1. Why CR2Cancer?
Chromatin regulators (CRs) can dynamically modulate chromatin architecture to epigenetically regulate gene expression in response to intrinsic and extrinsic signalling cues. Genomic alterations or dysregulation of CRs have now been identified in a wide range of cancer types and are increasingly regarded as novel therapeutic targets. To our knowledge, CR2Cancer is by far the most comprehensive database for CRs in human cancer. Users can obtain a whole picture of CRs in pan-cancer level (primary tumor tissues and cancer cell lines) and directly use the results from CR2Cancer in their studies.
2. What's in CR2Cancer?
CR2Cancer contains: (1) genomic, transcriptomic, proteomic, clinical and functional information for over 400 CRs across multiple cancer types. (2) diverse types of CR-associated relations, including cancer type dependent (CR-target and miRNA-CR) and independent (protein-protein interaction and drug-target) ones. (3) about 6000 items of aberrant molecular change (mutation or dysregulation) and interactions of CRs in cancer development manually curated from 5007 publications.
3. Where do you collect a list of CRs?
We compiled a comprehensive list of 429 CRs functioning as DNA modifiers, histone-modifying enzymes or chromatin remodelers from three recent publications [1-3].
4. What is the procedure of text mining?
It is comprised of three steps: (1) download all abstracts referring to cancer research from PubMed; (2) based on a dictionary of CR gene names, filter the publications focusing on CRs; (3) manually curate aberrant molecular change and interactions of CRs in cancer development.
5. How do you normalize RNA-Seq data from TCGA and carry out differential expression analysis?
After downloading the row counts of RNA-Seq data, genes were retained for downstream analysis if they had more than one count per million mapped reads in at least 5% samples. Voom method was used to convert the read counts to log2CPM [4]. A linear model was fitted to each gene, and empirical Bayes moderated t-statistics were used to assess differential expression between tumor and normal with limma package [5].
6. How do you determine methylation level of promoter for CRs?
We calculated Spearman correlation coefficient between expression and methylation for all CpG sites in the promoter region, and assigned the beta value of CpG site with the least rho value as promoter methylation level of CRs.
7. How do you predict the transcriptional targets of CRs?
We exploited two strategies to infer the targets of CRs. One is to process cancer gene expression profile with ARACNe-AP [6] that implement gene network reverse engineering through adaptive partitioning inference of mutual information. The other is to deal with ChIP-Seq data of chromatin regulators.
8. Which methods do you use to infer miRNA-CR regulatory relations?
The miRNA-CR regulatory relations were integrated from several experiment validation or sequence-based prediction databases such as RAID [7], mir2Disease [8], miRTarBase [9], miRDB [10], miRWalk [11] and TargetSCAN [12]. We then calculated Pearson correlations between the expression levels of miRNA and CR, and sorted the miRNA-CR pairs by the frequency of cancer types in which they are negatively correlated.
References
[1] Gonzalez-Perez A, et al. The mutational landscape of chromatin regulatory factors across 4,623 tumor samples. Genome biology 14.9 (2013): r106.
[2] Shah, MA, et al. A global assessment of cancer genomic alterations in epigenetic mechanisms. Epigenetics & chromatin 7.1 (2014): 29.
[3] Yang Z, et al. An integrative pan-cancer-wide analysis of epigenetic enzymes reveals universal patterns of epigenomic deregulation in cancer. Genome biology 16.1 (2015): 140.
[4] Law CW, et al. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome biology 15.2 (2014): R29.
[5] Ritchie ME, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research 43.7 (2015): e47-e47.
[6] Lachmann A, et al. ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics 32.14 (2016): 2233-2235.
[7] Yi, Y, et al. RAID v2. 0: an updated resource of RNA-associated interactions across organisms. Nucleic acids research 45.D1 (2017): D115-D118.
[8] Jiang, Q, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic acids research 37.suppl_1 (2008): D98-D104.
[9] Chou, C, et al. miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic acids research 44.D1 (2015): D239-D247.
[10] Wong, N, et al. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic acids research 43.D1 (2014): D146-D152.
[11] Dweep, H, et al. miRWalk2. 0: a comprehensive atlas of microRNA-target interactions. Nature methods 12.8 (2015): 697-697.
[12] Agarwal, V, et al. Predicting effective microRNA target sites in mammalian mRNAs. elife 4 (2015): e05005.