{'fraction_non_alphanumeric': 0.03392330383480826, 'fraction_numerical': 0.007374631268436578, 'mean_word_length': 5.058035714285714, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 1, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Using Genome Query Language ( GQL ) to uncover genetic variation

Motivation:With high throughput DNA sequencing costs dropping below $1, 000 for human genomes, data storage, retrieval, and analysis are the major bottlenecks in biological studies. In order to address the large-data challenges, we advocate a clean separation between the evidence collection and the inference in variant calling. We define and implement a Genome Query Language (GQL) that allows for the rapid collection of evidence needed for calling variants. Results: We provide a number of cases to showcase the use of GQL for complex evidence collection, such as the evidence for large structural variations. Specifically, typical GQL queries can be written in 5-10 lines of high level code, and search large data sets (100GB) in minutes. We also demonstrate its complementarity with other variant calling tools. Popular variant calling tools can achieve one order of magnitude speed-up by using GQL to retrieve evidence. Finally, we show how GQL can be used to query and compare multiple data-sets. By separating the evidence and inference for variant calling, it frees all variant detection tools from the data intensive evidence collection, and focus on statistical inference. Availability: GQL can be downloaded from http://cseweb.ucsd.edu/∼ckozanit/gql. Contact: ckozanit@ucsd.edu