{'fraction_non_alphanumeric': 0.026595744680851064, 'fraction_numerical': 0.0, 'mean_word_length': 5.223140495867769, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Engineering a high-performance SNP detection pipeline

We present Sprite, a bioinformatic data analysis pipeline for detecting single nucleotide polymorphisms (SNPs) in the human genome. A SNP detection pipeline for next-generation sequencing data uses several software tools, including tools for read preprocessing, read alignment, and SNP calling. We target end-to-end scalability and I/O efficiency in Sprite by merging tools in this pipeline and eliminating redundancies. A key component of our optimized pipeline is parsnip, a new parallel method and software tool for SNP calling. Experimental results on synthetic and real data sets indicate that the quality of results achieved by parsnip is comparable to state-of-the-art SNP calling software.