{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T19:44:04Z","timestamp":1792266244370,"version":"4.4.0"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Recent advancements in next-generation sequencing have combined massive data throughput with high read accuracy to facilitate large-scale, rapid, and sensitive analysis of genetic variation. High-depth, high-quality platforms such as the Illumina NovaSeq X and Ultima Genomics UG 100 are increasingly used in time-sensitive clinical contexts such as cancer screening to detect mutations as low as 0.01% without sequence error correction. This process usually involves the bioinformatic construction of a pileup, or a list of nucleotides aligned to one or more positions in a sequence alignment, to identify variants. Efficient pileup software is required to process large-scale sequencing datasets rapidly to allow for timely clinical decision-making. While foundational to many analytical pipelines, the de facto standard pileup software samtools mpileup faces scalability challenges with larger datasets and is restricted to one output format.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present pileup-hi, a multi-threaded pileup engine that is scalable to alignments containing billions of reads and extendable to support custom output formats. When set to emit the default mpileup format, pileup-hi is up to 13\u00d7 faster than samtools mpileup, 3.2\u00d7 faster than sambamba mpileup, and up to 8\u00d7 faster than perbase base-depth. The default output of pileup-hi has binary equivalence to the output of samtools mpileup across benchmark files and a variety of samtools regression tests. We present a new pileup-derived format that provides depth-invariant data storage proportional only to the reference genome length and number of unique indels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Pileup-hi is implemented in the Rust programming language and distributed as open-source code and precompiled binaries. Source code and installation instructions can be found at https:\/\/github.com\/greninger-lab\/pileup-hi.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag721","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:25:57Z","timestamp":1790594757000},"source":"Crossref","is-referenced-by-count":0,"title":["Pileup-hi: an ultra-high-throughput, customizable alignment pileup program for large datasets"],"prefix":"10.1093","volume":"42","author":[{"given":"E","family":"Piliper","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, University of Washington Medical Center , Seattle, WA 98109,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7443-0527","authenticated-orcid":false,"given":"A L","family":"Greninger","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine and Pathology, University of Washington Medical Center , Seattle, WA 98109,","place":["United States"]},{"name":"Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center , Seattle, WA 98109,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"2026101714562001200_btag721-B1","doi-asserted-by":"publisher","author":"Almogy","year":"2022","DOI":"10.1101\/2022.05.29.493900"},{"key":"2026101714562001200_btag721-B2","doi-asserted-by":"publisher","first-page":"3255","DOI":"10.1038\/s41467-019-11216-7","article-title":"Genomic epidemiology of syphilis reveals independent emergence of macrolide resistance across multiple circulating lineages","volume":"10","author":"Beale","year":"2019","journal-title":"Nat Commun"},{"key":"2026101714562001200_btag721-B3","author":"BLAKE3 team. 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