / یافته‌های نوین در علوم زیستی     Open Access   (Followers: 1)
Bioactive Compounds in Health and Disease     Open Access   (Followers: 1)
Applied Phycology     Open Access   (Followers: 1)
RSC Chemical Biology     Open Access   (Followers: 1)
Plant-Environment Interactions     Open Access   (Followers: 1)
Journal of Zoological and Botanical Gardens     Open Access   (Followers: 1)
Reproductive Medicine     Open Access   (Followers: 1)
Current Research in Neurobiology     Open Access   (Followers: 1)
Artificial Intelligence in the Life Sciences     Open Access   (Followers: 1)
Current Research in Chemical Biology     Open Access   (Followers: 1)
EFB Bioeconomy Journal     Open Access   (Followers: 1)
Biosystematics and Ecology     Open Access   (Followers: 1)
Fish and Shellfish Immunology Reports     Open Access  
Biomimetic Intelligence and Robotics     Open Access  
Fundamental Research     Open Access  
International Journal of Advanced Statistics and IT&C for Economics and Life Sciences     Open Access  
Clinical Spectroscopy     Open Access  
Natural Sciences     Open Access  
Quantitative Plant Biology     Open Access  
International Journal of Biological, Physical and Chemical Studies     Open Access  
Passer Journal of Basic and Applied Sciences     Open Access  
UNM Journal of Biological Education     Open Access  
Jurnal Biogenerasi     Open Access  
Journal of Biocommunication     Open Access  
Fungal Genetics Reports     Open Access  
Proceedings of the Nova Scotian Institute of Science     Full-text available via subscription  
Vegetation Classification and Survey     Open Access  
Food and Ecological Systems Modelling Journal     Open Access  
Caucasiana     Open Access  
Arabian Journal of Scientific Research / المجلة العربية للبحث العلمي     Open Access  
Journal of Transplantation & Stem Cell Biology     Open Access  
Journal of Toxins     Open Access  
International Journal of Reproductive BioMedicine     Open Access  
KnE Life Sciences     Open Access  
Natural Product Communications     Open Access  
Global Journal of Ecology     Open Access  

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NAR Genomics and Bioinformatics
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2631-9268
Published by Oxford University Press Homepage  [425 journals]
  • NFixDB (Nitrogen Fixation DataBase)—a comprehensive integrated database
           for robust ‘omics analysis of diazotrophs

    • First page: lqae063
      Abstract: AbstractBiological 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.
      PubDate: Thu, 06 Jun 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae063
      Issue No: Vol. 6, No. 2 (2024)
       
  • Characterization of trans-spliced chimeric RNAs: insights into the
           mechanism of trans-splicing

    • First page: lqae067
      Abstract: AbstractTrans-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.
      PubDate: Thu, 06 Jun 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae067
      Issue No: Vol. 6, No. 2 (2024)
       
  • Detecting co-selection through excess linkage disequilibrium in bacterial
           genomes

    • First page: lqae061
      Abstract: AbstractPopulation 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.
      PubDate: Thu, 06 Jun 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae061
      Issue No: Vol. 6, No. 2 (2024)
       
  • From RNA sequence to its three-dimensional structure: geometrical
           

    • First page: lqae062
      Abstract: AbstractIn 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.
      PubDate: Tue, 04 Jun 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae062
      Issue No: Vol. 6, No. 2 (2024)
       
  • G-quadruplex propensity in H. neanderthalensis, H. sapiens and Denisovans
           mitochondrial genomes

    • First page: lqae060
      Abstract: AbstractCurrent 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.
      PubDate: Thu, 30 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae060
      Issue No: Vol. 6, No. 2 (2024)
       
  • Interpretable prediction of mRNA abundance from promoter sequence using
           contextual regression models

    • First page: lqae055
      Abstract: AbstractWhile machine learning models have been successfully applied to predicting gene expression from promoter sequences, it remains a great challenge to derive intuitive interpretation of the model and reveal DNA motif grammar such as motif cooperation and distance constraint between motif sites. Previous interpretation approaches are often time-consuming or have difficulty to learn the combinatory rules. In this work, we designed interpretable neural network models to predict the mRNA expression levels from DNA sequences. By applying the Contextual Regression framework we developed, we extracted weighted features to cluster samples into different groups, which have different gene expression levels. We performed motif analysis in each cluster and found motifs with active or repressive regulation on gene expression. By comparing the co-occurrence locations of discovered motifs, we also uncovered multiple grammars of motif combination including communities of cooperative motifs and distance constraints between motif pairs. These results revealed new insights of the regulatory architecture of promoter sequences.
      PubDate: Tue, 28 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae055
      Issue No: Vol. 6, No. 2 (2024)
       
  • PitViper: a software for comparative meta-analysis and annotation of
           functional screening data

    • First page: lqae059
      Abstract: AbstractRecent advancements in shRNA and Cas protein technologies have enabled functional screening methods targeting genes or non-coding regions using single or pooled shRNA and sgRNA. CRISPR-based systems have also been developed for modulating DNA accessibility, resulting in CRISPR-mediated interference (CRISPRi) or activation (CRISPRa) of targeted genes or genomic DNA elements. However, there is still a lack of software tools for integrating diverse array of functional genomics screening outputs that could offer a cohesive framework for comprehensive data integration. Here, we developed PitViper, a flexible and interactive open-source software designed to fill this gap, providing reliable results for the type of elements being screened. It is an end-to-end automated and reproducible bioinformatics pipeline integrating gold-standard methods for functional screening analysis. Our sensitivity analyses demonstrate that PitViper is a useful tool for identifying potential super-enhancer liabilities in a leukemia cell line through genome-wide CRISPRi-based screening. It offers a robust, flexible, and interactive solution for integrating data analysis and reanalysis from functional screening methods, making it a valuable resource for researchers in the field.
      PubDate: Sat, 25 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae059
      Issue No: Vol. 6, No. 2 (2024)
       
  • Structural dynamics of therapeutic nucleic acids with phosphorothioate
           backbone modifications

    • First page: lqae058
      Abstract: AbstractAntisense oligonucleotides (ASOs) offer ground-breaking possibilities for selective pharmacological intervention for any gene product-related disease. Therapeutic ASOs contain extensive chemical modifications that improve stability to enzymatic cleavage and modulate binding affinity relative to natural RNA/DNA. Molecular dynamics (MD) simulation can provide valuable insights into how such modifications affect ASO conformational sampling and target binding. However, force field parameters for chemically modified nucleic acids (NAs) are still underdeveloped. To bridge this gap, we developed parameters to allow simulations of ASOs with the widely applied phosphorothioate (PS) backbone modification, and validated these in extensive all-atom MD simulations of relevant PS-modified NA systems representing B-DNA, RNA, and DNA/RNA hybrid duplex structures. Compared to the corresponding natural NAs, single PS substitutions had marginal effects on the ordered DNA/RNA duplex, whereas substantial effects of phosphorothioation were observed in single-stranded RNA and B-DNA, corroborated by the experimentally derived structure data. We find that PS-modified NAs shift between high and low twist states, which could affect target recognition and protein interactions for phosphorothioated oligonucleotides. Furthermore, conformational sampling was markedly altered in the PS-modified ssRNA system compared to that of the natural oligonucleotide, indicating sequence-dependent effects on conformational preference that may in turn influence duplex formation.
      PubDate: Sat, 25 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae058
      Issue No: Vol. 6, No. 2 (2024)
       
  • ViralFlow v1.0—a computational workflow for streamlining viral
           genomic surveillance

    • First page: lqae056
      Abstract: AbstractViralFlow v1.0 is a computational workflow developed for viral genomic surveillance. Several key changes turned ViralFlow into a general-purpose reference-based genome assembler for all viruses with an available reference genome. New virus-agnostic modules were implemented to further study nucleotide and amino acid mutations. ViralFlow v1.0 runs on a broad range of computational infrastructures, from laptop computers to high-performance computing (HPC) environments, and generates standard and well-formatted outputs suited for both public health reporting and scientific problem-solving. ViralFlow v1.0 is available at: https://viralflow.github.io/index-en.html.
      PubDate: Sat, 25 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae056
      Issue No: Vol. 6, No. 2 (2024)
       
  • A single-cell strategy for the identification of intronic variants related
           to mis-splicing in pancreatic cancer

    • First page: lqae057
      Abstract: AbstractMost clinical diagnostic and genomic research setups focus almost exclusively on coding regions and essential splice sites, thereby overlooking other non-coding variants. As a result, intronic variants that can promote mis-splicing events across a range of diseases, including cancer, are yet to be systematically investigated. Such investigations would require both genomic and transcriptomic data, but there currently exist very few datasets that satisfy these requirements. We address this by developing a single-nucleus full-length RNA-sequencing approach that allows for the detection of potentially pathogenic intronic variants. We exemplify the potency of our approach by applying pancreatic cancer tumor and tumor-derived specimens and linking intronic variants to splicing dysregulation. We specifically find that prominent intron retention and pseudo-exon activation events are shared by the tumors and affect genes encoding key transcriptional regulators. Our work paves the way for the assessment and exploitation of intronic mutations as powerful prognostic markers and potential therapeutic targets in cancer.
      PubDate: Sat, 25 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae057
      Issue No: Vol. 6, No. 2 (2024)
       
  • Escherichia coli non-coding regulatory regions are highly conserved

    • First page: lqae041
      Abstract: AbstractMicrobial genome sequences are rapidly accumulating, enabling large-scale studies of sequence variation. Existing studies primarily focus on coding regions to study amino acid substitution patterns in proteins. However, non-coding regulatory regions also play a distinct role in determining physiologic responses. To investigate intergenic sequence variation on a large-scale, we identified non-coding regulatory region alleles across 2350 Escherichia coli strains. This ‘alleleome’ consists of 117 781 unique alleles for 1169 reference regulatory regions (transcribing 1975 genes) at single base-pair resolution. We find that 64% of nucleotide positions are invariant, and variant positions vary in a median of just 0.6% of strains. Additionally, non-coding alleles are sufficient to recover E. coli phylogroups. We find that core promoter elements and transcription factor binding sites are significantly conserved, especially those located upstream of essential or highly-expressed genes. However, variability in conservation of transcription factor binding sites is significant both within and across regulons. Finally, we contrast mutations acquired during adaptive laboratory evolution with wild-type variation, finding that the former preferentially alter positions that the latter conserves. Overall, this analysis elucidates the wealth of information found in E. coli non-coding sequence variation and expands pangenomic studies to non-coding regulatory regions at single-nucleotide resolution.
      PubDate: Mon, 20 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae041
      Issue No: Vol. 6, No. 2 (2024)
       
  • Nanopore Current Events Magnifier (nanoCEM): a novel tool for visualizing
           current events at modification sites of nanopore sequencing

    • First page: lqae052
      Abstract: AbstractSummary: 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.
      PubDate: Mon, 20 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae052
      Issue No: Vol. 6, No. 2 (2024)
       
  • BaRDIC: robust peak calling for RNA–DNA interaction data

    • First page: lqae054
      Abstract: AbstractChromatin-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.
      PubDate: Mon, 20 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae054
      Issue No: Vol. 6, No. 2 (2024)
       
  • Advances in single-cell long-read sequencing technologies

    • First page: lqae047
      Abstract: AbstractWith 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.
      PubDate: Mon, 20 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae047
      Issue No: Vol. 6, No. 2 (2024)
       
  • Homorepeat variability within the human population

    • First page: lqae053
      Abstract: AbstractGenetic variation within populations plays a crucial role in driving evolution. Unlike the average protein sequence, the evolution of homorepeats can be influenced by DNA replication slippage, when DNA polymerases either add or skip repeats of nucleotides. While there are some diseases known to be caused by abnormal changes in the length of amino acid homorepeats, naturally occurring variations in homorepeat length remain relatively unexplored. In our study, we examined the variation in amino acid homorepeat length of human individuals by analyzing 125 748 exomes, as well as 15 708 whole genomes. Our analyses revealed significant variability in homorepeat length across the human population, indicating that these motifs are prone to mutations at higher rates than non repeat sequences. We focused our study on glutamine homorepeats, also known as polyQ sequences, and found that shorter polyQ sequences tend to exhibit greater length variation, while longer ones primarily undergo deletions. Notably, polyQ sequencesthat are more conserved across primates tend to show less variation within the human population, indicating stronger selective pressure to maintain their length. Overall, our results demonstrate that there is large natural variation in the length of homorepeats within the human population, with no apparent impact on observable traits.
      PubDate: Mon, 20 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae053
      Issue No: Vol. 6, No. 2 (2024)
       
  • MUFFIN: a suite of tools for the analysis of functional sequencing data

    • First page: lqae051
      Abstract: AbstractThe large diversity of functional genomic assays allows for the characterization of non-coding and coding events at the tissue level or at a single-cell resolution. However, this diversity also leads to protocol differences, widely varying sequencing depths, substantial disparities in sample sizes, and number of features. In this work, we have built a Python package, MUFFIN, which offers a wide variety of tools suitable for a broad range of genomic assays and brings many tools that were missing from the Python ecosystem. First, MUFFIN has specialized tools for the exploration of the non-coding regions of genomes, such as a function to identify consensus peaks in peak-called assays, as well as linking genomic regions to genes and performing Gene Set Enrichment Analyses. MUFFIN also possesses a robust and flexible count table processing pipeline, comprising normalization, count transformation, dimensionality reduction, Differential Expression, and clustering. Our tools were tested on three widely different scRNA-seq, ChIP-seq and ATAC-seq datasets. MUFFIN integrates with the popular Scanpy ecosystem and is available on Conda and at https://github.com/pdelangen/Muffin.
      PubDate: Tue, 14 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae051
      Issue No: Vol. 6, No. 2 (2024)
       
  • Predicting gene disease associations with knowledge graph embeddings for
           diseases with curtailed information

    • First page: lqae049
      Abstract: AbstractKnowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and, in particular, regarding their applications to prioritize genes associated with complex diseases with reduced genetic information. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGE by implementing state-of-the-art methods, and two novel algorithms: Dlemb and BioKG2vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both scenarios. Our findings underscore the significance of data quality, preprocessing, and integration in achieving accurate predictions. Additionally, we applied KGE to predict genes linked to Intervertebral Disc Degeneration (IDD) and illustrated that functions pertinent to the disease are enriched within the prioritized gene set.
      PubDate: Tue, 14 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae049
      Issue No: Vol. 6, No. 2 (2024)
       
  • State-of-the-RNArt: benchmarking current methods for RNA 3D structure
           prediction

    • First page: lqae048
      Abstract: AbstractRNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/.
      PubDate: Tue, 14 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae048
      Issue No: Vol. 6, No. 2 (2024)
       
  • DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and
           viruses

    • First page: lqae044
      Abstract: AbstractSequence classification facilitates a fundamental understanding of the structure of microbial communities. Binary metagenomic sequence classifiers are insufficient because environmental metagenomes are typically derived from multiple sequence sources. Here we introduce a deep-learning based sequence classifier, DeepMicroClass, that classifies metagenomic contigs into five sequence classes, i.e. viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. DeepMicroClass achieved high performance for all sequence classes at various tested sequence lengths ranging from 500 bp to 100 kbps. By benchmarking on a synthetic dataset with variable sequence class composition, we showed that DeepMicroClass obtained better performance for eukaryotic, plasmid and viral contig classification than other state-of-the-art predictors. DeepMicroClass achieved comparable performance on viral sequence classification with geNomad and VirSorter2 when benchmarked on the CAMI II marine dataset. Using a coastal daily time-series metagenomic dataset as a case study, we showed that microbial eukaryotes and prokaryotic viruses are integral to microbial communities. By analyzing monthly metagenomes collected at HOT and BATS, we found relatively higher viral read proportions in the subsurface layer in late summer, consistent with the seasonal viral infection patterns prevalent in these areas. We expect DeepMicroClass will promote metagenomic studies of under-appreciated sequence types.
      PubDate: Mon, 06 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae044
      Issue No: Vol. 6, No. 2 (2024)
       
  • Unravelling genetic architecture of circulatory amino acid levels, and
           their effect on risk of complex disorders

    • First page: lqae046
      Abstract: AbstractVariations in serum amino acid levels are linked to a multitude of complex disorders. We report the largest genome-wide association study (GWAS) on nine serum amino acids in the UK Biobank participants (117 944, European descent). We identified 34 genomic loci for circulatory levels of alanine, 48 loci for glutamine, 44 loci for glycine, 16 loci for histidine, 11 loci for isoleucine, 19 loci for leucine, 9 loci for phenylalanine, 32 loci for tyrosine and 20 loci for valine. Our gene-based analysis mapped 46–293 genes associated with serum amino acids, including MIP, GLS2, SLC gene family, GCKR, LMO1, CPS1 and COBLL1.The gene–property analysis across 30 tissues highlighted enriched expression of the identified genes in liver tissues for all studied amino acids, except for isoleucine and valine, in muscle tissues for serum alanine and glycine, in adrenal gland tissues for serum isoleucine and leucine, and in pancreatic tissues for serum phenylalanine. Mendelian randomization (MR) phenome-wide association study analysis and subsequent two-sample MR analysis provided evidence that every standard deviation increase in valine is associated with 35% higher risk of type 2 diabetes and elevated levels of serum alanine and branched-chain amino acids with higher levels of total cholesterol, triglyceride and low-density lipoprotein, and lower levels of high-density lipoprotein. In contrast to reports by observational studies, MR analysis did not support a causal association between studied amino acids and coronary artery disease, Alzheimer’s disease, breast cancer or prostate cancer. In conclusion, we explored the genetic architecture of serum amino acids and provided evidence supporting a causal role of amino acids in cardiometabolic health.
      PubDate: Mon, 06 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae046
      Issue No: Vol. 6, No. 2 (2024)
       
  • Malat1 affects transcription and splicing through distinct pathways in
           mouse embryonic stem cells

    • First page: lqae045
      Abstract: AbstractMalat1 is a long-noncoding RNA with critical roles in gene regulation and cancer metastasis, however its functional role in stem cells is largely unexplored. We here perform a nuclear knockdown of Malat1 in mouse embryonic stem cells, causing the de-regulation of 320 genes and aberrant splicing of 90 transcripts, some of which potentially affecting the translated protein sequence. We find evidence that Malat1 directly interacts with gene bodies and aberrantly spliced transcripts, and that it locates upstream of down-regulated genes at their putative enhancer regions, in agreement with functional genomics data. Consistent with this, we find these genes affected at both exon and intron levels, suggesting that they are transcriptionally regulated by Malat1. Besides, the down-regulated genes are regulated by specific transcription factors and bear both activating and repressive chromatin marks, suggesting that some of them might be regulated by bivalent promoters. We propose a model in which Malat1 facilitates the transcription of genes involved in chromatid dynamics and mitosis in one pathway, and affects the splicing of transcripts that are themselves involved in RNA processing in a distinct pathway. Lastly, we compare our findings with Malat1 perturbation studies performed in other cell systems and in vivo.
      PubDate: Mon, 06 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae045
      Issue No: Vol. 6, No. 2 (2024)
       
  • A computational approach for deciphering the interactions between proximal
           and distal gene regulators in GC B-cell response

    • First page: lqae050
      Abstract: AbstractDelineating the intricate interplay between promoter-proximal and -distal regulators is crucial for understanding the function of transcriptional mediator complexes implicated in the regulation of gene expression. The present study aimed to develop a computational method for accurately modeling the spatial proximal and distal regulatory interactions. Our method combined regression-based models to identify key regulators through gene expression prediction and a graph-embedding approach to detect coregulated genes. This approach enabled a detailed investigation of the gene regulatory mechanisms for germinal center B cells, accompanied by dramatic rearrangements of the genome structure. We found that while the promoter-proximal regulatory elements were the principal regulators of gene expression, the distal regulators fine-tuned transcription. Moreover, our approach unveiled the presence of modular regulators, such as cofactors and proximal/distal transcription factors, which were co-expressed with their target genes. Some of these modules exhibited abnormal expression patterns in lymphoma. These findings suggest that the dysregulation of interactions between transcriptional and architectural factors is associated with chromatin reorganization failure, which may increase the risk of malignancy. Therefore, our computational approach helps decipher the transcriptional cis-regulatory code spatially interacting.
      PubDate: Mon, 06 May 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae050
      Issue No: Vol. 6, No. 2 (2024)
       
  • Nextflow vs. plain bash: different approaches to the parallelization of
           SNP calling from the whole genome sequence data

    • First page: lqae040
      Abstract: AbstractThis study compared computational approaches to parallelization of an SNP calling workflow. The data comprised DNA from five Holstein-Friesian cows sequenced with the Illumina platform. The pipeline consisted of quality control, alignment to the reference genome, post-alignment, and SNP calling. Three approaches to parallelization were compared: (i) a plain Bash script in which a pipeline for each cow was executed as separate processes invoked at the same time, (ii) a Bash script wrapped in a single Nextflow process and (iii) a Nextflow script with each component of the pipeline defined as a separate process. The results demonstrated that on average, the multi-process Nextflow script performed 15–27% faster depending on the number of assigned threads, with the biggest execution time advantage over the plain Bash approach observed with 10 threads. In terms of RAM usage, the most substantial variation was observed for the multi-process Nextflow, for which it increased with the number of assigned threads, while RAM consumption of the other setups did not depend much on the number of threads assigned for computations. Due to intermediate and log files generated, disk usage was markedly higher for the multi-process Nextflow than for the plain Bash and for the single-process Nextflow.
      PubDate: Mon, 29 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae040
      Issue No: Vol. 6, No. 2 (2024)
       
  • Cancer drug sensitivity estimation using modular deep Graph Neural
           Networks

    • First page: lqae043
      Abstract: AbstractComputational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drugs components that are tailored to the transcriptomic profile of a given primary tumor. The SMILES representation of molecules that is used by state-of-the-art drug-sensitivity models is not conducive for neural networks to generalize to new drugs, in part because the distance between atoms does not generally correspond to the distance between their representation in the SMILES strings. Graph-attention networks, on the other hand, are high-capacity models that require large training-data volumes which are not available for drug-sensitivity estimation. We develop a modular drug-sensitivity graph-attentional neural network. The modular architecture allows us to separately pre-train the graph encoder and graph-attentional pooling layer on related tasks for which more data are available. We observe that this model outperforms reference models for the use cases of precision oncology and drug discovery; in particular, it is better able to predict the specific interaction between drug and cell line that is not explained by the general cytotoxicity of the drug and the overall survivability of the cell line. The complete source code is available at https://zenodo.org/doi/10.5281/zenodo.8020945. All experiments are based on the publicly available GDSC data.
      PubDate: Sat, 27 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae043
      Issue No: Vol. 6, No. 2 (2024)
       
  • Scalable and efficient DNA sequencing analysis on different compute
           infrastructures aiding variant discovery

    • First page: lqae031
      Abstract: AbstractDNA 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.
      PubDate: Thu, 25 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae031
      Issue No: Vol. 6, No. 2 (2024)
       
  • Proposing candidate genes under telomeric control based on cross-species
           position data

    • First page: lqae037
      Abstract: AbstractIn this paper, we present a comprehensive computational framework aimed at suggesting genes whose transcriptional regulation is likely to be influenced by their chromosomal position. This framework provides a user-friendly web interface, enabling researchers to explore the positional properties of all human genes and their orthologs across species, with a focus on their relation to the telomeres. Our approach involves multiple scoring methods, each adjustable by users, representing different features of the genes' positional variation across species. The resulting rankings can be combined to identify candidate genes that may be subject to position effects. Furthermore, the ranking can be tailored to a specific set of reference genes. We evaluate the method within the context of TPE-OLD, a mechanism where telomeres can exert a direct influence on gene expression across considerable genomic distances, and empower researchers to delve deeper into genes of interest, analyzing their position across species and estimating their susceptibility to position effects like TPE-OLD. We also provide simple enrichment analyses of user-provided gene lists in relation to top-ranking candidate genes.
      PubDate: Thu, 25 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae037
      Issue No: Vol. 6, No. 2 (2024)
       
  • Microbiome compositional data analysis for survival studies

    • First page: lqae038
      Abstract: AbstractThe growing interest in studying the relationship between the human microbiome and our health has also extended to time-to-event studies where researchers explore the connection between the microbiome and the occurrence of a specific event of interest. The analysis of microbiome obtained through high throughput sequencing techniques requires the use of specialized Compositional Data Analysis (CoDA) methods designed to accommodate its compositional nature. There is a limited availability of statistical tools for microbiome analysis that incorporate CoDA, and this is even more pronounced in the context of survival analysis. To fill this methodological gap, we present coda4microbiome for survival studies, a new methodology for the identification of microbial signatures in time-to-event studies. The algorithm implements an elastic-net penalized Cox regression model adapted to compositional covariates. We illustrate coda4microbiome algorithm for survival studies with a case study about the time to develop type 1 diabetes for non-obese diabetic mice. Our algorithm identified a bacterial signature composed of 21 genera associated with diabetes development. coda4microbiome for survival studies is integrated in the R package coda4microbiome as an extension of the existing functions for cross-sectional and longitudinal studies.
      PubDate: Thu, 25 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae038
      Issue No: Vol. 6, No. 2 (2024)
       
