An Optimized Data Structure for High Throughput 3D Proteomics Data: mzRTree

TL;DR

The paper introduces mzRTree, a scalable R-tree based data structure that significantly improves the efficiency of storing and querying high-throughput LC-MS proteomics data, reducing computational costs.

Contribution

It presents mzRTree, a novel, space-efficient index structure optimized for fast range queries on large LC-MS datasets, outperforming existing methods.

Findings

mzRTree outperforms existing data structures in range query speed.

mzRTree is more space-efficient than current solutions.

It reduces computational costs for large proteomics datasets.

Abstract

As an emerging field, MS-based proteomics still requires software tools for efficiently storing and accessing experimental data. In this work, we focus on the management of LC-MS data, which are typically made available in standard XML-based portable formats. The structures that are currently employed to manage these data can be highly inefficient, especially when dealing with high-throughput profile data. LC-MS datasets are usually accessed through 2D range queries. Optimizing this type of operation could dramatically reduce the complexity of data analysis. We propose a novel data structure for LC-MS datasets, called mzRTree, which embodies a scalable index based on the R-tree data structure. mzRTree can be efficiently created from the XML-based data formats and it is suitable for handling very large datasets. We experimentally show that, on all range queries, mzRTree outperforms other known structures used for LC-MS data, even on those queries these structures are optimized for. Besides, mzRTree is also more space efficient. As a result, mzRTree reduces data analysis computational costs for very large profile datasets.