1-20 of 9279
Journal Article
TEITbase: a database for transposable element (TE)-initiated transcripts in human cancers Open Access
Yun Zhang and others
Database, Volume 2026, 2026, baag025, https://doi.org/10.1093/database/baag025
Published: 19 May 2026
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Detection of TE-initiated transcripts. (A) Pipeline designed to identify TE... Open Access
Published: 19 May 2026
Figure 1
Detection of TE-initiated transcripts. (A) Pipeline designed to identify TE-initiated transcripts by using a deep learning model. (B) RAMPAGE and CapTrap-seq signals of the PRKACB gene, along with TSS predictions of our method. (C) The distances to the true TSSs of predicted TSSs from our method (
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RNA–miRNA correlation networks specific to four metastatic sites of BLCA. (... Open Access
Published: 19 May 2026
Figure 1
RNA–miRNA correlation networks specific to four metastatic sites of BLCA. (A) Subnetworks show top-ranked RNA–miRNA pairs (P .001) specific to bone (blue), liver (yellow), lung (purple), and lymph node (red) metastases. Yellow nodes in the networks represent miRNAs and cyan nodes represent RNAs.
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Lung metastasis associated RNA–miRNA pairs common to different primary canc... Open Access
Published: 19 May 2026
Figure 2
Lung metastasis associated RNA–miRNA pairs common to different primary cancer types. The orange bars in the left panel show the number of RNA–miRNA pairs from each primary cancer type. The blue bars in the top panel show the number of common pairs in each combination of primary cancer types, while t
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Example of using MetaCancerDB. (A) The Cancer Type panel visualized in a pi... Open Access
Published: 19 May 2026
Figure 3
Example of using MetaCancerDB. (A) The Cancer Type panel visualized in a pie chart shows the primary cancer types in MetaCancerDB. The number of samples of each cancer type is displayed when users move a mouse over a cancer type. When users click a primary cancer type, a table of metastatic sites of
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Schematic of the pipeline used in the construction of the FuNGI database. F... Open Access
Published: 19 May 2026
Figure 1
Schematic of the pipeline used in the construction of the FuNGI database. Fungal proteomes from UniProtKB were analysed to predict nucleolar localization signals (NoLSs) using the NoD server, nuclear localization signals (NLSs) using NLStradamus, and subcellular localization using WoLF PSORT. A comp
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Overview of nucleolar localization predictions. (a) Summary statistics of t... Open Access
Published: 19 May 2026
Figure 2
Overview of nucleolar localization predictions. (a) Summary statistics of the FuNGI database. The total number of proteomes analysed from each fungal phylum is indicated in square brackets. Horizontal bars represent the proportion of proteins predicted to contain nucleolar localization signals (NoLS
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Snapshot of user interface for exploring proteomes in FuNGI. (a) The Proteo... Open Access
Published: 19 May 2026
Figure 3
Snapshot of user interface for exploring proteomes in FuNGI. (a) The Proteomes page lists UniProt proteomes available in the database, allowing users to control the number of entries displayed per page and search for specific species. (b) The Taxonomy Browser enables users to search fungal proteomes
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Snapshot of individual proteome information page in FuNGI. (a) Proteome Det... Open Access
Published: 19 May 2026
Figure 4
Snapshot of individual proteome information page in FuNGI. (a) Proteome Details: metadata for the selected fungal species, including UniProt accession, taxonomy, strain name, gene and protein counts, and hierarchical lineage. (b) Summary and Statistics: a pie chart showing the categorization of prot
Journal Article
MetaCancerDB: a database of site-specific RNA–miRNA correlations in cancer metastasis Open Access
Myeonghun Cho and others
Database, Volume 2026, 2026, baag026, https://doi.org/10.1093/database/baag026
Published: 19 May 2026
Journal Article
FuNGI: Fungal Nucleolar Genomic Inventory—a comprehensive database of fungal proteins with predicted nucleolar localization signals Open Access
Gnanendra Shanmugam and others
Database, Volume 2026, 2026, baag023, https://doi.org/10.1093/database/baag023
Published: 19 May 2026
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Applications of TEITbase. (A) The homepage of TEITbase. (B) Box plot showin... Open Access
Published: 19 May 2026
Figure 3
Applications of TEITbase. (A) The homepage of TEITbase. (B) Box plot showing the promoter activity of L1HS in TCGA tumours (top) and GTEx normal tissues (bottom). (C) Box plot showing the promoter activity of L1HS in LUSC and adjacent normal tissues. (D) Box plot illustrating the promoter activity o
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Tumour-specific TE-initiated transcription promotes tumourigenesis. (A) The... Open Access
Published: 19 May 2026
Figure 2
Tumour-specific TE-initiated transcription promotes tumourigenesis. (A) The distribution of the activated TE loci across the TE classes. (B) The distribution of the neighbouring genes across the gene type. (C) The reactome pathway enrichment analysis of neighbouring genes associated with the tumour-
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Snapshot of NoLS information for an individual protein of the selected orga... Open Access
Published: 19 May 2026
Figure 5
Snapshot of NoLS information for an individual protein of the selected organism in FuNGI. (a) Detailed information of the selected protein, including confidence rank, general information, and highlighted NoLS segments (in red). (b) NoD server prediction results displayed as a line graph, where the
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Co-localization of MoNOP1-RFP with candidate nucleolar proteins predicted b... Open Access
Published: 19 May 2026
Figure 6
Co-localization of MoNOP1-RFP with candidate nucleolar proteins predicted by FuNGI in Magnaporthe oryzae hyphae. Fluorescence microscopy images of M. oryzae hyphae co-expressing the nucleolar marker MoNOP1-RFP and individual GFP-tagged candidate proteins predicted to localize to the nucleolus by
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Proportion of secondary structure elements within NoLS regions and correspo... Open Access
Published: 19 May 2026
Figure 7
Proportion of secondary structure elements within NoLS regions and corresponding full-length sequences of five experimentally validated M. oryzae proteins. PSIPRED predictions were grouped by structural class (α-helix, β-strand, coil) and subdivided into confidence score intervals ([0.0–0.2] to [0
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Simple tree representation of biochemical pathways involving seven Cys-PTMs... Open Access
Published: 12 May 2026
Figure 3
Simple tree representation of biochemical pathways involving seven Cys-PTMs (inwards). The pathway names appearing in >50 cysteine entries are shown for clarity (outwards). The figure was generated using Interactive Tree of Life (iTOL) v7. Simple tree for Cys-PTM related pathways.
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(Left panel)Query: CysDBase Online Webserver page Input: a) General Query S... Open Access
Published: 12 May 2026
Figure 12
(Left panel)Query: CysDBase Online Webserver page Input: a) General Query Section, b) FASTA Sequences Download, and c) protein structural microenvironment. (Right panel) Outputs: d) Biochemical information (tabular form) and e) protein microenvironment. CysDBase homepage.
Journal Article
CysDBase: a comprehensive database of cysteine post-translational modifications across protein sequence, structure, microenvironment, class, cellular localization, biological pathway, and taxonomy Open Access
Devarakonda Himaja and Debashree Bandyopadhyay
Database, Volume 2026, 2026, baag021, https://doi.org/10.1093/database/baag021
Published: 12 May 2026
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Schematic representation of dataset curation, database construction, deploy... Open Access
Published: 12 May 2026
Figure 2
Schematic representation of dataset curation, database construction, deployment, and homepage of CysDBase. CysDBase work flow.