LAFITE Reveals the Complexity of Transcript Isoforms in Subcellular Fractions
Source abstract
Abstract
Abstract Characterization of the subcellular distribution of RNA is essential for understanding the molecular basis of biological processes. Here, the subcellular nanopore direct RNA‐sequencing (DRS) of four lung cancer cell lines (A549, H1975, H358, and HCC4006) is performed, coupled with a computational pipeline, Low‐abundance Aware Full‐length Isoform clusTEr (LAFITE), to comprehensively analyze the full‐length cytoplasmic and nuclear transcriptome. Using additional DRS and orthogonal data sets, it is shown that LAFITE outperforms current methods for detecting full‐length transcripts, particularly for low‐abundance isoforms that are usually overlooked due to poor read coverage. Experimental validation of six novel isoforms exclusively identified by LAFITE further confirms the reliability of this pipeline. By applying LAFITE to subcellular DRS data, the complexity of the nuclear transcriptome is revealed in terms of isoform diversity, 3'‐UTR usage, m6A modification patterns, and intron retention. Overall, LAFITE provides enhanced full‐length isoform identification and enables a high‐resolution view of the RNA landscape at the isoform level.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: RNA modifications and cancer · Cancer-related molecular mechanisms research · RNA Research and Splicing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Life Sciences The Chinese University of Hong Kong Shatin Hong Kong SAR China
State Key Laboratory of Agrobiotechnology The Chinese University of Hong Kong Shatin Hong Kong SAR China
Department of Clinical Oncology Queen Elizabeth Hospital Kowloon Hong Kong SAR China
References (94)
Show source-provided references
1. 10.1126/science.aan239910.1126/science.aan2399 ↗
2. 10.1042/BC2004006610.1042/BC20040066 ↗
3. 10.1038/nrm164310.1038/nrm1643 ↗
4. 10.1016/j.cell.2007.08.00310.1016/j.cell.2007.08.003 ↗
5. 10.1126/science.117648810.1126/science.1176488 ↗
6. 10.1016/j.mcn.2017.12.00510.1016/j.mcn.2017.12.005 ↗
7. 10.1186/s13024-015-0036-510.1186/s13024-015-0036-5 ↗
8. 10.1158/0008-5472.CAN-20-421810.1158/0008-5472.CAN-20-4218 ↗
9. 10.1126/science.270.5237.78910.1126/science.270.5237.789 ↗
10. 10.1016/j.tcb.2010.03.00610.1016/j.tcb.2010.03.006 ↗
11. 10.1016/j.molcel.2019.02.00810.1016/j.molcel.2019.02.008 ↗
12. 10.1002/(SICI)1097-4644(19961101)63:2<185::AID-JCB6>3.0.CO;2-U10.1002/(SICI)1097-4644(19961101)63:2<185::AID-JCB6>3.0.CO;2-U ↗
13. 10.1126/science.280.5363.58510.1126/science.280.5363.585 ↗
14. 10.1093/nar/gkq04210.1093/nar/gkq042 ↗
15. 10.1016/j.ymeth.2017.05.01710.1016/j.ymeth.2017.05.017 ↗
16. 10.1038/nature2575710.1038/nature25757 ↗
17. 10.1016/j.celrep.2015.11.03610.1016/j.celrep.2015.11.036 ↗
18. 10.1101/gr.134445.11110.1101/gr.134445.111 ↗
19. 10.1038/s41467-022-30183-010.1038/s41467-022-30183-0 ↗
20. 10.1038/nature1123310.1038/nature11233 ↗
21. 10.3389/fgene.2019.0108110.3389/fgene.2019.01081 ↗
22. 10.1016/j.cell.2019.05.02710.1016/j.cell.2019.05.027 ↗
23. 10.1038/nrg277610.1038/nrg2776 ↗
24. 10.1093/nar/gkx116510.1093/nar/gkx1165 ↗
25. 10.1074/mcp.M111.01368010.1074/mcp.M111.013680 ↗
26. 10.1038/nmeth.271410.1038/nmeth.2714 ↗
27. 10.1038/nature0961610.1038/nature09616 ↗
28. 10.1093/nar/gkaa69910.1093/nar/gkaa699 ↗
29. 10.1016/j.neuron.2018.03.03010.1016/j.neuron.2018.03.030 ↗
30. 10.1038/s41592-019-0617-210.1038/s41592-019-0617-2 ↗
31. 10.1093/nar/gkaa58810.1093/nar/gkaa588 ↗
32. 10.1101/gr.250217.11910.1101/gr.250217.119 ↗
33. 10.1038/nmeth.457710.1038/nmeth.4577 ↗
34. 10.1093/nar/gkx118210.1093/nar/gkx1182 ↗
35. 10.1080/15476286.2021.188129110.1080/15476286.2021.1881291 ↗
36. 10.1371/journal.pone.013262810.1371/journal.pone.0132628 ↗
37. 10.1101/gr.222976.11710.1101/gr.222976.117 ↗
38. 10.1038/s41467-020-15171-610.1038/s41467-020-15171-6 ↗
39. 10.1186/s13059-019-1910-110.1186/s13059-019-1910-1 ↗
40. 10.1101/gr.251512.11910.1101/gr.251512.119 ↗
41. 10.1126/science.aaa035510.1126/science.aaa0355 ↗
42. 10.1111/j.1582-4934.2010.01055.x10.1111/j.1582-4934.2010.01055.x ↗
43. 10.1093/nar/gkx07610.1093/nar/gkx076 ↗
44. 10.1002/ijc.2840510.1002/ijc.28405 ↗
45. 10.1186/s13059-020-1935-510.1186/s13059-020-1935-5 ↗
46. 10.1101/gr.257766.11910.1101/gr.257766.119 ↗
47. 10.1038/ng178910.1038/ng1789 ↗
48. 10.1101/gr.132563.11110.1101/gr.132563.111 ↗
49. 10.1101/gr.151571.11210.1101/gr.151571.112 ↗
50. 10.1186/1471-2164-16-S8-S210.1186/1471-2164-16-S8-S2 ↗
51. 10.1093/nar/gky58710.1093/nar/gky587 ↗
52. 10.12688/f1000research.23297.110.12688/f1000research.23297.1 ↗
53. 10.1093/nar/gkab112910.1093/nar/gkab1129 ↗
54. 10.1038/s41586-020-2536-x10.1038/s41586-020-2536-x ↗
55. 10.1152/physiolgenomics.00196.201310.1152/physiolgenomics.00196.2013 ↗
56. 10.1038/s41598-017-06128-910.1038/s41598-017-06128-9 ↗
57. 10.1016/j.molcel.2005.10.03310.1016/j.molcel.2005.10.033 ↗
58. 10.1186/s12964-018-0295-110.1186/s12964-018-0295-1 ↗
59. 10.1016/0092-8674(78)90039-910.1016/0092-8674(78)90039-9 ↗
60. 10.1038/s41467-021-23303-910.1038/s41467-021-23303-9 ↗
61. 10.1126/science.110862510.1126/science.1108625 ↗
62. 10.1186/s13059-021-02261-x10.1186/s13059-021-02261-x ↗
63. 10.1016/j.tig.2003.09.01410.1016/j.tig.2003.09.014 ↗
64. 10.1371/journal.pgen.100136610.1371/journal.pgen.1001366 ↗
65. 10.1186/s12885-020-06794-510.1186/s12885-020-06794-5 ↗
66. 10.1038/s41592-019-0503-y10.1038/s41592-019-0503-y ↗
67. 10.1002/wrna.5610.1002/wrna.56 ↗
68. 10.1038/nrm.2016.13210.1038/nrm.2016.132 ↗
69. 10.1186/s13059-020-02241-710.1186/s13059-020-02241-7 ↗
70. 10.1016/j.molcel.2019.09.03210.1016/j.molcel.2019.09.032 ↗
71. 10.1038/nrm.2017.2710.1038/nrm.2017.27 ↗
72. 10.1186/s13059-018-1417-110.1186/s13059-018-1417-1 ↗
73. 10.1038/s41588-021-00851-w10.1038/s41588-021-00851-w ↗
74. 10.1093/nar/gkv116810.1093/nar/gkv1168 ↗
75. 10.1186/s13059-017-1339-310.1186/s13059-017-1339-3 ↗
76. 10.1038/ncomms1513410.1038/ncomms15134 ↗
77. 10.1038/s41477-020-0688-110.1038/s41477-020-0688-1 ↗
78. 10.1101/gad.247361.11410.1101/gad.247361.114 ↗
79. 10.1016/j.tranon.2016.10.00310.1016/j.tranon.2016.10.003 ↗
80. 10.1074/mcp.M113.03560010.1074/mcp.M113.035600 ↗
81. 10.1186/gb-2004-5-10-r7410.1186/gb-2004-5-10-r74 ↗
82. 10.1016/j.gde.2012.12.00410.1016/j.gde.2012.12.004 ↗
83. 10.1016/j.actbio.2008.05.00710.1016/j.actbio.2008.05.007 ↗
84. 10.1186/s13059-019-1727-y10.1186/s13059-019-1727-y ↗
85. 10.1093/bioinformatics/bty19110.1093/bioinformatics/bty191 ↗
86. 10.14806/ej.17.1.20010.14806/ej.17.1.200 ↗
87. 10.1093/bioinformatics/bts63510.1093/bioinformatics/bts635 ↗
88. 10.1038/sdata.2017.11210.1038/sdata.2017.112 ↗
89. 10.1038/s41467-020-16951-w10.1038/s41467-020-16951-w ↗
90. Herrmann C. J. · Nucleic Acids Res. · 2020 · 48 · D174
91. 10.1104/pp.19.0094310.1104/pp.19.00943 ↗
92. 10.1038/nbt.351910.1038/nbt.3519 ↗
93. 10.1089/omi.2011.011810.1089/omi.2011.0118 ↗
94. 10.2144/00011341810.2144/000113418 ↗