Dynamic Mapping Simulator.

GenStore is the first in-storage processing system designed for genome sequence analysis that greatly reduces both data movement and computational overheads of genome sequence analysis by exploiting low-cost and accurate in-storage filters. Described in the ASPLOS 2022 paper by Mansouri Ghiasi et al. at https://people.inf.ethz.ch/omutlu/pub/GenS…

The GPU-version and MPI-version of PerM

GateSeeder is the first near-memory CPU-FPGA co-design for alleviating both the compute-bound and memory-bound bottlenecks in short and long-read mapping. GateSeeder outperforms Minimap2 by up to 40.3×, 4.8×, and 2.3× when mapping real ONT, HiFi, and Illumina reads, respectively.

S3: Sequence Similarity Search (Protein Sequence)

DNA read mapping (seed-extension-aligner)

Pipeline to perform alignment & variant calling on whole-genome sequence data

coding problems from course 6 of the Bioinformatics specialization

The first work to provide a comprehensive survey of a prominent set of algorithmic improvement and hardware acceleration efforts for the entire genome analysis pipeline used for the three most prominent sequencing data, short reads (Illumina), ultra-long reads (ONT), and accurate long reads (HiFi). Described in arXiv (2022) by Alser et al. https…

SequenceLab is a benchmark suite for evaluating computational methods for comparing genomic sequences, such as pre-alignment filters and pairwise sequence alignment algorithms. SequenceLab is described by Rumpf et al. at https://arxiv.org/abs/2310.16908

Illumina (and SOLiD) sensitive read mapping tool (cloned from svn://scm.gforge.inria.fr/svnroot/storm/)

Mapping reads to a corresponding genome using a suffix tree (linear time construction) and Smith-Waterman local alignment algorithm

Space-efficient minimizer-based pangenome reference graph and haplotype mapping tool

Highly optimized genomic resources for GPUs

subset and spaced seed design tool

Reference-based read-mapper which performs ungapped alignment of sample reads on reference sequence.

A systematic survey of algorithmic foundations and methodologies across 107 alignment methods (1988-2021), for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. Described by Alser et al. at https://arxiv.…

BLEND is a mechanism that can efficiently find fuzzy seed matches between sequences to significantly improve the performance and accuracy while reducing the memory space usage of two important applications: 1) finding overlapping reads and 2) read mapping. Described by Firtina et al. (published in NARGAB https://doi.org/10.1093/nargab/lqad004)