ArticleJournal of proteome research2016
APOSTL: An Interactive Galaxy Pipeline for Reproducible Analysis of Affinity Proteomics Data.
Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Epigenetic priming promotes tyrosine kinase inhibitor resistance and oncogene amplification.Nature structural & molecular biology · 2026Article
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- Regulation of Enhancers by SUMOylation Through TFAP2C Binding and Recruitment of HDAC Complex to the Chromatin.Research square · 2024Article
- Extracellular Proximity Labeling Reveals an Expanded Interactome for the Matrisome Protein TIMP2.Research square · 2024Article
- Integrated Proteomics-Based Physical and Functional Mapping of AXL Kinase Signaling Pathways and Inhibitors Define Its Role in Cell Migration.Molecular cancer research : MCR · 2022Article
- CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry-based protein-protein interaction/association data.Molecular biology of the cell · 2021Article
- Network of Interactions between ZIKA Virus Non-Structural Proteins and Human Host Proteins.Cells · 2020Article
- Proximity labeling reveals novel interactomes in liveDevelopment (Cambridge, England) · 2019Article
- An immunoproteomic approach to characterize the CAR interactome and signalosome.Science signaling · 2019Article
- Off-target based drug repurposing opportunities for tivantinib in acute myeloid leukemia.Scientific reports · 2019Article
- The Galaxy Platform for Reproducible Affinity Proteomic Mass Spectrometry Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2019Article
- Targeting the BRD4-HOXB13 Coregulated Transcriptional Networks with Bromodomain-Kinase Inhibitors to Suppress Metastatic Castration-Resistant Prostate Cancer.Molecular cancer therapeutics · 2018Article
- Polypharmacology-based ceritinib repurposing using integrated functional proteomics.Nature chemical biology · 2017Article
- Sustained activation of the AKT/mTOR and MAP kinase pathways mediate resistance to the Src inhibitor, dasatinib, in thyroid cancer.Oncotarget · 2017Article
- An architecture for genomics analysis in a clinical setting using Galaxy and Docker.GigaScience · 2017Article
- Dual Targeting of WEE1 and PLK1 by AZD1775 Elicits Single Agent Cellular Anticancer Activity.ACS chemical biology · 2017Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
With continuously increasing scale and depth of coverage in affinity proteomics (AP-MS) data, the analysis and visualization is becoming more challenging. A number of tools have been developed to identify high-confidence interactions; however, a cohesive and intuitive pipeline for analysis and visualization is still needed. Here we present Automated Processing of SAINT Templated Layouts (APOSTL), a freely available Galaxy-integrated software suite and analysis pipeline for reproducible, interactive analysis of AP-MS data. APOSTL contains a number of tools woven together using Galaxy workflows, which are intuitive for the user to move from raw data to publication-quality figures within a single interface. APOSTL is an evolving software project with the potential to customize individual analyses with additional Galaxy tools and widgets using the R web application framework, Shiny. The source code, data, and documentation are freely available from GitHub ( https://github.com/bornea/APOSTL ) and other sources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.