Large-scale computational discovery and analysis of virus-derived microbial nanocompartments.
Ontology highlight
ABSTRACT:
SUBMITTER: Andreas MP
PROVIDER: S-EPMC8346489 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Accumulating evidence has suggested that tRNA-derived fragments (tRFs) could be loaded to Argonaute proteins and function as regulatory small RNAs. However, their mode of action remains largely unknown, and investigations of their binding mechanisms have been limited, revealing little more than microRNA-like seed regions in a handful of tRFs and a few targets. Here, we identified such regions of potential interaction on a larger scale, using in vivo formed hybrids of guides and targets in crosslinked chimeric reads in two orientations. We considered "forward pairs" (with guides located on the 5' ends and targets on the 3' ends of hybrids) and "reverse pairs" (opposite orientation) and compared them as independent sets of biological constructs. We observed intriguing differences between the two chimera orientations, including the paucity of tRNA halves and abundance of polyT-containing targets in forward pairs. We found a total of 197 quality-ranked motifs supported by ∼120,000 tRF-mRNA chimeras, with 103 interacting motifs common in forward and reverse pairs. By analyzing T→C conversions in human and mouse PAR-CLIP datasets, we detected Argonaute crosslinking sites in tRFs, conserved across species. We proposed a novel model connecting the formation of asymmetric pairs in two sets to the potential binding mechanisms of tRFs, involving the identified interaction motifs and crosslinking sites to Argonaute proteins. Our results suggest the way forward for further experimental elucidation of tRF-binding mechanisms.
| S-EPMC8258673 | biostudies-literature
Project description:Modern metagenomic environmental DNA studies are almost completely reliant on next-generation sequencing, making evaluations of these methods critical. We compare two next-generation sequencing techniques - amplicon and shotgun - on water samples across four of Brazil's major river floodplain systems (Amazon, Araguaia, Paraná, and Pantanal). Less than 50% of phyla identified via amplicon sequencing were recovered from shotgun sequencing, clearly challenging the dogma that mid-depth shotgun recovers more diversity than amplicon-based approaches. Amplicon sequencing also revealed ~27% more families. Overall the amplicon data were more robust across both biodiversity and community ecology analyses at different taxonomic scales. Our work doubles the sampling size in similar environmental studies, and novelly integrates environmental data (e.g., pH, temperature, nutrients) from each site, revealing divergent correlations depending on which data are used. While myriad variants on NGS techniques and bioinformatic pipelines are available, our results point to core differences that have not been highlighted in any studies to date. Given the low number of taxa identified when coupling shotgun data with clade-based taxonomic algorithms, previous studies that quantified biodiversity using such bioinformatic tools should be viewed cautiously or re-analyzed. Nonetheless, shotgun has complementary advantages that should be weighed when designing projects.
| S-EPMC5537354 | biostudies-literature
Project description:MotivationThe rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing.ResultsIn this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efficiency. The proposed framework was implemented on Apache Spark, which allows for easy and efficient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can significantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (?2.2B reads, 437 GB) demonstrated the excellent scalability of the proposed method.Availability and implementationOpen-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/~yijunsun/lab/SLAD.html.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6931356 | biostudies-literature
Project description:Mass spectrometry is widely used to probe the proteome and its modifications in an untargeted manner, with unrivalled coverage. Applied to phosphoproteomics, it has tremendous potential to interrogate phospho-signalling and its therapeutic implications. However, this task is complicated by issues of undersampling of the phosphoproteome and challenges stemming from its high-content but low-sample-throughput nature. Hence, methods using such data to reconstruct signalling networks have been limited to restricted data sets and insights (for example, groups of kinases likely to be active in a sample). We propose a new method to handle high-content discovery phosphoproteomics data on perturbation by putting it in the context of kinase/phosphatase-substrate knowledge, from which we derive and train logic models. We show, on a data set obtained through perturbations of cancer cells with small-molecule inhibitors, that this method can study the targets and effects of kinase inhibitors, and reconcile insights obtained from multiple data sets, a common issue with these data.
| S-EPMC4579397 | biostudies-literature
Project description:Development and function of the human heart depend on the dynamic control of tissue-specific gene expression by distant-acting transcriptional enhancers. While large numbers of heart enhancers have been identified using the mouse as a model system, many of these regulatory sequences are poorly conserved in the human genome. To generate an accurate genome-wide map of human heart enhancers, we used an epigenomic enhancer discovery approach and identified ~6,200 candidate enhancer sequences directly from fetal and adult human heart tissue. Consistent with their predicted function, these elements were markedly enriched near genes implicated in heart development, function and disease. To further validate their in vivo enhancer activity, we tested 65 of these human sequences in a transgenic mouse enhancer assay and observed that 43 (66%) drove reproducible reporter gene expression in the heart. These results support the discovery of a genome-wide set of non-coding sequences highly enriched in human heart enhancers which is likely to facilitate down-stream studies of the role of enhancers in development and pathological conditions of the heart. Examination of AcCBP/p300 binding in human adult heart, human fetal (16wk) heart and mouse postnatal day 2 heart
2011-12-04 | E-GEOD-32587 | biostudies-arrayexpress
Project description:Development and function of the human heart depend on the dynamic control of tissue-specific gene expression by distant-acting transcriptional enhancers. While large numbers of heart enhancers have been identified using the mouse as a model system, many of these regulatory sequences are poorly conserved in the human genome. To generate an accurate genome-wide map of human heart enhancers, we used an epigenomic enhancer discovery approach and identified ~6,200 candidate enhancer sequences directly from fetal and adult human heart tissue. Consistent with their predicted function, these elements were markedly enriched near genes implicated in heart development, function and disease. To further validate their in vivo enhancer activity, we tested 65 of these human sequences in a transgenic mouse enhancer assay and observed that 43 (66%) drove reproducible reporter gene expression in the heart. These results support the discovery of a genome-wide set of non-coding sequences highly enriched in human heart enhancers which is likely to facilitate down-stream studies of the role of enhancers in development and pathological conditions of the heart.
