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96 results for “prokaryotes”
Datasets - Unveiling Host-Parasite Relationships through Conserved MITEs in Prokaryote and Viral Genomes
<p><em><span>Title:</span></em></p> <p><strong><span>Unveiling Host-Parasite Relationships through Conserved MITEs in Prokaryote and Viral Genomes<span> </span></span></strong></p> <p><em><span> </span></em></p> <p><em><span>Authors:</span></em></p> <p><span>Francisco Nadal-Molero<sup>(1)</sup>, Riccardo Roselli<sup>(1)</sup>, Silvia Garcia-Juan<sup>(1)</sup>, Alicia Campos-Lopez<sup>(1)</sup>, Ana-Belen Martin-Cuadrado<sup>(1*)</sup></span></p> <p> </p> <p><strong><span>SUPPLEMENTARY FILES</span></strong></p> <p><strong><span> </span></strong></p> <p><strong><span>Supplementary File S1.</span></strong><span> Sequences of cMITEs detected in Bacteria genomes (<em>fasta</em> format). The hosting microbial species and inferred NCBI-taxonomy are indicated in the name of each sequence. The structure of the MITE name is: “Accession|Genome|start|end|TSD|TIRlength|MITETracker_group|Lineage”.</span></p> <p><strong><span>Supplementary File S2.</span></strong><span> Sequences of cMITEs detected in the Archaea genomes (<em>fasta</em> format). The hosting microbial species and inferred NCBI-taxonomy are indicated in the name of each sequence. The structure of the MITE name is: “Accession|Genome|start|end|TSD|TIRlength|MITETracker_group|Lineage”.</span></p> <p><strong><span>Supplementary File S3.</span></strong><span> Sequences of vMITEs detected in the virus sequences from the NCBI and IMG/VR v.4.1 database (<em>fasta</em> format). Virus, microbial host (if known) and inferred NCBI-taxonomy is stated in the name of each sequence. The structure of the MITE name is: </span></p> <p><span>“Accession|Genome|start|end|TSD|TIRlength|MITETracker_group|Virus|Name|Host”.</span></p> <p><strong><span>Supplementary File S4.</span></strong><span> Sequences of <span>si-vMITEs</span> detected in the virus sequences from the NCBI and IMG/VR v.4.1 database (<em>fasta</em> format). Virus, microbial host (if known) and inferred NCBI-taxonomy are stated in the name of each sequence. The structure of the MITE name is: “Accession|Genome|start|end|Ident.Method.by.DB|Host”.</span></p> <p><strong><span>Supplementary Files S5. </span></strong><span>Cytoscape networks. (A) Figure 1A, (B) Figure 1B.</span></p> <p><strong><span>Supplementary File S6.</span></strong><span> Sequences of cMITEs obtained from <span>5837</span> genomes of Neisseriales. The structure of the MITE name is: </span></p> <p><span>“Accession|NucleotideID|start|end|TSD|TIRlength|MITETracker_group|Genome|Lineage”.</span></p> <p><strong><span>Supplementary File S7.</span></strong><span> Sequences of si-vMITEs obtained from <span>5837</span> genomes of Neisseriales. The structure of the MITE name is: “Accession|Genome|start|end|Host”.</span></p> <p><strong><span>Supplementary File S8.</span></strong><span> Sequences of cMITEs obtained from 46051 genomes of Bacteroidota. The structure of the MITE name is:</span></p> <p><span>“Accession|NucleotideID|start|end|TSD|TIRlength|MITETracker_group|Genome|Lineage”.</span></p> <p><strong><span>Supplementary File S9.</span></strong><span> Sequences of si-vMITEs obtained from 46051 genomes of Bacteroidota. The structure of the MITE name is: “Accession|Genome|start|end|Host”.</span></p>
genomic sequencies of phages (prokaryotic vriuses) and their hosts
<p>This repository contains data relevant to the reserach <a href="https://github.com/yoheyokubo/MCL4PHI">Multi-Instance Contrastive Learning with Binomial K-Mers for Phage Host Interaction Prediction</a>. It is comprised of the preprocessed data (<em>data_preprocessed.zip</em>) to quickly reproduce our results and the raw genomic sequencies (<em>data_raw.zip</em>) of phages and hosts, originally from <a href="https://github.com/KennthShang/CHERRY">CHERRY's dataset</a>.</p>
Multidimensional specialization and generalization are pervasive in soil prokaryotes with generalists dominating communities and specialists more central in networks
<p>Project folder to run analyses in the manuscript "<strong>Multidimensional specialization and generalization are pervasive in soil prokaryotes with generalists dominating communities and specialists more central in networks</strong>" with necessary raw and processed data.</p> <p>Manuscript abstract:</p> <p>Habitat specialization underpins biological processes from species distributions to speciation. However, organisms are often described as specialists or generalists based on a single niche axis, despite facing complex, multidimensional environments. Here, we analyzed 236 prokaryotic communities across the United States demonstrating for the first time that 90% of >1,200 prokaryotes followed one of two trajectories: specialization on all niche axes (multidimensional specialization) or generalization on all axes (multidimensional generalization). We then documented that this pervasive multidimensional specialization/generalization had a wide range of ecological and evolutionary consequences. First, multidimensional specialization and generalization are highly conserved with very few transitions between these two trajectories. Second, multidimensional generalists dominated communities because they were 73 times more abundant than specialists. Lastly, multidimensional specialists played important roles in community structure with ~220% more connections in microbiome networks. These results indicate that multidimensional generalization and specialization are evolutionarily stable with multidimensional generalists supporting larger populations and multidimensional specialists playing important roles within communities likely stemming from their overrepresentation among pollutant detoxifiers and nutrient cyclers. Taken together, we demonstrate that the vast majority of soil prokaryotes are restricted to one of two multidimensional niche trajectories, multidimensional specialization or multidimensional generalization, which then has far-reaching consequences for evolutionary transitions, microbial dominance, and community roles. </p>
Comparing the impacts of an invasive grass on nitrogen cycling and ammonia-oxidizing Prokaryotes in high-nitrogen forests, open fields, and wetlands
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Data from: The hindgut-lumen prokaryotic microbiota of the lignocellulose-degrading termite Reticulitermes flavipes and its responses to dietary lignocellulose composition
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The ProkaBioDen database, a global database of benthic prokaryotic biomasses and densities in the marine realm
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Extended Data Table 3 in Isolation of an archaeon at the prokaryote eukaryote interface
Extended Data Table 3 | Growth of MK-D1 after incubation of 120 days with a range of substrates
Data from: Recent events dominate interdomain lateral gene transfers between prokaryotes and eukaryotes and, with the exception of endosymbiotic gene transfers, few ancient transfer events persist
While there is compelling evidence for the impact of endosymbiotic gene transfer (EGT; transfer from either mitochondrion or chloroplast to the nucleus) on genome evolution in eukaryotes, the role of interdomain transfer from bacteria and/or archaea (i.e. prokaryotes) is less clear. Lateral gene transfers (LGTs) have been argued to be potential sources of phylogenetic information, particularly for reconstructing deep nodes that are difficult to recover with traditional phylogenetic methods. We sought to identify interdomain LGTs by using a phylogenomic pipeline that generated 13 465 single gene trees and included up to 487 eukaryotes, 303 bacteria and 118 archaea. Our goals include searching for LGTs that unite major eukaryotic clades, and describing the relative contributions of LGT and EGT across the eukaryotic tree of life. Given the difficulties in interpreting single gene trees that aim to capture the approximately 1.8 billion years of eukaryotic evolution, we focus on presence–absence data to identify interdomain transfer events. Specifically, we identify 1138 genes found only in prokaryotes and representatives of three or fewer major clades of eukaryotes (e.g. Amoebozoa, Archaeplastida, Excavata, Opisthokonta, SAR and orphan lineages). The majority of these genes have phylogenetic patterns that are consistent with recent interdomain LGTs and, with the notable exception of EGTs involving photosynthetic eukaryotes, we detect few ancient interdomain LGTs. These analyses suggest that LGTs have probably occurred throughout the history of eukaryotes, but that ancient events are not maintained unless they are associated with endosymbiotic gene transfer among photosynthetic lineages.
Data from: Identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning
<h4>This repository contains the data and Docker image to reproduce the results of our paper: <strong>identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning</strong></h4> <p>Authors: Hengchuang Yin, Shufang Wu, Jie Tan, Qian Guo, Mo Li, Jinyuan Guo, Yaqi Wang, Xiaoqing Jiang, and Huaiqiu Zhu*</p> <p><strong>This work has been accepted by GigaScience. </strong></p> <p><strong>Hengchuang Yin, Shufang Wu, Jie Tan, Qian Guo, Mo Li, Jinyuan Guo, Yaqi Wang, Xiaoqing Jiang, and Huaiqiu Zhu. "IPEV: Identification of Prokaryotic and Eukaryotic Virus-Derived Sequences in Virome Using Deep Learning." GigaScience 13 (2024): giae018. <a href="https://doi.org/10.1093/gigascience/giae018" rel="nofollow">https://doi.org/10.1093/gigascience/giae018</a>.</strong></p> <div> </div> <p> </p> <p><strong>Background: </strong>The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses.</p> <p><strong>Findings: </strong>We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2D convolutional neural network combining trinucleotide pair relative distance and frequency. Cross-validation assessments of IPEV demonstrate its state-of-the-art precision, significantly improving the F1-score by approximately 22% on an independent test set compared to existing methods when query viruses share less than 30% sequence similarity with known viruses. Furthermore, IPEV outperforms other methods in accuracy on marine and gut virome samples based on annotations by sequence alignments. IPEV reduces runtime by at most 1,225 times compared to existing methods under the same computing configuration. We also utilized IPEV to analyze longitudinal samples and found that the gut virome exhibits a higher degree of temporal stability than previously observed in persistent personal viromes, providing novel insights into the resilience of the gut virome in individuals. </p> <p><strong>Conclusions: </strong>IPEV is a high-performance, user-friendly tool that assists biologists in identifying and classifying prokaryotic and eukaryotic viruses within viromes. The tool is available at https://github.com/basehc/IPEV.</p> <p> </p> <p><strong>5_fold_cross_validation.zip:</strong> Dataset of cross-validation of IPEV</p> <p><strong>Eukaryotic_virus_CV_Dataset-1.csv:</strong> GI, and accession ID for the cross-validation Dataset-1 (eukaryotic virus)</p> <p><strong>Prokaryotic_virus_CV_Dataset-1.csv:</strong> GI, and accession ID for the cross-validation Dataset-1 (prokaryotic virus)</p> <p><strong>Test_Prokaryotic_virus_Dataset-1.fasta: </strong>An independent test set of IPEV (prokaryotic virus)</p> <p><strong>Test_Eukaryotic_virus_Dataset-1.fasta: </strong>An independent test set of IPEV (eukaryotic virus)</p> <p> </p> <p> </p> <p><strong>Dataset_sequencing_error.zip: </strong>Simulated dataset with sequencing errors</p> <p><strong>Cap_enzyme_sequence.fasta: </strong>Accession IDs of Receptor Binding Proteins (RBPs) in phages collected by our article</p> <p><strong>Dataset_runtime_evaluation.zip: </strong>Dataset for evaluating the runtime of IPEV</p> <p><strong>Receptor_binding_protein_accession_id:</strong> Accession IDs of Receptor Binding Proteins (RBPs) in phages collected by our article</p> <p> </p> <p><strong>archaea_ID.txt</strong> Accession ID information for the reference archaea dataset</p> <p><strong>bacteria_ID.txt</strong> Accession ID information for the reference bacterial dataset</p> <p><strong>marine_virome_id.csv:</strong> Ocean virome data information used in our paper</p> <p><strong>gut_virome.csv</strong>:Gur virome data information used in our paper</p> <p><strong>fungi.txt: </strong>Negative sequence information used to train, validate, and test the model in the decontamination function</p> <p><strong>bacteria.txt: </strong>Negative sequence information used to train, validate, and test the model in the decontamination function</p> <p> </p> <p> </p> <h4><strong>Reproduce the results of our paper from a Docker image.</strong></h4> <p> </p> <p>We also provide a Docker image file that does not require any environment configuration. You can reproduce the results of our paper (e.g., train and test our IPEV model) in a Docker image.</p> <p>Pull the<a href="https://hub.docker.com/r/dryinhc/ipev_v1"> <em>dryinhc/ipev_v1</em></a> image from Docker Hub. Open a terminal window and run the following command:</p> <p><em>docker pull dryinhc/ipev_v1</em></p> <p>This will download the image to your local machine.</p> <p>Run the <em>dryinhc/ipev_v1</em> image. In the same terminal window, run the following command:</p> <p><em>docker run -it --rm dryinhc/ipev_v1</em></p> <p>This will start a container based on the image and run the IPEV tool.</p> <p>And you can run cd train or cd other file folders in the container.</p> <p>To exit the container, press <em>Ctrl+D</em> or type <em>exit</em>.</p> <p>It contains 4 directories, namely 5 fold cross validation, independent set, marine virome, and gut virome. The 5-fold cross-validation directory holds the scripts required for implementing the 5-fold cross-validation method. The independent set directory contains scripts necessary for working with an independent set. Lastly, the marine virome and gut virome directories store scripts for analyzing real datasets.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p>We hereby confirm that the dataset associated with the research described in this work is made available to the public under the Creative Commons Zero (CC0) license.</p> <p> </p> <p> </p> <h4><strong>Contact </strong></h4> <p> </p> <p>If you have any questions, please don't hesitate to ask me: yinhengchuang@pku.edu.cn or hqzhu@pku.edu.cn</p>
Exploring the Roles of Ribosomal Peptides in Prokaryote-Phage Interactions through Deep Learning-Enabled Metagenome Mining
<ol> <li><a href="../api/records/10202404/draft/files/OVD.fa.gz/content">OVD.fa.gz: The Ocean Virus Database containing the identified viral sequences</a></li> </ol> <p><a href="../api/records/8138131/draft/files/OVD_taxonomy.upload.txt/content">2. OVD_taxonomy.upload.txt: The taxonomic information of the OVD.</a></p> <p>3. <a href="../api/records/8138131/draft/files/host_phage_pair_summary.upload.txt/content">host_phage_pair_summary.upload.txt: The phage-host relationships.</a></p> <p>4. <a href="../api/records/8138131/draft/files/RiPP_vpf_host_phage_summary.upload.txt/content">RiPP_vpf_host_phage_summary.upload.txt: The RiPP-involving phage-host interaction network.</a></p> <p>5. virus_db_checkv_quality_summary_upload.tsv: The quality assessment of GOV2 and the identified phages in this study using CheckV.</p>
Salmon Softcliping Tutorial - Prokaryotic Dataset
<p>This is a paired-end short read simulation dataset of Salmonella along with two different representations of Salmonella transcriptome. "genes.fasta" is the regular gene/CDS-based transcriptome that is commonly used in prokaryotic expression quantification. "operons.fasta" is an operon-based transcriptome in which the sequences of the co-transcribed genes are concatenated together to form a united operon sequence. The gene annotation and gene to operon assignments are provided in "salmonella_operons.gff" file.</p>
Fig. 4 in Isolation and identification of 18 unrecorded prokaryotic species from the intestinal tracts of aquatic animals in Korea
Fig. 4. The phylogenetic tree based on 16S rRNA gene sequence showing the phylogenetic relatedness between the two isolated strains belonging to the phylum Bacteroidetes and the type species from each genus. The tree was mainly reconstructed using the NJ, ML, and MP algorithms. The filled diamonds indicate the branches present in the phylogenetic trees reconstructed using three different tree reconstruction methods. Numbers on the nodes represent bootstrap values with 1000 replicates (NJ/ML/MP). Escherichia coli ATCC 11775T was used as an outgroup. The bar indicates 0.05 accumulated substitutions per nucleotide.
Data from: Recent events dominate interdomain lateral gene transfers between prokaryotes and eukaryotes and, with the exception of endosymbiotic gene transfers, few ancient transfer events persist
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RNA-DNA interactome of a prokaryotic cell uncovered by proximity ligation
GEO Series GSE209901. Escherichia coli; Bacillus subtilis; Thermofilum adornatum. 6 samples. Type: Other.
Directed shotgun proteomics guided by RNA-Seq identifies a complete expressed prokaryotic proteome
GEO Series GSE44564. Bartonella henselae. 4 samples. Type: Expression profiling by high throughput sequencing.
smORFer: a modular algorithm to detect small ORFs in prokaryotes
GEO Series GSE150601. Staphylococcus aureus. 2 samples. Type: Expression profiling by high throughput sequencing.
Physiological role of 2’-O-methylation within prokaryotic and eukaryotic tRNA for innate immune activation via endosomal Toll-like receptors
GEO Series GSE125797. Homo sapiens. 24 samples. Type: Expression profiling by array.
Identification of a p-coumarate degradation regulon in Rhodopseudomonas palustris using Xpression, an integrated tool for prokaryotic RNA-seq data processing
GEO Series GSE39025. Rhodopseudomonas palustris CGA009. 3 samples. Type: Expression profiling by high throughput sequencing.
Gene Expression in Prokaryotic Microbial Assemblages Across Salinity Gradients in the Columbia River Coastal Margin
GEO Series GSE18303. Archaea; Bacteria; uncultured prokaryote. 64 samples. Type: Expression profiling by array.
Dataset of origins of replication in circular prokaryotic chromosomes
<p>This dataset includes two files downloaded from the DoriC 12.0 database [1] and one file with experimentally verified origins of replications (oriCs). The two DoriC files describe oriCs of circular chromosomes predicted with Ori-Finder on complete and chromosome level assemblies.</p> <p>The experimental_dataset file is a describes a set of experimentally verified oriCs, including accession numbers and and sources, collected via literature review. The oriC_number column is used in the case of bipartite origins.</p> <p>These files were used in the development of ORCA: <a href="https://github.com/ZoyavanMeel/ORCA">https://github.com/ZoyavanMeel/ORCA</a>.</p> <p> </p> <p>[1] Mei-Jing Dong, Hao Luo and Feng Gao, DoriC 12.0: an updated database of replication origins in both complete and draft prokaryotic genomes, Nucleic Acid Research, 2023, <a href="https://doi.org/10.1093/nar/gkac964">doi: 10.1093/nar/gkac964</a>.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.