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Booeshaghi, A. S.

Publications and source records attributed to Booeshaghi, A. S..

At least 19 recordsLinked to original sources
bioRxiv2026

Denoising single-cell ATAC-seq data with Shannon entropy

Single-cell Assay for Transposase Accessible Chromatin with sequencing (scATAC-seq) is widely used to investigate chromatin accessibility but experiments often contain empty droplets, droplets containing damaged nuclei or cellular-debris. Accurately filtering high-quality nuclei is important as noisy droplets contribute reads that can distort downstream analysis and obscure rare cell population. Current approaches often rely on read depth and or predefined genomic features that are not exactly the same as chromatin accessibility landscape. Here, we present a Shannon entropy-based method that filters high-quality nuclei more robustly by quantifying genome-wide accessibility complexity. This approach improves signal-to-noise ratio, increases sensitivity for accessible-region detection, and enables more reliable downstream inference in scATAC-seq studies.

bioinformatics↗
bioRxiv2026

Token Alignment for Verifying LLM-Extracted Text

Large language models excel at text extraction, but they sometimes hallucinate. A simple way to avoid hallucinations is to remove any extracted text that does not appear in the original source. This is easy when the extracted text is contiguous (findable with exact string matching), but much harder when it is discontiguous. Techniques for finding discontiguous phrases depend heavily on how the text is split--i.e., how it is tokenized. In this study, we show that splitting text along subword boundaries, with LLM-specific tokenizers, and aligning extracted text with ordered alignment algorithms, improves alignment by about 50% compared to word-level tokenization. To demonstrate this, we introduce the Berkeley Ordered Alignment of Text (BOAT) dataset, a modification of the Stanford Question Answering Dataset (SQuAD) that includes non-contiguous phrases, and BIO-BOAT a biomedical variant built from 51 bioRxiv preprints. We show that text-alignment methods form a partially ordered set, and that ordered alignment is the most practical choice for verifying LLM-extracted text. We implement this approach in taln, which enumerates ordinal subword alignments.

bioinformatics↗
bioRxiv2026

Science should be machine-readable

We develop a machine-automated approach for extracting results from papers, which we assess via a comprehensive review of the entire eLife corpus. Our method facilitates a direct comparison of machine and peer review, and sheds light on key challenges that must be overcome in order to facilitate AI-assisted science. In particular, the results point the way towards a machine-readable framework for disseminating scientific information. We therefore argue that publication systems should optimize separately for the dissemination of data and results versus the conveying of novel ideas, and the former should be machine-readable.

scientific communication and education↗
bioRxiv2025

Determining gene specificity from multivariate single-cell RNA sequencing data

An important application of single-cell genomics experiments is to identify genes specific to biological categories or experimental conditions. Although numerous approaches have been proposed to identify such genes, we consider an axiomatic approach based on defining properties that a specificity measure should have. This leads us to develop ember (Entropy Metrics for Biological ExploRation), which we show is the only method satisfying four key desired properties for a specificity measure. Applying ember to eight tissues from eight founder mouse strains, we find that gene specificity is often unintuitive: canonical markers can be supplanted, housekeeping genes are context-dependent, and mouse strain can drive unexpected cell type switching. Unsupervised learning on entropy metrics uncovers shared genes specialized to male gonads and kidney, as well as genes specific to non-consecutive developmental stages in the kidney. To facilitate further exploration of gene specificity in mice, we have also developed a comprehensive specificity database, along with a web interface, API and MCP server. Extending ember to a human PBMC dataset collected from 255 diverse individuals, we find that variation in PBMCs is largely localized to classical monocytes. We also find genes with unique specificity by sex, age and ancestral background. Together, these applications establish ember as a powerful tool and provide a roadmap for elucidating the impact of human genetic variation using the murine model.

bioinformatics↗
bioRxiv2025

Systematic cell-type resolved transcriptomes of 8 tissues in 8 lab and wild-derived mouse strains captures global and local expression variation

Mapping the impact of genomic variation on gene expression facilitates an understanding of the molecular basis of complex phenotypic traits and disease predisposition. Mouse models provide a controlled and reproducible framework for capturing the breadth of genomic variation observed in different genotypes across a wide variety of tissues. As part of the IGVF consortiums effort to catalog the effects of genetic variation, we uniformly characterized the transcriptomes of eight tissues from each mouse founder strain used to derive the Collaborative Cross strains, comprising five classical laboratory inbred strains and three wild-derived inbred strains. We sequenced samples from four male and four female replicates per tissue using single-nucleus RNA-seq to generate an "8-cube" dataset of 5.2 million nuclei across 106 cell types and cell states. As expected, the overall extent of transcriptome variation correlates positively with genetic divergence across the strains with the greatest differential between PWK/PhJ and CAST/EiJ. At the individual tissue level, heart and brain are relatively more similar across strains compared with gonads, adrenal, skeletal muscle, kidney, and liver. Further analyses revealed substantial strain variation, often concentrated in a few cell types as well as cell-state signatures that especially reflect strain-associated immune and metabolic trait differences. The founder 8-cube dataset provides rich transcriptome variation signatures to help explain strain-specific phenotypic traits and disease states, as illustrated by examples in tissue-resident immune cells, muscle degeneration, kidney sex differences, and the hypothalamicpituitary-adrenal axis. This data further provides a systematic foundation for the analysis of these tissues in the founder strains as well as the Collaborative Cross.

