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Found 8 papers, 8 papers with code
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
We identify an urgent need for a flexible, high-performance, and energy-efficient HW/SW co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs.
FastRemap: A Tool for Quickly Remapping Reads between Genome Assemblies
Also available in Bioconda at: https://anaconda. org/bioconda/fastremap-bio.
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis
We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seed matches.
GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis
Unfortunately, it is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data.
Hardware Architecture Genomics
AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes
There are several tools that attempt to accelerate the process of updating a read data set from one reference to another (i. e., remapping).
Understanding the Interactions of Workloads and DRAM Types: A Comprehensive Experimental Study
As a result, the combined DRAM-workload behavior is often difficult to intuitively determine today, which can hinder memory optimizations in both hardware and software.
Hardware Architecture Performance
Apollo: A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm
Our experiments with real read sets demonstrate that Apollo is the only algorithm that 1) uses reads from any sequencing technology within a single run and 2) scales well to polish large assemblies without splitting the assembly into multiple parts.
GRIM-Filter: Fast Seed Location Filtering in DNA Read Mapping Using Processing-in-Memory Technologies
State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i. e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i. e., sequence alignment) to determine the origin of the read.
