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DNA/RNA sequences, gene expression, protein structures, metagenomics, single-cell sequencing
22,984 datasets
A 21.3 KB document details a four-week study on juvenile cobia (Rachycentron canadum) exposed to different light wavelengths. Compared to natural light, blue and green light increased body weight by 27.6% and 23.2%, respectively, and upregulated specific genes related to appetite and lipid synthesis. The dataset, authored by Yafan Zhu and last updated in May 2026, is shared under a CC-BY-4.0 license.
A longitudinal transcriptomic analysis of skin lesions and draining lymph nodes in a BALB/c mouse model infected with Leishmania braziliensis, spanning from 2 hours to 77 days post-infection. The dataset, created by Jessica Lobo-Silva and shared on figshare, includes differential gene expression, pathway enrichment, and gene co-expression network data. It was last updated in May 2026.
A longitudinal transcriptomic analysis of skin lesions and draining lymph nodes in a BALB/c mouse model infected with Leishmania braziliensis, spanning from 2 hours to 77 days post-infection. The dataset, created by Jessica Lobo-Silva and last updated in May 2026, includes bulk RNA sequencing results characterizing differential gene expression, pathway enrichment, and gene co-expression networks. It recapitulates 77% of inflammatory pathways found in human cutaneous leishmaniasis and identifies conserved non-coding RNAs and epigenetic regulators.
Jessica Lobo-Silva's dataset contains bulk RNA sequencing data from skin lesions and draining lymph nodes in a BALB/c mouse model infected with Leishmania braziliensis. The data includes differential gene expression, pathway enrichment, and gene co-expression network analyses across six time points from 2 hours to 77 days post-infection. It was published on figshare under a CC-BY-4.0 license.
A longitudinal transcriptomic dataset characterizes host responses to Leishmania braziliensis infection in a BALB/c mouse ear dermal model. The data includes bulk RNA sequencing from skin lesions and draining lymph nodes at six time points from 2 hours to 77 days post-infection. The dataset was authored by Jessica Lobo-Silva and published on figshare in May 2026.
Longitudinal bulk RNA sequencing data from skin lesions and draining lymph nodes in a BALB/c mouse model infected with Leishmania braziliensis. The dataset captures host responses at six time points from 2 hours to 77 days post-infection, characterizing differential gene expression and pathway enrichment. It was authored by Jessica Lobo-Silva and last updated on May 12, 2026.
A longitudinal transcriptomic analysis of skin lesions and draining lymph nodes from BALB/c mice infected with Leishmania braziliensis, sampled at 2, 6, and 48 hours and at 14, 35, and 77 days post-infection. The dataset includes differential gene expression, pathway enrichment, and gene co-expression network analysis, identifying conserved inflammatory pathways and potential therapeutic targets. It was authored by Jessica Lobo-Silva and shared under a CC-BY-4.0 license.
A longitudinal transcriptomic analysis of skin lesions and draining lymph nodes in BALB/c mice infected with Leishmania braziliensis, performed at 2, 6, and 48 hours and at 14, 35, and 77 days post-infection. The dataset characterizes differential gene expression, pathway enrichment, and gene co-expression networks, with ulcerated mouse lesions recapitulating 77% of inflammatory pathways described in human CL. Jessica Lobo-Silva authored this dataset, last updated on 2026-05-12.
Longitudinal bulk RNA sequencing data from skin lesions and draining lymph nodes in a BALB/c mouse model infected with Leishmania braziliensis. The dataset includes samples collected at six time points from 2 hours to 77 days post-infection, characterizing differential gene expression, pathway enrichment, and gene co-expression networks. The data was generated by Jessica Lobo-Silva and shared under a CC-BY-4.0 license.
A longitudinal transcriptomic analysis of skin lesions and draining lymph nodes in a BALB/c mouse model infected with Leishmania braziliensis. The dataset includes bulk RNA sequencing data from six time points spanning 2 hours to 77 days post-infection. It was authored by Jessica Lobo-Silva and last updated on 2026-05-12.
Gustavo Reis de Brito's dataset contains scientometric analysis records for sweetpotato genetics research. It includes 590 records from Web of Science spanning 1960 to 2022, with 213 full-text papers retained after relevance filtering. The analysis maps research evolution, identifies gaps, and assesses the integration of omics approaches with Traditional Knowledge.
108.9 KB of mechanistic model data investigating the bell-shaped dependence of protein production on EF-Tu concentration in the PURE cell-free system. The dataset, authored by Shunnosuke Ban and last updated in May 2026, likely contains computational analysis results from a model describing hundreds of molecular species and reactions. It was used to identify a resource competition mechanism and guide experimental optimization of protein synthesis.
Six signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, CFD) identified as potential pathogenic markers for a monocyte subset in myasthenia gravis. The dataset was generated by Shuang Li through multi-omics integration and machine learning analysis and was last updated on May 1, 2026. The file is 1.5 KB in size.
A table of results from a multi-omics integration and machine learning analysis of myasthenia gravis. The dataset was created by Shuang Li and last updated on 2026-05-01. It is derived from scRNA-seq data, GWAS summary data, and expression quantitative trait loci integrated from GEO, GWAS catalog, and GTEx databases.
A 2026 study by Shuang Li integrates transcriptome-wide association study (TWAS) and single-cell RNA sequencing (scRNA-seq) data to investigate myasthenia gravis pathogenesis. The dataset, shared under a CC-BY-4.0 license, contains results from a machine learning benchmark that identified six signature genes associated with a pathogenic monocyte subset. It is derived from multi-omics data sourced from GEO, GWAS catalog, and GTEx databases.
A 2026 dataset from figshare by Shuang Li presents results from a multi-omics integration and machine learning analysis of myasthenia gravis. The data likely contains activity scores or signature gene expression for CFD+CD14+ monocyte subsets identified as a potential pathogenic driver. It is derived from integrating scRNA-seq, GWAS, and GTEx data, validated with a new scRNA-seq dataset from MG patients and controls.
A 2026 multi-omics integration study identifies a potential pathogenic monocyte subset in myasthenia gravis. The dataset, authored by Shuang Li and shared on figshare, results from integrating scRNA-seq, GWAS, and GTEx data with machine learning analysis. It highlights six signature genes, including CFD, associated with high TWAS activity in CD14+ monocytes from MG patients.
A 2026 dataset by Shuang Li presents results from a multi-omics integration study of myasthenia gravis. It identifies six signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, and CFD) associated with a pathogenic monocyte subset. The data was generated by integrating scRNA-seq, GWAS, and GTEx data, followed by machine learning analysis.
A 1.3 KB CSV file published on figshare in May 2026 by Shuang Li. It contains results from a multi-omics integration study identifying a potential pathogenic monocyte subset in myasthenia gravis. The data likely contains signature genes and activity scores derived from transcriptome-wide association studies and single-cell RNA sequencing analyzed with seven machine learning algorithms.
Kaiying Wang published this dataset on 2026-04-27. It contains results from a systematic evaluation of four next-generation sequencing configurations for Chikungunya virus genomic surveillance, using 13 clinical samples with varying viral loads. The data compares sequencing depth, genome coverage, and variant detection between second-generation and third-generation platforms with different amplicon schemes.