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18 results for “KEGG”
NOMIS-KEGG-data
<p>KEGG data and associated interim file to reproduce figures in https://www.researchsquare.com/article/rs-2697617/v1</p>
10 eADAGE models used for the PAO1 KEGG pathways case study in PathCORE
<p>ensemble Analysis using Denoising Autoencoders for Gene Expression<strong> </strong>(<strong>eADAGE</strong>) is an unsupervised feature construction algorithm developed by Tan et al. that uses an ensemble of neural networks (an ensemble of ADAGE models) to capture biological signatures embedded in the expression compendium. By initializing eADAGE with different random seeds, Tan et al. produced 10 eADAGE models that each extracted k=300 features from the compendium of genome-scale <em>P. aeruginosa</em> data.</p> <p>eADAGE is described in Tan et al.'s "System-wide automatic extraction of functional signatures in <em>Pseudomonas aeruginosa</em> with eADAGE" (https://doi.org/10.1101/078659). The code to construct these 10 models is available in this repository: https://bitbucket.org/greenelab/eadage (see eADAGE_construction.sh). </p>
Paulinella micropore KR01 manually corrected gene models from selected KEGG pathways
<p>Major results files produced from the analysis of the nucleotide biosynthesis, DNA replication, and histidine metabolism pathways in <em>Paulinella micropore</em> KR01.</p> <p> </p> <p><code>manually_corrected_genes.genome.gff3.gz</code></p> <p>Gene models (in GFF3 format) of manually corrected <em>P. micropora</em> KR01 genes.</p> <p> </p> <p><code>manually_corrected_genes.cds.fna.gz</code></p> <p>CDS of manually corrected <em>P. micropora</em> KR01 genes.</p> <p> </p> <p><code>manually_corrected_genes.pep.faa.gz</code></p> <p>Protein sequences of manually corrected <em>P. micropora</em> KR01 genes.</p> <p> </p> <p><code>fasta.tar.gz</code></p> <p>Sequences used for phylogenetic analysis of major KEGG Orthologs from the nucleotide biosynthesis, DNA replication, and histidine metabolism pathways.</p> <p> </p> <p><code>aln.tar.gz</code></p> <p>Alignments produced by <code>mafft</code> v7.453 (‘--localpair --maxiterate 1000’) that were used for phylogenetic analysis of the major KEGG Orthologs.</p> <p> </p> <p><code>tree.tar.gz</code></p> <p>Consensus trees produced by <code>iqtree</code> v1.6.12 (‘-m LG+R7 -bb 2000 -quiet’) that were used for phylogenetic analysis of the major KEGG Orthologs.</p>
Supplementary material for "Genes divided according to the relative position of the longest intron show increased representation in different KEGG pathways"
<p>This is a 4th version of the Supplementary Information related to the article "Genes divided according to the relative position of the longest intron show increased representation in different KEGG pathways". It contains upgraded primary and control data used for gene set enrichment analyzes, results of these analyzes as well as our code for calculation of intron lenghts. Detailed information can be found in the above-mentioned article, which deals with the connection between the position of the longest introns in genes and biological functions of genes.</p>
KEGG Orthology annotation of Avena sativa (cv. Sang) proteins
<p>KofamScan v1.3.0 was used to assign KEGG Orthologs (KOs) to the <em>Avena sativa</em> (cv. Sang) proteins.</p> <p>exec_annotation Asativa_sang.v1.1.aa.fa -o Asativa_sang.v1.1.aa.ko.txt --profile profiles/eukaryote.hal --ko-list ko_list</p> <p>The Oat proteins (Asativa_sang.v1.1.aa.fa) were downloaded from https://doi.org/10.5447/ipk/2022/2<br> The KOfam database was downloaded from https://www.genome.jp/ftp/db/kofam/archives/2021-12-01/</p>
Supplementary Table S1: KEGG pathway enrichment
<p>This is a dataset excel file directly related to my manuscript (MBO3_2019_11_0405.R1). The dataset excel file is a detailed result of the KEGG pathway enrichment analysis. </p>
10 eADAGE models for the PAO1 KEGG pathways case study in PathCORE
<p>ensemble Analysis using Denoising Autoencoders for Gene Expression<strong> </strong>(<strong>eADAGE</strong>) is an unsupervised feature construction algorithm developed by Tan et al. that uses an ensemble of neural networks (an ensemble of ADAGE models) to capture biological signatures embedded in the expression compendium. By initializing eADAGE with different random seeds, Tan et al. produced 10 eADAGE models that each extracted k=300 features from the compendium of genome-scale <em>P. aeruginosa</em> data.</p> <p>eADAGE is described in Tan et al.'s "System-wide automatic extraction of functional signatures in <em>Pseudomonas aeruginosa</em> with eADAGE" (https://doi.org/10.1101/078659).</p> <p>The code to construct these 10 models is available in this repository: https://bitbucket.org/greenelab/eadage (see eADAGE_construction.sh). </p>
