ArticleNucleic acids research2020
To Petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- RabbitSketch: a high-performance sketching library for genome analysis.Bioinformatics (Oxford, England) · 2025Article
- When less is more: sketching with minimizers in genomics.Genome biology · 2024Review
- Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics.Nucleic acids research · 2024Article
- Indexing and searching petabase-scale nucleotide resources.Nature methods · 2024Article
- RabbitKSSD: accelerating genome distance estimation on modern multi-core architectures.Bioinformatics (Oxford, England) · 2023Article
- Strain level microbial detection and quantification with applications to single cell metagenomics.Nature communications · 2022Article
- SPRISS: approximating frequent k-mers by sampling reads, and applications.Bioinformatics (Oxford, England) · 2022Article
- Simplitigs as an efficient and scalable representation of de Bruijn graphs.Genome biology · 2021Article
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Authors and funding
10 authors.
Funding
Abstract
As computational biologists continue to be inundated by ever increasing amounts of metagenomic data, the need for data analysis approaches that keep up with the pace of sequence archives has remained a challenge. In recent years, the accelerated pace of genomic data availability has been accompanied by the application of a wide array of highly efficient approaches from other fields to the field of metagenomics. For instance, sketching algorithms such as MinHash have seen a rapid and widespread adoption. These techniques handle increasingly large datasets with minimal sacrifices in quality for tasks such as sequence similarity calculations. Here, we briefly review the fundamentals of the most impactful probabilistic and signal processing algorithms. We also highlight more recent advances to augment previous reviews in these areas that have taken a broader approach. We then explore the application of these techniques to metagenomics, discuss their pros and cons, and speculate on their future directions.
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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.