  • CWGCNA: an R package to perform causal inference from the WGCNA framework

    • First page: lqae042
      Abstract: AbstractWGCNA (weighted gene co-expression network analysis) is a very useful tool for identifying co-expressed gene modules and detecting their correlations to phenotypic traits. Here, we explored more possibilities about it and developed the R package CWGCNA (causal WGCNA), which works from the traditional WGCNA pipeline but mines more information. It couples a mediation model with WGCNA, so the causal relationships among WGCNA modules, module features, and phenotypes can be found, demonstrating whether the module change causes the phenotype change or vice versa. After that, when annotating the module gene set functions, it uses a novel network-based method, considering the modules' topological structures and capturing their influence on the gene set functions. In addition to conducting these biological explorations, CWGCNA also contains a machine learning section to perform clustering and classification on multi-omics data, given the increasing popularity of this data type. Some basic functions, such as differential feature identification, are also available in our package. Its effectiveness is proved by the performance on three single or multi-omics datasets, showing better performance than existing methods. CWGCNA is available at: https://github.com/yuabrahamliu/CWGCNA.
      PubDate: Thu, 25 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae042
      Issue No: Vol. 6, No. 2 (2024)
       
  • A comprehensive analysis of pneumococcal two-component system regulatory
           networks

    • First page: lqae039
      Abstract: AbstractTwo-component systems are key signal-transduction systems that enable bacteria to respond to a wide variety of environmental stimuli. The human pathogen, Streptococcus pneumoniae (pneumococcus) encodes 13 two-component systems and a single orphan response regulator, most of which are significant for pneumococcal pathogenicity. Mapping the regulatory networks governed by these systems is key to understand pneumococcal host adaptation. Here we employ a novel bioinformatic approach to predict the regulons of each two-component system based on publicly available whole-genome sequencing data. By employing pangenome-wide association studies (panGWAS) to predict genotype-genotype associations for each two-component system, we predicted regulon genes of 11 of the pneumococcal two-component systems. Through validation via next-generation RNA-sequencing on response regulator overexpression mutants, several top candidate genes predicted by the panGWAS analysis were confirmed as regulon genes. The present study presents novel details on multiple pneumococcal two-component systems, including an expansion of regulons, identification of candidate response regulator binding motifs, and identification of candidate response regulator-regulated small non-coding RNAs. We also demonstrate a use for panGWAS as a complementary tool in target gene identification via identification of genotype-to-genotype links. Expanding our knowledge on two-component systems in pathogens is crucial to understanding how these bacteria sense and respond to their host environment, which could prove useful in future drug development.
      PubDate: Mon, 22 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae039
      Issue No: Vol. 6, No. 2 (2024)
       
  • A data-driven estimation of the ribosome drop-off rate in S. cerevisiae
           reveals a correlation with the genes length

    • First page: lqae036
      Abstract: AbstractRibosomes 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.
      PubDate: Thu, 18 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae036
      Issue No: Vol. 6, No. 2 (2024)
       
  • A method to comprehensively identify germline SNVs, INDELs and CNVs from
           whole exome sequencing data of BRCA1/2 negative breast cancer patients

    • First page: lqae033
      Abstract: AbstractIn the rapidly evolving field of genomics, understanding the genetic basis of complex diseases like breast cancer, particularly its familial/hereditary forms, is crucial. Current methods often examine genomic variants—such as Single Nucleotide Variants (SNVs), insertions/deletions (Indels), and Copy Number Variations (CNVs)—separately, lacking an integrated approach. Here, we introduced a robust, flexible methodology for a comprehensive variants’ analysis using Whole Exome Sequencing (WES) data. Our approach uniquely combines meticulous validation with an effective variant filtering strategy. By reanalyzing two germline WES datasets from BRCA1/2 negative breast cancer patients, we demonstrated our tool’s efficiency and adaptability, uncovering both known and novel variants. This contributed new insights for potential diagnostic, preventive, and therapeutic strategies. Our method stands out for its comprehensive inclusion of key genomic variants in a unified analysis, and its practical resolution of technical challenges, offering a pioneering solution in genomic research. This tool presents a breakthrough in providing detailed insights into the genetic alterations in genomes, with significant implications for understanding and managing hereditary breast cancer.
      PubDate: Wed, 17 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae033
      Issue No: Vol. 6, No. 2 (2024)
       
  • Spinach genomes reveal migration history and candidate genes for important
           crop traits

    • First page: lqae034
      Abstract: AbstractSpinach (Spinacia oleracea) is an important leafy crop possessing notable economic value and health benefits. Current genomic resources include reference genomes and genome-wide association studies. However, the worldwide genetic relationships and the migration history of the crop remained uncertain, and genome-wide association studies have produced extensive gene lists related to agronomic traits. Here, we re-analysed the sequenced genomes of 305 cultivated and wild spinach accessions to unveil the phylogeny and history of cultivated spinach and to explore genetic variation in relation to phenotypes. In contrast to previous studies, we employed machine learning methods (based on Extreme Gradient Boosting, XGBoost) to detect variants that are collectively associated with agronomic traits. Variant-based cluster analyses revealed three primary spinach groups in the Middle East, Asia and Europe/US. Combining admixture analysis and allele-sharing statistics, migration routes of spinach from the Middle East to Europe and Asia are presented. Using XGBoost machine learning models we predict genomic variants influencing bolting time, flowering time, petiole color, and leaf surface texture and propose candidate genes for each trait. This study enhances our understanding of the history and phylogeny of domesticated spinach and provides valuable information on candidate genes for future genetic improvement of the crop.
      PubDate: Wed, 17 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae034
      Issue No: Vol. 6, No. 2 (2024)
       
