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20172022
most citedNanopore Sequencing Technology and Tools for Genome Assembly: Computational Analysis of the Current State, Bottlenecks and Future Directions

269 citations · 752 across the 16 of their papers we have counts for

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11 papers · 1 filter

cs.AR2022★ 2 cited

GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping

Haiyu Mao, Mohammed Alser, Mohammad Sadrosadati +6

Nanopore sequencing is a widely-used high-throughput genome sequencing technology that can sequence long fragments of a genome into raw electrical signals at low cost. Nanopore seq…

cs.AR2022★ 19 cited

Scrooge: A Fast and Memory-Frugal Genomic Sequence Aligner for CPUs, GPUs, and ASICs

Joël Lindegger, Damla Senol Cali, Mohammed Alser +3

Pairwise sequence alignment is a very time-consuming step in common bioinformatics pipelines. Speeding up this step requires heuristics, efficient implementations, and/or hardware…

cs.AR2022

ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis

Can Firtina, Kamlesh Pillai, Gurpreet S. Kalsi +10

Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequen…

cs.AR2022★ 38 cited

SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping

Damla Senol Cali, Konstantinos Kanellopoulos, Joel Lindegger +15

A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e.,…

cs.AR2022

Algorithmic Improvement and GPU Acceleration of the GenASM Algorithm

Joël Lindegger, Damla Senol Cali, Mohammed Alser +2

We improve on GenASM, a recent algorithm for genomic sequence alignment, by significantly reducing its memory footprint and bandwidth requirement. Our algorithmic improvements redu…

cs.AR2022★ 13 cited

GenStore: A High-Performance and Energy-Efficient In-Storage Computing System for Genome Sequence Analysis

Nika Mansouri Ghiasi, Jisung Park, Harun Mustafa +11

Read mapping is a fundamental, yet computationally-expensive step in many genomics applications. It is used to identify potential matches and differences between fragments (called…

cs.AR2021

Accelerating Genome Sequence Analysis via Efficient Hardware/Algorithm Co-Design

Damla Senol Cali

Genome sequence analysis plays a pivotal role in enabling many medical and scientific advancements in personalized medicine, outbreak tracing, and forensics. However, the analysis…

cs.AR2021★ 74 cited

FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications

Gagandeep Singh, Mohammed Alser, Damla Senol Cali +4

Modern data-intensive applications demand high computation capabilities with strict power constraints. Unfortunately, such applications suffer from a significant waste of both exec…

cs.AR2020★ 84 cited

Accelerating Genome Analysis: A Primer on an Ongoing Journey

Mohammed Alser, Zülal Bingöl, Damla Senol Cali +4

Genome analysis fundamentally starts with a process known as read mapping, where sequenced fragments of an organism's genome are compared against a reference genome. Read mapping i…

cs.AR2020★ 2 cited

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

Damla Senol Cali, Gurpreet S. Kalsi, Zülal Bingöl +13

Genome sequence analysis has enabled significant advancements in medical and scientific areas such as personalized medicine, outbreak tracing, and the understanding of evolution. U…

cs.AR2019★ 7 cited

Understanding the Interactions of Workloads and DRAM Types: A Comprehensive Experimental Study

Saugata Ghose, Tianshi Li, Nastaran Hajinazar +2

It has become increasingly difficult to understand the complex interaction between modern applications and main memory, composed of DRAM chips. Manufacturers are now selling and pr…