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Workflow Development for the Functional Characterization of ncRNAs

  • Markus WolfienEmail author
  • David Leon Brauer
  • Andrea Bagnacani
  • Olaf Wolkenhauer
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1912)

Abstract

During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.

Key words

Workflow ncRNA Transcript identification Experimental RNA discovery Data analysis Next Generation Sequencing Network analysis Co-expression analysis Machine learning 

Notes

Acknowledgments

We acknowledge the partners and management of the German Network for Bioinformatics Infrastructure (de.NBI) for continuous support and guidance. Financial support for this work by the German Federal Ministry for Education and Research (BMBF) and European Social Fund (ESF) is greatly acknowledged (Grant 031L0106C, 02NUK043C, ESF/14-BM-A55-0027/18).

References

  1. 1.
    Anastasiadou E, Jacob LS, Slack FJ (2017) Non-coding RNA networks in cancer. Nat Rev Cancer 18:5–18.  https://doi.org/10.1038/nrc.2017.99 CrossRefPubMedGoogle Scholar
  2. 2.
    Delihas N (2015) Discovery and characterization of the first non-coding RNA that regulates gene expression, micF RNA: a historical perspective. World J Biol Chem 6:272.  https://doi.org/10.4331/WJBC.V6.I4.272 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Schmitz U, Naderi-Meshkin H, Gupta SK et al (2016) The RNA world in the 21st century—a systems approach to finding non-coding keys to clinical questions. Brief Bioinform 17:380–392.  https://doi.org/10.1093/bib/bbv061 CrossRefPubMedGoogle Scholar
  4. 4.
    Tripathi R, Chakraborty P, Varadwaj PK (2017) Unraveling long non-coding RNAs through analysis of high-throughput RNA-sequencing data. Non-coding RNA Res 2:111–118.  https://doi.org/10.1016/J.NCRNA.2017.06.003 CrossRefGoogle Scholar
  5. 5.
    Xuan J, Yu Y, Qing T et al (2013) Next-generation sequencing in the clinic: promises and challenges. Cancer Lett 340:284–295.  https://doi.org/10.1016/j.canlet.2012.11.025 CrossRefPubMedGoogle Scholar
  6. 6.
    Metzker ML (2010) Sequencing technologies—the next generation. Nat Rev Genet 11:31–46.  https://doi.org/10.1038/nrg2626 CrossRefGoogle Scholar
  7. 7.
    Bernhart SH, Hofacker IL (2009) From consensus structure prediction to RNA gene finding. Briefings Funct Genomics Proteomics 8:461–471.  https://doi.org/10.1093/bfgp/elp043 CrossRefGoogle Scholar
  8. 8.
    Derrien T, Johnson R, Bussotti G et al (2012) The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res 22:1775–1789.  https://doi.org/10.1101/gr.132159.111 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Moran VA, Perera RJ, Khalil AM (2012) Emerging functional and mechanistic paradigms of mammalian long non-coding RNAs. Nucleic Acids Res 40:6391–6400.  https://doi.org/10.1093/nar/gks296 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Bejerano G, Pheasant M, Makunin I et al (2004) Ultraconserved elements in the human genome. Science 304:1321–1325.  https://doi.org/10.1126/science.1098119 CrossRefPubMedGoogle Scholar
  11. 11.
    Johnsson P, Lipovich L, Grandér D, Morris KV (2014) Evolutionary conservation of long non-coding RNAs; sequence, structure, function. Biochim Biophys Acta 1840:1063–1071.  https://doi.org/10.1016/J.BBAGEN.2013.10.035 CrossRefPubMedGoogle Scholar
  12. 12.
    Hammond SM (2015) An overview of microRNAs. Adv Drug Deliv Rev 87:3–14.  https://doi.org/10.1016/j.addr.2015.05.001 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Volders P-J, Verheggen K, Menschaert G et al (2015) An update on LNCipedia: a database for annotated human lncRNA sequences. Nucleic Acids Res 43:D174–D180.  https://doi.org/10.1093/nar/gku1060 CrossRefPubMedGoogle Scholar
  14. 14.
    Quek XC, Thomson DW, Maag JLV et al (2015) lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs. Nucleic Acids Res 43:D168–D173.  https://doi.org/10.1093/nar/gku988 CrossRefPubMedGoogle Scholar
  15. 15.
    Fang Y, Fullwood MJ (2016) Roles, functions, and mechanisms of long non-coding RNAs in cancer. Genomics Proteomics Bioinformatics 14:42–54.  https://doi.org/10.1016/j.gpb.2015.09.006 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Vincent AT, Derome N, Boyle B et al (2017) Next-generation sequencing (NGS) in the microbiological world: how to make the most of your money. J Microbiol Methods 138:60–71.  https://doi.org/10.1016/J.MIMET.2016.02.016 CrossRefPubMedGoogle Scholar
  17. 17.
    Tripathi R, Sharma P, Chakraborty P, Varadwaj PK (2016) Next-generation sequencing revolution through big data analytics. Front Life Sci 9:119–149.  https://doi.org/10.1080/21553769.2016.1178180 CrossRefGoogle Scholar
  18. 18.
    Kim J, Park W-Y, Kim NKD et al (2017) Good laboratory standards for clinical next-generation sequencing cancer panel tests. J Pathol Transl Med 51:191–204.  https://doi.org/10.4132/jptm.2017.03.14 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Ounzain S, Micheletti R, Beckmann T et al (2015) Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs. Eur Heart J 36:353–68a.  https://doi.org/10.1093/eurheartj/ehu180 CrossRefPubMedGoogle Scholar
  20. 20.
    Ryu AH, Eckalbar WL, Kreimer A et al (2017) Use antibiotics in cell culture with caution: genome-wide identification of antibiotic-induced changes in gene expression and regulation. Sci Rep 7:7533.  https://doi.org/10.1038/s41598-017-07757-w CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Ekblom R, Wolf JBW (2014) A field guide to whole-genome sequencing, assembly and annotation. Evol Appl 7:1026–1042.  https://doi.org/10.1111/eva.12178 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Rizzetto S, Eltahla AA, Lin P et al (2017) Impact of sequencing depth and read length on single cell RNA sequencing data of T cells. Sci Rep 7:12781.  https://doi.org/10.1038/s41598-017-12989-x CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Lott SC, Wolfien M, Riege K et al (2017) Customized workflow development and data modularization concepts for RNA-sequencing and metatranscriptome experiments. J Biotechnol 261:85–96.  https://doi.org/10.1016/j.jbiotec.2017.06.1203 CrossRefPubMedGoogle Scholar
  24. 24.
    Spjuth O, Bongcam-Rudloff E, Dahlberg J et al (2016) Recommendations on e-infrastructures for next-generation sequencing. Gigascience 5:26.  https://doi.org/10.1186/s13742-016-0132-7 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Lampa S, Dahlö M, Olason PI et al (2013) Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data. Gigascience 2:9.  https://doi.org/10.1186/2047-217X-2-9 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Celesti A, Celesti F, Fazio M et al (2017) Are next-generation sequencing tools ready for the cloud? Trends Biotechnol 35:486–489.  https://doi.org/10.1016/J.TIBTECH.2017.03.005 CrossRefPubMedGoogle Scholar
  27. 27.
    Grüning BA, Fallmann J, Yusuf D et al (2017) The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy. Nucleic Acids Res 45:D626–D634.  https://doi.org/10.1093/nar/gkx409 CrossRefGoogle Scholar
  28. 28.
    Afgan E, Baker D, van den Beek M et al (2016) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 44:W3–W10.  https://doi.org/10.1093/nar/gkw343 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    da Veiga Leprevost F, Grüning BA, Alves Aflitos S et al (2017) BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics 33:2580–2582.  https://doi.org/10.1093/bioinformatics/btx192 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Wolfien M, Rimmbach C, Schmitz U et al (2016) TRAPLINE: a standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation. BMC Bioinformatics 17:21.  https://doi.org/10.1186/s12859-015-0873-9 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Schulz W, Durant T, Siddon A, Torres R (2016) Use of application containers and workflows for genomic data analysis. J Pathol Inform 7:53.  https://doi.org/10.4103/2153-3539.197197 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    FASTQC (2010) Babraham Institute. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 20 Jun 2018
  33. 33.
    Patel RK, Jain M (2012) NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS One 7:e30619.  https://doi.org/10.1371/journal.pone.0030619 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Okonechnikov K, Conesa A, García-Alcalde F (2016) Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32:292–294.  https://doi.org/10.1093/bioinformatics/btv566 CrossRefPubMedGoogle Scholar
  35. 35.
    Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10.  https://doi.org/10.14806/ej.17.1.200 CrossRefGoogle Scholar
  36. 36.
    Jiang H, Lei R, Ding S-W, Zhu S (2014) Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15:182.  https://doi.org/10.1186/1471-2105-15-182 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Wood DE, Salzberg SL (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 15:R46.  https://doi.org/10.1186/gb-2014-15-3-r46 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120.  https://doi.org/10.1093/bioinformatics/btu170 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    TrimGalore! (2012) Babraham Institute. https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/. Accessed 20 Jun 2018
  40. 40.
    Kim D, Pertea G, Trapnell C et al (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14:R36.  https://doi.org/10.1186/gb-2013-14-4-r36 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360.  https://doi.org/10.1038/nmeth.3317 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21.  https://doi.org/10.1093/bioinformatics/bts635 CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Hoffmann S, Otto C, Doose G et al (2014) A multi-split mapping algorithm for circular RNA, splicing, trans-splicing and fusion detection. Genome Biol 15:R34.  https://doi.org/10.1186/gb-2014-15-2-r34 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Engström PG, Steijger T, Sipos B et al (2013) Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods 10:1185–1191.  https://doi.org/10.1038/nmeth.2722 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34:525–527.  https://doi.org/10.1038/nbt.3519 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Patro R, Duggal G, Love MI et al (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14:417–419.  https://doi.org/10.1038/nmeth.4197 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Robert C, Watson M (2015) Errors in RNA-Seq quantification affect genes of relevance to human disease. Genome Biol 16:177.  https://doi.org/10.1186/s13059-015-0734-x CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Conesa A, Madrigal P, Tarazona S et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13.  https://doi.org/10.1186/s13059-016-0881-8 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Trapnell C, Hendrickson DG, Sauvageau M et al (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31:46–53.  https://doi.org/10.1038/nbt.2450 CrossRefPubMedGoogle Scholar
  50. 50.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550.  https://doi.org/10.1186/s13059-014-0550-8 CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Pimentel H, Bray NL, Puente S et al (2017) Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods 14:687–690.  https://doi.org/10.1038/nmeth.4324 CrossRefPubMedGoogle Scholar
  52. 52.
    Riege K, Hölzer M, Klassert TE et al (2017) Massive effect on LncRNAs in human monocytes during fungal and bacterial infections and in response to vitamins A and D. Sci Rep 7:40598.  https://doi.org/10.1038/srep40598 CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Batut B, Hiltemann S, Bagnacani A et al (2017) Community-driven data analysis training for biology. bioRxiv: 225680. doi:  https://doi.org/10.1101/225680
  54. 54.
    Signal B, Gloss BS, Dinger ME (2016) Computational approaches for functional prediction and characterisation of long noncoding RNAs. Trends Genet 32:620–637.  https://doi.org/10.1016/J.TIG.2016.08.004 CrossRefPubMedGoogle Scholar
  55. 55.
    Smalter Hall A, Shan Y, Lushington G, Visvanathan M (2013) An overview of computational life science databases & exchange formats of relevance to chemical biology research. Comb Chem High Throughput Screen 16:189–198CrossRefGoogle Scholar
  56. 56.
    Chakraborty S, Deb A, Maji RK et al (2014) LncRBase: an enriched resource for lncRNA information. PLoS One 9:e108010.  https://doi.org/10.1371/journal.pone.0108010 CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Gusev A, Ko A, Shi H et al (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48:245–252.  https://doi.org/10.1038/ng.3506 CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Lopez-Maestre H, Brinza L, Marchet C et al (2016) SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence. Nucleic Acids Res 44:e148.  https://doi.org/10.1093/nar/gkw655 CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Schlötterer C, Tobler R, Kofler R, Nolte V (2014) Sequencing pools of individuals — mining genome-wide polymorphism data without big funding. Nat Rev Genet 15:749–763.  https://doi.org/10.1038/nrg3803 CrossRefPubMedGoogle Scholar
  60. 60.
    Lai X, Bhattacharya A, Schmitz U et al (2013) A systems’ biology approach to study microRNA-mediated gene regulatory networks. Biomed Res Int 2013:703849.  https://doi.org/10.1155/2013/703849 CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Schmitz U, Lai X, Winter F et al (2014) Cooperative gene regulation by microRNA pairs and their identification using a computational workflow. Nucleic Acids Res 42:7539–7552.  https://doi.org/10.1093/nar/gku465 CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Chou C-H, Chang N-W, Shrestha S et al (2016) miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res 44:D239–D247.  https://doi.org/10.1093/nar/gkv1258 CrossRefPubMedGoogle Scholar
