The brain is the control center for the body. It has specific regions that do certain types of work, including frontal lobe, parietal lobe, temporal lobe, occipital lobe and cerebellum. All of your senses – sight, smell, hearing, touch, and taste – depend on your brain.
When the brain is damaged, it can affect many things. There is a broad category of brain diseases, which vary greatly in symptoms and severity, such as brain injuries, brain tumors and neurodegenerative diseases. Among the brain diseases, glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk.
Comprehensive integration of multi-omics data and annotation of brain-specific genes, brain-region-specific genes and brain diseases on the molecular level will assist us to elucidate the mechanism of brain diseases. Although a number of brain-disease associated databases have been created, they lack detailed annotation information or omics datasets, posing great challenges for better understanding the molecular functional significance in brain diseases.
Here we present BrainBase (https://bigd.big.ac.cn/brainbase), a knowledgebase of brain diseases. As one of the resources in the National Genomics Data Center of China, BrainBase is devoted to providing a comprehensive annotation of brain diseases on molecular level (mRNA, lncRNA, miRNA, protein and CpG site) with high-quality articles and particularly, it provides the analyzed glioma multi-omics figures/tables. Users can perform multi-omics analyses such as survival analysis and differential analysis among different glioma grades or subtypes.
We collect a comprehensive assemble of multi-omics datasets (genome, transcriptome, epigenome and proteome) from public resources. As a result, a total of 22 datasets are collected. The datasets can be downloaded from the website:
|Transcriptome||CGGA_301, CGGA_325, CGGA_693
GSE111260, GSE16011, GSE2223, GSE4290, GSE50161, GSE59612
GSE36278, GSE50923, GSE60274, GSE61160
The glioma and medulloblastoma publications are collected from PubMed with the criterion of literature “IF>10” and keywords “glioma” or “medulloblastoma”. We create a curation model for these two brain tumors, which covers omics level, associated pathways, tumor cell state and description, PMID and journal information, omics level, etc.
1) Which omics level the associated gene influence the disease: genome, epigenome, transcriptome and proteome
2) What regulate the gene and the gene influence what: Upstream: regulator type, regulator, interaction and direction Downstream: target type, target, interaction and effect
3) Pathway: The pathway of the gene and molecular effect
4) Tumor cell state: How the molecular influence the tumor cell state and detailed description
5) Sample information: sample species and source (tissue/cell line)
6) Disease information: classification, subtype, grade and the potential of the molecular (diagnosis, prognosis, predictive and therapy)
7) Literature information: PMID, title, journal ,year, authors, corresponding author and email
|genomics||Mutation; Wildtype; Copy number variation; Gene fusion|
|epigenetics||DNA; RNA; Histone|
|transcriptomics||up-regulated; down-regulated; activated; inhibited; alternative splicing|
|proteomics||up-regulated; down-regulated; activated; inhibited; overexpressed|
|regulator type||protein; mRNA; miRNA; pathway; lncRNA; virus|
|direction||promote; activate; inhibit; cooperation; co-target|
|target type||protein; mRNA; miRNA; pathway; lncRNA; signaling; telomere|
|effect||co-active; inhibit; activate; promote; methylate; phosphorylate; demethylation; forming complex|
|grade||G2; G3; G4|
|classification||Glioma; Glioblastoma; Medulloblastoma|
|subtype||Adult glioblastoma; Adult anaplastic oligodendroglioma; Astrocytoma; Angiocentric Glioma; Anaplastic oligodendroglioma; Brainstem glioma Chordoid glioma; Classical Glioblastoma; CML glioblastomaDiffuse glioma; Diffuse intrinsic pontine glioma (DIPG); Early gliomagenesis; Ependymoma Glioblastoma; Glioblastoma multiforme; High-grade glioma; Low-grade glioma; MES glioblastoma; PN glioblastoma; Proneural glioblastomag|
|tumor cell state||aggressive; angiogenesis; apoptosis; asymmetric division and differentiation; autophagy; clonogenicity; cell death; diffusion; expansion; formation; growth; infiltration; inhibition; invasion; maintenance; metastases; migration; oncogene capacity; proliferation; radioresistance; self-renewal; stemness; transformation; tumorigenesis; viability; promote oncogenesis; stimulated tumor vascularization|
|species||Homo Sapiens; Mus musculus; Drosophila; Rattus norvegicus|
The knowledge of other brain diseases are collected from public resources, including LncBook, EWAS Atlas, EDK, HMDD, Developmental Brain Disorders Database and Brain Disease Knowledgebase. The disease-gene association annotation with link information of the public databases mentioned above are collected. The descriptions about the diseases are mainly collected from Wikipedia and Disease Ontology.
In this study, except the knowledge annotation of brain disease, we provide the function for users to analyze the multi-omics data of glioma. The codes of figure are written by R, users can search a gene to do the survival analysis or compare the multi-omics level between different grades or subtypes of glioma on multi-omics level (genome, transcriptome, epigenome and proteome). All the analyzed figures can be browsed on the website and downloaded with a pdf format by clicking the figures on the website. The analysis types of the four omics are described below.
Genome: G2 vs G3 vs G4, IDH status, 1p19q status, IDH & 1p19q status, MGMT status and survival.
Transcriptome: Normal and Tumor tissue profile, Normal vs Glioma, G2 vs G3 vs G4, IDH status, 1p19q status, IDH & 1p19q status, MGMT status, cell type and survival.
Epigenome: Glioma vs Normal, G2 vs G3 vs G4, IDH status, IDH & 1p19q status, MGMT status and survival.
Four types of featured genes are included in BrainBase. They are brain-specific genes, brain-region-specific genes, HPA brain proteins and cerebrospinal fluid proteins. The details of these genes are described below.
1) Brain-specific genes: To identify brain-specific genes, we use the RNA-Seq dataset from GTEx (2016-01-15; v7), which contains 11,688 samples across 53 tissue sites of 714 donors. Considering that several tissues have multiple different sites, gene expression levels are averaged over sites that are from the same tissue. To reduce background noise, genes with maximal expression levels smaller than 1TPM (Transcripts Per Million) are removed. Finally, based on the expression levels across 31 tissues, we calculate the tissue specificity index τ (Itai, 2005) for each gene to identify tissue-specific genes. τ is valued between 0 and 1, where 0 represents housekeeping genes that are consistently expressed in different tissues, and 1 indicates tissue-specific genes that are exclusively expressed in only one tissue. In this study, brain-specific genes are defined as those genes that are maximally expressed in the brain with τ > 0.9. As a consequence, a list of 655 brain-specific genes were identified.
2) Brain-region-specific genes: Based on GTEx data, similar to the brain-specific genes mentioned above, 575 brain-region-specific genes of 13 brain regions are identified.
3) HPA brain proteins: The expression profiles for proteins in human tissues based on immunohistochemistry using tissue microarrays. The data and reliability score are obtained from the public database HPA.
4) Cerebrospinal fluid proteins: To achieve the detectability in the periphery, 1,126 proteins detected in cerebrospinal fluid with their fluorescence intensity are obtained from GEO ( GSE83710).
Note: The brain-region figure was downloaded from the website https://www.brainhq.com/brain-resources/cool-brain-facts-myths/brain-101/