ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies Article Swipe
Jing Tang
,
Jianbo Fu
,
Yunxia Wang
,
Bo Li
,
Yinghong Li
,
Qingxia Yang
,
Xuejiao Cui
,
Jiajun Hong
,
Xiaofeng Li
,
Yu Chen
,
Weiwei Xue
,
Feng Zhu
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bby127
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bby127
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bby127
- https://academic.oup.com/bib/article-pdf/21/2/621/33399114/bby127.pdf
- OA Status
- bronze
- Cited By
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- References
- 148
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W2910249797Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bib/bby127Digital Object Identifier
- Title
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ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studiesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-12-07Full publication date if available
- Authors
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Jing Tang, Jianbo Fu, Yunxia Wang, Bo Li, Yinghong Li, Qingxia Yang, Xuejiao Cui, Jiajun Hong, Xiaofeng Li, Yu Chen, Weiwei Xue, Feng ZhuList of authors in order
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https://doi.org/10.1093/bib/bby127Publisher landing page
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https://academic.oup.com/bib/article-pdf/21/2/621/33399114/bby127.pdfDirect link to full text PDF
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/bib/article-pdf/21/2/621/33399114/bby127.pdfDirect OA link when available
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Workflow, Computer science, DatabaseTop concepts (fields/topics) attached by OpenAlex
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168Total citation count in OpenAlex
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2025: 2, 2024: 20, 2023: 24, 2022: 47, 2021: 24Per-year citation counts (last 5 years)
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148Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.possible | 95 |
| abstract_inverted_index.provides | 168 |
| abstract_inverted_index.ranking. | 150 |
| abstract_inverted_index.research | 28 |
| abstract_inverted_index.response | 37 |
| abstract_inverted_index.revealed | 189 |
| abstract_inverted_index.specific | 6, 56, 78 |
| abstract_inverted_index.studies. | 263 |
| abstract_inverted_index.workflow | 10, 58, 125, 223 |
| abstract_inverted_index.available | 178 |
| abstract_inverted_index.criteria. | 104 |
| abstract_inverted_index.dependent | 61 |
| abstract_inverted_index.developed | 112 |
| abstract_inverted_index.discovery | 88, 227 |
| abstract_inverted_index.essential | 69 |
| abstract_inverted_index.generated | 161 |
| abstract_inverted_index.landscape | 33 |
| abstract_inverted_index.organisms | 48 |
| abstract_inverted_index.potential | 245 |
| abstract_inverted_index.proteins. | 210 |
| abstract_inverted_index.proteomic | 262 |
| abstract_inverted_index.requiring | 253 |
| abstract_inverted_index.technique | 24 |
| abstract_inverted_index.validated | 114 |
| abstract_inverted_index.workflows | 96 |
| abstract_inverted_index.ANPELA’s | 190 |
| abstract_inverted_index.Label-free | 1 |
| abstract_inverted_index.Systematic | 184 |
| abstract_inverted_index.Therefore, | 241 |
| abstract_inverted_index.assessment | 121, 127, 199 |
| abstract_inverted_index.benchmarks | 188 |
| abstract_inverted_index.evaluation | 103 |
| abstract_inverted_index.integrated | 9 |
| abstract_inverted_index.processing | 17, 174 |
| abstract_inverted_index.technique, | 13 |
| abstract_inverted_index.theories), | 135 |
| abstract_inverted_index.underlying | 134 |
| abstract_inverted_index.validating | 205 |
| abstract_inverted_index.validation | 185 |
| abstract_inverted_index.workflows. | 240 |
| abstract_inverted_index.(collective | 126 |
| abstract_inverted_index.acquisition | 12 |
| abstract_inverted_index.appropriate | 74 |
| abstract_inverted_index.challenging | 84 |
| abstract_inverted_index.discovering | 194 |
| abstract_inverted_index.performance | 53, 120, 149, 219, 235 |
| abstract_inverted_index.stand-alone | 182 |
| abstract_inverted_index.techniques, | 255 |
| abstract_inverted_index.workflow(s) | 145 |
| abstract_inverted_index.applications | 247 |
| abstract_inverted_index.capabilities | 191 |
| abstract_inverted_index.interactions | 45 |
| abstract_inverted_index.multifaceted | 99 |
| abstract_inverted_index.perspectives | 202 |
| abstract_inverted_index.sequentially | 8 |
| abstract_inverted_index.workflow(s), | 196 |
| abstract_inverted_index.automatically | 154 |
| abstract_inverted_index.comprehensive | 32, 148, 234 |
| abstract_inverted_index.metaproteomic | 27, 187, 249 |
| abstract_inverted_index.identification | 140 |
| abstract_inverted_index.quantification | 2, 14, 164 |
| abstract_inverted_index.well-performing | 195 |
| abstract_inverted_index.well-established | 130 |
| abstract_inverted_index.(https://idrblab.org/anpela/) | 110 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5100755257 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I158842170, https://openalex.org/I76130692 |
| citation_normalized_percentile.value | 0.99316865 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |