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We now need more than ever to make genome analysis more intelligent. We needto read, analyze, and interpret our genomes not only quickly, but alsoaccurately and efficiently enough to scale the analysis to population level.There currently exist major computational bottlenecks and inefficienciesthroughout the entire genome analysis pipeline, because state-of-the-art genomesequencing technologies are still not able to read a genome in its entirety. Wedescribe the ongoing journey in significantly improving the performance,accuracy, and efficiency of genome analysis using intelligent algorithms andhardware architectures. We explain state-of-the-art algorithmic methods andhardware-based acceleration approaches for each step of the genome analysispipeline and provide experimental evaluations. Algorithmic approaches exploitthe structure of the genome as well as the structure of the underlyinghardware. Hardware-based acceleration approaches exploit specializedmicroarchitectures or various execution paradigms (e.g., processing inside ornear memory) along with algorithmic changes, leading to new hardware/softwareco-designed systems. We conclude with a foreshadowing of future challenges,benefits, and research directions triggered by the development of both very lowcost yet highly error prone new sequencing technologies and specializedhardware chips for genomics. We hope that these efforts and the challenges wediscuss provide a foundation for future work in making genome analysis moreintelligent. The analysis script and data used in our experimental evaluationare available at: https://github.com/CMU-SAFARI/Molecules2Variations

Going From Molecules to Genomic Variations to Scientific Discovery: Intelligent Algorithms and Architectures for Intelligent Genome Analysis