Alexander Dobin

Predicting cellular responses to perturbation across diverse contexts with STATE

State, a set-based transformer trained on over 100 million perturbed cells, predicts perturbation responses in new cellular contexts better than mean, linear and deep learning baselines.

Stack: In-Context Learning of Single-Cell Biology

SO_SCPLOWTACKC_SCPLOW is a foundation model trained on human single-cell data that enables in-context learning for predicting perturbation effects without fine-tuning.

BINSEQ: A Family of High-Performance Binary Formats for Nucleotide Sequences

BINSEQ introduces binary formats for sequencing data to enable faster parallel processing, reducing analysis time for genome and transcriptome studies.

Spatially resolved, multimodal in vivo Perturb-seq using antibody-based cell hashing

This paper introduces PerturbSpace, a method combining CRISPR perturbations with spatially hashed single-cell multiomics to study gene function in tissue architecture.

cyto: ultra high-throughput processing of 10x-flex single cell sequencing

cyto processes 10x Genomics Flex single-cell sequencing data faster and more efficiently than existing tools, enabling large-scale single-cell genomics projects.

Pathogenic Viruses, Genome Integrations, and Viral::Human Chimeric Transcripts Detected by VirusIntegrationFinder Across >30k Human Tumor and Normal Samples

The paper develops a tool to detect viral integrations in human genomes and applies it to over 30,000 tumor samples, identifying virus-human chimeric transcripts and insertion hotspots.

scBaseCount: an AI agent-curated, uniformly processed, and autonomously updated single cell data repository

scBaseCount is a single-cell RNA sequencing database that automates metadata extraction and data processing using AI, offering a large, standardized collection of gene expression data across multiple organisms and tissues.

STAR+WASP reduces reference bias in the allele-specific mapping of RNA-seq reads

STAR+WASP improves allele-specific mapping by reducing reference bias in RNA-seq data, achieving faster performance and similar ASE accuracy to the original WASP algorithm.

Comparative transcriptomics reveals human-specific cortical features

This study uses single nucleus transcriptomics to compare human and non-human primate cortical tissues, identifying human-specific gene expression patterns and their potential functional roles.

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector

FusionInspector identifies and characterizes biologically relevant fusion transcripts from RNA-seq data in tumor and normal tissues using clustering and machine learning.

The ENCODE Uniform Analysis Pipelines

ENCODE's containerised Workflow Description Language pipelines for ChIP-seq, ATAC-seq, DNase-seq, RNA-seq variants, Hi-C and bisulfite sequencing run on cloud or local clusters, with documented compute costs and provenance.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

The paper introduces the EN-TEx resource, which provides personal epigenomes and variant-impact models across multiple tissues, revealing allele-specific loci and their associations with GWAS loci.

Conserved coexpression at single cell resolution across primate brains

The paper uses single-cell expression data from five primates to identify conserved and divergent gene coexpression patterns in the middle temporal gyrus, revealing human-specific genes associated with brain disorders.

Virtue as the mean: Pan-human consensus genome significantly improves the accuracy of RNA-seq analyses

This study evaluates the impact of using a Pan-human consensus genome on RNA-seq alignment accuracy, finding significant improvements in mapping error reduction and splice junction quantification.

Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data

Genome-aligning STARsolo matches simulated truth (R≈0.99) and CellRanger's counts almost exactly, running up to 4.4 times faster than CellRanger; pseudoaligners call hundreds of false-positive genes per cell.

A limited set of transcriptional programs define major cell types

The paper identifies five major cell types based on transcriptional programs in human tissues and shows their composition correlates with tissue morphology and disease.

Management, Analyses, and Distribution of the MaizeCODE Data on the Cloud

The paper describes the development of a cloud-based platform for managing, analyzing, and distributing MaizeCODE data, enhancing reproducibility and accessibility.

Dynamics of microRNA expression during mouse prenatal development

The paper analyzes miRNA expression dynamics across mouse embryonic development, identifying tissue-specific clusters and their regulatory roles.

Is it time to change the reference genome?

The paper discusses the limitations of the current human reference genome and suggests a consensus reference may offer advantages for variant calling.

The fractured landscape of RNA-seq alignment: The default in our STARs

This study evaluates STAR aligner performance across alignment parameters and biological tasks, finding technical metrics uninformative and performance mostly robust except in challenging genomic regions.

STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq

Filtering STAR's chimeric alignments against gene annotations outscored 15 other fusion callers on 60 cancer cell lines and ran fastest, averaging 2.3 single-core hours per sample.

STAR: ultrafast universal RNA-seq aligner

Maximum mappable seed search in uncompressed suffix arrays, then seed clustering and stitching, aligns 550 million paired-end reads per hour on 12 cores with better sensitivity and precision.