Srinivas Aluru

scSAGA improves multimodal single-cell data integration by preserving geometry and scaling efficiently, outperforming existing methods on various datasets.

SCEMENT integrates large-scale single cell RNA-seq data efficiently, outperforming existing methods in speed and memory usage while preserving quantitative gene expression information.

MCPNet constructs high-quality gene networks efficiently and scalably using a novel maximum-capacity-path metric, outperforming existing methods in speed and accuracy.

Chromap provides a fast and accurate method for aligning and preprocessing chromatin profiles, outperforming existing tools in speed while maintaining alignment accuracy.

The paper introduces a variant selection framework to reduce variation graph size while preserving paths with limited differences, achieving significant graph reduction in human chromosome 1.

This paper evaluates long read error correction methods using real data, comparing their performance on quality, runtime, memory, and impact on genome assembly.

GRNUlar infers gene regulatory networks from scRNA-Seq data using unrolled deep learning, outperforming existing methods with and without TF information.

This paper proves sequence to graph alignment is NP-complete under Hamming and edit distances for alphabets of size ≥2 and presents an efficient algorithm for when only sequence changes are allowed.

The paper introduces an algorithm to validate distance constraints in graph-based genomic references using sparse matrix operations, demonstrating efficiency on real-world datasets.

The paper introduces a parallel algorithm for sequence-to-graph alignment, achieving significant speedup for aligning DNA reads to variation graphs.

FastANI accurately estimates average nucleotide identity (ANI) up to 782-fold faster than BLAST; across 90K prokaryotic genomes, an 83-95% ANI gap supports a 95% species boundary.

The paper introduces a fast algorithm for computing whole-genome homology maps, achieving efficient alignment with high accuracy and coverage.

The paper identifies four computational building blocks that dominate runtime in NGS secondary analysis tools across sequence mapping, de novo assembly, and variant calling.

The paper introduces a fast approximate algorithm for mapping long reads to large reference databases, achieving high speed and precision by combining minimizers with MinHash identity estimation.