pileup-hi: an ultra-high-throughput, customizable alignment pileup program for large datasets

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ID: 329978
2026
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Abstract
Abstract Motivation 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. Results 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 13x faster than samtools mpileup, 3.2x faster than sambamba mpileup, and up to 8x 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. Availability and implementation 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. Supplementary information Supplementary data are available at Bioinformatics online.
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Authors E Piliper, A L Greninger
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag721
URL
https://doi.org/10.1093/bioinformatics/btag721
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