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Autoturbo-DNA: Turbo-Autoencoders for the DNA data storage channel

Welzel, M.; Dressler, H.; Heider, D.

2023-09-17 bioinformatics
10.1101/2023.09.15.557887 bioRxiv
Show abstract

DNA, with its high storage density and long-term stability, is a potential candidate for a next-generation storage device. The DNA data storage channel, comprised of synthesis, amplification, storage, and sequencing, exhibits error probabilities and error profiles specific to the components of the channel. Here, we present Autoturbo-DNA, a PyTorch framework for training error-correcting, overcomplete autoencoders specifically tailored for the DNA data storage channel. It allows training different architecture combinations and using a wide variety of channel component models for noise generation during training. It further supports training the encoder to generate DNA sequences that adhere to user-defined constraints.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 0.4%
34.4%
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2
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○ 457 papers in training set
Top 0.5%
11.9%
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3
Bioinformatics Advances
◐ 203 papers in training set
Top 0.3%
9.7%
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50% of probability mass above
4
Scientific Reports
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Top 12%
6.3%
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5
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Top 31%
4.3%
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6
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● 1863 papers in training set
Top 10%
3.2%
7
Nature Communications
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Top 40%
2.4%
8
IEEE/ACM Transactions on Computational Biology and Bioinformatics
● 38 papers in training set
Top 0.5%
2.0%
9
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
1.7%
10
GigaScience
◐ 212 papers in training set
Top 3%
1.4%
11
Nucleic Acids Research
◐ 1281 papers in training set
Top 10%
1.4%
12
Nature Machine Intelligence
○ 70 papers in training set
Top 2%
1.3%
13
Briefings in Bioinformatics
◐ 354 papers in training set
Top 6%
1.1%
14
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 5%
1.1%
15
BioData Mining
○ 22 papers in training set
Top 0.4%
1.1%
16
IEEE Transactions on Computational Biology and Bioinformatics
● 20 papers in training set
Top 0.5%
1.1%
17
iScience
○ 1154 papers in training set
Top 30%
1.0%
18
Biology Methods and Protocols
◐ 61 papers in training set
Top 2%
0.8%
19
Genome Biology
○ 637 papers in training set
Top 9%
0.8%

"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.