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Melsted, P.

Publications and source records attributed to Melsted, P..

At least 19 recordsLinked to original sources
bioRxiv2026

BloodVariome: a high-resolution atlas of inherited genetic effects in human immune cells

Genome-wide association studies have linked thousands of sequence variants to immune-mediated diseases, yet their cellular mechanisms remain largely unresolved. Here we present BloodVariome, a high-resolution atlas of genetic effects across the human immune cell hierarchy. Combining deep immunophenotyping with automated pattern-recognition, we quantified 1,533 traits across 127 immune cell populations in 11,983 individuals. We identified 259 significant associations, the vast majority of which are not captured by conventional bulk blood trait studies. Most associations were restricted to single immune lineages or cell populations, revealing a fine-grained genetic compartmentalization of the immune system. By linking known disease risk alleles to specific immune cell phenotypes, BloodVariome illuminates cellular mechanisms underlying autoimmunity, immunodeficiency, and hematologic malignancy. Moreover, we implicate novel regulators of human immune cell development and function. By bridging the gap between cohort size and phenotypic depth, BloodVariome establishes a high-resolution framework for interpreting how genetic variation shapes cellular immunity at population-scale.

genomics↗
bioRxiv2026

Population-scale immunoglobulin genetics resolves the human B-cell system

Immunoglobulins (Ig) mediate adaptive humoral immunity, yet the regulation of B-cell responses in vivo in humans remains inaccessible to direct experimentation. Here we use population-scale Ig genetics to resolve molecular regulation of the human B-cell system. Analysis of circulating IgA, IgG, IgM, and six composite Ig traits in 114,697 individuals identifies 504 genetic associations. Integration with regulatory genomics, plasma proteomics, and immunophenotyping maps these effects across the B-cell hierarchy, recovering known regulators and revealing previously unrecognized genes in humoral immunity. At key control nodes - including Fc{gamma} receptors, the immunoglobulin heavy-chain locus and the TACI-APRIL signaling axis - variants form allelic series generating graded perturbations of antibody output. Ig-associated loci show extensive overlap with autoimmunity, immunodeficiency and B-cell malignancy. These findings demonstrate that Ig traits, analyzed at population scale, encode fine-grained information about the regulation of the human B-cell system and link natural variation in humoral immunity to immune-mediated disease.

genomics↗
bioRxiv2026

RNA-seq analysis in seconds using GPUs

We present a GPU implementation of kallisto for RNA-seq transcript quantification. By redesigning the core algorithms: pseudoalignment, equivalence class intersection, and the EM algorithm; for massively parallel execution on GPUs, we achieve a 30-50x speedup over multithreaded CPU kallisto. On a benchmark of 100 Geuvadis samples from Human cell lines the GPU version processes paired-end reads at a rate of 3.6 million per second, completing a typical sample in seconds rather than minutes. For a large dataset of 295 million reads, runtime drops from 40 minutes to 50 seconds. Our implementation demonstrates that careful algorithmic redesign, rather than naive porting of software, is necessary to fully exploit the computing power of GPUs in sequence analysis.

bioinformatics↗
bioRxiv2024

The impact of package selection and versioning on single-cell RNA-seq analysis

Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting raw read data into cell-gene count matrices through sequence alignment, followed by analyses including filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely-used packages implementing such workflows, and are generally thought to implement individual steps similarly. We investigate in detail the algorithms and methods underlying Seurat and Scanpy and find that there are, in fact, considerable differences in the outputs of Seurat and Scanpy. The extent of differences between the programs is approximately equivalent to the variability that would be introduced in benchmarking scRNA-seq datasets by sequencing less than 5% of the reads or analyzing less than 20% of the cell population. Additionally, distinct versions of Seurat and Scanpy can produce very different results, especially during parts of differential expression analysis. Our analysis highlights the need for users of scRNA-seq to carefully assess the tools on which they rely, and the importance of developers of scientific software to prioritize transparency, consistency, and reproducibility for their tools.

bioinformatics↗
bioRxiv2024

Batch correction methods used in single cell RNA-sequencing analyses are often poorly calibrated

As the number of experiments that employ single-cell RNA-sequencing (scRNA-seq) grows it opens up the possibility of combining results across experiments or processing cells from the same experiment assayed in separate sequencing runs. The gain in the number of cells that can be compared comes at the cost of batch effects that may be present. Several methods have been proposed to combat this for scRNA-seq datasets. We compared seven widely used method used for batch correction of scRNA-seq datasets. We present a novel approach to measure the degree to which the methods alter the data in the process of batch correction, both at the fine scale comparing distances between cells as well as measuring effects observed across clusters of cells. We demonstrate that many of the published method are poorly calibrated in the sense that the process of correction creates measurable artifacts in the data. In particular, MNN, SCVI and LIGER performed poorly in our tests, often altering the data considerably. Batch correction with Combat, BBKNN and Seurat introduced artifacts that could be detected in our setup. However, we found that Harmony was the only method that consistently performed well, in all the testing methodology we present. Due to these result Harmony is the only method we can safely recommend using when performing batch correction of scRNA-seq data.

