Detecting m6A RNA modification from nanopore sequencing using a semi-supervised learning framework Article Swipe
Haotian Teng
,
Marcus H. Stoiber
,
Ziv Bar‐Joseph
,
Carl Kingsford
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.01.06.574484
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.01.06.574484
Direct nanopore-based RNA sequencing can be used to detect post-transcriptional base modifications, such as m6A methylation, based on the electric current signals produced by the distinct chemical structures of modified bases. A key challenge is the scarcity of adequate training data with known methylation modifications. We present Xron, a hybrid encoder-decoder framework that delivers a direct methylation-distinguishing basecaller by training on synthetic RNA data and immunoprecipitation-based experimental data in two steps. First, we generate data with more diverse modification combinations through in silico cross-linking. Second, we use this dataset to train an end-to-end neural network basecaller followed by fine-tuning on immunoprecipitation-based experimental data with label-smoothing. The trained neural network basecaller outperforms existing methylation detection methods on both read-level and site-level prediction scores. Xron is a standalone, end-to-end m6A-distinguishing basecaller capable of detecting methylated bases directly from raw sequencing signals, enabling de novo methylome assembly.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.01.06.574484
- https://www.biorxiv.org/content/biorxiv/early/2024/01/07/2024.01.06.574484.full.pdf
- OA Status
- green
- Cited By
- 3
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390660489
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https://openalex.org/W4390660489Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.01.06.574484Digital Object Identifier
- Title
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Detecting m6A RNA modification from nanopore sequencing using a semi-supervised learning frameworkWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-07Full publication date if available
- Authors
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Haotian Teng, Marcus H. Stoiber, Ziv Bar‐Joseph, Carl KingsfordList of authors in order
- Landing page
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https://doi.org/10.1101/2024.01.06.574484Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2024/01/07/2024.01.06.574484.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/01/07/2024.01.06.574484.full.pdfDirect OA link when available
- Concepts
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Nanopore, Nanopore sequencing, Computational biology, Computer science, Artificial intelligence, RNA, Machine learning, Biology, DNA sequencing, Nanotechnology, Materials science, Genetics, GeneTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4306786775, https://openalex.org/W2915777758, https://openalex.org/W2625191040, https://openalex.org/W3091905639, https://openalex.org/W4283024773, https://openalex.org/W2091724447, https://openalex.org/W2064676287, https://openalex.org/W2078964320, https://openalex.org/W2065847836, https://openalex.org/W4233030298, https://openalex.org/W2180404227, https://openalex.org/W776567260, https://openalex.org/W2461624292, https://openalex.org/W2995260704, https://openalex.org/W3197583636, https://openalex.org/W2161890613, https://openalex.org/W2899193720, https://openalex.org/W2953460056, https://openalex.org/W2955930197, https://openalex.org/W2973421587, https://openalex.org/W2922152634, https://openalex.org/W4220674994, https://openalex.org/W2160475169, https://openalex.org/W2996159306, https://openalex.org/W3016950990, https://openalex.org/W3202435251, https://openalex.org/W2950063384, https://openalex.org/W3043981686, https://openalex.org/W2982772605, https://openalex.org/W3208156641, https://openalex.org/W2910850717, https://openalex.org/W4362635637, https://openalex.org/W3119507732, https://openalex.org/W3184504420, https://openalex.org/W4221050963, https://openalex.org/W4308772822, https://openalex.org/W2062821957, https://openalex.org/W2010792435, https://openalex.org/W2632544498, https://openalex.org/W4223962268, https://openalex.org/W3156817477, https://openalex.org/W3015537910, https://openalex.org/W3104137212, https://openalex.org/W4213092805, https://openalex.org/W2752782242, https://openalex.org/W4223461291, https://openalex.org/W2591367881, https://openalex.org/W2030520626, https://openalex.org/W3125877494, https://openalex.org/W1971094148, https://openalex.org/W2127141656, https://openalex.org/W2949279762, https://openalex.org/W2086699924, https://openalex.org/W2989499631, https://openalex.org/W2789843538, https://openalex.org/W3119738125, https://openalex.org/W4224227778, https://openalex.org/W2971454256, https://openalex.org/W2126165721, https://openalex.org/W4225743429 |
| referenced_works_count | 60 |
| abstract_inverted_index.A | 32 |
| abstract_inverted_index.a | 49, 55, 125 |
