Indexing Next-Generation Sequencing data

Abstract

Next-Generation Sequencing (NGS), also known as high-throughput sequencing, has opened the possibility of a comprehensive characterization of the genomic and epigenomic landscapes, giving answers to fundamental questions for biological and clinical research, e.g., how DNA-protein interactions and chromatin structure affect gene activity, how cancer develops, how much complex diseases such as diabetes or cancer depend on personal (epi)genomic traits, opening the road to personalized and precision medicine.In this context, our research has focused on sense-making, e.g., discovering how heterogeneous DNA regions concur to determine particular biological processes or phenotypes. Towards such discovery, characteristic operations to be performed on region data regard identifying co-occurrences of regions, from different biological tests and/or of distinct semantic types, possibly within a certain distance from each others and/or from DNA regions with known structural or functional properties.In this paper, we present Di3, a 1D Interval Inverted Index, acting as a multi-resolution single-dimension data structure for interval-based data queries. Di3 is defined at data access layer, independent from data layer, business logic layer, and presentation layer; this design makes Di3 adaptable to any underlying persistence technology based on key-value pairs, spanning from classical B+ tree to LevelDB and Apache HBase, and makes Di3 suitable for different business logic and presentation layer scenarios.We demonstrate the effectiveness of Di3 as a general purpose genomic region manipulation tool, with a console-level interface, and as a software component used within MuSERA, a tool for comparative analysis of region data replicates from NGS ChIP-seq and DNase-seq tests.