Glutamatergic Signaling System Data for Metazoan Phylogenetic Reconstruction
by Ankit Thakur·Updated 15d ago
145.4 MB21files
Available on 1 platform
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Description
145.4 MB of curated genomic and phylogenetic data generated for evolutionary reconstruction of glutamatergic signaling across Metazoa. Ankit Thakur published this dataset on figshare in 2026, which includes species metadata, protein sequences, orthology assignments, and phylogenetic trees. Processed outputs from tools like PSI-BLAST, InterProScan, IQ-TREE2, and GeneRax are provided alongside supporting computational workflows.
Use Cases
Reconstructing evolutionary histories of glutamatergic signaling components based on the provided phylogenetic trees and reconciliation analyses.
Identifying orthologous protein groups across Metazoa using the curated orthology assignments and sequence collections.
Analyzing network topology and modularity of signaling systems based on the entropy and modularity analyses mentioned.
Reproducing or extending comparative genomic studies using the included multiple sequence alignments and computational workflows.
Strengths
Dataset size of 145.4 MB indicates substantial processed and raw data content.
Includes outputs from multiple established bioinformatics tools (e.g., PSI-BLAST, IQ-TREE2, GeneRax) supporting reproducibility.
Broad taxonomic scope covering glutamatergic systems across Metazoa.
Published under a permissive CC-BY-4.0 license, facilitating reuse.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count is unknown, which may limit suitability assessment for specific analytical tasks.
Description metadata is limited; actual data quality and structure require manual inspection after download.
Provenance
Source
figshare
Collection Method
Curated comparative genomic and phylogenetic analyses, including computational workflows for reproducibility.
Freshness
Last updated 2026-05-23 11:33:21; freshness should be verified.
File formats include ZIP and CSV; users must unpack the archive to access the full dataset.