Search Results for "proteomics"

....; Shadbahr, T.; Tanoli Z; Pessia, A.; Tang, J. SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets. Genomics, Proteomics & Bioinformatics 2022, 20, 587-596 https://doi.org/10.1016/j.gpb.2022.01.004

...The central anchor for the data congregation is the modificationSpecificPeptides.txt or diann-output.pr_matrix.tsv file - additional data is inferred from different other source files from the corresponding folder. Maybe also useful for normal proteomics purposes but this script is heavily optimized for protein neo-termini identification and validation. A graphical interface is available as Yoğurtlu_MANTI (a Perl/Tk script) + execut. application versions for Win1x without the need to have Perl installed locally. For a very detailed explanation of script parameters and the evaluation strategy, please consult the extensive manual PDF

...The central anchor for the data congregation is the modificationSpecificPeptides.txt file - additional data is inferred from different other source files from the MaxQuant txt folder but the starting point for the data assembly is solely the modificationSpecificPeptides.txt file. Maybe also useful for normal proteomics purposes but this script is heavily optimized for protein neo-termini identification and validation. For a more thorough explanation of script parameters and evaluation strategy, please consult the extensive manual PDF.

MetaErg is a stand-alone and fully automated metagenome and metaproteome annotation pipeline published at: https://www.frontiersin.org/articles/10.3389/fgene.2019.00999/full. If you are using this pipeline for your work, please cite: Dong X and Strous M (2019) An Integrated Pipeline for Annotation and Visualization of Metagenomic Contigs. Front. Genet. 10:999. doi: 10.3389/fgene.2019.00999 The instructions on configuring and running the MetaErg pipeline is available at GitHub...

...In experiments with real-life data, the algorithms were shown to be able to construct complete non-redundant sets of patterns for chemical datasets involving several thousands of molecules as well as for comparably large datasets from genomics or proteomics. The included relational learning algorithms are tailored towards so-called tree-like features for which some otherwise very hard sub-problems (NP-hard) become tractable.