  63. 63.
    Betel D, Koppal A, Agius P et al (2010) Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 11:R90.  https://doi.org/10.1186/gb-2010-11-8-r90 CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Lai X, Gupta SK, Schmitz U et al (2018) MiR-205-5p and miR-342-3p cooperate in the repression of the E2F1 transcription factor in the context of anticancer chemotherapy resistance. Theranostics 8:1106–1120.  https://doi.org/10.7150/thno.19904 CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Veneziano D, Nigita G, Ferro A (2015) Computational approaches for the analysis of ncRNA through deep sequencing techniques. Front Bioeng Biotechnol 3:77.  https://doi.org/10.3389/fbioe.2015.00077 CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Yan K, Arfat Y, Li D et al (2016) Structure prediction: new insights into decrypting long noncoding RNAs. Int J Mol Sci 17:132.  https://doi.org/10.3390/IJMS17010132 CrossRefPubMedCentralGoogle Scholar
  67. 67.
    Guo X, Gao L, Wang Y et al (2016) Advances in long noncoding RNAs: identification, structure prediction and function annotation. Brief Funct Genomics 15:38–46.  https://doi.org/10.1093/bfgp/elv022 CrossRefPubMedGoogle Scholar
  68. 68.
    Volders P-J, Helsens K, Wang X et al (2013) LNCipedia: a database for annotated human lncRNA transcript sequences and structures. Nucleic Acids Res 41:D246–D251.  https://doi.org/10.1093/nar/gks915 CrossRefPubMedGoogle Scholar
  69. 69.
    Ebert MS, Sharp PA (2012) Roles for MicroRNAs in conferring robustness to biological processes. Cell 149:515–524.  https://doi.org/10.1016/J.CELL.2012.04.005 CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Yamamura S, Imai-Sumida M, Tanaka Y, Dahiya R (2018) Interaction and cross-talk between non-coding RNAs. Cell Mol Life Sci 75:467–484.  https://doi.org/10.1007/s00018-017-2626-6 CrossRefPubMedGoogle Scholar
  71. 71.
    Alcaraz N, Kücük H, Weile J et al (2011) KeyPathwayMiner: detecting case-specific biological pathways using expression data. Internet Math 7:299–313.  https://doi.org/10.1080/15427951.2011.604548 CrossRefGoogle Scholar
  72. 72.
    Hausburg F, Jung JJ, Hoch M et al (2017) (Re-)programming of subtype specific cardiomyocytes. Adv Drug Deliv Rev 120:142–167.  https://doi.org/10.1016/j.addr.2017.09.005 CrossRefPubMedGoogle Scholar
  73. 73.
    Khan FM, Schmitz U, Nikolov S et al (2014) Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic. Biochim Biophys Acta 1844:289–298.  https://doi.org/10.1016/J.BBAPAP.2013.05.007 CrossRefPubMedGoogle Scholar
  74. 74.
    Khan FM, Marquardt S, Gupta SK et al (2017) Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures. Nat Commun 8:198.  https://doi.org/10.1038/s41467-017-00268-2 CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Wiwie C, Rauch A, Haakonsson A, et al (2017) Elucidation of time-dependent systems biology cell response patterns with time course network enrichment. arXiv.org arXiv:1710.10262Google Scholar
  76. 76.
    Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302:249–255.  https://doi.org/10.1126/science.1087447 CrossRefPubMedGoogle Scholar
  77. 77.
    Yavari A, Bellahcene M, Bucchi A et al (2017) Mammalian γ2 AMPK regulates intrinsic heart rate. Nat Commun 8:1258.  https://doi.org/10.1038/s41467-017-01342-5 CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559.  https://doi.org/10.1186/1471-2105-9-559 CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Li S, Li B, Zheng Y et al (2017) Exploring functions of long noncoding RNAs across multiple cancers through co-expression network. Sci Rep 7:754.  https://doi.org/10.1038/s41598-017-00856-8 CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57.  https://doi.org/10.1038/nprot.2008.211 CrossRefGoogle Scholar
  81. 81.
    Chen EY, Tan CM, Kou Y et al (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14:128.  https://doi.org/10.1186/1471-2105-14-128 CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Kuleshov MV, Jones MR, Rouillard AD et al (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44:W90–W97.  https://doi.org/10.1093/nar/gkw377 CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Caicedo JC, Cooper S, Heigwer F et al (2017) Data-analysis strategies for image-based cell profiling. Nat Methods 14:849–863.  https://doi.org/10.1038/nmeth.4397 CrossRefPubMedGoogle Scholar