bioinformatics↗
bioRxiv2023

kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq

The term "RNA-seq" refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data.

bioinformatics↗
bioRxiv2023

Voyager: exploratory single-cell genomics data analysis with geospatial statistics

Exploratory spatial data analysis (ESDA) can be a powerful approach to understanding single-cell genomics datasets, but it is not yet part of standard data analysis workflows. In particular, geospatial analyses, which have been developed and refined for decades, have yet to be fully adapted and applied to spatial single-cell analysis. We introduce the Voyager platform, which systematically brings the geospatial ESDA tradition to (spatial) -omics, with local, bivariate, and multivariate spatial methods not yet commonly applied to spatial -omics, united by a uniform user interface. Using Voyager, we showcase biological insights that can be derived with its methods, such as biologically relevant negative spatial autocorrelation. Underlying Voyager is the SpatialFeatureExperiment data structure, which combines Simple Feature with SingleCellExperiment and AnnData to represent and operate on geometries bundled with gene expression data. Voyager has comprehensive tutorials demonstrating ESDA built on GitHub Actions to ensure reproducibility and scalability, using data from popular commercial technologies. Voyager is implemented in both R/Bioconductor and Python/PyPI, and features compatibility tests to ensure that both implementations return consistent results.

bioinformatics↗
bioRxiv2023

A Chromosome-Level Genome Assembly for the Rock Ptarmigan (Lagopus muta)

The Rock Ptarmigan (Lagopus muta) is a cold-adapted, largely sedentary, game bird with a Holarctic distribution. The species represents an important example of an organism likely to be affected by ongoing climatic shifts across a disparate range. We provide here a high-quality reference genome and mitogenome for the Rock Ptarmigan assembled from PacBio HiFi and Hi-C sequencing of a female bird from Iceland. The total size of the genome is 1.03 Gb with a scaffold N50 of 71.23 Mb and a contig N50 of 17.91 Mb. The final scaffolds represent all 40 predicted chromosomes, and the mitochondria with a BUSCO score of 98.6%. Gene annotation resulted in 16,078 protein-coding genes out of a total 19,831 predicted (81.08% excluding pseudogenes). The genome included 21.07% repeat sequences, and the average length of genes, exons, and introns were, 33605, 394, and 4265 bp respectively. The availability of a new reference-quality genome will contribute to understanding the Rock Ptarmigans unique evolutionary history, vulnerability to climate change, and demographic trajectories around the globe and serve as a reference genome for the species in the family Tetraonidae (order Galliformes).

molecular biology↗
bioRxiv2022

BUSZ: Compressed BUS files

SummaryWe describe a compression scheme for BUS files and an implementation of the algorithm in the bustools software. Our compression algorithm yields smaller file sizes than gzip, at significantly faster compression and decompression speeds. We evaluated our algorithm on 533 BUS files from scRNA-seq experiments with a total size of 1Tb. Our compression is more than 2x faster than the fastest gzip option and results in 1.5x smaller files than the best gzip compression. This amounts to an 8.3x reduction in the file size, resulting in a compressed size of 122Gb for the dataset. Availability and ImplementationA complete description of the format is available at https://github.com/BUStools/BUSZ-format and an implementation at https://github.com/BUStools/bustools Contactpmelsted@hi.is online.

bioinformatics↗
bioRxiv2022

Annotation-agnostic discovery of associations between novel gene isoforms and phenotypes

We present a novel method for associating phenotypes with RNA expression, that can identify expression associations resulting from a wide variety of underlying transcriptional and post-transcriptional events, without relying on annotations of these events. We show that we can reliably detect, de novo, phenotypically relevant transcriptional structures

bioinformatics↗
bioRxiv2022

Accurate quantification of single-nucleus and single-cell RNA-seq transcripts

In single-cell and single-nucleus RNA sequencing, the coexistence of nascent (unprocessed) and mature (processed) mRNA poses challenges in accurate read mapping and the interpretation of count matrices. The traditional transcriptome reference, defining the region of interest in bulk RNA-seq, restricts its focus to mature mRNA transcripts. This restriction leads to two problems: reads originating outside of the region of interest are prone to mismapping within this region, and additionally, such external reads cannot be matched to specific transcript targets. Expanding the region of interest to encompass both nascent and mature mRNA transcript targets provides a more comprehensive framework for RNA-seq analysis. Here, we introduce the concept of distinguishing flanking k-mers (DFKs) to improve mapping of sequencing reads. We have developed an algorithm to identify DFKs, which serve as a sophisticated background filter, enhancing the accuracy of mRNA quantification. This dual strategy of an expanded region of interest coupled with the use of DFKs enhances the precision in quantifying both mature and nascent mRNA molecules, as well as in delineating reads of ambiguous status.

