Tsachy Weissman

This paper uses compression metrics to detect changes in spiking neural data from cortical lesions, finding that compression-based methods outperform single-neuron metrics in lesion detection accuracy.

This paper introduces a compression framework for neural recording systems that reduces bandwidth demands while preserving spike detection accuracy.

This paper introduces MDRAM, a DNA-based random access memory system that enables efficient and scalable readouts using nanopore sequencing and magnetic bead conjugation.

NanoSpring compresses nanopore sequencing reads with a reference-free approximate assembly approach, achieving lower bit per base rates than gzip and competitive performance with CoLoRd.

The paper analyzes the Human Pangenome to identify conserved and polymorphic structural variations across diverse haploid assemblies.

KmerKeys provides a web tool for efficient k-mer-based querying of genome assemblies and variant databases.

The paper introduces computational tools to analyze crosslink-ligation RNA data, revealing new RNA structures and homodimers.

This study evaluates the impact of lossy compression on nanopore sequencing data, showing minimal effects on basecalling and consensus accuracy while significantly reducing storage needs.

The paper introduces SAMDUDE, a denoising method for aligned genomic data that improves variant calling by reducing false positives and increasing true variant detection in both chromosome and whole genome sequencing data.

The paper develops an automated method for RNA secondary structure analysis using network techniques, validated against manual analysis and known structures.

This paper evaluates the impact of lossy quality score compression on variant calling accuracy using genomic datasets and finds that it can maintain or even improve performance while reducing storage costs.