Avi Srivastava

ERVmancer is a phylogeny-informed method that resolves read-mapping ambiguity for HERVs, outperforming existing approaches and revealing new biology in breast cancer.

BenchDrop-seq provides a microfluidics-free, benchtop platform for single-cell long-read RNA sequencing, enabling isoform-resolved transcript analysis from thousands of cells using standard equipment.

This study uses an ex vivo PBMC model to show that monocyte and Treg dynamics determine EBV infection outcomes, with LIPA and Tregs playing key roles in antiviral responses and LCL outgrowth.

This study identifies TET2-mutant clonal hematopoiesis in thyroid cancer microenvironments and shows that TGFβ signaling from these macrophages mediates resistance to BRAF/MEK inhibition, with TGFβ inhibition restoring sensitivity.

Dictionary learning enables assumption-free cross-modality single-cell mapping via multi-omic bridge datasets and scales single-modality integration to millions of cells in under an hour via atomic sketching.

Airpart identifies cell-type-specific allelic imbalance from single-cell RNA-seq data using statistical models and provides interpretable partitions of genes and cells.

The paper introduces scCUT&Tag-pro and scChromHMM to analyze chromatin states in single cells, showing their ability to characterize dynamic changes and regulatory priming across immune cell types.

Alevin-fry provides a fast, memory-efficient, and accurate method for quantifying single-cell and single-nucleus RNA-seq data, including RNA velocity inputs.

Signac extends Seurat with a ChromatinAssay for single-cell chromatin and multimodal analysis, adds modified TF-IDF weighting, peak-gene linkage and mitochondrial-variant clonotyping, and scales to 733,526 cells.

Weighted-nearest neighbor (WNN) analysis in Seurat v4 learns per-cell modality weights to define cell states, enabling a 228-antibody human PBMC CITE-seq reference for RNA-only query mapping.

This paper revises a benchmark to compare kallisto-bustools and alevin-fry, showing alevin-fry is faster and more memory-efficient when configured similarly to kallisto-bustools.

The paper confirms that considering unexpected fragment origins improves transcript quantification accuracy in RNA-seq simulations and shows that Srivastava et al.'s methodology enhances this accuracy.

This study compares RNA velocity results across different quantification tools for droplet scRNA-seq data, showing significant differences in abundance estimates and their impact on velocity analysis.

The paper introduces a method to compress quantification uncertainty in scRNA-seq data by storing mean and variance instead of full inferential replicates, improving computational efficiency without loss of performance.

The paper evaluates how alignment and mapping methods affect transcript quantification accuracy and introduces a new method called selective alignment.

The paper introduces a Bayesian framework for improving dscRNA-seq quantification by sharing information across cells and modalities, enhancing gene abundance estimates for multi-mapping reads.

Terminus groups transcripts based on inferential uncertainty to enable robust transcript-level or group-level analysis from RNA-seq data.

The paper introduces a nonparametric method for RNA-seq differential expression analysis that accounts for inferential uncertainty, showing improved false discovery rate control compared to existing methods.

Alevin improves gene abundance estimation in droplet-based single-cell RNA sequencing by accurately handling UMI deduplication for both gene-unique and gene-ambiguous reads.

The paper introduces a space and time-efficient index for the compacted colored de Bruijn graph, balancing speed and space usage for k-mer lookup and pattern matching.

RapMap introduces a quasi-mapping approach to efficiently align RNA-seq reads to transcriptomes, improving speed and accuracy over existing tools.