Biomedical subjects

Ben Langmead

Publications and source records attributed to Ben Langmead.

5 recordsLinked to original sources

PubMed2026

ImpuT2T: Pangenome-Based Patching for Human Genome Assemblies.

With improvements in sequencing and assembly have come many high-quality telomere-to-telomere assemblies and reference pangenomes. However, the long-read sequencing recipes needed for high quality assemblies are expensive, and out of reach for many research groups. Here we propose ImpuT2T, a method that takes an assembly produced via inexpensive HiFi sequencing reads, and uses a panel of T2T (or near-T2T) assemblies to scaffold and fill ("patch") the gaps between the HiFi contigs. Benchmarking against reference assemblies demonstrates that ImpuT2T is highly effective at patching human HiFi assemblies, consistently outperforming existing patching approaches. Moreover, we show that including more haplotypes in the pangenome improves the quality of the patched assemblies, with the greatest gains achieved using the full HPRC Release 2 pangenome.

Journal Article↗

PubMed2026

Movi 2: fast and space-efficient queries on pangenomes.

SUMMARY: Space-efficient compressed indexing methods are critical for pangenomics and for avoiding reference bias. In the Movi study, we implemented the move-structure index, highlighting its locality-of-reference and speed. However, Movi had a high memory footprint compared to other compressed indexes. Here, we introduce Movi 2 and describe new methods that greatly reduce size and memory footprint of move structure-based indexes. The most compressed version of Movi 2 reduces the Movi index's space footprint more than five-fold. We also introduce sampling approaches that enable trade-offs between query and space efficiency. To demonstrate, we show that Movi 2 achieves advantageous time and space tradeoffs when applied to large pangenome collections, including both the first and second releases of the Human Pangenome Reference Consortium (HPRC) collection, the latter of which spans over 460 human haplotypes. We show that Movi 2 dominates prior methods on both speed and memory footprint, including both r-index-based and our previous move-structure-based method. AVAILABILITY AND IMPLEMENTATION: The methods we developed for Movi 2 are publicly available at https://github.com/mohsenzakeri/Movi.

Humans↗

PubMed2026

RLBWT-based LCP computation in compressed space for terabase-scale pangenome analysis.

MOTIVATION: Lossless full text indexes are utilized in a myriad of applications in bioinformatics. The continuously decreasing cost of generating biological data has resulted in the need to build full text indexes on biological datasets of increasing size. Many compressed full text indexes have been developed to address this problem. In particular, run-length Burrows-Wheeler transform (RLBWT) based compressed full text indexes have seen wide development and adoption. However, the construction of these RLBWT-based compressed full text indexes is still computationally expensive, sometimes prohibitively so, even for current dataset sizes. RESULTS: Therefore, we present algorithms for the construction of RLBWT-based compressed full text indexes and their supporting data structures in compressed space. The algorithms have a space complexity of O(r) words and run in O(n) time for repetitive datasets, where r is the number of runs in the BWT, n is the length of the text, and repetitive datasets implies nr∈Ω(log n). We provide the first algorithm to compute LCP-related information for repetitive datasets in optimal time and O(r) space, greatly reducing memory requirements. The key idea behind this algorithm is the utilization of r samples of the inverse suffix array at regular intervals. For example, on the Human Pangenome Reference Consortium Release 2 dataset, this reduces peak memory from 2135 GiB to 170 GiB (12.6x reduction) compared to the previous best method (pfp-thresholds). AVAILABILITY AND IMPLEMENTATION: The implementation is available at https://github.com/ucfcbb/TeraTools.

Algorithms↗

PubMed2025

Fast and flexible minimizer digestion with digest.

SUMMARY: Minimizer digestion is an increasingly common component of bioinformatics tools, including tools for de Bruijn graph assembly and sequence classification. We describe a new open source tool and library to facilitate efficient digestion of genomic sequences. It can produce digests based on the related ideas of minimizers, modimizers or syncmers. Digest uses efficient data structures, scales well to many threads, and produces digests with expected spacings between digested elements. AVAILABILITY AND IMPLEMENTATION: Digest is implemented in C++17 with a Python API, and is available open-source at https://github.com/VeryAmazed/digest. The python library is available on Bioconda. Rust bindings are available as a public crate at https://crates.io/crates/digest-rs.

Software↗

PubMed2025

Fast and flexible minimizer digestion with digest.

Minimizer digestion is an increasingly common component of bioinformatics tools, including tools for De Bruijn-Graph assembly and sequence classification. We describe a new open source tool and library to facilitate efficient digestion of genomic sequences. It can produce digests based on the related ideas of minimizers, modimizers or syncmers. Digest uses efficient data structures, scales well to many threads, and produces digests with expected spacings between digested elements. Digest is implemented in C++17 with a Python API, and is available open-source at https://github.com/VeryAmazed/digest.

digestion↗