P. aeruginosa expression compendium for the PAO1 KEGG pathways case study in PathCORE.
<p>The normalized P. aeruginosa (PAO1) gene expression compendium uses datasets from ArrayExpress. Each gene expression vector is linearly normalized to the range between 0 and 1.</p> <p>The script used to download the PAO1 datasets and process all samples into a single expression compendium is available here: https://bitbucket.org/greenelab/eadage/src/d8a98586e0f448a5b7d497f5516cc4e241635361/data_collection/data_collection.sh</p>
MicrobeAnnotator KEGG Database (UNOFFICIAL MIRROR)
<p>Contains the following files: </p><p>total 128K</p><p>-rw-r--r-- 1 jespinoz staff 5.8K Oct 18 16:39 KEGG_Bifurcating_Module_Information.pkl</p><p>-rw-r--r-- 1 jespinoz staff 96 Oct 18 16:41 KEGG_Bifurcating_Module_Information.pkl.md5</p><p>-rw-r--r-- 1 jespinoz staff 37K Oct 18 16:39 KEGG_Module_Information.txt</p><p>-rw-r--r-- 1 jespinoz staff 84 Oct 18 16:41 KEGG_Module_Information.txt.md5</p><p>-rw-r--r-- 1 jespinoz staff 59K Oct 18 16:39 KEGG_Regular_Module_Information.pkl</p><p>-rw-r--r-- 1 jespinoz staff 92 Oct 18 16:41 KEGG_Regular_Module_Information.pkl.md5</p><p>-rw-r--r-- 1 jespinoz staff 1.2K Oct 18 16:39 KEGG_Structural_Module_Information.pkl</p><p>-rw-r--r-- 1 jespinoz staff 95 Oct 18 16:41 KEGG_Structural_Module_Information.pkl.md5</p>
Gene catalogs and KEGG annotation related to root metagenomic samples
<p><span>Reference genomes of root microbes are essential for metagenomic analyses and mechanistic studies of </span><span>crop </span><span>root microbiome</span><span>s</span><span>. Combining high-throughput bacterial cultivation with metagenomic sequencing, we constructed comprehensive bacterial and viral genome collections from the roots of wheat, rice, maize, and Medicago. The crop root bacterial genome collection (CRBC) significantly expands the quantity and phylogenetic diversity of publicly available crop root bacterial genomes, with 6,699 bacterial genomes (</span><span>68.9 % </span><span>from isolates) </span><span>and 1,817 novel species,</span><span> expanding crop root bacterial diversity by </span><span>290.6%</span><span>. The crop root viral genome collection (CRVC) contains 9,736 nonredundant viral genomes, with 1,572 previously unreported genus-level clusters in crop roots. From these data, we identified conserved bacterial </span><span>functions</span><span> enriched in root microbiomes across soils and host species and uncovered previously unexplored bacteria–virus </span><span>connections</span><span> in crop root ecosystems. Together, the CRBC and CRVC serve as valuable resources for investigating microbial mechanisms and applications, supporting sustainable agriculture. </span></p> <p> </p> <p>The gene sets of 14 datasets are available under this accession.</p>
GO and KEGG terms analysis files between TLR2-4 and WT cells
<p><em><span>Staphylococcus aureus</span></em><span> infections pose a potential threat to livestock production and public health. A novel strategy is needed to control <em>S. aureus</em> infection due to its adaptive evolution to antibiotics. Autophagy plays a key role in degrading bacteria for innate immune cells.</span> <span>In order to promote <em>S. aureus</em> clearance via TLR induced autophagy pathway, the domain fusion TLR2-4 with the extracellular domain of TLR2, specific recognizing <em>S. aureus</em>, and transmembrane and intracellular domains of TLR4 is assembled, then the goats expressing TLR2-4 is generated. TLR2-4 substantially augments the removal of <em>S. aureus</em> within macrophages by elevating autophagy level. Phosphorylated JNK/ERK1/2 promote LC3-puncta in TLR2-4 macrophages during <em>S. aureus</em>-induced autophagy via MyD88-mediated the TAK1 signaling cascade. Meantime, the TRIF-dependent TBK1-TFEB-OPTN signaling is involved in TLR2-4-triggered autophagy after <em>S. aureus</em> challenge. Moreover, the transcript of <em>ATG5 </em>and <em>ATG12 </em>is significantly increased via cAMP-PKA-NF-</span><span>k</span><span>B signaling, which facilitates <em>S. aureus</em>-induced autophagy in TLR2-4 macrophages. Overall, the novel receptor TLR2-4 enhances the autophagy-dependent clearance of <em>S. aureus</em> in macrophages via TAK1/TBK1-JNK/ERK, TBK1-TFEB-OPTN and cAMP-PKA-NF-</span><span>k</span><span>B-ATGs signaling pathways, which provide an alternative </span><span>approach to resistant against <em>S. aureus</em> infection.</span></p>
Kegg Fertility Study
ClinicalTrials.gov study NCT06862440. IPD Sharing: NO. Countries: 1. Publications: 1.
GO and KEGG terms analysis files between TLR2-4 and WT cells
Open the record for dataset details and reuse information.
Table S2: Gene set enrichment analysis and KEGG
Open the record for dataset details and reuse information.
PA up-regulates the KEGG pathway ‘Lysosome’ in cultured PTECs
GEO Series GSE191216. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Atg5 knockout down-regulates the KEGG pathway ‘Oxidative Phosphorylation’ in erastin2-treated primary PTECs
GEO Series GSE298268. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Appendix 2 (KEGG pathway enrichment)
<p>This is a dataset excel file directly related to my manuscript (MBO3_2019_11_0405). The dataset excel file is a detailed result of the KEGG pathway enrichment analysis. The dataset excel file has been uploaded to an external repository (response to reviewer's comments).</p>
Table S1: Relative abundance of KEGG pathways obtained by PICRUSt
<p>Relative abundance of KEGG pathways obtained by PICRUSt</p>
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