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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.27130
|2 doi
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|a pubmed24n1225.xml
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|a (DE-627)NLM356806138
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|a (NLM)37177839
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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1 |
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|a Kelich, Payam
|e verfasserin
|4 aut
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|a BinderSpace
|b A package for sequence space analyses for datasets of affinity-selected oligonucleotides and peptide-based molecules
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 10.07.2023
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|a Date Revised 14.12.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2023 Wiley Periodicals LLC.
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|a Discovery of target-binding molecules, such as aptamers and peptides, is usually performed with the use of high-throughput experimental screening methods. These methods typically generate large datasets of sequences of target-binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low-throughput experiments or other approaches. Bioinformatics-based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders. BinderSpace is an open-source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text-based and visual output of motifs, can also provide heat maps of previously measured user-defined functional properties for all the motif-containing molecules. Users can also run principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analyses on whole datasets and on motif-related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t-SNE maps. If points (sequences) in two-dimensional maps in PCA or t-SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use of BinderSpace on a dataset of oligonucleotides binding to single-wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein. BinderSpace is openly accessible to the public via the GitHub website: https://github.com/vukoviclab/BinderSpace
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|a Journal Article
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|a Research Support, U.S. Gov't, Non-P.H.S.
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|a affinity selection datasets
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|a clustering analysis
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|a dimensionality reduction
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|a high affinity binding
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|a sequence motif analysis
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|a sequence space
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|a Oligonucleotides
|2 NLM
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| 650 |
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|a Nanotubes, Carbon
|2 NLM
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| 650 |
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|a Peptides
|2 NLM
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| 700 |
1 |
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|a Zhao, Huanhuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Orona, Jose R
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Vuković, Lela
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 44(2023), 22 vom: 15. Aug., Seite 1836-1844
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
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|g volume:44
|g year:2023
|g number:22
|g day:15
|g month:08
|g pages:1836-1844
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|u http://dx.doi.org/10.1002/jcc.27130
|3 Volltext
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