  84. 84.
    Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26.  https://doi.org/10.18637/jss.v028.i05 CrossRefGoogle Scholar
  85. 85.
    Ray SS, Maiti S (2015) Noncoding RNAs and their annotation using metagenomics algorithms. Wiley Interdiscip Rev Data Min Knowl Discov 5:1–20.  https://doi.org/10.1002/widm.1142 CrossRefGoogle Scholar
  86. 86.
    Saeb S, Lonini L, Jayaraman A, et al (2016) Voodoo machine learning for clinical predictions. bioRxiv: 059774.  https://doi.org/10.1101/059774
  87. 87.
    Yu N, Cho KH, Cheng Q, Tesorero RA (2009) A hybrid computational approach for the prediction of small non-coding RNAs from genome sequences. In: 2009 International Conference on Computational Science and Engineering. IEEE, pp 1071–1076Google Scholar
  88. 88.
    van der ML, Hinton G (2008) Visualizing Data using t-SNE. J Mach Learn Res 9:2579–2605Google Scholar
  89. 89.
    Sun K, Chen X, Jiang P et al (2013) iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data. BMC Genomics 14(Suppl 2):S7.  https://doi.org/10.1186/1471-2164-14-S2-S7 CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Xiao Y, Lv Y, Zhao H et al (2015) Predicting the functions of long noncoding RNAs using RNA-Seq based on Bayesian network. Biomed Res Int 2015:1–14.  https://doi.org/10.1155/2015/839590 CrossRefGoogle Scholar
  91. 91.
    Lertampaiporn S, Thammarongtham C, Nukoolkit C et al (2014) Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm. Nucleic Acids Res 42:e93.  https://doi.org/10.1093/nar/gku325 CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Abbas Q, Raza SM, Biyabani AA, Jaffar MA (2016) A review of computational methods for finding non-coding RNA genes. Genes (Basel) 7:113.  https://doi.org/10.3390/genes7120113 CrossRefGoogle Scholar
  93. 93.
    Lee B, Baek J, Park S, Yoon S (2016) deepTarget: end-to-end learning framework for microRNA target prediction using deep recurrent neural networks. arXiv.org arXiv:1603.09123Google Scholar
  94. 94.
    Cheng S, Guo M, Wang C et al (2016) MiRTDL: a deep learning approach for miRNA target prediction. IEEE/ACM Trans Comput Biol Bioinform 13:1161–1169.  https://doi.org/10.1109/TCBB.2015.2510002 CrossRefGoogle Scholar
  95. 95.
    Karczewski KJ, Snyder MP (2018) Integrative omics for health and disease. Nat Rev Genet 19:299–310.  https://doi.org/10.1038/nrg.2018.4 CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Ison J, Rapacki K, Ménager H et al (2016) Tools and data services registry: a community effort to document bioinformatics resources. Nucleic Acids Res 44:D38–D47.  https://doi.org/10.1093/nar/gkv1116 CrossRefPubMedPubMedCentralGoogle Scholar
  97. 97.
    Ison J, Kalas M, Jonassen I et al (2013) EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics 29:1325–1332.  https://doi.org/10.1093/bioinformatics/btt113 CrossRefPubMedPubMedCentralGoogle Scholar
  98. 98.
    Wolkenhauer O (2014) Why model? Front Physiol 5:21.  https://doi.org/10.3389/fphys.2014.00021 CrossRefPubMedPubMedCentralGoogle Scholar
  99. 99.
    Doudna JA, Charpentier E (2014) Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science 346:1258096.  https://doi.org/10.1126/science.1258096 CrossRefPubMedPubMedCentralGoogle Scholar
  100. 100.
    Palazzo AF, Lee ES (2015) Non-coding RNA: what is functional and what is junk? Front Genet 6:2.  https://doi.org/10.3389/fgene.2015.00002 CrossRefPubMedPubMedCentralGoogle Scholar
  101. 101.
    Scarano D, Rao R, Corrado G (2017) In silico identification and annotation of non-coding RNAs by RNA-seq and de novo assembly of the transcriptome of Tomato Fruits. PLoS One 12:e0171504.  https://doi.org/10.1371/journal.pone.0171504 CrossRefPubMedPubMedCentralGoogle Scholar
  102. 102.
    Garalde DR, Snell EA, Jachimowicz D et al (2018) Highly parallel direct RNA sequencing on an array of nanopores. Nat Methods 15:201–206.  https://doi.org/10.1038/nmeth.4577 CrossRefPubMedGoogle Scholar
  103. 103.
    Webb S (2018) Deep learning for biology. Nature 554:555–557.  https://doi.org/10.1038/d41586-018-02174-z CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Markus Wolfien
    • 1
    Email author
  • David Leon Brauer
    • 1
  • Andrea Bagnacani
    • 1
  • Olaf Wolkenhauer
    • 1
    • 2
  1. 1.Department of Systems Biology and Bioinformatics, Institute of Computer ScienceUniversity of RostockRostockGermany
  2. 2.Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research CentreStellenbosch UniversityStellenboschSouth Africa

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