Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ProDOMA: improve PROtein DOMAin classification for third-generation
  sequencing reads using deep learning   Motivation: With the development of third-generation sequencing technologies,
people are able to obtain DNA sequences with lengths from 10s to 100s of kb.
These long reads allow protein domain annotation without assembly, thus can
produce important insights into the biological functions of the underlying
data. However, the high error rate in third-generation sequencing data raises a
new challenge to established domain analysis pipelines. The state-of-the-art
methods are not optimized for noisy reads and have shown unsatisfactory
accuracy of domain classification in third-generation sequencing data. New
computational methods are still needed to improve the performance of domain
prediction in long noisy reads. Results: In this work, we introduce ProDOMA, a
deep learning model that conducts domain classification for third-generation
sequencing reads. It uses deep neural networks with 3-frame translation
encoding to learn conserved features from partially correct translations. In
addition, we formulate our problem as an open-set problem and thus our model
can reject unrelated DNA reads such as those from noncoding regions. In the
experiments on simulated reads of protein coding sequences and real reads from
the human genome, our model outperforms HMMER and DeepFam on protein domain
classification. In summary, ProDOMA is a useful end-to-end protein domain
analysis tool for long noisy reads without relying on error correction.
Availability: The source code and the trained model are freely available at
https://github.com/strideradu/ProDOMA. Contact: yannisun@cityu.edu.hk

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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: bayes-an