ReviewComputational and structural biotechnology journal2022
From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Singletrack: an algorithm for improving memory consumption and performance of gap-affine sequence alignment.Bioinformatics (Oxford, England) · 2026Article
- Article
- Gene Surfing: An efficient and versatile tool for targeted enzyme mining in metagenomics.Synthetic and systems biotechnology · 2025Article
- Dissecting the effects of single amino acid substitutions in SARS-CoV-2 Mpro.Protein science : a publication of the Protein Society · 2025Article
- Taming large-scale genomic analyses via sparsified genomics.Nature communications · 2025Article
- Advances in Whole Genome Sequencing: Methods, Tools, and Applications in Population Genomics.International journal of molecular sciences · 2025Review
- BIMSA: accelerating long sequence alignment using processing-in-memory.Bioinformatics (Oxford, England) · 2024Article
- Sigmoni: classification of nanopore signal with a compressed pangenome index.Bioinformatics (Oxford, England) · 2024Article
- RUBICON: a framework for designing efficient deep learning-based genomic basecallers.Genome biology · 2024Article
- An overlooked phenomenon: complex interactions of potential error sources on the quality of bacterial de novo genome assemblies.BMC genomics · 2024Article
- TargetCall: eliminating the wasted computation in basecalling via pre-basecalling filtering.Frontiers in genetics · 2024Article
- Physics-Based Signal Analysis of Genome Sequences: An Overview of GenomeBits.Microorganisms · 2023Article
- Sigmoni: classification of nanopore signal with a compressed pangenome index.bioRxiv : the preprint server for biology · 2023Article
- Variant calling and benchmarking in an era of complete human genome sequences.Nature reviews. Genetics · 2023Review
- Scrooge: a fast and memory-frugal genomic sequence aligner for CPUs, GPUs, and ASICs.Bioinformatics (Oxford, England) · 2023Article
- A framework for high-throughput sequence alignment using real processing-in-memory systems.Bioinformatics (Oxford, England) · 2023Article
- BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis.NAR genomics and bioinformatics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We now need more than ever to make genome analysis more intelligent. We need to read, analyze, and interpret our genomes not only quickly, but also accurately and efficiently enough to scale the analysis to population level. There currently exist major computational bottlenecks and inefficiencies throughout the entire genome analysis pipeline, because state-of-the-art genome sequencing technologies are still not able to read a genome in its entirety. We describe the ongoing journey in significantly improving the performance, accuracy, and efficiency of genome analysis using intelligent algorithms and hardware architectures. We explain state-of-the-art algorithmic methods and hardware-based acceleration approaches for each step of the genome analysis pipeline and provide experimental evaluations. Algorithmic approaches exploit the structure of the genome as well as the structure of the underlying hardware. Hardware-based acceleration approaches exploit specialized microarchitectures or various execution paradigms (e.g., processing inside or near memory) along with algorithmic changes, leading to new hardware/software co-designed systems. We conclude with a foreshadowing of future challenges, benefits, and research directions triggered by the development of both very low cost yet highly error prone new sequencing technologies and specialized hardware chips for genomics. We hope that these efforts and the challenges we discuss provide a foundation for future work in making genome analysis
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.