Statistical significance of overlapping regions.

<p>The statistical significance tab allows the user to determine the statistical significance of the extent of overlap of two sets of regions. Active datasets are loaded into two dropdown lists and the user selects one dataset as a query and the other one as a reference. Only regions of both datasets that are within the selected domain dataset are included in the calculation. Clicking the Execute button calls up a command-line window and executes the IntervalStat tool. The command-line window stays open to display the messages from the tool. When the terminal window is closed BiSA calculates Overlap Correlation Value of the two datasets.</p

Analysis is the main overlap analysis tab of BiSA.

<p>BiSA offers six types of analysis: Overlap finding option a) reports overlap percentage with respect to the total Dataset-A regions and percentage with respect to the other active dataset regions. Overlapping or non-overlapping regions of Dataset-A can be extracted by options b), d) or e). Whereas, option c) or f) can be used to extract overlapping sections of regions common in all or two datasets. The results of overlap analysis type b), c), d), e) and f) can be saved back into the Knowledge Base by the ‘Save to KB’ button, allowing them to go into downstream analysis and independent annotation. Ticking the “Extract both datasets, bp overlap and centre distance between the regions” for options d), e) and f) displays both Dataset-A and Dataset-B regions, bp overlap and distance between the two sets.</p

Administration of datasets.

<p>From the Administration tab users can delete a dataset, save the data in tab delimited text format, and view the distribution of region sizes over the dataset as a table and histogram.</p

Proximal features.

<p>This tab allows users to search for genomic features located in proximity to a specific gene or genomic locus. Searching multiple datasets returns the numbers of binding sites for each factor identified. Selecting a single factor returns detailed binding region information for interactions in proximity to the gene or locus of interest.</p

Example study of overlap between FoxA1 and FoxA3, CTCF and SA1, ZNF263 and c-Fos datasets.

<p>A) Venn diagram representation of 2,929 overlapping regions in FoxA1 (8,175 regions) and FoxA3 (4,598 regions) datasets. B) Common sections of overlapping regions are saved back into the KB, and for bin size 100 a histogram of the size distribution of region overlaps is drawn. The histogram shows that there are more than 1,600 regions that have at least ∼300 bp in common between the two datasets. C) Overlap of 39,144 regions between CTCF and SA1 datasets. D) Distribution of overlapping sections of CTCF and SA1. E) Overlap of 559 regions between ZNF263 and c-Fos datasets. D) Distribution of overlapping sections of ZNF263 and c-Fos. We also observed that in three comparisons >94% of the overlapping sections are >200 bases long, suggesting that overlapping regions usually share a significant section of the two sets.</p

Select Datasets.

<p>This tab displays a list of all datasets in the KB, populated by default or as a consequence of uploading in Step-1. Clicking on the text of any row displays the reference link of the article, raw data link and notes, if any, below the table. Website addresses are hyperlinked to the websites/articles from where the data are obtained. (A) Changing the Active ticks and clicking on the Update button implements the selection. (B) Users can search the KB by organism, cell line, factor label, reference genome or peak caller.</p

New gene definitions.

<p>New gene definitions may be uploaded in the software. The user must specify columns for chromosome, strand, lower and upper coordinates.</p

Venn diagrams.

<p>BiSA can cross-compare a maximum of three active KB as a Venn diagram. (A) If there are more than three active datasets then a pop-up window appears that allows the investigator to select three datasets to be analysed. (B) Google Charts is used to draw Venn diagrams. The diagram can be saved as a high quality PNG file.</p

Import Datasets to Knowledge Base (KB).

<p>This step is optional and users can study data already saved in the KB, without importing datasets. In this step, the user can upload their own transcription factor DNA binding sites or histone modification locations, usually as BED or GFF peak files. If the file extension is other than BED or GFF, BiSA prompts the user to choose the right format. It is important to specify a Reference Genome (encircled), for instance hg18/hg19 for human or mm9/mm8 for mouse. BiSA will only allow comparisons between datasets of the same reference genome.</p

How did socio-demographic status and personal attributes influence compliance to COVID-19 preventive behaviours during the early outbreak in Japan? Lessons for pandemic management

This study focuses on how socio-demographic status and personal attributes influence self-protective behaviours during a pandemic, with protection behaviours being assessed through three perspectives – social distancing, personal protection behaviour and social responsibility awareness. The research considers a publicly available and recently collected dataset on Japanese citizens during the COVID-19 early outbreak and utilises a data analysis framework combining Classification and Regression Tree (CART), a data mining approach, and regression analysis to gain deep insights. The analysis reveals Socio-demographic attributes – sex, marital family status and having children – as having played an influential role in Japanese citizens' abiding by the COVID-19 protection behaviours. Especially women with children are noted as more conscious than their male counterparts. Work status also appears to have some impact concerning social distancing. Trust in government also appears as a significant factor. The analysis further identifies smoking behaviour as a factor characterising subjective prevention actions with non-smokers or less-frequent smokers being more compliant to the protection behaviours. Overall, the findings imply the need of public policy campaigning to account for variations in protection behaviour due to socio-demographic and personal attributes during pandemics and national emergencies