Conceitos essenciais
Studying the permutation recovery problem against deletion errors for DNA data storage.
Resumo
Introduction
Need for durable and compact storage systems.
DNA as a promising storage medium due to its density and durability.
DNA Storage Systems
Components: DNA synthesis, storage container, next-generation sequencing.
Unordered nature of DNA storage systems poses challenges in data retrieval.
Clustering Approaches
Clustering based on edit distance is computationally expensive.
Distributed approximate clustering algorithm proposed for efficiency.
Bee Identification Approach
Generalization of bee identification problem for multi-draw channels.
Utilizes address information to solve the task efficiently.
Deletions vs Erasures
Previous approaches designed for binary erasure channel.
More realistic noise model considered: deletions in this study.
Problem Formulation
Defining N-permutations and addresses in the context of DNA data storage.
Algorithm Design
Two-step approach: partitioning using clustering, followed by labeling with minimum-cost algorithm.
Theoretical Analysis
Theoretical bounds and probabilities derived to ensure accurate permutation recovery.
Estatísticas
Let N and M be positive integers.
An N-permutation π over [M] is an NM-tuple where every symbol appears exactly N times.
Citações
"We study the permutation recovery problem against deletions errors for DNA data storage."
"DNA has emerged as a promising storage medium due to its immense density and durability."