Implementation of an IOS app to compare documents and visualize common paragraphs using a local alignment software.

Background

The goal of this thesis is to adapt the the STELLAR algorithm [1] in SeqAn to compare two text files (input is ASCII or PDF) postprocess the resulting alignments and visualize them in an iOS app. The app should be able to (optional in italics):

1. Parse documents locally
   • Dropbox and icloud
2. Be abel to prcoess ASCII texts
   • read PDFs (usinf prodomo or xdpf)
3. Provide interface to call stellar
   • minimum length (10-200)
   • Error rate in %
   • xdrop parameter
   • alphabet is fixed to char
   • keep kmer kmer small (max 3mer)
   • Tooltips for parameters
   • change of parameters adapts UI
4. Postprocessing of results
   • Smooth eges of match
5. Visulisation
   • Highlight local alignments
   • map ASCII to orignial document (if PDF etc)
   • edit distance in %
6. statistics
   • coverage plots

References

[1] Kehr, B., David Weese, and Knut Reinert. 2011. “STELLAR: Fast and Exact Local Alignments.” BMC Bioinformatics 12 (Suppl 9). BioMed Central Ltd: S15.