| Name | CRACD like |
| Summary |
{"type": "root", "children": [{"type": "t", "text": "<head>\n <meta charset=\"UTF-8\">\n <title>Summary of CRACDL Function</title>\n </head>\n \n "}, {"type": "p", "children": [{"type": "t", "text": "\n A review of the abstracts provided reveals that none of these studies mention or describe any data related to “CRACDL” – its molecular role, signaling pathways, or contributions to disease mechanisms. In other words, within the context of these publications, there is no functional annotation or experimental evidence regarding CRACDL.\n "}]}, {"type": "t", "text": "\n "}, {"type": "p", "children": [{"type": "t", "text": "\n For instance, one abstract focuses on T‐cell prolymphocytic leukemia and the interplay between TCL1 overexpression and ATM defects in governing DNA damage responses and T‐cell receptor signaling."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "1"}]}, {"type": "t", "text": " Other studies explore topics such as genetic variants associated with opioid use disorder"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "2"}]}, {"type": "t", "text": ", age‐related changes in breast epithelial cells"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "3"}]}, {"type": "t", "text": ", molecular signatures in pediatric acute lymphoblastic leukemia"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "4"}]}, {"type": "t", "text": ", prognostic long non‐coding RNA biomarkers in pancreatic ductal adenocarcinoma"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "5"}]}, {"type": "t", "text": ", and novel genotype–phenotype relationships in bipolar disorder."}, {"type": "fg", "children": [{"type": "fg_f", "ref": "6"}]}, {"type": "t", "text": "\n "}]}, {"type": "t", "text": "\n "}, {"type": "p", "children": [{"type": "t", "text": "\n Additional investigations in non‐human models related to behavioral traits in laying hens"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "7"}]}, {"type": "t", "text": "and copy number variant analyses in discordant monozygotic twins for schizophrenia"}, {"type": "fg", "children": [{"type": "fg_f", "ref": "8"}]}, {"type": "t", "text": "delineate various genes and pathways involved in these specific conditions; however, none of them include any reference to CRACDL.\n "}]}, {"type": "t", "text": "\n "}, {"type": "p", "children": [{"type": "t", "text": "\n In summary, while these abstracts cover a wide range of biomedical topics—including leukemia pathogenesis, substance use disorders, cancer biology, neuropsychiatric disorders, and behavioral genetics—they do not address CRACDL. As such, the function (if any) of CRACDL remains to be elucidated by future targeted research.\n "}]}, {"type": "rg", "children": [{"type": "r", "ref": 1, "children": [{"type": "t", "text": "A Schrader, G Crispatzu, S Oberbeck, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Actionable perturbations of damage responses by TCL1/ATM and epigenetic lesions form the basis of T-PLL."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Nat Commun (2018)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1038/s41467-017-02688-6"}], "href": "https://doi.org/10.1038/s41467-017-02688-6"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "29449575"}], "href": "https://pubmed.ncbi.nlm.nih.gov/29449575"}]}, {"type": "r", "ref": 2, "children": [{"type": "t", "text": "Hiba Alblooshi, Habiba Al Safar, Ahmed El Kashef, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Stratified analyses of genome wide association study data reveal haplotypes for a candidate gene on chromosome 2 (KIAA1211L) is associated with opioid use in patients of Arabian descent."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "BMC Psychiatry (2020)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1186/s12888-019-2425-8"}], "href": "https://doi.org/10.1186/s12888-019-2425-8"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "32005204"}], "href": "https://pubmed.ncbi.nlm.nih.gov/32005204"}]}, {"type": "r", "ref": 3, "children": [{"type": "t", "text": "James C Garbe, Francois Pepin, Fanny A Pelissier, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Accumulation of multipotent progenitors with a basal differentiation bias during aging of human mammary epithelia."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Cancer Res (2012)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1158/0008-5472.CAN-12-0157"}], "href": "https://doi.org/10.1158/0008-5472.CAN-12-0157"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "22552289"}], "href": "https://pubmed.ncbi.nlm.nih.gov/22552289"}]}, {"type": "r", "ref": 4, "children": [{"type": "t", "text": "Chindo Hicks, Jitsuda Sitthi-Amorn, Jessica Douglas, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Molecular Analysis of Central Nervous System Disease Spectrum in Childhood Acute Lymphoblastic Leukemia."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Clin Med Insights Oncol (2016)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.4137/CMO.S18180"}], "href": "https://doi.org/10.4137/CMO.S18180"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "26997880"}], "href": "https://pubmed.ncbi.nlm.nih.gov/26997880"}]}, {"type": "r", "ref": 5, "children": [{"type": "t", "text": "Jukun Song, Qiuyan Xu, Haodeng Zhang, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Five key lncRNAs considered as prognostic targets for predicting pancreatic ductal adenocarcinoma."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "J Cell Biochem (2018)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1002/jcb.26598"}], "href": "https://doi.org/10.1002/jcb.26598"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "29239017"}], "href": "https://pubmed.ncbi.nlm.nih.gov/29239017"}]}, {"type": "r", "ref": 6, "children": [{"type": "t", "text": "René Breuer, Manuel Mattheisen, Josef Frank, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Int J Bipolar Disord (2018)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1186/s40345-018-0132-x"}], "href": "https://doi.org/10.1186/s40345-018-0132-x"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "30415424"}], "href": "https://pubmed.ncbi.nlm.nih.gov/30415424"}]}, {"type": "r", "ref": 7, "children": [{"type": "t", "text": "Clemens Falker-Gieske, Hanna Iffland, Siegfried Preuß, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Meta-analyses of genome wide association studies in lines of laying hens divergently selected for feather pecking using imputed sequence level genotypes."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "BMC Genet (2020)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1186/s12863-020-00920-9"}], "href": "https://doi.org/10.1186/s12863-020-00920-9"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "33004014"}], "href": "https://pubmed.ncbi.nlm.nih.gov/33004014"}]}, {"type": "r", "ref": 8, "children": [{"type": "t", "text": "Christina A Castellani, Zain Awamleh, Melkaye G Melka, et al. "}, {"type": "b", "children": [{"type": "t", "text": "Copy number variation distribution in six monozygotic twin pairs discordant for schizophrenia."}]}, {"type": "t", "text": " "}, {"type": "i", "children": [{"type": "t", "text": "Twin Res Hum Genet (2014)"}]}, {"type": "t", "text": " DOI: "}, {"type": "a", "children": [{"type": "t", "text": "10.1017/thg.2014.6"}], "href": "https://doi.org/10.1017/thg.2014.6"}, {"type": "t", "text": " PMID: "}, {"type": "a", "children": [{"type": "t", "text": "24556202"}], "href": "https://pubmed.ncbi.nlm.nih.gov/24556202"}]}]}]}
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| NCBI Gene ID | 343990 |
| API | |
| Download Associations | |
| Predicted Functions |
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| Co-expressed Genes |
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| Expression in Tissues and Cell Lines |
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CRACDL has 1,703 functional associations with biological entities spanning 6 categories (functional term, phrase or reference, disease, phenotype or trait, chemical, cell line, cell type or tissue, gene, protein or microRNA, sequence feature) extracted from 20 datasets.