  • ChimericFragments: computation, analysis and visualization of global
           RNA networks

    • First page: lqae035
      Abstract: AbstractRNA–RNA interactions are a key feature of post-transcriptional gene regulation in all domains of life. While ever more experimental protocols are being developed to study RNA duplex formation on a genome-wide scale, computational methods for the analysis and interpretation of the underlying data are lagging behind. Here, we present ChimericFragments, an analysis framework for RNA-seq experiments that produce chimeric RNA molecules. ChimericFragments implements a novel statistical method based on the complementarity of the base-pairing RNAs around their ligation site and provides an interactive graph-based visualization for data exploration and interpretation. ChimericFragments detects true RNA–RNA interactions with high precision and is compatible with several widely used experimental procedures such as RIL-seq, LIGR-seq or CLASH. We further demonstrate that ChimericFragments enables the systematic detection of novel RNA regulators and RNA–target pairs with crucial roles in microbial physiology and virulence. ChimericFragments is written in Julia and available at: https://github.com/maltesie/ChimericFragments.
      PubDate: Wed, 17 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae035
      Issue No: Vol. 6, No. 2 (2024)
       
  • Properties and predicted functions of large genes and proteins of
           apicomplexan parasites

    • First page: lqae032
      Abstract: AbstractEvolutionary constraints greatly favor compact genomes that efficiently encode proteins. However, several eukaryotic organisms, including apicomplexan parasites such as Toxoplasma gondii, Plasmodium falciparum and Babesia duncani, the causative agents of toxoplasmosis, malaria and babesiosis, respectively, encode very large proteins, exceeding 20 times their average protein size. Although these large proteins represent <1% of the total protein pool and are generally expressed at low levels, their persistence throughout evolution raises important questions about their functions and possible evolutionary pressures to maintain them. In this study, we examined the trends in gene and protein size, function and expression patterns within seven apicomplexan pathogens. Our analysis revealed that certain large proteins in apicomplexan parasites harbor domains potentially important for functions such as antigenic variation, erythrocyte invasion and immune evasion. However, these domains are not limited to or strictly conserved within large proteins. While some of these proteins are predicted to engage in conventional metabolic pathways within these parasites, others fulfill specialized functions for pathogen–host interactions, nutrient acquisition and overall survival.
      PubDate: Thu, 04 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae032
      Issue No: Vol. 6, No. 2 (2024)
       
  • The determinants of the rarity of nucleic and peptide short sequences in
           nature

    • First page: lqae029
      Abstract: AbstractThe 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.
      PubDate: Thu, 04 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae029
      Issue No: Vol. 6, No. 2 (2024)
       
  • VIBES: a workflow for annotating and visualizing viral sequences
           integrated into bacterial genomes

    • First page: lqae030
      Abstract: AbstractBacteriophages are viruses that infect bacteria. Many bacteriophages integrate their genomes into the bacterial chromosome and become prophages. Prophages may substantially burden or benefit host bacteria fitness, acting in some cases as parasites and in others as mutualists. Some prophages have been demonstrated to increase host virulence. The increasing ease of bacterial genome sequencing provides an opportunity to deeply explore prophage prevalence and insertion sites. Here we present VIBES (Viral Integrations in Bacterial genomES), a workflow intended to automate prophage annotation in complete bacterial genome sequences. VIBES provides additional context to prophage annotations by annotating bacterial genes and viral proteins in user-provided bacterial and viral genomes. The VIBES pipeline is implemented as a Nextflow-driven workflow, providing a simple, unified interface for execution on local, cluster and cloud computing environments. For each step of the pipeline, a container including all necessary software dependencies is provided. VIBES produces results in simple tab-separated format and generates intuitive and interactive visualizations for data exploration. Despite VIBES’s primary emphasis on prophage annotation, its generic alignment-based design allows it to be deployed as a general-purpose sequence similarity search manager. We demonstrate the utility of the VIBES prophage annotation workflow by searching for 178 Pf phage genomes across 1072 Pseudomonas spp. genomes.
      PubDate: Thu, 04 Apr 2024 00:00:00 GMT
      DOI: 10.1093/nargab/lqae030
      Issue No: Vol. 6, No. 2 (2024)
       
