Lior S. Pachter

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

SWALO improves genome assembly by using likelihood-based scaffolding, achieving more correct joins and fewer errors than existing methods.

The paper introduces a method to address amplification bias in single-cell RNA-seq using unique molecular identifiers, showing it can correct gene abundance estimates in cases of pooled amplification paradox.

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 Odd-paired (Opa) acts as a late-acting pioneer factor that coordinates with Zelda to regulate gene expression in early Drosophila embryos.

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

The paper introduces an interpretable variational autoencoder for single-cell RNA-seq to identify gene programs in large datasets.

The paper extends RNA velocity to incorporate protein data, enabling temporal cell state inference from single-cell multiomics experiments.

The paper curates a database of single-cell transcriptomics studies to analyze trends in the field and provides insights into cell type identification.

The paper quantifies the tradeoff between sequencing depth and cell number in single-cell RNA-seq, finding that deeper sequencing per cell provides greater benefits than increasing cell numbers beyond 15,000 reads per cell.

The paper introduces the BUS format for single-cell RNA-seq pseudoalignments and the BUStools suite for analyzing data in this format.

The paper introduces absSimSeq, a tool for simulating RNA-Seq data that accounts for compositional data properties, and shows that compositional normalization improves differential analysis performance.

This paper introduces a deterministic column subset selection method for scRNA-Seq clustering, showing it performs similarly to full data clustering while avoiding some limitations of PCA and thresholding methods.

The paper introduces a method to identify and error-correct DNA barcodes in single-cell sequencing by traversing a de Bruijn graph, improving transcriptome recovery.

The paper introduces the Poseidon syringe pump system as an open-source alternative to commercial systems, highlighting design principles for open-source bioinstrumentation.

The paper introduces a method to identify cell types and their transcriptional signatures from single-cell RNA-Seq data by analyzing transcript compatibility counts.

The paper identifies population structure in gene expression data using canonical correlation analysis between genotype and expression principal components, finding genes associated with diseases affecting African Americans.

The paper compares genome alignment and transcriptome pseudoalignment, finding they produce similar quantifications and are interchangeable for many applications.

The paper introduces a neural network model to predict translation elongation rates and validates it by designing synonymous variants that match predicted protein output in yeast.

This paper introduces an alignment-free method for genome-wide association studies using k-mer counts, demonstrating its ability to detect associations with structural variations and sequences absent from reference genomes.

The paper introduces a multiplexing method for single-cell RNA-seq that enables cost-effective analysis of large-scale perturbation experiments, revealing cell population and transcriptional changes not detectable by bulk methods.

The paper introduces a method to perform gene-level differential analysis with transcript-level resolution by aggregating transcript-level results and p-value aggregation methods, enhancing sensitivity and enabling application to perturbation analysis.

Read pairs that fail kallisto pseudoalignment because of conflicting matches are filtered by pizzly to call, assemble and quantify gene fusions from raw RNA-seq in minutes rather than hours.

The paper analyzes RNA-Seq data from Zika-infected human neural progenitor cells to identify transcriptional changes and isoform divergence in neurodevelopmental pathways.

The paper presents a probabilistic method for RNA-seq quantification that achieves near-optimal accuracy.