COMBINE-lab is a research group in the University of Maryland’s Department of Computer Science. It develops algorithms, data structures, and open-source software for analyzing high-throughput genomics data, with an emphasis on transcriptomics, single-cell assays, sequence indexing, and scalable inference. Its work spans RNA-seq quantification and transcriptome analysis, long-read sequencing, single-cell workflows, quality control, sequence mapping, and compressed representations of genomic data.

The lab maintains tools including salmon, alevin-fry, simpleaf, piscem, oarfish, and QCatch, which support researchers working with bulk and single-cell RNA-seq, long-read data, and large sequence collections. Its methods also address feature barcoding, sparse data, multimodal sequencing protocols, and graph-based sequence representations.

COMBINE-lab emphasizes scientific accuracy, computational performance, and usability. Its approach combines careful benchmarking and algorithm engineering with software that researchers can install, document, cite, inspect, and extend. Many projects involve performance-conscious implementation, and new projects are commonly developed in Rust. The lab is especially suited to students interested in building robust computational methods and software for biological data analysis.