Gagandeep Singh

Open-source, Hardware-Independent GPU Acceleration for Scalable Nanopore Basecalling with Slorado and Openfish

This paper introduces open-source GPU-accelerated basecalling tools for nanopore sequencing that are hardware-independent and competitive with proprietary solutions.

TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering

TargetCall filters out off-target reads before basecalling to reduce computational waste, improving basecalling performance and efficiency.

RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes

RawHash enables fast and accurate real-time analysis of nanopore raw signals for large genomes, outperforming existing methods in throughput and accuracy.

BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis

BLEND improves genomic sequence analysis by efficiently finding fuzzy seed matches, enhancing speed, memory use, and accuracy in read overlapping and mapping.

A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers

This paper introduces RUBICON, a framework for designing efficient deep learning-based genomic basecallers, and demonstrates its effectiveness with RUBICALL, a hardware-optimized mixed-precision basecaller that outperforms existing methods.