Nucleotide String Indexing using Range Matching

Alon Rashelbach; Ori Rottenstreich; Mark Silberstein

doi:https://doi.org/10.1145/3584371.3613024

Nucleotide String Indexing using Range Matching

Alon Rashelbach, Ori Rottenstreich, Mark Silberstein

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The most common data-structures for genome indexing exhibit a fundamental trade-off between memory footprint and performance. We present Ranger, a new indexing technique that is both memory efficient and fast, and integrate it into the popular Minimap2 tool. Ranger achieves almost identical end-to-end performance as the original Minimap2, while occupying up to 1.7× and 1.2× less memory. With a limited memory capacity, Ranger achieves up to 4.3× performance speedup compares to current indexing techniques.

Original language	English
Title of host publication	ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
ISBN (Electronic)	9798400701269
DOIs	https://doi.org/10.1145/3584371.3613024
State	Published - 3 Sep 2023
Event	14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2023 - Houston, United States Duration: 3 Sep 2023 → 6 Sep 2023

Publication series

Name	ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Conference

Conference	14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2023
Country/Territory	United States
City	Houston
Period	3/09/23 → 6/09/23

All Science Journal Classification (ASJC) codes

Software
Health Informatics
Biomedical Engineering
Computer Science Applications

Access to Document

https://doi.org/10.1145/3584371.3613024

Cite this

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title = "Nucleotide String Indexing using Range Matching",

abstract = "The most common data-structures for genome indexing exhibit a fundamental trade-off between memory footprint and performance. We present Ranger, a new indexing technique that is both memory efficient and fast, and integrate it into the popular Minimap2 tool. Ranger achieves almost identical end-to-end performance as the original Minimap2, while occupying up to 1.7× and 1.2× less memory. With a limited memory capacity, Ranger achieves up to 4.3× performance speedup compares to current indexing techniques.",

author = "Alon Rashelbach and Ori Rottenstreich and Mark Silberstein",

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year = "2023",

month = sep,

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doi = "https://doi.org/10.1145/3584371.3613024",

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Rashelbach, A, Rottenstreich, O & Silberstein, M 2023, Nucleotide String Indexing using Range Matching. in ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics., 83, ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2023, Houston, United States, 3/09/23. https://doi.org/10.1145/3584371.3613024

Nucleotide String Indexing using Range Matching. / Rashelbach, Alon; Rottenstreich, Ori; Silberstein, Mark.
ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. 2023. 83 (ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Nucleotide String Indexing using Range Matching

AU - Rashelbach, Alon

AU - Rottenstreich, Ori

AU - Silberstein, Mark

PY - 2023/9/3

Y1 - 2023/9/3

N2 - The most common data-structures for genome indexing exhibit a fundamental trade-off between memory footprint and performance. We present Ranger, a new indexing technique that is both memory efficient and fast, and integrate it into the popular Minimap2 tool. Ranger achieves almost identical end-to-end performance as the original Minimap2, while occupying up to 1.7× and 1.2× less memory. With a limited memory capacity, Ranger achieves up to 4.3× performance speedup compares to current indexing techniques.

AB - The most common data-structures for genome indexing exhibit a fundamental trade-off between memory footprint and performance. We present Ranger, a new indexing technique that is both memory efficient and fast, and integrate it into the popular Minimap2 tool. Ranger achieves almost identical end-to-end performance as the original Minimap2, while occupying up to 1.7× and 1.2× less memory. With a limited memory capacity, Ranger achieves up to 4.3× performance speedup compares to current indexing techniques.

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U2 - https://doi.org/10.1145/3584371.3613024

DO - https://doi.org/10.1145/3584371.3613024

M3 - منشور من مؤتمر

T3 - ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

BT - ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

T2 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2023

Y2 - 3 September 2023 through 6 September 2023

ER -

Nucleotide String Indexing using Range Matching

Abstract

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All Science Journal Classification (ASJC) codes

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