I have whole genome of chickpea data base from other sites. I have other one is with diseased tissues of chickpea database. Is it possible by mathematically .pathogen +tissue minus healthy tissue = pathogen genome size. Can it possible for me to submit in ncbi. Or is it possible to get good amount of annotations/ real pathogen size. For example one person working in wilt of chickpea and this trascriptome data base minus icrisat data base of chickpea equalt to wilt genome. Should I used such ideas in drylabs and submit database in ncbi.
However, I am not able to understand your question properly, but main thing which i would like to bring to your notice is, you cannot submit the data in NCBI if already exist as a part of another project. Though you are doing modification in existing data but the fact is, it already exist in NCBI database and people who are actually interested in studying wilt transcriptome and chickpea transcriptome can bifurcate by mapping the reads to wilt genome and chickpea genome respectively.
But in case if you have your original transcriptome data generated for wilt infected chickpea by your own then you can map total reads to existing wilt genome and can use mapped reads for recreating wild genome by denovo assembly and now you can submit it to NCBI as wilt data.
Hope it helps
If you have the raw reads of the diseased genome you might be able to separate them by using an uneven sequencing depth assembler like those for metagenomes. Followed by binning and/or comparison against a chickpea "pure" genome (anything that wouldn't match would likely be your pathogen).
As a quick and dirty approach based on the reads you could use tools like BBSplit (http://seqanswers.com/forums/showthread.php?t=41288) to separate first reads that don't map to chickpea and then do an assembly of the rest. I know for viruses that pre-host substraction makes the assembly faster and putatively better (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129059).
The quality of the resulting assemblies you might analyse with tools like quast (http://bioinf.spbau.ru/quast).
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