Short Read Mapping: An Algorithmic Tour

Canzar, Stefan; Salzberg, Steven L.

doi:10.1109/jproc.2015.2455551

Cited by 74 publications

(53 citation statements)

References 118 publications

(198 reference statements)

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“…Different approaches employing the seed-and-extend paradigm [4] to align short reads to DAGs have been proposed [22,9] with the main goal of fast (yet heuristic) alignment suitable for applications. For aligning sequences to cyclic graphs, the graph can be "unrolled" into a DAG.…”

Section: Introductionmentioning

confidence: 99%

Aligning sequences to general graphs in O(V + mE) time

Rautiainen

Marschall

2017

Preprint

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Graphs are commonly used to represent sets of sequences. Either edges or nodes can be labeled by sequences, so that each path in the graph spells a concatenated sequence. Examples include graphs to represent genome assemblies, such as string graphs and de Bruijn graphs, and graphs to represent a pan-genome and hence the genetic variation present in a population. Being able to align sequencing reads to such graphs is a key step for many analyses and its applications include genome assembly, read error correction, and variant calling with respect to a variation graph. Given the wide range of applications of this basic problem, it is surprising that algorithms with optimal runtime are, to the best of our knowledge, yet unknown. In particular, aligning sequences to cyclic graphs currently represents a challenge both in theory and practice. Here, we introduce an algorithm to compute the minimum edit distance of a sequence of length m to any path in a node-labeled directed graph (V, E) in O(|V |+m|E|) time and O(|V |) space. The corresponding alignment can be obtained in the same runtime using O( √ m|V |) space. The time complexity depends only on the length of the sequence and the size of the graph. In particular, it does not depend on the cyclicity of the graph, or any other topological features.not peer-reviewed)

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Section: Introductionmentioning

confidence: 99%

Aligning sequences to general graphs in O(V + mE) time

Rautiainen

Marschall

2017

Preprint

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“…Our goal in this analysis is to not to perform a side-by-side analysis of execution time or accuracy of various aligners. The optimal aligner choice is a complex analysis topic covered by prior research [5], [8]- [10] as there is a delicate balance between accuracy and performance that must be met depending on the expected usage. As our focus in this paper is on microarchitectural analysis of sequence aligners, we run each aligner only in the single threaded mode.…”

Section: A Hardware-software Setupmentioning

confidence: 99%

“…In this section, we will provide a brief overview of sequence alignment techniques and modern processor microarchitecture to set the context for this work. We refer the reader to prior work for an in-depth algorithmic survey of sequence alignment algorithms and experimental analysis of aligners [5], [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Sequence Alignment Through the Looking Glass

Appuswamy

Fellay

Chaturvedi

2018

Preprint

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Index Terms-sequence alignment, computational genomics, microarchitecture Abstract-Rapid advances in sequencing technologies are producing genomic data on an unprecedented scale. The first, and often one of the most time consuming, step of genomic data analysis is sequence alignment, where sequenced reads must be aligned to a reference genome. Several years of research on alignment algorithms has led to the development of several stateof-the-art sequence aligners that can map tens of thousands of reads per second.In this work, we answer the question "How do sequence aligners utilize modern processors?" We examine four state-ofthe-art aligners running on an Intel processor and identify that all aligners leave the processor substantially underutilized. We perform an in-depth microarchitectural analysis to explore the interaction between aligner software and processor hardware. We identify bottlenecks that lead to processor underutilization and discuss the implications of our analysis on next-generation sequence aligner design.

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“…However, it is challenging to efficiently store, process and analyze the huge amounts of genomic data generated by se- ‡This work was supported in part by NSF CCF-1909509. quencing machines that translate organic nucleotides to digital symbols. The genome sequencing machines are improving faster than Moore's Law [8]. For instance, a recent Illumina NovaSeq machine [9] produces nearly 750GB of data per day.…”

Section: Introductionmentioning

confidence: 99%

FindeR: Accelerating FM-Index-Based Exact Pattern Matching in Genomic Sequences through ReRAM Technology

Zokaee

Zhang

Jiang

2019

2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT)

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Genomics is the critical key to enabling precision medicine, ensuring global food security and enforcing wildlife conservation. The massive genomic data produced by various genome sequencing technologies presents a significant challenge for genome analysis. Because of errors from sequencing machines and genetic variations, approximate pattern matching (APM) is a must for practical genome analysis. Recent work proposes FPGA, ASIC and even process-in-memory-based accelerators to boost the APM throughput by accelerating dynamic-programmingbased algorithms (e.g., Smith-Waterman). However, existing accelerators lack the efficient hardware acceleration for the exact pattern matching (EPM) that is an even more critical and essential function widely used in almost every step of genome analysis including assembly, alignment, annotation and compression.State-of-the-art genome analysis adopts the FM-Index that augments the space-efficient BWT with additional data structures permitting fast EPM operations. But the FM-Index is notorious for poor spatial locality and massive random memory accesses. In this paper, we propose a ReRAM-based process-in-memory architecture, FindeR, to enhance the FM-Index EPM search throughput in genomic sequences. We build a reliable and energyefficient Hamming distance unit to accelerate the computing kernel of FM-Index search using commodity ReRAM chips without introducing extra CMOS logic. We further architect a full-fledged FM-Index search pipeline and improve its search throughput by lightweight scheduling on the NVDIMM. We also create a system library for programmers to invoke FindeR to perform EPMs in genome analysis. Compared to state-of-the-art accelerators, FindeR improves the FM-Index search throughput by 83% ∼ 30K× and throughput per Watt by 3.5× ∼ 42.5K×.

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Short Read Mapping: An Algorithmic Tour

Cited by 74 publications

References 118 publications

Aligning sequences to general graphs in O(V + mE) time

Aligning sequences to general graphs in O(V + mE) time

Sequence Alignment Through the Looking Glass

FindeR: Accelerating FM-Index-Based Exact Pattern Matching in Genomic Sequences through ReRAM Technology

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