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Appia: simpler chromatography analysis and visualization

Posert, R.; Baconguis, I.

2022-08-24 biochemistry
10.1101/2022.04.01.486784 bioRxiv
Show abstract

Chromatography is an essential family of assays for molecular biology and chemistry. Typically, only a qualitative assessment of peak height, position, and shape are sufficient to proceed. However, there are more advanced forms of chromatography that require integration of peak width, baseline subtraction, and precise peak position calling. To handle these precise workflows, instrument manufacturer software is complicated and arcane; this makes simpler day-to-day analyses inefficient. Additionally, each manufacturer reports the data in their own proprietary format, requiring that analysis is performed at instrument computers and making data sharing between labs difficult. Here we present Appia, a free, open-source chromatography processing and visualization package focused on making analysis, collaboration, and publication quick and easy.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Analytical Chemistry
● 218 papers in training set
Top 0.2%
18.9%
Similar papers in this journal
2
Nature Protocols
○ 33 papers in training set
Top 0.1%
16.7%
Similar papers in this journal
3
Journal of Proteome Research
● 234 papers in training set
Top 0.4%
8.0%
Similar papers in this journal
4
Analytical Biochemistry
○ 26 papers in training set
Top 0.1%
5.6%
Similar papers in this journal
5
SoftwareX
○ 15 papers in training set
Top 0.1%
4.1%
Similar papers in this journal
50% of probability mass above
6
PLOS ONE
● 5266 papers in training set
Top 32%
4.1%
7
Bioinformatics
◐ 1204 papers in training set
Top 5%
4.1%
8
Protein Science
○ 246 papers in training set
Top 1%
3.2%
9
Journal of Visualized Experiments
● 34 papers in training set
Top 0.2%
2.8%
10
HardwareX
○ 18 papers in training set
Top 0.1%
2.2%
11
Current Protocols
○ 15 papers in training set
Top 0.1%
1.9%
12
Metabolites
○ 53 papers in training set
Top 0.5%
1.8%
13
Molecular & Cellular Proteomics
183 papers in training set
Top 1%
1.7%
14
Nucleic Acids Research
◐ 1281 papers in training set
Top 9%
1.5%
15
Nature Communications
○ 5641 papers in training set
Top 46%
1.5%
16
Journal of the American Society for Mass Spectrometry
● 37 papers in training set
Top 0.3%
1.5%
17
The Analyst
● 16 papers in training set
Top 0.2%
1.1%
18
Methods
○ 34 papers in training set
Top 0.4%
1.1%
19
Wellcome Open Research
○ 67 papers in training set
Top 0.9%
1.1%
20
eLife
● 5828 papers in training set
Top 62%
1.1%
21
Journal of Molecular Biology
○ 232 papers in training set
Top 3%
1.0%
22
MethodsX
○ 16 papers in training set
Top 0.3%
0.6%
23
Synthetic Biology
◐ 24 papers in training set
Top 0.3%
0.6%
24
Journal of Neuroscience Methods
○ 122 papers in training set
Top 2%
0.6%
25
PROTEOMICS
○ 43 papers in training set
Top 0.8%
0.6%
26
SLAS Discovery
○ 25 papers in training set
Top 0.3%
0.6%
27
SLAS Technology
○ 14 papers in training set
Top 0.2%
0.6%
28
Communications Chemistry
○ 48 papers in training set
Top 1%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.