bioinformatics↗
bioRxiv2021

The sequences of 150,119 genomes in the UK biobank

We describe the analysis of whole genome sequences (WGS) of 150,119 individuals from the UK biobank (UKB). This constitutes a set of high quality variants, including 585,040,410 SNPs, representing 7.0% of all possible human SNPs, and 58,707,036 indels. The large set of variants allows us to characterize selection based on sequence variation within a population through a Depletion Rank (DR) score for windows along the genome. DR analysis shows that coding exons represent a small fraction of regions in the genome subject to strong sequence conservation. We define three cohorts within the UKB, a large British Irish cohort (XBI) and smaller African (XAF) and South Asian (XSA) cohorts. A haplotype reference panel is provided that allows reliable imputation of most variants carried by three or more sequenced individuals. We identified 895,055 structural variants and 2,536,688 microsatellites, groups of variants typically excluded from large scale WGS studies. Using this formidable new resource, we provide several examples of trait associations for rare variants with large effects not found previously through studies based on exome sequencing and/or imputation.

genetics↗
bioRxiv2019

Bifrost - Highly parallel construction and indexing of colored and compacted de Bruijn graphs

MotivationDe Bruijn graphs are the core data structure for a wide range of assemblers and genome analysis software processing High Throughput Sequencing datasets. For population genomic analysis, the colored de Bruijn graph is often used in order to take advantage of the massive sets of sequenced genomes available for each species. However, memory consumption of tools based on the de Bruijn graph is often prohibitive, due to the high number of vertices, edges or colors in the graph. In order to process large and complex genomes, most short-read assemblers based on the de Bruijn graph paradigm reduce the assembly complexity and memory usage by compacting first all maximal non-branching paths of the graph into single vertices. Yet, de Bruijn graph compaction is challenging as it requires the uncompacted de Bruijn graph to be available in memory.\n\nResultsWe present a new parallel and memory efficient algorithm enabling the direct construction of the compacted de Bruijn graph without producing the intermediate uncompacted de Bruijn graph. Bifrost features a broad range of functions such as sequence querying, storage of user data alongside vertices and graph editing that automatically preserve the compaction property. Bifrost makes full use of the dynamic index efficiency and proposes a graph coloring method efficiently mapping each k-mer of the graph to the set of genomes in which it occurs. Experimental results show that our algorithm is competitive with state-of-the-art de Bruijn graph compaction and coloring tools. Bifrost was able to build the colored and compacted de Bruijn graph of about 118,000 Salmonella genomes on a mid-class server in about 4 days using 103 GB of main memory.\n\nAvailabilityhttps://github.com/pmelsted/bifrost available with a BSD-2 license\n\nContactguillaumeholley@gmail.com

bioinformatics↗
bioRxiv2019

Modular and efficient pre-processing of single-cell RNA-seq

Analysis of single-cell RNA-seq data begins with pre-processing of sequencing reads to generate count matrices. We investigate algorithm choices for the challenges of pre-processing, and describe a workflow that balances efficiency and accuracy. Our workflow is based on the kallisto (https://pachterlab.github.io/kallisto/) and bustools (https://bustools.github.io/) programs, and is near-optimal in speed and memory. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses. Documentation and tutorials for using the kallisto | bus workflow are available at https://www.kallistobus.tools/.

bioinformatics↗
bioRxiv2018

The Barcode, UMI, Set format and BUStools

We introduce the Barcode-UMI-Set format (BUS) for representing pseudoalignments of reads from single-cell RNA-seq experiments. The format can be used with all single-cell RNA-seq technologies, and we show that BUS files can be efficiently generated. BUStools is a suite of tools for working with BUS files and facilitates rapid quantification and analysis of single-cell RNA-seq data. The BUS format therefore makes possible the development of modular, technology-specific, and robust workflows for single-cell RNA-seq analysis.

bioinformatics↗
bioRxiv2018

A direct comparison of genome alignment and transcriptome pseudoalignment

MotivationGenome alignment of reads is the first step of most genome analysis workflows. In the case of RNA-Seq, transcriptome pseudoalignment of reads is a fast alternative to genome alignment, but the different \"coordinate systems\" of the genome and transcriptome have made it difficult to perform direct comparisons between the approaches.\n\nResultsWe have developed tools for converting genome alignments to transcriptome pseudoalignments, and conversely, for projecting transcriptome pseudoalignments to genome alignments. Using these tools, we performed a direct comparison of genome alignment with transcriptome pseudoalignment. We find that both approaches produce similar quantifications. This means that for many applications genome alignment and transcriptome pseudoalignment are interchangeable.\n\nAvailability and Implementationbam2tcc is a C++14 software for converting alignments in SAM/BAM format to transcript compatibility counts (TCCs) and is available at https://github.com/pachterlab/bam2tcc. kallisto genomebam is a user option of kallisto that outputs a sorted BAM file in genome coordinates as part of transcriptome pseudoalignment. The feature has been released with kallisto v0.44.0, and is available at https://pachterlab.github.io/kallisto/.\n\nSupplementary MaterialN/A\n\nContactLior Pachter (lpachter@caltech.edu)

bioinformatics↗
bioRxiv2018

Identification of transcriptional signatures for cell types from single-cell RNA-Seq

Single-cell RNA-Seq makes it possible to characterize the transcriptomes of cell types and identify their transcriptional signatures via differential analysis. We present a fast and accurate method for discriminating cell types that takes advantage of the large numbers of cells that are assayed. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3 single-cell RNA-Seq that can identify previously undetectable marker genes.

bioinformatics↗