A. Sina Booeshaghi

This paper introduces a Shannon entropy-based method to filter high-quality nuclei in scATAC-seq data, improving signal-to-noise ratio and downstream analysis reliability.

The paper identifies distinct adipocyte subsets in human white adipose tissue with different metabolic profiles and links TSHZ3 to their regulation and metabolic disease associations.

This paper introduces concordex, a method to identify spatially homogeneous regions in tissues by decomposing gene expression patterns into cell-type and spatial variation, demonstrating its effectiveness across datasets.

This study compares Seurat and Scanpy, finding significant differences in their scRNA-seq analysis outputs, comparable to low sequencing depth or cell coverage.

This paper introduces a method to verify LLM-extracted text by aligning it with ordered subword tokenization, showing improved accuracy over word-level tokenization.

This paper develops a machine-automated approach to extract results from scientific papers and evaluates it using the eLife corpus, highlighting the need for machine-readable scientific dissemination.

The paper characterizes transcriptomes across 8 tissues in 8 mouse strains to identify expression variation linked to genetic differences and phenotypic traits.

The paper develops lr-kallisto, a tool for accurate long-read transcriptome quantification using ONT data, showing improved results with exome capture.

The paper introduces ember, an entropy-based framework to assess gene specificity in single-cell RNA sequencing data, revealing unexpected patterns in gene expression across tissues and mouse strains.

The paper describes using kallisto, bustools, and kb-python to quantify RNA-seq data from bulk, single-cell, and single-nucleus samples.

The paper introduces seqspec, a machine-readable specification for genomics assays to standardize preprocessing and enable tracking and comparison of assays.

The paper introduces algorithms and tools to automate cell type annotation and analysis in cell atlas projects, demonstrating their use in building a human atlas.

The paper presents a Human Commons Cell Atlas with 2.9 million cells across 27 tissues, demonstrating its use in studying OAS1 isoform cell type specificity, particularly locating the p44b isoform in testicular spermatids.

The paper introduces a modular approach and quality control metrics for improving barcode quantification in sequence census assays.

The paper describes a universal preprocessing workflow for single-cell genomics data using the cellatlas package.

Geospatial exploratory statistics, including local, bivariate and multivariate spatial methods, are packaged for spatial omics behind one interface, with matched R/Bioconductor and Python implementations.

The paper introduces a command-line tool called ffq for querying user-generated data and metadata from sequence databases.

The paper evaluates pseudoalignment for improving read assignment in Ultima Genomics single-cell RNA-seq data, finding it more accurate than standard alignment.

The paper introduces a normalization method for single-cell RNA-seq data that meets three criteria and outperforms existing methods on benchmarks.

The paper develops snATAK for preprocessing single-nucleus chromatin accessibility data and evaluates tradeoffs between multimodal assays.

The paper uses full-length single-cell RNA-seq to identify isoform-specific markers in mouse primary motor cortex cells, revealing isoform shifts masked at the gene level.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

The paper presents a modular and efficient workflow for single-cell RNA-seq pre-processing using kallisto and bustools, demonstrating its flexibility for RNA velocity analyses.

The paper highlights issues with log(1+x) normalization for low-expressed genes in single-cell RNA-seq and suggests using the fraction of cells with non-zero expression as an alternative.

This study benchmarks two lightweight-mapping tools for single-cell RNA-seq pre-processing, finding similar results but noting Salmon-Alevin-fry's higher computational cost.

The paper shows that aged mouse lung has reduced ACE2 mRNA expression in ciliated cells compared to younger mice.