Developing a workflow to determine the rate of mutation of the african cassava mosaic virus.

Cassava is one of the alternative crops to Zambia’s staple crop maize. Unlike maize,
cassava is drought resistant; maize has adversely been affected by drought. Unfortunately
cassava has been ravaged by the African cassava mosaic virus for over one hundred years.
It causes cassava mosaic disease which can result into a reduction in tuberous yield of
even up to seventy percent. Biological and agricultural science researchers world over
have not yet found a solution to the adverse effects of the virus partly because of the
software tools used to study the virus. The tools are not comprehensive, are fragmented
and have a complicated workflow. These factors cause the virus to mutate before any
solution to its effects is found. The tools used are also mostly proprietary resulting in
only few researchers participating in the study of the African cassava mosaic virus. A
case study of the research on the African cassava mosaic virus (ACMV) mutation gives
the picture of the foregoing. Researchers’ efforts to curb its effects are usually outdone by
its unpredictable and rapid rate of mutation. This research, therefore, used the ACMV
case study to develop a workflow that would be used to determine the rate of mutation
of the ACMV. The workflow brings together pre-existing fragmented tools via an XML
communication workflow which results into a tool that is easy to use. The analysis
of ACMV takes three to four stages: percentage identity analysis through a BLAST,
multiple sequence alignment and/or pairwise sequence analysis and phylogenetic tree
creation. All the stages, put together, bring out information on whether the ACMV
strain being studied has mutated or not. The five pre-existing tools (Bioedit, Geneious,
Mega, NCBI and SDT) are not interoperable. They use varying file formats for data input
and output although they all include xml. This research proposes the use of open source
biopython modules to build applications that would be ably used to study the ACMV.
An easier workflow based on xml related protocols is also proposed in order to reduce
total response time of the applications used to study the ACMV. The proposed solution
was tested and proved to be three times faster than the pre-existing tools used by Mount
Makulu research station researchers, in Zambia. A proof of concept on the development of
a single page application based on html, xml and svg was done. Ultimately, the reduction in response time of the applications used to study the ACMV would improve the rate
at which mutation in the ACMV is determined and thus help come up with solutions
against the virus fast enough to curb its effects and improve cassava yields. The solution
to the effects of the ACMV would increase food security in Zambia and other parts of
Africa that depend on cassava as staple food.
Keywords: African cassava mosaic virus; bioinformatics; biopython; computational
framework; data sharing techniques; HTML; metagenomics; open source tools; software;
SVG; XML; workflow