(1) DupHMM.py has continued to be improved upon with the k-means clustering with the aid of a genetic algorithm. Not 100% operational yet, as the Viterbi portion of the program does not seem to work correctly.
(2) RNA_Seq_SAM_Parse.py creates a tabulated gene count table for RNA-Seq data files. This was used to compare expression in A. suecica vs. A. thaliana. An R script was also added which compares gene expression in leaf for 2 biological replicate A. suecica data sets vs. 2 biological replicate, 5 technical each A. thaliana (Col) data sets. A second R script was also added which compares the expression between the two Col biological replicates.
(3) HighVsLowPerformerDupCoverage.py looks at Isabelle's SxN F2's, which have been grouped into high performing and low performing groups based on their abilities to produce viable pollen, and compares the coverage of meiosis-related genes that DupHMM found to be duplicated in the high group vs. the low group. Looking for ~2X coverage in high vs. low; also looking for the presence of SUNs as found by SUNFinder.py. RemoveCorruptedReadsFromSxNF2s.py removes corrupted reads from the SxN F2s, which were likely produced due to an outdated FASTQ format going through aligners.
(4) CompDupGeneExpToRandomList.py looks at the expression of DupHMM.py-found duplicated genes, totals the number of counts within these genes, and then compares this list of gene counts to many randomly generated lists of genes and their summed read counts. The results found that to >96% C.I., the duplicated gene set has higher coverage than an equally sized list of randomly selected genes' coverage.