Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA–RNA Interaction Data
The genomic revolution and subsequent advances in large-scale genomic and transcriptomic technologies highlighted hidden genomic treasures. Among them stand out non-coding small RNAs (sRNAs), shown to play important roles in post-transcriptional regulation of gene expression in both pro- and eukaryo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-05-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2021.635070/full |
| _version_ | 1819087931234582528 |
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| author | Amir Bar Liron Argaman Yael Altuvia Hanah Margalit |
| author_facet | Amir Bar Liron Argaman Yael Altuvia Hanah Margalit |
| author_sort | Amir Bar |
| collection | DOAJ |
| description | The genomic revolution and subsequent advances in large-scale genomic and transcriptomic technologies highlighted hidden genomic treasures. Among them stand out non-coding small RNAs (sRNAs), shown to play important roles in post-transcriptional regulation of gene expression in both pro- and eukaryotes. Bacterial sRNA-encoding genes were initially identified in intergenic regions, but recent evidence suggest that they can be encoded within other, well-defined, genomic elements. This notion was strongly supported by data generated by RIL-seq, a RNA-seq-based methodology we recently developed for deciphering chaperon-dependent sRNA-target networks in bacteria. Applying RIL-seq to Hfq-bound RNAs in Escherichia coli, we found that ∼64% of the detected RNA pairs involved known sRNAs, suggesting that yet unknown sRNAs may be included in the ∼36% remaining pairs. To determine the latter, we first tested and refined a set of quantitative features derived from RIL-seq data, which distinguish between Hfq-dependent sRNAs and “other RNAs”. We then incorporated these features in a machine learning-based algorithm that predicts novel sRNAs from RIL-seq data, and identified high-scoring candidates encoded in various genomic regions, mostly intergenic regions and 3′ untranslated regions, but also 5′ untranslated regions and coding sequences. Several candidates were further tested and verified by northern blot analysis as Hfq-dependent sRNAs. Our study reinforces the emerging concept that sRNAs are encoded within various genomic elements, and provides a computational framework for the detection of additional sRNAs in Hfq RIL-seq data of E. coli grown under different conditions and of other bacteria manifesting Hfq-mediated sRNA-target interactions. |
| first_indexed | 2024-12-21T21:43:59Z |
| format | Article |
| id | doaj.art-5b8f2476b3644a6f91d447add060d82b |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-12-21T21:43:59Z |
| publishDate | 2021-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-5b8f2476b3644a6f91d447add060d82b2022-12-21T18:49:17ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2021-05-011210.3389/fmicb.2021.635070635070Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA–RNA Interaction DataAmir BarLiron ArgamanYael AltuviaHanah MargalitThe genomic revolution and subsequent advances in large-scale genomic and transcriptomic technologies highlighted hidden genomic treasures. Among them stand out non-coding small RNAs (sRNAs), shown to play important roles in post-transcriptional regulation of gene expression in both pro- and eukaryotes. Bacterial sRNA-encoding genes were initially identified in intergenic regions, but recent evidence suggest that they can be encoded within other, well-defined, genomic elements. This notion was strongly supported by data generated by RIL-seq, a RNA-seq-based methodology we recently developed for deciphering chaperon-dependent sRNA-target networks in bacteria. Applying RIL-seq to Hfq-bound RNAs in Escherichia coli, we found that ∼64% of the detected RNA pairs involved known sRNAs, suggesting that yet unknown sRNAs may be included in the ∼36% remaining pairs. To determine the latter, we first tested and refined a set of quantitative features derived from RIL-seq data, which distinguish between Hfq-dependent sRNAs and “other RNAs”. We then incorporated these features in a machine learning-based algorithm that predicts novel sRNAs from RIL-seq data, and identified high-scoring candidates encoded in various genomic regions, mostly intergenic regions and 3′ untranslated regions, but also 5′ untranslated regions and coding sequences. Several candidates were further tested and verified by northern blot analysis as Hfq-dependent sRNAs. Our study reinforces the emerging concept that sRNAs are encoded within various genomic elements, and provides a computational framework for the detection of additional sRNAs in Hfq RIL-seq data of E. coli grown under different conditions and of other bacteria manifesting Hfq-mediated sRNA-target interactions.https://www.frontiersin.org/articles/10.3389/fmicb.2021.635070/fullsRNA (small RNA)RIL-seqpredictionE. coli – Escherichia colipost-transcriptional regulationHfq |
| spellingShingle | Amir Bar Liron Argaman Yael Altuvia Hanah Margalit Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA–RNA Interaction Data Frontiers in Microbiology sRNA (small RNA) RIL-seq prediction E. coli – Escherichia coli post-transcriptional regulation Hfq |
| title | Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA–RNA Interaction Data |
| title_full | Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA–RNA Interaction Data |
| title_fullStr | Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA–RNA Interaction Data |
| title_full_unstemmed | Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA–RNA Interaction Data |
| title_short | Prediction of Novel Bacterial Small RNAs From RIL-Seq RNA–RNA Interaction Data |
| title_sort | prediction of novel bacterial small rnas from ril seq rna rna interaction data |
| topic | sRNA (small RNA) RIL-seq prediction E. coli – Escherichia coli post-transcriptional regulation Hfq |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2021.635070/full |
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