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

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          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 ( 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.

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          Most cited references 113

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          Missing value estimation methods for DNA microarrays.

          Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.
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            DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics.

            The components of complex peptide mixtures can be separated by liquid chromatography, fragmented by tandem mass spectrometry, and identified by the SEQUEST algorithm. Inferring a mixture's source proteins requires that the identified peptides be reassociated. This process becomes more challenging as the number of peptides increases. DTASelect, a new software package, assembles SEQUEST identifications and highlights the most significant matches. The accompanying Contrast tool compares DTASelect results from multiple experiments. The two programs improve the speed and precision of proteomic data analysis.
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              PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry.

              A number of different approaches have been described to identify proteins from tandem mass spectrometry (MS/MS) data. The most common approaches rely on the available databases to match experimental MS/MS data. These methods suffer from several drawbacks and cannot be used for the identification of proteins from unknown genomes. In this communication, we describe a new de novo sequencing software package, PEAKS, to extract amino acid sequence information without the use of databases. PEAKS uses a new model and a new algorithm to efficiently compute the best peptide sequences whose fragment ions can best interpret the peaks in the MS/MS spectrum. The output of the software gives amino acid sequences with confidence scores for the entire sequences, as well as an additional novel positional scoring scheme for portions of the sequences. The performance of PEAKS is compared with Lutefisk, a well-known de novo sequencing software, using quadrupole-time-of-flight (Q-TOF) data obtained for several tryptic peptides from standard proteins. Copyright 2003 John Wiley & Sons, Ltd.

                Author and article information

                Brief Bioinform
                Brief. Bioinformatics
                Briefings in Bioinformatics
                Oxford University Press
                March 2020
                15 January 2019
                15 January 2019
                : 21
                : 2
                : 621-636
                [1 ] College of Pharmaceutical Sciences , Zhejiang University, Hangzhou, China
                [2 ] School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science , Chongqing University, Chongqing, China
                [3 ] Bioinformatics and Drug Design Group , Department of Pharmacy, National University of Singapore, Singapore, Singapore
                Author notes
                Corresponding author: Feng Zhu, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China. Tel.: +86-571-88208444; Fax: +86-571-88208444; E-mail: zhufeng@ ; prof.zhufeng@
                © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact

                Page count
                Pages: 16
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 81872798
                Funded by: National Key Research and Development Program of China, DOI 10.13039/501100012166;
                Award ID: 2018YFC0910500
                Funded by: Innovation Project on Industrial Generic Key Technologies of Chongqing;
                Award ID: cstc2015zdcy-ztzx120003
                Funded by: Fundamental Research Funds for Central University;
                Award ID: 2018QNA7023
                Award ID: 10611CDJXZ238826
                Award ID: 2018CDQYSG0007
                Award ID: CDJZR14468801
                Award ID: CDJKXB14011
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