# DREAM-Stellar: parallel and space efficient exact local alignment

**Authors:** Evelin Aasna, Simon Gene Gottlieb, Marcel Ehrhardt, Knut Reinert

PMC · DOI: 10.1186/s12859-026-06389-0 · 2026-02-18

## TL;DR

DREAM-Stellar is a fast and efficient tool for finding local alignments in genomic sequences using parallel computing and improved filtering techniques.

## Contribution

DREAM-Stellar introduces a parallelized and space-efficient local aligner with a new prefilter and IBF data structure for improved performance on genomic data.

## Key findings

- DREAM-Stellar is up to 900 times faster on 32 threads than its single-threaded predecessor.
- The tool can find all alignments between a pair of genomes in minutes.
- Heuristic tools like BLAST miss or overwhelm significant local alignments with less significant matches.

## Abstract

Searching large genomic data sets for local alignments poses a computational challenge. A particular obstacle is the handling of repetitive sequences that appear in various contexts and incur a high runtime cost. For practical homology search, it is important to develop a specific but sensitive filter. Good filters reduce the search space before alignment without missing significant matches.

We introduce DREAM-Stellar, a parallelized, updated version of the pairwise local aligner Stellar. The new aligner, DREAM-Stellar, is composed of four steps: preprocessing the queries and references, building a data structure for distributing the queries, computing in parallel the results and finally combining them. For distributing the queries we use the IBF data structure and a new prefilter for local alignments. We present our comparison of five local aligners on simulated and real genomic data and conclude that heuristic tools like BLAST miss a large percentage of significant local alignments or "drown" them in millions of less significant matches. This new version of Stellar is up to 900 times faster on 32 parallel threads than its single-threaded predecessor and can find all alignments between a pair of genomes in minutes. With that, the runtime of DREAM-Stellar is on par with tools like BLAST etc.

DREAM-Stellar is very practical and fast on very long sequences which makes it a suitable new tool for finding local alignments between genomic sequences under the edit distance model. The software is freely available for Linux and Mac OS X at https://github.com/seqan/dream-stellar

## Full-text entities

- **Genes:** FPR1 (formyl peptide receptor 1) [NCBI Gene 2357] {aka FMLP, FPR}, Kcnip3 (Kv channel interacting protein 3, calsenilin) [NCBI Gene 56461] {aka 4933407H12Rik, Csen, DREAM, KChIP3}, CTCF (CCCTC-binding factor) [NCBI Gene 10664] {aka CFAP108, FAP108, MRD21}, KCNIP3 (potassium voltage-gated channel interacting protein 3) [NCBI Gene 30818] {aka CSEN, DREAM, KCHIP3}
- **Diseases:** SINES (MESH:D031368), IBF (MESH:D001816)
- **Chemicals:** RAM (MESH:C071315)
- **Species:** Drosophila melanogaster (fruit fly, species) [taxon 7227], Homo sapiens (human, species) [taxon 9606], Mus musculus (house mouse, species) [taxon 10090]

## Figures

13 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13015089/full.md

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Source: https://tomesphere.com/paper/PMC13015089