Click the + buttons to view associations for CRACDL from the datasets below.
If available, associations are ranked by standardized value
| Dataset | Summary | |
|---|---|---|
| Allen Brain Atlas Aging Dementia and Traumatic Brain Injury Tissue Sample Gene Expression Profiles | tissue samples with high or low expression of CRACDL gene relative to other tissue samples from the Allen Brain Atlas Aging Dementia and Traumatic Brain Injury Tissue Sample Gene Expression Profiles dataset. | |
| CellMarker Gene-Cell Type Associations | cell types associated with CRACDL gene from the CellMarker Gene-Cell Type Associations dataset. | |
| CM4AI KOLF21J CRISPRi Gene Perturbation Atlas | gene perturbations changing expression of CRACDL gene from the CM4AI KOLF21J CRISPRi Gene Perturbation Atlas dataset. | |
| CM4AI U2OS Cell Map Protein Localization Assemblies | assemblies containing CRACDL protein from integrated AP-MS and IF data from the CM4AI U2OS Cell Map Protein Localization Assemblies dataset. | |
| COMPARTMENTS Experimental Protein Localization Evidence Scores 2025 | cellular components containing CRACDL protein in low- or high-throughput protein localization assays from the COMPARTMENTS Experimental Protein Localization Evidence Scores 2025 dataset. | |
| COMPARTMENTS Text-mining Protein Localization Evidence Scores 2025 | cellular components co-occuring with CRACDL protein in abstracts of biomedical publications from the COMPARTMENTS Text-mining Protein Localization Evidence Scores 2025 dataset. | |
| GTEx eQTL 2025 | SNPs regulating expression of CRACDL gene from the GTEx eQTL 2025 dataset. | |
| GTEx Tissue Gene Expression Profiles 2023 | tissues with high or low expression of CRACDL gene relative to other tissues from the GTEx Tissue Gene Expression Profiles 2023 dataset. | |
| GWAS Catalog SNP-Phenotype Associations 2025 | phenotypes associated with CRACDL gene in GWAS datasets from the GWAS Catalog SNP-Phenotype Associations 2025 dataset. | |
| IMPC Knockout Mouse Phenotypes | phenotypes of mice caused by CRACDL gene knockout from the IMPC Knockout Mouse Phenotypes dataset. | |
| JASPAR Predicted Human Transcription Factor Targets 2025 | transcription factors regulating expression of CRACDL gene predicted using known transcription factor binding site motifs from the JASPAR Predicted Human Transcription Factor Targets dataset. | |
| JASPAR Predicted Mouse Transcription Factor Targets 2025 | transcription factors regulating expression of CRACDL gene predicted using known transcription factor binding site motifs from the JASPAR Predicted Mouse Transcription Factor Targets 2025 dataset. | |
| LINCS L1000 CMAP Chemical Perturbation Consensus Signatures | small molecule perturbations changing expression of CRACDL gene from the LINCS L1000 CMAP Chemical Perturbations Consensus Signatures dataset. | |
| Rummagene Transcription Factor Associations 2026 | transcription factors regulating expression of CRACDL gene from the Rummagene Transcription Factor Associations 2026 dataset. | |
| RummaGEO Drug Perturbation Signatures | drug perturbations changing expression of CRACDL gene from the RummaGEO Drug Perturbation Signatures dataset. | |
| RummaGEO Gene Perturbation Signatures | gene perturbations changing expression of CRACDL gene from the RummaGEO Gene Perturbation Signatures dataset. | |
| Sci-Plex Drug Perturbation Signatures | drug perturbations changing expression of CRACDL gene from the Sci-Plex Drug Perturbation Signatures dataset. | |
| Tahoe Therapeutics Tahoe 100M Perturbation Atlas | drug perturbations changing expression of CRACDL gene from the Tahoe Therapeutics Tahoe 100M Perturbation Atlas dataset. | |
| TISSUES Experimental Tissue Protein Expression Evidence Scores 2025 | tissues with high expression of CRACDL protein in proteomics datasets from the TISSUES Experimental Tissue Protein Expression Evidence Scores 2025 dataset. | |
| TISSUES Text-mining Tissue Protein Expression Evidence Scores 2025 | tissues co-occuring with CRACDL protein in abstracts of biomedical publications from the TISSUES Text-mining Tissue Protein Expression Evidence Scores 2025 dataset. | |