Cited by 167

The RNA Atlas expands the catalog of human non-coding RNAs

Lorenzi, Lucia, Chiu, Hua-Sheng, Avila Cobos, Francisco, Gross, Stephen, Volders, Pieter-Jan, Cannoodt, Robrecht, Nuytens, Justine, Vanderheyden, Katrien +28 more·2021·Nature Biotechnology

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Crossref · 10.1038/s41587-021-00936-1

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  • Too many 'novel miRNAs' lack clear miRNA features and have only served to further complicate the miRNA field.

    “Too many “novel miRNAs” lack clear miRNA features and have only served to further complicate the miRNA field [17, 35, 36].”
  • Of 640 samples from the RNA-Atlas project, 608 nonimmune cells consistently clustered together irrespective of their class/cell type expression and were removed.

    “Further, of 640 samples from the RNA-Atlas project [17], 608 (the nonimmune cells) consistently clustered together irrespective of their class/cell type expression. These were also removed, resulting in 2,077 final samples from 196 cell types.”

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Cites

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Research works printed but not linked: 22

  1. Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).
  2. Wang, L. et al. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res. 41, e74 (2013).
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  6. Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
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  9. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 46, 2699 (2018).
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  11. Lefever, S. et al. High-throughput PCR assay design for targeted resequencing using primerXL. BMC Bioinformatics 18, 400 (2017).
  12. Gleeson, J., Lane, T. A., Harrison, P. J., Haerty, W. & Clark, M. B. Nanopore direct RNA sequencing detects differential expression between human cell populations. Preprint at bioRxiv https://doi.org/10.1101/2020.08.02.232785 (2020). · DOI
  13. Leger, A. et al. RNA modifications detection by comparative nanopore direct RNA sequencing. Preprint at bioRxiv https://doi.org/10.1101/843136 (2019). · DOI
  14. Nicorici, D. et al. FusionCatcher-a tool for finding somatic fusion genes in paired-end RNA-sequencing data. Preprint at https://doi. org/10.1101/011650 (2014). · DOI
  15. Goovaerts, T. et al. A comprehensive overview of genomic imprinting in breast and its deregulation in cancer. Nat. Commun. 9, 4120 (2018).
  16. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
  17. Liao Y, Smyth GK, Shi W. FeatureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics https://doi.org/10.1093/bioinformatics/btt656 (2014). · DOI · Paper page
  18. Bovolenta, L. A., Acencio, M. L. & Lemke, N. HTRIdb: an open-access database for experimentally verified human transcriptional regulation interactions. BMC Genomics 13, 405 (2012).
  19. Whitfield, T. W. et al. Functional analysis of transcription factor binding sites in human promoters. Genome Biol. 13, R50 (2012).
  20. Haeussler, M. et al. The UCSC Genome Browser database: 2019 update. Nucleic Acids Res. 47, D853-D858 (2019). Reprints and permissions information is available at www.nature.com/reprints.
  21. Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260-D266 (2018). 100. Pachkov, M., Balwierz, P. J., Arnold, P., Ozonov, E. & Van Nimwegen, E. SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates. Nucleic Acids Res. 41, D214-D220 (2013). 101. Smith, A. D., Sumazin, P., Xuan, Z. & Zhang, M. Q. DNA motifs in human and mouse proximal promoters predict tissue-specific expression. Proc. Natl Acad. Sci. USA 103, 6275-6280 (2006). 102. Smith, A. D., Sumazin, P., Das, D. & Zhang, M. Q. Mining ChIP-chip data for transcription factor and cofactor binding sites. Bioinformatics 21 (Suppl. 1), i403-i412 (2005). 103.
  22. Lury, D. A. & Fisher, R. A. Statistical methods for research workers. J. R. Stat. Soc. Ser. D Statistician https://doi.org/10.2307/2986695 (1972). 106. Brown, M. B. 400: a method for combining non-independent, one-sided tests of significance. Biometrics 31, 987-992 (1975). 107. · DOI

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Authors
Lorenzi, L., Chiu, H.-S., Avila Cobos, F., Gross, S., Volders, P.-J., Cannoodt, R., Nuytens, J., Vanderheyden, K., Anckaert, J., Lefever, S., Tay, A. P., de Bony, E. J., Trypsteen, W., Gysens, F., Vromman, M., Goovaerts, T., Hansen, T. B., Kuersten, S., Nijs, N., Taghon, T., Vermaelen, K., Bracke, K. R., Saeys, Y., De Meyer, T., Deshpande, N. P., Anande, G., Chen, T.-W., Wilkins, M. R., Unnikrishnan, A., De Preter, K., Kjems, J., Koster, J., Schroth, G. P., Vandesompele, J., Sumazin, P., & Mestdagh, P.
Venue
Nature Biotechnology
Published
2021-06-17
Volume
39
Issue
11
Pages
1453-1465
ISSN
  • 1087-0156
  • 1546-1696
Cited by
167
References
14
Language
en

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