Abstract

Modern sequencing technology has enabled the cheap, rapid production of
whole genomes. There is a need for visualization tools to show the data
collected about a whole genome such as genes, proteins, annotations, and
expression data. Many common approaches are developed such as the genome browser where
sequence features are displayed as visual elements in tracks and features are
aligned with their genome coordinates, visual networks where the data elements represented as nodes and relationship as edges, and traditional spreadsheet where each row
captures the information about a gene/genome where the information is textual in
nature, such as identifiers, descriptions, or sequences.
Our study is focusing in the last approach with introducing some advanced features.


To build the system, the common used similar systems are reviewed, and during the implementation some software artifacts are reused such as reusing some JavaScript libraries to reduce the complexity of software development.
Generally, an incremental method is used to develop the webpage starting from collecting the data from AspGD database, analyzing them, coding then testing them once at time.
Our research group studies fungal genomes, so the spreadsheets are tested by displaying each of the Aspergilli genomes in the AspGD database (www.aspgd.org).


We have developed CGene and CGenome, pronounced See-Gene and See-Genome respectively, as a HTML5 web-based spreadsheets that can incorporate visual displays, as well as text, within the spreadsheet cells. Current displays use Scalable Vector Graphics (SVG) to present these spreadsheets which are generated from standard GFF3 files, standard output files from InterProScan, aspgd files from AspGD Gene Ontology Annotations File, and Chromosomal Feature File. All these files are analyzed to present them in a visual way that requires less effort to understand.

The main aim of our study is to take the advantages of the ability of humans to recognize patterns. The user can see the gene/genomes of interest as row-by-row of visualization. This can play powerful roll to ease the understanding of quantitive data by replacing them by graphical figures that make the comparison easier.