Mass spectrometry-based proteomics is a key enabler for personalized healthcare, providing a deep dive into the complex protein compositions of biological systems. This technology has vast applications in biotechnology and biomedicine but faces significant computational bottlenecks. Current methodologies often require multiple hours or even days to process extensive datasets, particularly in the domain of spectral clustering. To tackle these inefficiencies, we introduce SpecHD, a hyperdimensional computing (HDC) framework supplemented by an FPGA-accelerated architecture with integrated near-storage preprocessing. Utilizing streamlined binary operations in an HDC environment, SpecHD capitalizes on the low-latency and parallel capabilities of FPGAs. This approach markedly improves clustering speed and efficiency, serving as a catalyst for real-time, high-throughput data analysis in future healthcare applications. Our evaluations demonstrate that SpecHD not only maintains but often surpasses existing clustering quality metrics while drastically cutting computational time. Specifically, it can cluster a large-scale human proteome dataset-comprising 25 million MS/MS spectra and 131 GB of MS data-in just 5 minutes. With energy efficiency exceeding 31x and a speedup factor that spans a range of 6x to 54x over existing state-of-the-art solutions, SpecHD emerges as a promising solution for the rapid analysis of mass spectrometry data with great implications for personalized healthcare.

MOTIVATION: The genomic surveillance of viral pathogens such as SARS-CoV-2 and HIV-1 has been critical to modern epidemiology and public health, but the use of sequence analysis pipelines requires computational expertise, and web-based platforms require sending potentially sensitive raw sequence data to remote servers. RESULTS: We introduce ViralWasm, a user-friendly graphical web application suite for viral genomics. All ViralWasm tools utilize WebAssembly to execute the original command line tools client-side directly in the web browser without any user setup, with a cost of just 2-3x slowdown with respect to their command line counterparts. AVAILABILITY AND IMPLEMENTATION: The ViralWasm tool suite can be accessed at: https://niema-lab.github.io/ViralWasm.

MOTIVATION: Genomic distance estimation is a critical workload since exact computation for whole-genome similarity metrics such as Average Nucleotide Identity (ANI) incurs prohibitive runtime overhead. Genome sketching is a fast and memory-efficient solution to estimate ANI similarity by distilling representative k-mers from the original sequences. In this work, we present HyperGen that improves accuracy, runtime performance, and memory efficiency for large-scale ANI estimation. Unlike existing genome sketching algorithms that convert large genome files into discrete k-mer hashes, HyperGen leverages the emerging hyperdimensional computing (HDC) to encode genomes into quasi-orthogonal vectors (Hypervector, HV) in high-dimensional space. HV is compact and can preserve more information, allowing for accurate ANI estimation while reducing required sketch sizes. In particular, the HV sketch representation in HyperGen allows efficient ANI estimation using vector multiplication, which naturally benefits from highly optimized general matrix multiply (GEMM) routines. As a result, HyperGen enables the efficient sketching and ANI estimation for massive genome collections. RESULTS: We evaluate HyperGen's sketching and database search performance using several genome datasets at various scales. HyperGen is able to achieve comparable or superior ANI estimation error and linearity compared to other sketch-based counterparts. The measurement results show that HyperGen is one of the fastest tools for both genome sketching and database search. Meanwhile, HyperGen produces memory-efficient sketch files while ensuring high ANI estimation accuracy. AVAILABILITY AND IMPLEMENTATION: A Rust implementation of HyperGen is freely available under the MIT license as an open-source software project at https://github.com/wh-xu/Hyper-Gen. The scripts to reproduce the experimental results can be accessed at https://github.com/wh-xu/experiment-hyper-gen.