| abstract_inverted_index.We | 46 |
| abstract_inverted_index.an | 92 |
| abstract_inverted_index.as | 14 |
| abstract_inverted_index.be | 6 |
| abstract_inverted_index.by | 24, 59, 98 |
| abstract_inverted_index.de | 141 |
| abstract_inverted_index.in | 69, 82 |
| abstract_inverted_index.is | 35, 124 |
| abstract_inverted_index.of | 29, 38, 131 |
| abstract_inverted_index.on | 18, 61, 100, 116 |
| abstract_inverted_index.to | 8, 90 |
| abstract_inverted_index.we | 73, 86 |
| abstract_inverted_index.RNA | 3, 63 |
| abstract_inverted_index.The | 106 |
| abstract_inverted_index.and | 65, 119 |
| abstract_inverted_index.can | 5 |
| abstract_inverted_index.key | 33 |
| abstract_inverted_index.m6A | 15 |
| abstract_inverted_index.raw | 137 |
| abstract_inverted_index.the | 19, 25, 36 |
| abstract_inverted_index.two | 70 |
| abstract_inverted_index.use | 87 |
| abstract_inverted_index.Xron | 123 |
| abstract_inverted_index.base | 11 |
| abstract_inverted_index.both | 117 |
| abstract_inverted_index.data | 41, 64, 68, 75, 103 |
| abstract_inverted_index.from | 136 |
| abstract_inverted_index.more | 77 |
| abstract_inverted_index.novo | 142 |
| abstract_inverted_index.such | 13 |
| abstract_inverted_index.that | 53 |
| abstract_inverted_index.this | 88 |
| abstract_inverted_index.used | 7 |
| abstract_inverted_index.with | 42, 76, 104 |
| abstract_inverted_index.Xron, | 48 |
| abstract_inverted_index.based | 17 |
| abstract_inverted_index.bases | 134 |
| abstract_inverted_index.known | 43 |
| abstract_inverted_index.train | 91 |
| abstract_inverted_index.Direct | 1 |
| abstract_inverted_index.First, | 72 |
| abstract_inverted_index.bases. | 31 |
| abstract_inverted_index.detect | 9 |
| abstract_inverted_index.direct | 56 |
| abstract_inverted_index.hybrid | 50 |
| abstract_inverted_index.neural | 94, 108 |
| abstract_inverted_index.silico | 83 |
| abstract_inverted_index.steps. | 71 |
| abstract_inverted_index.Second, | 85 |
| abstract_inverted_index.capable | 130 |
| abstract_inverted_index.current | 21 |
| abstract_inverted_index.dataset | 89 |
| abstract_inverted_index.diverse | 78 |
| abstract_inverted_index.methods | 115 |
| abstract_inverted_index.network | 95, 109 |
| abstract_inverted_index.present | 47 |
| abstract_inverted_index.scores. | 122 |
| abstract_inverted_index.signals | 22 |
| abstract_inverted_index.through | 81 |
| abstract_inverted_index.trained | 107 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.adequate | 39 |
| abstract_inverted_index.chemical | 27 |
| abstract_inverted_index.delivers | 54 |
| abstract_inverted_index.directly | 135 |
| abstract_inverted_index.distinct | 26 |
| abstract_inverted_index.electric | 20 |
| abstract_inverted_index.enabling | 140 |
| abstract_inverted_index.existing | 112 |
| abstract_inverted_index.followed | 97 |
| abstract_inverted_index.generate | 74 |
| abstract_inverted_index.modified | 30 |
| abstract_inverted_index.produced | 23 |
| abstract_inverted_index.scarcity | 37 |
| abstract_inverted_index.signals, | 139 |
| abstract_inverted_index.training | 40, 60 |
| abstract_inverted_index.assembly. | 144 |
| abstract_inverted_index.challenge | 34 |
| abstract_inverted_index.detecting | 132 |
| abstract_inverted_index.detection | 114 |
| abstract_inverted_index.framework | 52 |
| abstract_inverted_index.methylome | 143 |
| abstract_inverted_index.synthetic | 62 |
| abstract_inverted_index.basecaller | 58, 96, 110, 129 |
| abstract_inverted_index.end-to-end | 93, 127 |
| abstract_inverted_index.methylated | 133 |
| abstract_inverted_index.prediction | 121 |
| abstract_inverted_index.read-level | 118 |
| abstract_inverted_index.sequencing | 4, 138 |
| abstract_inverted_index.site-level | 120 |
| abstract_inverted_index.structures | 28 |
| abstract_inverted_index.fine-tuning | 99 |
| abstract_inverted_index.methylation | 44, 113 |
| abstract_inverted_index.outperforms | 111 |
| abstract_inverted_index.standalone, | 126 |
| abstract_inverted_index.combinations | 80 |
| abstract_inverted_index.experimental | 67, 102 |
| abstract_inverted_index.methylation, | 16 |
| abstract_inverted_index.modification | 79 |
| abstract_inverted_index.cross-linking. | 84 |
| abstract_inverted_index.modifications, | 12 |
| abstract_inverted_index.modifications. | 45 |
| abstract_inverted_index.nanopore-based | 2 |
| abstract_inverted_index.encoder-decoder | 51 |
| abstract_inverted_index.label-smoothing. | 105 |
| abstract_inverted_index.m6A-distinguishing | 128 |
| abstract_inverted_index.post-transcriptional | 10 |
| abstract_inverted_index.immunoprecipitation-based | 66, 101 |
| abstract_inverted_index.methylation-distinguishing | 57 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5074597014, https://openalex.org/A5070239856, https://openalex.org/A5065673127, https://openalex.org/A5113653378 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I4210155982, https://openalex.org/I74973139 |
| citation_normalized_percentile.value | 0.72438888 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |