![]() ![]() This phenomenon underlies what can be described as the “quantum leap” in publications of high-throughput studies, when the analysis leaps -often with sparse analytical justification-from considering statistically prioritized lists of candidates to a handful of hits that are selected for further validation guided by the a posteriori knowledge of the authors ( Lotterhos et al., 2016). This bridging analysis step is significantly constrained by attempts to balance the challenges of analysing large datasets in an unbiased manner to excavate novel insights, with the appropriate recognition of known gene candidates considered as validation hits. Yet, critical to the utility of the data they yield,is the ability to translate their results into a constrained list of prioritized candidates that can be rigorously investigated on a feasible scale. High-throughput approaches - such as RNA and CRISPR-based screens, Next Generation sequencing methods, and proteomic analysis - permit the unbiased measurement of the contribution of each gene in the genome to the outcome of a specific biological process these methods continue to be some of the most powerful tools in research biology ( Heckl and Charpentier, 2015, Gilbert et al., 2014, Moffat et al., 2006, Lee et al., 2003). SIGNAL is accessible as a rapid user-friendly web-based application ( ).Ī record of this paper’s Transparent Peer Review process is included in the Supplemental Information. We describe Selection by Iterative pathway Group and Network Analysis Looping (SIGNAL), an integrated, iterative approach which uses both pathway and network methods to optimize gene prioritization. Using comparative analysis of parallel independent studies as a benchmark, we characterize the specific complementary contributions of each approach and demonstrate an optimal framework by which to integrate these methods. The specific limitations of these individual approaches and the lack of a systematic way by which to integrate their rankings has contributed to limited overlap in the reported results from comparable genome-wide studies and costly inefficiencies in secondary validation efforts. ![]() Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical tradeoffs. ![]() Hit selection from high throughput assays remains a critical bottleneck in realizing the potential of omic-scale studies in biology. ![]()
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