Mass spectrometry, commonly used for protein identification, generates a massive number of spectra that need to be matched against a large database. In reality, most of them remain unidentified or mismatched due to unexpected post-translational modifications. Open modification search (OMS) has been proposed as a strategy to improve the identification rate by considering changes in spectra, but it expands the search space exponentially. In this work, we propose HyperOMS, an algorithm-hardware co-design for boosted OMS, to cope with the enlarged database and expanded search space. HyperOMS encodes spectral data into binary vectors and performs the efficient OMS in high-dimensional space. We accelerate the HyperOMS algorithm using a DRAM-based PIM accelerator, which combines processing-using-memory and near-memory processing technologies. In order to maximize the parallelization and efficiency of the accelerator, we optimize the data allocation and devise an approximation strategy for similarity computation. Experimental results show that the HyperOMS accelerator yields up to 3.8× speedup and 119W higher energy efficiency compared to running HyperOMS on GPU, and up to 99× speedup and 1984× higher energy efficiency over the state-of-the-art OMS tool, ANN-SoLo 1, while providing comparable search quality to competing tools.

MOTIVATION: Driven by technological advances, the throughput and cost of mass spectrometry (MS) proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications (PTMs), will remain unknown. Open modification searching (OMS) is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of MS proteomics datasets. RESULTS: We propose an OMS algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering PTMs. We accelerate HOMS-TC on NVIDIA's tensor core units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31× faster on average than alternative search engines and provides comparable accuracy to competing search tools. AVAILABILITY AND IMPLEMENTATION: HOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc.

MOTIVATION: In viral molecular epidemiology, reconstruction of consensus genomes from sequence data is critical for tracking mutations and variants of concern. However, as the number of samples that are sequenced grows rapidly, compute resources needed to reconstruct consensus genomes can become prohibitively large. RESULTS: ViralConsensus is a fast and memory-efficient tool for calling viral consensus genome sequences directly from read alignment data. ViralConsensus is orders of magnitude faster and more memory-efficient than existing methods. Further, unlike existing methods, ViralConsensus can pipe data directly from a read mapper via standard input and performs viral consensus calling on-the-fly, making it an ideal tool for viral sequencing pipelines. AVAILABILITY AND IMPLEMENTATION: ViralConsensus is freely available at https://github.com/niemasd/ViralConsensus as an open-source software project.

Genome sequence alignment is the core of many biological applications. The advancement of sequencing technologies produces a tremendous amount of data, making sequence alignment a critical bottleneck in bioinformatics analysis. The existing hardware accelerators for alignment suffer from limited on-chip memory, costly data movement, and poorly optimized alignment algorithms. They cannot afford to concurrently process the massive amount of data generated by sequencing machines. In this article, we propose a ReRAM-based accelerator, RAPIDx, using processing in-memory (PIM) for sequence alignment. RAPIDx achieves superior efficiency and performance via software–hardware co-design. First, we propose an adaptive banded parallelism alignment algorithm suitable for PIM architecture. Compared to the original dynamic programming-based alignment, the proposed algorithm significantly reduces the required complexity, data bit width, and memory footprint at the cost of negligible accuracy degradation. Then, we propose the efficient PIM architecture that implements the proposed algorithm. The data flow in RAPIDx achieves four-level parallelism and we design an in-situ alignment computation flow in ReRAM, delivering$5.5-9.7\times $efficiency and throughput improvements compared to our previous PIM design, RAPID. The proposed RAPIDx is reconfigurable to serve as a co-processor integrated into the existing genome analysis pipeline to boost sequence alignment or edit distance calculation. On short-read alignment, RAPIDx delivers$131.1\times $and$46.8\times $throughput improvements over state-of-the-art CPU and GPU libraries, respectively. As compared to ASIC accelerators for long-read alignment, the performance of RAPIDx is$1.8{\times }-2.9{\times }$higher.

State-of-the-art high-throughput DNA sequencers output terabytes of short reads that typically need to be aligned to a reference genome in order to perform downstream analyses. Because alignment typically dominates the total run time of bioinformatics pipelines, a number of recent work sought to accelerate it in hardware. However, existing FPGA implemen-tations did not fully optimize the alignment algorithms for the FPGA hardware and mainly focused on a subset of alignment problems, e.g., ungapped alignment with a limited number of mismatches, which hinder their practical utility. In this work, we analyze the existing alignment methods and identify and leverage opportunities for FPGA acceleration. Our alignment framework, SALIENT, first carries out an ultra-fast ungapped alignment, which supports a flexible number of mismatches. Based on the underlying bioinformatics pipeline and the information provided by the ungapped aligner, SALIENT then identifies a fraction of reads that need to go through its gapped aligner, thus improving alignment throughput. We extensively evaluate SALIENT using diverse datasets. Experimental results indicate that SALIENT, running on a single Xilinx Alveo U280 device, delivers an average throughput of 546 million bases/second, outperforming the state- of-the-art minimap2 software by 40x, and Bowtie2 by up to 107 x, with a similar or slightly better (~O.l %-0.5 %) alignment and error (false negative/positive) rate. Compared to the existing ungapped FPGA aligners [1]–[4], SALIENT has 9.4-18x higher throughput/Watt, while compared to the gapped aligners [5], [6], it is 28–35 x better. SALIENT achieves 7.6 x higher throughput than Illumina DRAGEN Bio-IT Platform [7].

De novo assembly of genomes for which there is no reference, is essential for novel species discovery and metagenomics. In this work, we accelerate two key performance bottlenecks of DBG-based assembly, graph construction and graph traversal, with a near-data processing (NDP) architecture based on 3D-stacking. The proposed framework distributes key operations across NDP cores to exploit a high degree of parallelism and high memory bandwidth. We propose several optimizations based on domain-specific properties to improve the performance of our design. We integrate the proposed techniques into an existing DBG assembly tool, and our simulation-based evaluation shows that the proposed NDP implementation can improve the performance of graph construction by 33× and traversal by 16× compared to the state-of-the-art.

Identifying genome functionality changes our understanding of humans and helps us in disease diagnosis; as well as drug, bio-material, and genetic engineering of plants and animals. Comparing the structure of the protein sequences, when only sequence information is available, against a database with known functionality helps us to identify and recognize the functionality of the unknown sequence. The process of predicting the possible RNA sequence that a specific protein has originated from is called back-translation. Aligning the back-translated RNA sequence against the database locates the most similar sequences, which is used to predict the functionality of the unknown protein sequence. Providing massive parallelism, FPGAs can accelerate bioinformatics applications substantially. In this paper, we propose, FabP11FabP is also the name of a family of proteins, “Fatty-Acid-Binding Proteins”., an optimized FPGA-based accelerator for aligning a back-translated protein sequence against a database of DNA/RNA sequences. FabP is deeply optimized to fully utilize the FPGA resources and the DRAM memory bandwidth to maximize the performance. FabP on a mid-range FPGA provides 8.1 % and 23.3× (24.8× and 266.8 ×) speedup and higher energy efficiency as compared to the GPU-based implementation on a high-end NVIDIA GPU (state-of-the-art CPU implementation), respectively.

MOTIVATION: In molecular epidemiology, the identification of clusters of transmissions typically requires the alignment of viral genomic sequence data. However, existing methods of multiple sequence alignment (MSA) scale poorly with respect to the number of sequences. RESULTS: ViralMSA is a user-friendly reference-guided MSA tool that leverages the algorithmic techniques of read mappers to enable the MSA of ultra-large viral genome datasets. It scales linearly with the number of sequences, and it is able to align tens of thousands of full viral genomes in seconds. However, alignments produced by ViralMSA omit insertions with respect to the reference genome. AVAILABILITY AND IMPLEMENTATION: ViralMSA is freely available at https://github.com/niemasd/ViralMSA as an open-source software project. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conferences are a mainstay of most scientific disciplines, where scientists of all career stages come together to share cutting-edge ideas and approaches.If you do research, chances are you will attend one or more of these meetings in your career.Conferences are a microcosm of their discipline, and while conferences offer different perspectives in different disciplines, they all offer experiences that range from a casual chat waiting in line for coffee to watching someone present their groundbreaking, hot-off-the-press research.Here, we share tips for trainees and their mentors.Our recommendations are based on our experiences of attending conferences and mentoring students to improve their conference experiences.As you head to your first scientific conference, these rules will help you navigate the conference environment and make the most of your experience. In-person conferencesScientific meetings have historically been in person, where all attendees travel to one location to share and learn about recent work in the field.Conferences, especially when well organized, can provide valuable opportunities to meet and interact with other researchers.Our rules include considerations for all parts of attending an in-person meeting, including deciding whether to attend and present research, navigating travel and the conference schedule, and debriefing after the conference. Virtual conferencesDuring the Coronavirus Disease 2019 (CAU : PleasenotethatCOVID À 19hasbeendefinedasCoronaviru OVID-19) pandemic, many conferences have been and continue to be offered remotely [1][2][3].Even before 2020, some conferences had already moved to a virtual format in response to climate change [4] and/or travel bans [5], noting the environmental, financial, and geopolitical challenges posed by in-person meetings.Virtual conferences are more inclusive than their in-person counterparts in many ways: More people can attend them from all over the world, virtual meetings are less costly and easier to organize, and attendees can participate from the comfort of their homes [3,[6][7][8].Given that virtual conferences are becoming more commonplace [3,7-10], we include considerations for each rule when attending one.

DNA pattern matching is widely applied in many bioinformatics applications. The increasing volume of the DNA data exacerbates the runtime and power consumption to discover DNA patterns. In this paper, we propose a hardware-software co-design, called GenieHD, which efficiently parallelizes the DNA pattern matching task. We exploit brain-inspired hyperdimensional (HD) computing which mimics pattern-based computations in human memory. We transform inherent sequential processes of the DNA pattern matching to highly-parallelizable computation tasks using HD computing. The proposed technique first encodes the whole genome sequence and target DNA pattern to high-dimensional vectors. Once encoded, a light-weight operation on the high-dimensional vectors can identify if the target pattern exists in the whole sequence. We also design an accelerator architecture which effectively parallelizes the HD-based DNA pattern matching while significantly reducing the number of memory accesses. The architecture can be implemented on various parallel computing platforms to meet target system requirements, e.g., FPGA for low-power devices and ASIC for high-performance systems. We evaluate GenieHD on practical large-size DNA datasets such as human and Escherichia Coli genomes. Our evaluation shows that GenieHD significantly accelerates the DNA matching procedure, e.g., 44.4× speedup and 54.1× higher energy efficiency as compared to a state-of-the-art FPGA-based design.

MOTIVATION: The ability to simulate epidemics as a function of model parameters allows insights that are unobtainable from real datasets. Further, reconstructing transmission networks for fast-evolving viruses like Human Immunodeficiency Virus (HIV) may have the potential to greatly enhance epidemic intervention, but transmission network reconstruction methods have been inadequately studied, largely because it is difficult to obtain 'truth' sets on which to test them and properly measure their performance. RESULTS: We introduce FrAmework for VIral Transmission and Evolution Simulation (FAVITES), a robust framework for simulating realistic datasets for epidemics that are caused by fast-evolving pathogens like HIV. FAVITES creates a generative model to produce contact networks, transmission networks, phylogenetic trees and sequence datasets, and to add error to the data. FAVITES is designed to be extensible by dividing the generative model into modules, each of which is expressed as a fixed API that can be implemented using various models. We use FAVITES to simulate HIV datasets and study the realism of the simulated datasets. We then use the simulated data to study the impact of the increased treatment efforts on epidemiological outcomes. We also study two transmission network reconstruction methods and their effectiveness in detecting fast-growing clusters. AVAILABILITY AND IMPLEMENTATION: FAVITES is available at https://github.com/niemasd/FAVITES, and a Docker image can be found on DockerHub (https://hub.docker.com/r/niemasd/favites). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Author(s): Rule, Adam; Birmingham, Amanda; Zuniga, Cristal; Altintas, Ilkay; Huang, Shih-Cheng; Knight, Rob; Moshiri, Niema; Nguyen, Mai H; Rosenthal, Sara Brin; Pérez, Fernando; Rose, Peter W | Editor(s): Lewitter, Fran