ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Abstract

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA’s capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility

“…By selecting the top‐ranked genes (top 100 as frequently applied and widely accepted in DEGs study), a variety of signatures were identified by the Student's t test (Table S3) and SAM (Table S4). The CS values have been frequently used for quantitative evaluation on the consistency among the signatures discovered from multiple independent datasets . Therefore, based on the signatures identified from nine independent datasets, the CS for each method was calculated.…”

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

“…By combining multiple weak classifiers, the final results can be voted or averaged to obtain an overall model with higher accuracy, better general performance, and resistance to overfitting. This algorithm has been extensively used in bioinformatics and other areas, and has been confirmed to be an effective modeling technique in various domains (Ding et al, 2016a,b;Mrozek et al, 2016;Qiu et al, 2016;Wang et al, 2017;Wei et al, 2017a,b,c;Yu et al, 2017a;Zheng et al, 2017;Tang et al, 2018Tang et al, , 2019aXue et al, 2018;Degenhardt et al, 2019;Xu et al, 2019). In this study, the scikit-learn toolkit, available at https://scikit-learn.org, was used to establish the models.…”

Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction

“…CTD features represent the structural or physicochemical distribution patterns of amino acids in protein or peptide sequences (Dubchak et al, 1999;Tang et al, 2020). Thirteen types of physicochemical properties were used to calculate these characteristics, including hydrophobicity, standardized van der Waals volume, solvent accessibility, polarity, secondary structure, polarizability, and charge.…”