Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems

Vasimuddin,; Misra, Sanchit; Li, Heng; Aluru, Srinivas

doi:10.1109/ipdps.2019.00041

Cited by 1,184 publications

(563 citation statements)

References 16 publications

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“…Briefly raw reads were trimmed to remove adapters and low quality regions using BBDuk and a k-mer based filter was used to pull out reads matching the reference sequence NC_045512. Filtered reads were de novo assembled using SPAdes ( 30 ) and contigs were ordered against the reference using BWA-MEM ( 31 ) . Gaps were filled by remapping reads against the assembled scaffold and a consensus sequence was called from this alignment using a custom script in R/Bioconductor.…”

Section: Diagnostics and Sequencingmentioning

confidence: 99%

Cryptic transmission of SARS-CoV-2 in Washington State

Bedford

Greninger

Roychoudhury

et al. 2020

Preprint

141

157

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Section: Diagnostics and Sequencingmentioning

confidence: 99%

Cryptic transmission of SARS-CoV-2 in Washington State

Bedford

Greninger

Roychoudhury

et al. 2020

Preprint

141

157

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“…We used BWA-MEM v0.7.17 (Li, 2013) and BWA-MEM2 (Vasimuddin et al, 2019) v2.0.pre2 as baselines for comparison. We replace FMD-index seeding in BWA-MEM2 with ERT-based seeding.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, the occurrence table used for performing range queries on the FMD-index is decompressed by 4× and the suffix array to identify locations of substrings in the reference genome is fully decompressed. These changes increase the FMDindex size to 42 GB (12 GB occurrence table + 30 GB suffix array) (Vasimuddin et al, 2019) compared to 4.3 GB in BWA-MEM. Starting from a single character in the read, the FMD-index enables forward and backward MEM searches to determine the number of hits of progressively longer substrings using at most 2 extra memory lookups per character.…”

Section: Fmd-index Seeding Bottlenecksmentioning

confidence: 99%

“…There have also been efforts to improve the locality of the location operation using the FMindex (Cheng et al, 2018) although these have not yet been integrated into read alignment software. Similar to ERT, recently released BWA-MEM2 (Vasimuddin et al, 2019) also trades-off memory space for memory bandwidth, but by departing from the FMD-index and proposing a bandwidth-friendly data structure we further improve bandwidth efficiency while producing identical seeds. Data-parallel architectures such as GPUs have also been leveraged to accelerate FMD-index search (Chacón et al, 2015) by virtue of their high memory bandwidth and memory level parallelism.…”

Section: Related Workmentioning

confidence: 99%

“…The seeding step is a major bottleneck in read alignment software such as BWA-MEM. For instance, seeding contributes ∼38% to the overall run time of BWA-MEM2 (Vasimuddin et al, 2019). In addition to read alignment, seeding is also an important kernel in several other applications: metagenomics classification (e.g., Centrifuge (Kim et al, 2016)), de-novo assembly (Simpson and Durbin, 2012)) and read error correction (Greenstein et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Accelerating Maximal-Exact-Match Seeding with Enumerated Radix Trees

Subramaniyan

Wadden

Goliya

et al. 2020

Preprint

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Motivation: Read alignment is a time-consuming step in genome sequence analysis. In the read alignment software BWA-MEM and the recently published faster version BWA-MEM2, the seeding step is a major bottleneck, for instance, contributing 38% to the overall execution time in BWA-MEM2 when aligning single-end whole human genome reads from the Platinum Genomes dataset. This is because both BWA-MEM and BWA-MEM2 use a compressed index structure called the FMD-Index, which results in high memory bandwidth requirements for seeding, primarily due to its character-by-character processing of reads. Results: We propose a memory bandwidth-aware data structure for maximal-exact-match seeding called Enumerated Radix Tree (ERT). ERT trades off memory capacity to improve seeding performance (∼60 GB index for human genome). Together with optimizations to the seeding algorithm and mate-rescue step, ERT when integrated into BWA-MEM2 speeds up overall read alignment by 1.28× and provides up to 2.1× higher seeding performance while guaranteeing identical output to the original software. Furthermore, we prototype an FPGA implementation of ERT on Amazon EC2 F1 cloud and observe 1.6× higher seeding throughput over a 48-thread optimized CPU-ERT implementation. Availability and implementation: https://github.com/arun-sub/bwa-mem2

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Lasting response by vertical inhibition with cetuximab and trametinib in KRAS‐mutated colorectal cancer patient‐derived xenografts

Reissig,

Ladigan‐Badura,

Steinberg

et al. 2023

Molecular Oncology

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Although approximately half of all metastatic colorectal cancers (mCRCs) harbour mutations in KRAS or NRAS, hardly any progress has been made regarding targeted treatment for this group over the last few years. Here, we investigated the efficacy of vertical inhibition of the RAS‐pathway by targeting epidermal growth factor receptor (EGFR) and mitogen‐activated protein kinase kinase (MEK) in patient‐derived xenograft (PDX) tumours with primary KRAS mutation. In total, 19 different PDX models comprising 127 tumours were tested. Responses were evaluated according to baseline tumour volume changes and graded as partial response (PR; ≤ − 30%), stable disease (SD; between −30% and +20%) or progressive disease (PD; ≥ + 20%). Vertical inhibition with trametinib and cetuximab induced SD or PR in 74% of analysed models, compared to 24% by monotherapy with trametinib. In cases of PR by vertical inhibition (47%), responses were lasting (as long as day 137), with a low incidence of secondary resistance (SR). Molecular analyses revealed that primary and SR was driven by transcriptional reprogramming activating the RAS pathway in a substantial fraction of tumours. Together, these preclinical data strongly support the translation of this combination therapy into clinical trials for CRC patients.

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Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems

Cited by 1,184 publications

References 16 publications

Cryptic transmission of SARS-CoV-2 in Washington State

Cryptic transmission of SARS-CoV-2 in Washington State

Accelerating Maximal-Exact-Match Seeding with Enumerated Radix Trees

Lasting response by vertical inhibition with cetuximab and trametinib in KRAS‐mutated colorectal cancer patient‐derived xenografts

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