Rob Patro

The paper introduces Cuttlefish 2, a tool that significantly speeds up and reduces memory usage for constructing compacted de Bruijn graphs from large genomic datasets.

The paper introduces SEESAW, a method to detect isoform-level allelic imbalance while accounting for inferential uncertainty, and evaluates it on simulated and real RNA-seq data.

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

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

PuffAligner is a fast and accurate sequence aligner that balances speed, memory usage, and alignment quality, offering performance comparable to Bowtie2 and STAR.

Cuttlefish constructs compacted de Bruijn graphs from genome collections efficiently, outperforming existing tools in speed and memory usage.

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 introduces an updatable and scalable exact sequence-search index based on Mantis, demonstrating its efficiency and scalability over large datasets.

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 an efficient encoding method for color information in de Bruijn graphs, improving compression and scalability for large-scale genomic applications.

Tximeta automatically identifies reference transcriptomes and gathers metadata for RNA-seq analysis using checksums, improving reproducibility and reducing errors.

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 junction coverage compatibility score to assess the reliability of transcript abundance estimates and annotation catalogs by comparing observed and predicted junction-spanning reads.

Bioconda provides a sustainable, comprehensive distribution of bioinformatics software using Conda for reproducible analysis across platforms.

Mantis provides a fast, small, and exact sequence search index for large RNA-seq datasets, outperforming existing methods in speed and accuracy.

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.

Squeakr is a k-mer counting and multiset representation system that improves speed and memory efficiency compared to existing methods.

The paper introduces a selective-alignment algorithm to improve transcript-level quantification accuracy while maintaining efficiency, resolving complex mapping scenarios and reducing spurious alignments.

Quark compresses RNA-seq data using a semi-reference-based approach, achieving better compression than reference-free methods while allowing reference-free decompression.

Grouping k-mer color sets into equivalence classes and entropy-compressing them shrinks colored de Bruijn graph color storage up to 20-fold versus VARI, keeping BOSS for topology.

Salmon provides accurate and fast transcript expression estimates by modeling and correcting GC content bias using a dual-phase inference algorithm.

GRASS improves de novo transcriptome annotation by using graph regularization and semi-supervised learning to enhance completeness and accuracy.

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

The paper introduces a method to predict hierarchical chromatin domains from Hi-C data, showing conservation across cell types and species.