Spaced seed data structures

Abstract

This past decade, genome sciences have benefitted from rapid advances in DNA sequencing technologies, and development of efficient algorithms for processing short nucleotide sequences played a key role in enabling their uptake in the field. In particular, reassembly of human genomes (de novo or reference-guided) from short DNA sequence reads had a substantial impact on health research. De novo assembly of a genome is essential in the absence of a reference genome sequence of a species. It is also gaining traction even when one is available, due to the utility of the method to resolve ambiguous or rearranged genomic regions with high specificity. With commercial high-throughput sequencing technologies increasing their throughput and their read lengths, the de Bruijn graph (DBG) paradigm used by many assembly algorithms needs to be revisited. DBG uses a table of k-mers, sequences of length k base pairs derived from the reads, and their k-1 base pair overlaps to assemble sequences. Despite longer k-mers unlocking longer genomic features for assembly, associated increases in memory usage and other compute resources are tradeoffs that limit the practicability of DBG over other assembly archetypes already designed for longer reads. Here, we introduce three data structure designs for paired k-mers, or spaced seeds, each addressing memory and run time constraints imposed by longer reads. In spaced seeds, a fixed distance separates k-mer pairs, providing increased sequence specificity with increased distance, while keeping memory usage low. Further, we describe a data structure based on Bloom filters that would be suitable to implicitly store spaced seeds, and would be tolerant to sequencing errors. Building on the spaced seeds Bloom filter, we describe a data structure for tracking the frequencies of observed spaced seeds. We expect the data structure designs we introduce in this study to have broad applications in genomics research, with niche applications in genome, transcriptome and metagenome assemblies, and in read error correction.

Improved white spruce (Picea glauca) genome assemblies and annotation of large gene families of conifer terpenoid and phenolic defense metabolism

“…Recent efforts to minimize the genome assembly resource footprint have led to the implementation of several memory‐efficient assemblers (Simpson and Durbin, ; Conway and Bromage, ; Chikhi and Rizk, ; Ye et al ., ), but usually at the expense of time and accuracy. We have been preoccupied by the scale problem for some time (Simpson et al ., ) and have recently outlined and presented the theory behind assembly by spaced seeds, a re‐design of the traditional k‐ mer that, even in current data structure implementations, has potential for an over two‐fold speed‐up and a four‐fold reduction in memory without compromising on assembly correctness (Birol et al ., ).…”

Best hits of 11110110111: model-free selection and parameter-free sensitivity calculation of spaced seeds

“…Several extensions of the spaced seed model have then been proposed on the two aforementioned problems: vector seeds [ 5 ], one gapped q -grams [ 6 ] or indel seeds [ 7 , 8 ], neighbor seeds [ 9 , 10 ], transition seeds [ 11 – 15 ], multiple seeds [ 16 – 19 ], adaptive seeds [ 20 ] and related work on the associated indexes [ 21 – 26 ], just to mention a few.…”

Skip-mers: increasing entropy and sensitivity to detect conserved genic regions with simple cyclic q-grams

“…A single base difference, due to real biological variation or a sequencing error, affects all k-mers crossing that position thus impeding direct analyses by identity. Also, given the strong interdependence of local sequence, contiguous sections capture less information about genome structure and are thus more affected by sequence repetition (Chaisson et al, 2009; Birol et al, 2015).…”