Harun Mustafa

Nika Mansouri Ghiasi, Harun Mustafa, Talu Güloglu et al.

Graph-based representations of genome sequences have emerged as a powerful approach for representing massive genomic databases in an expressive and efficient way. Despite their benefits, analysis on large-scale genome graphs incurs significant data movement overhead from the storage system due to accessing large amounts of low-reuse data. Processing data directly inside the storage device can be a fundamental solution for mitigating this overhead. However, none of the existing tools for graph-based genome analysis can be efficiently used inside the storage system due to the limited internal hardware resources in modern SSDs. At the same time, prior storage-centric systems developed for (i) traditional, linear non-graph-based genome analysis or (ii) conventional, non-genomic graph analysis are not suitable for the unique data structures and access patterns of graph-based genome analysis. We propose GRAINS, the first system for analysis with large-scale genome graphs in storage. Through our detailed examination of typical analysis pipelines that operate on genome graphs, we perform storage-aware algorithm-architecture co-design to (i) make these pipelines more storage-friendly and (ii) further improve performance, energy-efficiency, and cost via in-storage and in-flash processing. GRAINS's co-design is based on three key aspects. First, we propose a new batching and execution flow, based on unique features of genome graphs. Second, via in-flash and in-storage processing, we avoid transferring low-reused flash pages. Third, to leverage the full parallelism of flash dies, we design an effective, yet lightweight, scheduling technique, enabled by re-purposing the existing SSD structures. GRAINS provides 2.7x-47.8x speedup (4.4x-31.6x energy reduction) over the state-of-the-art software baselines, and 1.5x-17.0x speedup (3.1x-20.7x energy reduction) over a hardware-accelerated baseline.

Andreas Georgiou, Vincent Fortuin, Harun Mustafa et al.

Metagenomic studies have increasingly utilized sequencing technologies in order to analyze DNA fragments found in environmental samples.One important step in this analysis is the taxonomic classification of the DNA fragments. Conventional read classification methods require large databases and vast amounts of memory to run, with recent deep learning methods suffering from very large model sizes. We therefore aim to develop a more memory-efficient technique for taxonomic classification. A task of particular interest is abundance estimation in metagenomic samples. Current attempts rely on classifying single DNA reads independently from each other and are therefore agnostic to co-occurence patterns between taxa. In this work, we also attempt to take these patterns into account. We develop a novel memory-efficient read classification technique, combining deep learning and locality-sensitive hashing. We show that this approach outperforms conventional mapping-based and other deep learning methods for single-read taxonomic classification when restricting all methods to a fixed memory footprint. Moreover, we formulate the task of abundance estimation as a Multiple Instance Learning (MIL) problem and we extend current deep learning architectures with two different types of permutation-invariant MIL pooling layers: a) deepsets and b) attention-based pooling. We illustrate that our architectures can exploit the co-occurrence of species in metagenomic read sets and outperform the single-read architectures in predicting the distribution over taxa at higher taxonomic ranks.