# An Investigation of Alternatives to Transform Protein Sequence Databases to a Columnar Index Schema
## 1 Introduction  
Mass spectrometers are widely-used devices (the mass spectrometry market will grow from $5.3 billion to $10.5 billion from 2016-2025 [1]) enabling the identification of proteins from any sample and thus to understand the ongoing biological process in the sample [2, 3, 4]. The identified proteins can help to optimize biochemical processes, for example those in biogas plants [5, 6], or diagnose diseases such as inflammatory bowel diseases [7] or even the spike proteins of the SARS-CoV2 virus [8]. The measurement of a mass spectrometer takes up to two hours and is followed by converting the measured data into a readable format. In order to gain insights into the converted data, an analysis step is required: the protein identification [9]. The state-of-the-art protein identification approach uses a peptide-centric approach comparing the experimental (spectrum) data with theoretical spectra from a protein sequence database. For this purpose, the proteins are digested into peptides, and theoretical spectra are calculated, resulting in billions of data sets. Subsequently, protein database search algorithms compare the measured spectra with the theoretical spectra to find the highest similarity. As a consequence of all these steps, bioinformatic protein identification takes several hours to complete. In order to speed up the bioinformatic processes, we are currently developing a cloud-based pipeline for protein identification [10]. One key challenge for this pipeline is the well-performing and efficient storage and retrieval of the proteins. Therefore, we tested and evaluated an index schema to query only suitable candidates of the sequence data and reduce thesearch area to a minimum [11; 12], using a column-based index in a distributed database management system (DBMS) that allows streamlining of the analysis step. In this work, we present an indexing schema for the sequence data of a protein sequence database, using a column-based index in a distributed database management system (DBMS) that allows streamlining of the analysis step. This step's key challenges are the memory-efficient and fast transformation of the protein sequence data from the current state-of-the-art FASTA format to the DBMS indexed schema. The transformation involves separating the sequence data, deduplication of the sequence data, and mass calculation steps, which have to be calculated before inserting into the DBMS. After presenting the transformation process, we describe four methods to aggregate the data into the index structure. The first one is the naive in-memory approach; the second is the structured hard disk approach; the third method uses DBMS queries; and the last one is the radix tree-based method. In the end, we evaluate those methods and show that the radix tree is very efficient for the storage of sequence data and has the best overall performance of all approaches. In summary, we make the following contributions:  
* Database schema: We propose a suitable schema for protein sequence data in a real-time cloud system as an extension to our prior paper [13]. * Protein data transformation: We review the process of protein data transformation into the proposed database schema and detail suitable techniques for the transformation steps. * Indexing: We propose a radix tree for efficient deduplication (including an extended description compared to our prior paper [13]). * Performance comparison: We evaluate the best method to transform the protein sequence data using the four proposed approaches. This paper is structured as follows: In Section 2, we explain the basics of the mass spectrometry workflow, the protein identification, the protein data, and the radix tree data structure. In Section 3, we describe our architecture for the real-time processing of mass spectrometer data and explain the index of the protein data and how to transform this one into an indexed structure. Next, in Section 4, we present the four methods for the data transformation.