IDentity with Locality: An ideal hash for gene sequence search

• URL: http://arxiv.org/abs/2406.14901v1
• Date: Fri, 21 Jun 2024 06:47:39 GMT
• Title: IDentity with Locality: An ideal hash for gene sequence search
• Authors: Aditya Desai, Gaurav Gupta, Tianyi Zhang, Anshumali Shrivastava,
• Abstract summary: Most gene search systems now use hashing-based data structures such as Bloom Filters (BF) We propose a novel hash function called the Identity with Locality (IDL) hash family, which co-locates the keys close in input space without causing collisions.
• Score: 34.574424989422674
• License:
• Abstract: Gene sequence search is a fundamental operation in computational genomics. Due to the petabyte scale of genome archives, most gene search systems now use hashing-based data structures such as Bloom Filters (BF). The state-of-the-art systems such as Compact bit-slicing signature index (COBS) and Repeated And Merged Bloom filters (RAMBO) use BF with Random Hash (RH) functions for gene representation and identification. The standard recipe is to cast the gene search problem as a sequence of membership problems testing if each subsequent gene substring (called kmer) of Q is present in the set of kmers of the entire gene database D. We observe that RH functions, which are crucial to the memory and the computational advantage of BF, are also detrimental to the system performance of gene-search systems. While subsequent kmers being queried are likely very similar, RH, oblivious to any similarity, uniformly distributes the kmers to different parts of potentially large BF, thus triggering excessive cache misses and causing system slowdown. We propose a novel hash function called the Identity with Locality (IDL) hash family, which co-locates the keys close in input space without causing collisions. This approach ensures both cache locality and key preservation. IDL functions can be a drop-in replacement for RH functions and help improve the performance of information retrieval systems. We give a simple but practical construction of IDL function families and show that replacing the RH with IDL functions reduces cache misses by a factor of 5x, thus improving query and indexing times of SOTA methods such as COBS and RAMBO by factors up to 2x without compromising their quality. We also provide a theoretical analysis of the false positive rate of BF with IDL functions. Our hash function is the first study that bridges Locality Sensitive Hash (LSH) and RH to obtain cache efficiency.

Related papers

• Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery [65.16724941038052]
  Category-aware Prototype Generation (CPG) and Discrimi Category 5.3% (DCE) are proposed. CPG enables the model to fully capture the intra-category diversity by representing each category with multiple prototypes. DCE boosts the discrimination ability of hash code with the guidance of the generated category prototypes.
  arXiv  Detail & Related papers  (2024-10-24T23:51:40Z)
• Sparse-Inductive Generative Adversarial Hashing for Nearest Neighbor Search [8.020530603813416]
  We propose a novel unsupervised hashing method, termed Sparsity-Induced Generative Adversarial Hashing (SiGAH) SiGAH encodes large-scale high-scale high-dimensional features into binary codes, which solves the two problems through a generative adversarial training framework. Experimental results on four benchmarks, i.e. Tiny100K, GIST1M, Deep1M, and MNIST, have shown that the proposed SiGAH has superior performance over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-06-12T08:07:23Z)
• Unified Functional Hashing in Automatic Machine Learning [58.77232199682271]
  We show that large efficiency gains can be obtained by employing a fast unified functional hash. Our hash is "functional" in that it identifies equivalent candidates even if they were represented or coded differently. We show dramatic improvements on multiple AutoML domains, including neural architecture search and algorithm discovery.
  arXiv  Detail & Related papers  (2023-02-10T18:50:37Z)
• Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning [95.54688542786863]
  Existing hashing methods assume that the query and retrieval samples lie in homogeneous feature space within the same domain. We propose an Asymmetric Transfer Hashing (ATH) framework with its unsupervised/semi-supervised/supervised realizations. By jointly optimizing asymmetric hash functions and the bipartite graph, not only can knowledge transfer be achieved but information loss caused by feature alignment can also be avoided.
  arXiv  Detail & Related papers  (2022-06-25T08:24:34Z)
• A Genetic Algorithm for Obtaining Memory Constrained Near-Perfect Hashing [0.0]
  We present an approach based on hash tables that focuses on both minimizing the number of comparisons performed during the search and minimizing the total collection size. The paper results show that near-perfect hashing is faster than binary search, yet uses less memory than perfect hashing.
  arXiv  Detail & Related papers  (2020-07-16T12:57:15Z)
• Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator [62.26981903551382]
  Variational auto-encoders (VAEs) with binary latent variables provide state-of-the-art performance in terms of precision for document retrieval. We propose a pairwise loss function with discrete latent VAE to reward within-class similarity and between-class dissimilarity for supervised hashing. This new semantic hashing framework achieves superior performance compared to the state-of-the-arts.
  arXiv  Detail & Related papers  (2020-05-21T06:11:33Z)
• Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice [36.8421113576893]
  Given a CNF formula F on n variables, the problem of model counting or #SAT is to compute the number of satisfying assignments of F. Recent years have witnessed a surge of effort towards developing efficient algorithmic techniques.
  arXiv  Detail & Related papers  (2020-04-30T11:17:26Z)
• Reinforcing Short-Length Hashing [61.75883795807109]
  Existing methods have poor performance in retrieval using an extremely short-length hash code. In this study, we propose a novel reinforcing short-length hashing (RSLH) In this proposed RSLH, mutual reconstruction between the hash representation and semantic labels is performed to preserve the semantic information. Experiments on three large-scale image benchmarks demonstrate the superior performance of RSLH under various short-length hashing scenarios.
  arXiv  Detail & Related papers  (2020-04-24T02:23:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.