2011-12-04 | GSE32587 | GEO
Project description:The advent of Next Generation Sequencing (NGS) has vastly increased our ability to discover novel viruses and to systematically define the spectrum of viruses present in a given specimen. Such studies have led to the discovery of novel viral pathogens as well as broader associations of the virome with diverse diseases including inflammatory bowel disease, severe acute malnutrition and HIV/AIDS. Critical to the success of these efforts are robust bioinformatic pipelines for rapid classification of microbial sequences. Existing computational tools are typically focused on either eukaryotic virus discovery or virome composition analysis but not both. Here we present VirusSeeker, a BLAST-based NGS data analysis pipeline designed for both purposes. VirusSeeker has been successfully applied in several previously published virome studies. Here we demonstrate the functionality of VirusSeeker in both novel virus discovery and virome composition analysis.
| S-EPMC5326578 | biostudies-literature
Project description:Marburg virus (MARV) disease is lethal, with fatality rates up to 90%. Neutralizing antibodies (Abs) are promising drug candidates to prevent or treat the disease. Current efforts are focused in part on vaccine development to induce such MARV-neutralizing Abs. We analyzed the antibody repertoire from healthy unexposed and previously MARV-infected individuals to assess if naïve repertoires contain suitable precursor antibodies that could become neutralizing with a limited set of somatic mutations. We computationally searched the human Ab variable gene repertoire for predicted structural homologs of the neutralizing Ab MR78 that is specific to the receptor binding site (RBS) of MARV glycoprotein (GP). Eight Ab heavy-chain complementarity determining region 3 (HCDR3) loops from MARV-naïve individuals and one from a previously MARV-infected individual were selected for testing as HCDR3 loop chimeras on the MR78 Ab framework. Three of these chimerized antibodies bound to MARV GP. We then tested a full-length native Ab heavy chain encoding the same 17-residue-long HCDR3 loop that bound to the MARV GP the best among the chimeric Abs tested. Despite only 57% amino acid sequence identity, the Ab from a MARV-naïve donor recognized MARV GP and possessed neutralizing activity against the virus. Crystallization of both chimeric and full-length native heavy chain-containing Abs provided structural insights into the mechanism of binding for these types of Abs. Our work suggests that the MARV GP RBS is a promising candidate for epitope-focused vaccine design to induce neutralizing Abs against MARV.
| S-EPMC7733803 | biostudies-literature
Project description:Learning to read is foundational for literacy development, yet many children in primary school fail to become efficient readers despite normal intelligence and schooling. This condition, referred to as developmental dyslexia, has been hypothesized to occur because of deficits in vision, attention, auditory and temporal processes, and phonology and language. Here, we used a developmentally plausible computational model of reading acquisition to investigate how the core deficits of dyslexia determined individual learning outcomes for 622 children (388 with dyslexia). We found that individual learning trajectories could be simulated on the basis of three component skills related to orthography, phonology, and vocabulary. In contrast, single-deficit models captured the means but not the distribution of reading scores, and a model with noise added to all representations could not even capture the means. These results show that heterogeneity and individual differences in dyslexia profiles can be simulated only with a personalized computational model that allows for multiple deficits.
| S-EPMC6419236 | biostudies-literature
Project description:High-Throughput (HT) SELEX combines SELEX (Systematic Evolution of Ligands by EXponential Enrichment), a method for aptamer discovery, with massively parallel sequencing technologies. This emerging technology provides data for a global analysis of the selection process and for simultaneous discovery of a large number of candidates but currently lacks dedicated computational approaches for their analysis. To close this gap, we developed novel in-silico methods to analyze HT-SELEX data and utilized them to study the emergence of polymerase errors during HT-SELEX. Rather than considering these errors as a nuisance, we demonstrated their utility for guiding aptamer discovery. Our approach builds on two main advancements in aptamer analysis: AptaMut-a novel technique allowing for the identification of polymerase errors conferring an improved binding affinity relative to the 'parent' sequence and AptaCluster-an aptamer clustering algorithm which is to our best knowledge, the only currently available tool capable of efficiently clustering entire aptamer pools. We applied these methods to an HT-SELEX experiment developing aptamers against Interleukin 10 receptor alpha chain (IL-10RA) and experimentally confirmed our predictions thus validating our computational methods.
| S-EPMC4499121 | biostudies-literature