 
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Hydrobiology     Open Access   (Followers: 39)
Sustainability and Climate Change     Full-text available via subscription   (Followers: 32)
Climate Change Ecology     Open Access   (Followers: 30)
F&S Science : Official journal of the American Society for Reproductive Medicine     Open Access   (Followers: 27)
Gut Microbiome     Open Access   (Followers: 26)
Biomaterials Advances     Full-text available via subscription   (Followers: 25)
Zoonotic Diseases     Open Access   (Followers: 24)
Anatomia     Open Access   (Followers: 15)
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Alfarama Journal of Basic & Applied Sciences     Open Access   (Followers: 12)
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Ecological Solutions and Evidence     Open Access   (Followers: 9)
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STAR Protocols     Open Access   (Followers: 7)
Sensors and Actuators Reports     Open Access   (Followers: 7)
Animal Microbiome     Open Access   (Followers: 7)
Carbon Capture Science & Technology     Open Access   (Followers: 7)
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Research     Open Access   (Followers: 6)
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Cell Reports Medicine     Open Access   (Followers: 5)
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Proceedings of the Indian National Science Academy     Full-text available via subscription   (Followers: 5)
Small Structures     Hybrid Journal   (Followers: 4)
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The Lancet Microbe     Open Access   (Followers: 4)
Ecosystem Health and Sustainability     Open Access   (Followers: 4)
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Biosis : Biological Systems     Open Access   (Followers: 4)
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Cell Reports Methods     Open Access   (Followers: 3)
Heilpflanzen     Hybrid Journal   (Followers: 3)
Peer Community Journal     Open Access   (Followers: 3)
Bioeduca : Journal of Biology Education     Open Access   (Followers: 3)
Journal of Plant Biology & Soil Health     Open Access   (Followers: 2)
Biogeographia : The Journal of Integrative Biogeography     Open Access   (Followers: 2)
Phenomics     Hybrid Journal   (Followers: 2)
All Life     Open Access   (Followers: 2)
Organs-on-a-Chip     Open Access   (Followers: 2)
International Journal of Bioelectromagnetism     Open Access   (Followers: 2)
Analytical Science Advances     Open Access   (Followers: 2)
Matrix Biology Plus     Open Access   (Followers: 2)
Contact (CTC)     Open Access   (Followers: 2)
Reproduction and Breeding     Open Access   (Followers: 2)
Advances in Biomarker Sciences and Technology     Open Access   (Followers: 2)
Journal of Life Science and Biomedicine     Open Access   (Followers: 2)
Current Research in Parasitology & Vector-Borne Diseases     Open Access   (Followers: 2)
JID Innovations     Open Access   (Followers: 2)
Medicine in Omics     Open Access   (Followers: 2)
Frontiers in Network Physiology     Open Access   (Followers: 2)
Biodiversity Observations     Open Access   (Followers: 2)
International Science and Technology Journal of Namibia     Open Access   (Followers: 2)
Journal of Metabolomics & Systems Biology     Open Access   (Followers: 1)
Bioethica     Open Access   (Followers: 1)
iBOL Barcode Bulletin     Open Access   (Followers: 1)
Canadian Journal of Bioethics     Open Access   (Followers: 1)
VITIS : Journal of Grapevine Research     Open Access   (Followers: 1)
Bionature     Open Access   (Followers: 1)
Journal of Bio-X Research     Open Access   (Followers: 1)
NAR Genomics and Bioinformatics     Open Access   (Followers: 1)
Nova Biologica Reperta / یافته‌های نوین در علوم زیستی     Open Access   (Followers: 1)
Bioactive Compounds in Health and Disease     Open Access   (Followers: 1)
Applied Phycology     Open Access   (Followers: 1)
RSC Chemical Biology     Open Access   (Followers: 1)
Plant-Environment Interactions     Open Access   (Followers: 1)
Journal of Zoological and Botanical Gardens     Open Access   (Followers: 1)
Reproductive Medicine     Open Access   (Followers: 1)
Current Research in Neurobiology     Open Access   (Followers: 1)
Artificial Intelligence in the Life Sciences     Open Access   (Followers: 1)
Current Research in Chemical Biology     Open Access   (Followers: 1)
EFB Bioeconomy Journal     Open Access   (Followers: 1)
Biosystematics and Ecology     Open Access   (Followers: 1)
Fish and Shellfish Immunology Reports     Open Access  
Biomimetic Intelligence and Robotics     Open Access  
Fundamental Research     Open Access  
International Journal of Advanced Statistics and IT&C for Economics and Life Sciences     Open Access  
Clinical Spectroscopy     Open Access  
Natural Sciences     Open Access  
Quantitative Plant Biology     Open Access  
International Journal of Biological, Physical and Chemical Studies     Open Access  
Passer Journal of Basic and Applied Sciences     Open Access  
UNM Journal of Biological Education     Open Access  
Jurnal Biogenerasi     Open Access  
Journal of Biocommunication     Open Access  
Fungal Genetics Reports     Open Access  
Proceedings of the Nova Scotian Institute of Science     Full-text available via subscription  
Vegetation Classification and Survey     Open Access  
Food and Ecological Systems Modelling Journal     Open Access  
Caucasiana     Open Access  
Arabian Journal of Scientific Research / المجلة العربية للبحث العلمي     Open Access  
Journal of Transplantation & Stem Cell Biology     Open Access  
Journal of Toxins     Open Access  
International Journal of Reproductive BioMedicine     Open Access  
KnE Life Sciences     Open Access  
Natural Product Communications     Open Access  
Global Journal of Ecology     Open Access  

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