The Parallelism Motifs of Genomic Data Analysis

Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share this data with the research community, but some of these genomic data analysis problems require large scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high end parallel systems today and place different requirements on programming support, software libraries, and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high performance genomics analysis, including alignment, profiling, clustering, and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or motifs that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing

diBELLA: Distributed Long Read to Long Read Alignment

We present a parallel algorithm and scalable implementation for genome analysis, specifically the problem of finding overlaps and alignments for data from "third generation" long read sequencers. While long sequences of DNA offer enormous advantages for biological analysis and insight, current long read sequencing instruments have high error rates and therefore require different approaches to analysis than their short read counterparts. Our work focuses on an efficient distributed-memory parallelization of an accurate single-node algorithm for overlapping and aligning long reads. We achieve scalability of this irregular algorithm by addressing the competing issues of increasing parallelism, minimizing communication, constraining the memory footprint, and ensuring good load balance. The resulting application, diBELLA, is the first distributed memory overlapper and aligner specifically designed for long reads and parallel scalability. We describe and present analyses for high level design trade-offs and conduct an extensive empirical analysis that compares performance characteristics across state-of-the-art HPC systems as well as a commercial cloud architectures, highlighting the advantages of state-of-the-art network technologies.Comment: This is the authors' preprint of the article that appears in the proceedings of ICPP 2019, the 48th International Conference on Parallel Processin

Scalable Parallel Algorithms for Genome Analysis

A critical problem for computational genomics is the problem of de novo genome assembly: the development of robust scalable methods for transforming short randomly sampled “shotgun” sequences, namely reads, into the contiguous and accurate reconstruction of complex genomes. These reads are significantly shorter (e.g. hundreds of bases long) than the size of chromosomes and also include errors. While advanced methods exist for assembling the small and haploid genomes of prokaryotes, the genomes of eukaryotes are more complex. Moreover, de novo assembly has been unable to keep pace with the flood of data, due to the dramatic increases in genome sequencer capabilities, combined with the computational requirements and the algorithmic complexity of assembling large scale genomes and metagenomes.In this dissertation, we address this challenge head on by developing parallel algorithms for de novo genome assembly with the ambition to scale to massive concurrencies. Our work is based on the Meraculous assembler, a state-of-the-art de novo assembler for short reads developed at JGI. Meraculous identifies non-erroneous overlapping substrings of length k (k-mers) with high quality extensions and uniquely assembles genome regions into uncontested sequences called contigs by constructing and traversing a de Bruijn graph of k-mers, a special graph that is used to represent overlaps among k-mers. The original reads are subsequently aligned onto the contigs to obtain information regarding the relative orientation of the contigs. Contigs are then linked together to create scaffolds, sequences of contigs that may contain gaps among them. Finally gaps are filled using localized assemblies based on the original reads.First, we design efficient scalable algorithms for k-mer analysis and contig generation. K-mer analysis is characterized by intensive communication and I/O requirements and our parallel algorithms successfully reduce the memory requirements by 7×. Then, contig generation relies on efficient parallelization of the de Bruijn graph construction and traversal, which necessitates a distributed hash table and is a key component of most de novo assemblers. We present a novel algorithm that leverages one-sided communication capabilities of the UPC to facilitate the requisite fine-grained, irregular parallelism and the avoidance of data hazards. The sequence alignment is characterized by intensive I/O and large computation requirements. We introduce mer-Aligner, a highly parallel sequence aligner that employs parallelism in all of its components. Finally, this thesis details the parallelization of the scaffolding modules, enabling the first massively scalable, high quality, complete end-to-end de novo assembly pipeline. Experimental large-scale results using human and wheat genomes demonstrate efficient performance and scalability on thousands of cores. Compared to the original Meraculous code, which requires approximately 48 hours to assemble the human genome, our pipeline called HipMer computes the assembly in only 4 minutes using 23,040 cores of Edison – an overall speedup of approximately 720×.In the last part of the dissertation we tackle the problem of metagenome assembly. Metagenomics is currently the leading technology to study the uncultured microbial diversity. While accessing an unprecedented number of environmental samples that consist of thousands of individual microbial genomes is now possible, the bottleneck is becoming computational, since the sequencing cost improvements exceed that of Moore’s Law. Metagenome assembly is further complicated by repeated sequences across genomes, polymorphisms within a species and variable frequency of the genomes within the sample. In our work we repurpose HipMer components for the problem of metagenome assembly and we design a versatile, high-performance metagenome assembly pipeline that outperforms state-of-the-art tools in both quality and performance