Thesis advisor: Gabor T. Marth / To help improve the analysis of forward genetic screens, we have developed an efficient and automated pipeline for mutational profiling using our reference guided tools including MOSAIK and FREEBAYES. Studies using next generation sequencing technologies currently employ either reference guided alignment or de novo assembly to analyze the massive amount of short read data produced by second generation sequencing technologies; the far more common approach being reference guided alignment due to the massive computational and sequencing costs associated with de novo assembly. The success of reference guided alignment is dependent on three factors; the accuracy of the reference, the ability of the mapper to correctly place a read, and the degree to which a variant allele differs from the reference. Reference assemblies are not perfect and none are entirely complete. Moreover, read mappers can only map reads in genomic locations that are unique enough to confidently place reads; paralogous sections, such as related gene families, cannot be characterized and are often ignored. Further, variant alleles that drastically alter the subject's DNA, such as insertions or deletions (INDELs), will not map to the reference and are either entirely missed or require further downstream analysis to characterize. Most importantly, reference guided methods are restricted to organisms for which such reference genomes have been assembled. The current alternative, de novo assembly of a genome, is prohibitively expensive for most labs requiring deep read coverage from numerous different library preparations as well as massive computing power. To address the shortcomings of current methods, while eliminating the costs intrinsic to de novo sequence assembly, we developed RUFUS, a novel, completely reference-independent variant discovery tool. RUFUS directly compares raw sequence data from two or more samples and identifies groups of reads unique to one or the other sample. RUFUS has at least the same variant detection sensitivity as mapping methods, with greatly increased specificity for SNPs and INDEL variation events. RUFUS is also capable of extremely sensitive copy number detection, without any restriction on event length. By modeling the underlying k-mer distribution, RUFUS produces a specific copy number spectrum for each individual sample. Applying a Bayesian detection method to detect changes in k-mer content between two samples, RUFUS produces copy number calls that are equally as sensitive as traditional copy number detection methods with far fewer false positives. Our data suggest that RUFUS' reference-free approach to variant discovery is able to substantially improve upon existing variant detection methods: reducing reference biases, reducing false positive variants, and detecting copy number variants with excellent sensitivity and specificity. / Thesis (PhD) — Boston College, 2014. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.