ChromatoShiny: an Interactive R/Shiny App for Plotting Chromatography Profiles

Natalia Y Kochanova,Maria Alba Abad,Petra Vizjak,A Arockia Jeyaprakash,William C Earnshaw,Georg Kustatscher

Wellcome open research（2024）

Wellcome Centre for Cell Biology | Ludwig-Maximilians-Universität München

Cited 0|Views21

Abstract

Background Unicorn™ software on Äkta liquid chromatography instruments outputs chromatography profiles of purified biological macromolecules. While the plots generated by the instrument software are very helpful to inspect basic chromatogram properties, they lack a range of useful annotation, customization and export options. Methods We use the R Shiny framework to build an interactive app that facilitates the interpretation of chromatograms and the generation of figures for publications. Results The app allows users to fit a baseline, to highlight selected fractions and elution volumes inside or under the plot (e.g. those used for downstream biochemical/biophysical/structural analysis) and to zoom into the plot. The app is freely available at https://ChromatoShiny.bio.ed.ac.uk. Conclusions It requires no programming experience, so we anticipate that it will enable chromatography users to create informative, annotated chromatogram plots quickly and simply.

Translated text

Key words

Feature Selection

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper