One Thousand and One Software for Proteomics: Tales of the Toolmakers of Science

Abstract

Proteomics is a very active field driven by frequent introduction of new technological approaches, leading to high demand for new software tools and the concurrent development of many methods for data analysis, processing and storage. The rapidly changing landscape of proteomics software makes finding a tool fit for a particular purpose a significant challenge. The comparison of software and the selection of tools capable to perform a certain operation on a given type of data relies on their detailed annotation using well-defined descriptors. However, finding accurate information including tool input/output capabilities can be challenging and often heavily depends on manual curation efforts. This is further hampered by a rather low half-life of most of the tools, thus demanding the maintenance of a resource with updated information about the tools. We present here our approach to curate a collection of 189 software tools with detailed information about their functional capabilities. We furthermore describe our efforts to reach out to the proteomics community for their engagement, which further increased the catalogue to >750 tools being about 70% of the estimated number of 1,097 tools existing for proteomics data analysis. Descriptions of all annotated tools are available through https://proteomics.bio.tools

Quick microbial molecular phenotyping by differential shotgun proteomics

“…This statistical exploration of label-free quantification data can be performed with a myriad of different tools that cover different levels of expertise of the end-user (Tsiamis et al, 2019). Packages such as RforProteomics (Gatto and Christoforou, 2014) or mixOmics (Rohart et al, 2017) are available in R for deep data exploration, statistics and visualization.…”

Deep learning neural network tools for proteomics

“…Peptides must also be quantified, usually based on their elution profile, which can be challenging due to missing values, interfering masses, or shifts in chromatographic retention time. Countless software tools exist to facilitate and expedite various proteomic data analysis, including for design of data collection, feature selection, peptide and protein identification and quantification, and biological interpretation ( Marx, 2020 ; Tsiamis et al., 2019 ). Still, many computational challenges prevent more sensitive and accurate peptide identification and quantification in the field of shotgun proteomics ( Schubert et al., 2017 ; Sinitcyn et al., 2018 ).…”

Proteomics Software in bio.tools: Coverage and Annotations

“…This has led to over 1000 tools servicing a wide range of functions from single operations to multifunctional software suites in proteomics. 1 Given such a large number of tools, initiatives like bio.tools (https://bio.tools) 2−4 and ms-utils.org (https://ms-utils.org) collect information about proteomics resources to facilitate the identification of suitable software that is capable of performing certain data operations. The benefits of findable, well-annotated software tools are widespread.…”

“…5 In 2019, we presented a collection of about 800 proteomics tools in the bio.tools registry and described their curation process. 1 It was the result of a crowdsourced community effort to collect relevant information from different sources, such as manuscripts, software documentation, and even source code. This extensive collection was supported by an engaged proteomics community 6,7 that contributed by annotating tools and promoting the usage of the bio.tools registry.…”