Small-scale sequencing enables quality assessment of Ribo-Seq data: an example from Arabidopsis cell culture.
Evaluation of sequencing library quality
Ribo-Seq
Ribosomal profiling
Translation
Translational profiling
Journal
Plant methods
ISSN: 1746-4811
Titre abrégé: Plant Methods
Pays: England
ID NLM: 101245798
Informations de publication
Date de publication:
24 Aug 2021
24 Aug 2021
Historique:
received:
31
03
2021
accepted:
13
08
2021
entrez:
25
8
2021
pubmed:
26
8
2021
medline:
26
8
2021
Statut:
epublish
Résumé
Translation is a tightly regulated process, controlling the rate of protein synthesis in cells. Ribosome sequencing (Ribo-Seq) is a recently developed tool for studying actively translated mRNA and can thus directly address translational regulation. Ribo-Seq libraries need to be sequenced to a great depth due to high contamination by rRNA and other contaminating nucleic acid fragments. Deep sequencing is expensive, and it generates large volumes of data, making data analysis complicated and time consuming. Here we developed a platform for Ribo-Seq library construction and data analysis to enable rapid quality assessment of Ribo-Seq libraries with the help of a small-scale sequencer. Our data show that several qualitative features of a Ribo-Seq library, such as read length distribution, P-site distribution, reading frame and triplet periodicity, can be effectively evaluated using only the data generated by a benchtop sequencer with a very limited number of reads. Our pipeline enables rapid evaluation of Ribo-Seq libraries, opening up possibilities for optimization of Ribo-Seq library construction from difficult samples, and leading to better decision making prior to more costly deep sequencing.
Sections du résumé
BACKGROUND
BACKGROUND
Translation is a tightly regulated process, controlling the rate of protein synthesis in cells. Ribosome sequencing (Ribo-Seq) is a recently developed tool for studying actively translated mRNA and can thus directly address translational regulation. Ribo-Seq libraries need to be sequenced to a great depth due to high contamination by rRNA and other contaminating nucleic acid fragments. Deep sequencing is expensive, and it generates large volumes of data, making data analysis complicated and time consuming.
METHODS AND RESULTS
RESULTS
Here we developed a platform for Ribo-Seq library construction and data analysis to enable rapid quality assessment of Ribo-Seq libraries with the help of a small-scale sequencer. Our data show that several qualitative features of a Ribo-Seq library, such as read length distribution, P-site distribution, reading frame and triplet periodicity, can be effectively evaluated using only the data generated by a benchtop sequencer with a very limited number of reads.
CONCLUSION
CONCLUSIONS
Our pipeline enables rapid evaluation of Ribo-Seq libraries, opening up possibilities for optimization of Ribo-Seq library construction from difficult samples, and leading to better decision making prior to more costly deep sequencing.
Identifiants
pubmed: 34429136
doi: 10.1186/s13007-021-00791-w
pii: 10.1186/s13007-021-00791-w
pmc: PMC8386038
doi:
Types de publication
Journal Article
Langues
eng
Pagination
92Subventions
Organisme : Knut och Alice Wallenbergs Stiftelse
ID : KAW 2016.0025
Organisme : Knut och Alice Wallenbergs Stiftelse
ID : KAW 2016.0341 and KAW 2016.0352
Organisme : Energimyndigheten
ID : Bio4Energy
Organisme : VINNOVA
ID : 2016-00504
Informations de copyright
© 2021. The Author(s).
Références
RNA. 2015 Oct;21(10):1731-45
pubmed: 26286745
PLoS Comput Biol. 2013;9(8):e1003118
pubmed: 23950696
Bioinformatics. 2016 Jul 15;32(14):2089-95
pubmed: 27153568
Bioinformatics. 2012 Dec 15;28(24):3211-7
pubmed: 23071270
Nat Protoc. 2012 Jul 26;7(8):1534-50
pubmed: 22836135
Nat Rev Genet. 2014 Mar;15(3):205-13
pubmed: 24468696
Cell. 2015 Oct 22;163(3):684-97
pubmed: 26496608
Nucleic Acids Res. 2015 Apr 20;43(7):e47
pubmed: 25605792
Plant Cell. 2013 Oct;25(10):3699-710
pubmed: 24179124
Science. 2009 Apr 10;324(5924):218-23
pubmed: 19213877
Nat Methods. 2012 Mar 04;9(4):357-9
pubmed: 22388286
Methods. 2015 Dec;91:69-74
pubmed: 26164698
Int J Mol Sci. 2019 Jan 08;20(1):
pubmed: 30626072
Nature. 2011 May 19;473(7347):337-42
pubmed: 21593866
Nat Methods. 2016 Feb;13(2):165-70
pubmed: 26657557
Proc Natl Acad Sci U S A. 2014 Jan 7;111(1):E203-12
pubmed: 24367078
Bioinformatics. 2014 Aug 1;30(15):2114-20
pubmed: 24695404
Genome Biol. 2014;15(12):550
pubmed: 25516281
Nat Biotechnol. 2016 May;34(5):525-7
pubmed: 27043002
Brief Funct Genomics. 2016 Jan;15(1):22-31
pubmed: 25380596
Elife. 2014 May 09;3:e01257
pubmed: 24842990
Genome Res. 2015 Dec;25(12):1836-47
pubmed: 26338483
Comp Funct Genomics. 2012;2012:475731
pubmed: 22693426
Bioinformatics. 2016 Oct 1;32(19):3047-8
pubmed: 27312411
EMBO J. 1988 Nov;7(11):3559-69
pubmed: 2850168
Proc Natl Acad Sci U S A. 2016 Nov 8;113(45):E7126-E7135
pubmed: 27791167
RNA Biol. 2016;13(3):316-9
pubmed: 26821742
Genome Biol. 2006;7(8):R76
pubmed: 16916443
Trends Genet. 2017 Oct;33(10):728-744
pubmed: 28887026
Mol Syst Biol. 2010 Aug 24;6:406
pubmed: 20739928
PLoS Comput Biol. 2018 Aug 13;14(8):e1006169
pubmed: 30102689