title: Fast search of thousands of short-read sequencing experiments.
abstract: The amount of sequence information in public repositories is growing at a rapid rate. Although these data are likely to contain clinically important information that has not yet been uncovered, our ability to effectively mine these repositories is limited. Here we introduce Sequence Bloom Trees (SBTs), a method for querying thousands of short-read sequencing experiments by sequence, 162 times faster than existing approaches. The approach searches large data archives for all experiments that involve a given sequence. We use SBTs to search 2,652 human blood, breast and brain RNA-seq experiments for all 214,293 known transcripts in under 4 days using less than 239 MB of RAM and a single CPU. Searching sequence archives at this scale and in this time frame is currently not possible using existing tools.
authors: Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
authors: Brad
authors: B
authors: Solomon
authors: Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
authors: Carl
authors: C
authors: Kingsford
mesh_terms: D000465:Algorithms; D057225:Data Mining; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D012313:RNA / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D012313:RNA