Mohsen Zakeri

Movi 2 improves pangenome indexing by reducing memory footprint and enabling space-time tradeoffs for efficient querying.

Movi Color improves long-read classification accuracy and speed using a move structure and color-based indexing, outperforming existing methods in precision and recall.

The paper benchmarks ANI estimation algorithms using simulated and real datasets, showing that ANIb is most accurate but least efficient, while k-mer approaches are efficient and accurate, with some clades requiring multiple k-values.

Movi is a fast pangenome index that enables efficient querying of nanopore sequencing reads using move structures, achieving faster speeds than existing methods.

Sigmoni classifies nanopore signals using a compressed pangenome index, enabling efficient and accurate classification of reads against large references.

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.

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.

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.

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

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