Biology subjects

Brown, N. K.

Publications and source records attributed to Brown, N. K..

7 recordsLinked to original sources

bioRxiv2026

RLBWT-Based LCP Computation in Compressed Space for Terabase-Scale Pangenome Analysis

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. 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 the average run length is at least 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 2,135 GiB to 170 GiB (12.6x reduction) compared to the previous best method (pfp-thresholds). AvailabilityThe implementation is available at https://github.com/ucfcbb/TeraTools. Supplementary InformationSupplementary Material is available online at bioRxiv.

bioinformatics↗

bioRxiv2025

Movi 2: Fast and Space-Efficient Queries on Pangenomes

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 indexs space footprint more than fivefold. 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 haplotyes. 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. The methods we developed for Movi 2 are publicly available at https://github.com/mohsenzakeri/Movi.

genomics↗

bioRxiv2024

Improved pangenomic classification accuracy with chain statistics

Compressed full-text indexes enable efficient sequence classification against a pangenome or tree-of-life index. Past work on compressed-index classification used matching statistics or pseudo-matching lengths to capture the fine-grained co-linearity of exact matches. But these fail to capture coarse-grained information about whether seeds appear co-linearly in the reference. We present a novel approach that additionally obtains coarse-grained co-linearity ("chain") statistics. We do this without using a chaining algorithm, which would require superlinear time in the number of matches. We start with a collection of strings, avoiding the multiple-alignment step required by graph approaches. We rapidly compute multi-maximal unique matches (multi-MUMs) and identify BWT sub-runs that correspond to these multi-MUMs. From these, we select those that can be "tunneled," and mark these with the corresponding multi-MUM identifiers. This yields an [order](r + n/d)-space index for a collection of d sequences having a length-n BWT consisting of r maximal equal-character runs. Using the index, we simultaneously compute fine-grained matching statistics and coarse-grained chain statistics in linear time with respect to query length. We found that this substantially improves classification accuracy compared to past compressed-indexing approaches and reaches the same level of accuracy as less efficient alignmentbased methods.

bioinformatics↗

bioRxiv2024

MEM-based pangenome indexing for k-mer queries

Pangenomes are growing in number and size, thanks to the prevalence of high-quality long-read assemblies. However, current methods for studying sequence composition and conservation within pangenomes have limitations. Methods based on graph pangenomes require a computationally expensive multiple-alignment step, which can leave out some variation. Indexes based on k-mers and de Bruijn graphs are limited to answering questions at a specific substring length k. We present Maximal Exact Match Ordered (MEMO), a pangenome indexing method based on maximal exact matches (MEMs) between sequences. A single MEMO index can handle arbitrary-length queries over pangenomic windows. MEMO enables both queries that test k-mer presence/absence (membership queries) and that count the number of genomes containing k-mers in a window (conservation queries). MEMOs index for a pangenome of 89 human autosomal haplotypes fits in 2.04 GB, 8.8x smaller than a comparable KMC3 index and 11.4x smaller than a PanKmer index. MEMO indexes can be made smaller by sacrificing some counting resolution, with our decile-resolution HPRC index reaching 0.67 GB. MEMO can conduct a conservation query for 31-mers over the human leukocyte antigen locus in 13.89 seconds, 2.5x faster than other approaches. MEMOs small index size, lack of k-mer length dependence, and efficient queries make it a flexible tool for studying and visualizing substring conservation in pangenomes.

bioinformatics↗

bioRxiv2023

Movi: a fast and cache-efficient full-text pangenome index

Efficient pangenome indexes are promising tools for many applications, including rapid classification of nanopore sequencing reads. Recently, a compressed-index data structure called the "move structure" was proposed as an alternative to other BWT-based indexes like the FM index and r-index. The move structure uniquely achieves both O(r) space and O(1)-time queries, where r is the number of runs in the pangenome BWT. We implemented Movi, an efficient tool for building and querying move-structure pangenome indexes. While the size of the Movis index is larger than the r-index, it scales at a smaller rate for pangenome references, as its size is exactly proportional to r, the number of runs in the BWT of the reference. Movi can compute sophisticated matching queries needed for classification - such as pseudo-matching lengths and backward search - at least ten times faster than the fastest available methods, and in some cases more than 30-fold faster. Movi achieves this speed by leveraging the move structures strong locality of reference, incurring close to the minimum possible number of cache misses for queries against large pangenomes. We achieve still further speed improvements by using memory prefetching to attain a degree of latency hiding that would be difficult with other index structures like the r-index. Movis fast constant-time query loop makes it well suited to real-time applications like adaptive sampling for nanopore sequencing, where decisions must be made in a small and predictable time interval.

bioinformatics↗

bioRxiv2023

m6A mRNA Methylation Regulates Early Pancreatic β-Cell Differentiation

N6-methyladenosine (m6A) is the most abundant chemical modification in mRNA, and plays important roles in human and mouse embryonic stem cell pluripotency, maintenance, and differentiation. We have recently reported, for the first time, the role of m6A in the postnatal control of {beta}-cell function in physiological states and in Type 1 and 2 Diabetes. However, the precise mechanisms by which m6A acts to regulate the development of human and mouse {beta}-cells are unexplored. Here, we show that the m6A landscape is dynamic during human pancreas development, and that METTL14, one of the m6A writer complex proteins, is essential for the early differentiation of both human and mouse {beta}-cells.

developmental biology↗

bioRxiv2023

Redox Regulation of m6A Methyltransferase METTL3 in Human β-cells Controls the Innate Immune Response in Type 1 Diabetes

Type 1 Diabetes (T1D) is characterized by autoimmune-mediated destruction of insulin-producing {beta}-cells. Several observations have renewed interest in the innate immune system as an initiator of the disease process against {beta}-cells. Here, we show that N6-Methyladenosine (m6A) is an adaptive {beta}-cell safeguard mechanism that accelerates mRNA decay of the 2-5-oligoadenylate synthetase (OAS) genes to control the antiviral innate immune response at T1D onset. m6A writer methyltransferase 3 (METTL3) levels increase drastically in human and mouse {beta}-cells at T1D onset but rapidly decline with disease progression. Treatment of human islets and EndoC-{beta}H1 cells with pro-inflammatory cytokines interleukin-1 {beta} and interferon mimicked the METTL3 upregulation seen at T1D onset. Furthermore, m6A-sequencing revealed the m6A hypermethylation of several key innate immune mediators including OAS1, OAS2, and OAS3 in human islets and EndoC-{beta}H1 cells challenged with cytokines. METTL3 silencing in human pseudoislets or EndoC-{beta}H1 cells enhanced OAS levels by increasing its mRNA stability upon cytokine challenge. Consistently, in vivo gene therapy, to prolong Mettl3 overexpression specifically in {beta}-cells, delayed diabetes progression in the non-obese diabetic (NOD) mouse model of T1D by limiting the upregulation of Oas pointing to potential therapeutic relevance. Mechanistically, the accumulation of reactive oxygen species blocked METTL3 upregulation in response to cytokines, while physiological levels of nitric oxide promoted its expression in human islets. Furthermore, for the first time to our knowledge, we show that the cysteines in position C276 and C326 in the zinc finger domain of the METTL3 protein are sensitive to S-nitrosylation (SNO) and are significant for the METTL3 mediated regulation of OAS mRNA stability in human {beta}-cells in response to cytokines. Collectively, we report that m6A regulates human and mouse {beta}-cells to control the innate immune response during the onset of T1D and propose targeting METTL3 to prevent {beta}-cell death in T1D.

molecular biology↗