genomics↗
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

Algorithms for a Commons Cell Atlas

Cell atlas projects curate representative datasets, cell types, and marker genes for tissues across an organism. Despite their ubiquity, atlas projects rely on duplicated and manual effort to curate marker genes and annotate cell types. The size of atlases coupled with a lack of data-compatible tools make reprocessing and analysis of their data near-impossible. To overcome these challenges, we present a collection of data, algorithms, and tools to automate cataloging and analyzing cell types across tissues in an organism, and demonstrate its utility in building a human atlas.

bioinformatics↗
bioRxiv2024

A human commons cell atlas reveals cell type specificity for OAS1 isoforms

We describe an open source Human Commons Cell Atlas comprising 2.9 million cells across 27 tissues that can be easily updated and that is structured to facilitate custom analyses. To showcase the flexibility of the atlas, we demonstrate that it can be used to study isoforms of genes at cell resolution. In particular, we study cell type specificity of isoforms of OAS1, which has been shown to offer SARS-CoV-2 protection in certain individuals that display higher expression of the p46 isoform. Using our commons cell atlas we localize the OAS1 p44b isoform to the testis, and find that it is specific to round and elongating spermatids. By virtue of enabling customized analyses via a modular and dynamic atlas structure, the commons cell atlas should be useful for exploratory analyses that are intractable within the rigid framework of current gene-centric cell atlases.

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

Universal preprocessing of single-cell genomics data

We describe a workflow for preprocessing a wide variety of single-cell genomics data types. The approach is based on parsing of machine-readable seqspec assay specifications to customize inputs for kb-python, which uses kallisto and bustools to catalog reads, error correct barcodes, and count reads. The universal preprocessing method is implemented in the Python package cellatlas that is available for download at: https://github.com/cellatlas/cellatlas/.

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

Quantitative assessment of single-cell RNA-seq clustering with CONCORDEX

The rapid advancement of spatially resolved transcriptomics (SRT) technologies has facilitated exploration of how gene expression varies across tissues. However, identifying spatially variable genes remains challenging due to confounding variation introduced by the spatial distribution of cell types. We introduce a new approach to identifying spatial domains that are homogeneous with respect to cell-type composition that facilitates the decomposition of gene expression patterns by cell-type and spatial variation. Our method, called concordex, is efficient and effective across technological platforms and tissue types, and using several biological datasets we show that it can be used to identify genes with subtle variation patterns that are missed when considering only cell-type variation, or spatial variation, alone. The con-cordex tool is freely available at https://github.com/pachterlab/concordexR.

bioinformatics↗
bioRxiv2023

A machine-readable specification for genomics assays

Understanding the structure of sequenced fragments from genomics libraries is essential for accurate read preprocessing. Currently, different assays and sequencing technologies require custom scripts and programs that do not leverage the common structure of sequence elements present in genomics libraries. We present seqspec, a machine-readable specification for libraries produced by genomics assays that facilitates standardization of preprocessing and enables tracking and comparison of genomics assays. The specification and associated seqspec command line tool is available at https://github.com/IGVF/seqspec.

bioinformatics↗
bioRxiv2022

Quantifying orthogonal barcodes for sequence census assays

Barcode-based sequence census assays utilize custom or random oligonucloetide sequences to label various biological features, such as cell-surface proteins or CRISPR perturbations. These assays all rely on barcode quantification, a task that is complicated by barcode design and technical noise. We introduce a modular approach to quantifying barcodes that achieves speed and memory improvements over existing tools. We also introduce a set of quality control metrics, and accompanying tool, for validating barcode designs.

bioinformatics↗
bioRxiv2022

Pseudoalignment facilitates assignment of error-prone Ultima Genomics reads

We analyze single-cell RNA-seq data sequenced with Ultima Genomics technology and find high error rates in and near homopolymers. To compensate for these errors, we explore the use of pseudoalignment for read assignment, and find that it can perform better than standard read alignment. Our pseudoalignment read assignment for Ultima Genomics data is available as part of the kallisto-bustools kb-python package available at https://github.com/pachterlab/kb_python.

genomics↗
bioRxiv2022

Depth normalization for single-cell genomics count data

Genomics data analysis requires normalization of feature counts that stabilizes technical variance, accounts for variable cell sequencing depth, and preserves monotonicity of within-cell feature abundances. We show that normalization via an optimal variance stabilizing transform for negative binomial count data followed by a proportional fitting step (PFlog) is the only feature-relabeling-equivariant method satisfying the three desiderata. We demonstrate superior performance of this method, which is equivalent to a shifted centered-log ratio transform, in comparison to other normalizations on numerous benchmarks across hundreds of single-cell RNA-seq datasets. We further show that both the shifted-log scale and centered-log ratio geometry are important for preserving PCA and k-NN structure.

bioinformatics↗
bioRxiv2021

Low-cost, scalable, and automated fluid sampling for fluidics applications

We present colosseum, a low-cost, modular, and automated fluid sampling device for scalable fluidic applications. The colosseum fraction collector uses a single motor, can be built for less than $100 using off-the-shelf and 3D-printed components, and can be assembled in less than an hour. Build Instructions and source files are available at https://github.com/pachterlab/colosseum.

bioengineering↗