Multi-level Queries, Motif Clustering and Non-B Burden heterogeneity.
Non-B DNA and Genomic instability in Cancer
Alternate (non-B) DNA-forming structures, such as Z-DNA, G-quadruplex, mirror repeats have demonstrated a potential role in cancer etiology. It has been found that non-B DNA-forming sequences can stimulate genetic instability in human cancer genomes, implicating them in the development of cancer and other genetic diseases. While there exist several non-B prediction databases published, they lack the ability to both analyze and visualize non-B data within the context of cancer gene signature sets.
Here, we introduce NBBC, A Non-B DNA Burden Explorer in Cancer to serve the purpose of analyzing and visualization non-B motif data. We introduce 'Non-B Burden', as a metric to summarize the prevalence of non-B DNA motifs at a gene level or within a genomic region. The non-B DNA data is collected from Non-B DB v2.0 database.
I. Computation of Non-B Burden
(Goal: To query non-B forming regions in genes and calculate non-B burdens.)
Based on the user input gene list, the computation module calculates non-B burden composition for the gene signature and offers multiple normalizations to enable comparisons across genes or non-B structures.
II. Module1: Gene Screen
(Goal: To help users select a subset of genes with high genomic instability evaluated by non-B burden. )
The gene layer analyzes non-B burdens and provides several visualizations for descriptive analysis of burden values, burden distribution, and burden-based gene clustering. A stacked barplot is used to visualize the total non-B burden. A bubble plot allows users to see the non-B burden by gene and type. A burden clustering function is also available within the heatmap format. Worth mentioning, the module, “Burden in Batch” (BiB) is provided process multiple queries in batch.
III. Module2: Motif Screen
(Goal: To further select high-quality motifs that are more likely to form non-B structure in the interested genes and to provide the specific sequences that can be used for wet lab experiments.)
The motif layer performs sequence-level motif clustering for high-quality non-B motif detection. For example, the length and guanine contents (%G) are two major factors in deciding motif quality for non-B forming. We employ unsupervised clustering to detect non-B motifs with both high G-contents and proper length. The app supports multiple features for clustering including length, guanine, adenine compositions in the non-B motif sequences.
Browser compatibility
To cite our work
Example References:
Xu Q, Kowalski J (2023). NBBC: A Non-B DNA Burden Explorer in Cancer. <https://kowalski-labapps.dellmed.utexas.edu/NBBC/>.
Example Text Citation:
e.g. “We used the Non-B DNA Burden Explorer in Cancer. (Xu and Kowalski, 2023)”
Kowalski-Muegge Lab
Dell Medical School, University of Texas at Austin
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Input options
The input options for NBBC include a single gene, a list of genes, a gene signature, or genomic coordinates. To accommodate these input types, we have provided four options below for users to choose from.
Option 1 offers pre-populated gene sets related to cancer, while Option 2 provides molecular signatures of cancer cell lines. Alternatively, users may manually input gene symbols in Option 3 or upload genomic coordinates of interesting regions in Option 4.
Please select an option and then input geneset below.
This example includes multiple groups of mutation sites. Mutation sites are the query genomic regions for Gene are only group names and can be any strings.
This example includes a list of genomic regions with [gene symbols] as labels
This example includes a list of genomic regions with [tags] as labels.
Preview of selected genes/regions
Table: Genomic Coordinates uplaoded from Option 4:
For below example, the mutation sites (chromosome, start, end) of multiple genes (hync_symbol) are listed.
In this case, the query regions are mutation sites and the groups are tagged by gene symbols.
Each row in the table represent a region in query.
The four column names are required to be the same as the example.
- [hgnc_symbol] can be either gene name or any tags to annotation the region.
- [chromosome, start, end] takes genomic coordinates as input.
