Back

Rapid and sensitive detection of native glycoRNAs

Hemberger, H.; Chai, P.; Lebedenko, C. G.; Caldwell, R. M.; George, B. M.; Flynn, R. A.

2023-02-26 molecular biology Community evaluation
10.1101/2023.02.26.530106 bioRxiv
Show abstract

Chemical tools enable precise characterization of many biopolymers, including glycoconjugates. Metabolic chemical reporters enabled the discovery of glycoRNAs, however they have certain limitations due the requirement of having living cells to incorporate the modified sugar. Here we develop a periodate oxidation and aldehyde ligation method to detect and characterize native sialoglycoRNAs, termed rPAL. With optimized RNA biochemistry to enhance recovery and analysis of small RNAs, we show rPAL is at least an order of magnitude more sensitive than previous methods for detecting sialoglycoRNAs. These improvements allow rPAL to detect sialoglycoRNA from human clinical samples as demonstrated by defining the abundance and patterns of sialoglycoRNAs from sorted populations of peripheral blood mononuclear cells. The sensitivity, robustness, and flexibility of rPAL will allow greater access towards characterizing glycoRNA biology.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
RNA
● 189 papers in training set
Top 0.2%
8.1%
Similar papers in this journal
2
Journal of Biological Chemistry
○ 690 papers in training set
Top 1%
8.1%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 20%
8.1%
Similar papers in this journal
4
Nucleic Acids Research
◐ 1281 papers in training set
Top 2%
7.4%
Similar papers in this journal
5
ACS Chemical Biology
● 167 papers in training set
Top 0.3%
7.4%
Similar papers in this journal
6
Nature Biotechnology
○ 172 papers in training set
Top 0.4%
6.9%
7
eLife
● 5828 papers in training set
Top 24%
5.3%
50% of probability mass above
8
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 15%
3.3%
9
Scientific Reports
○ 3612 papers in training set
Top 30%
3.3%
10
PLOS ONE
● 5266 papers in training set
Top 37%
3.2%
11
Cell
○ 431 papers in training set
Top 4%
2.7%
12
Nature Methods
○ 385 papers in training set
Top 3%
2.7%
13
Cell Reports Methods
○ 165 papers in training set
Top 1%
2.0%
14
Journal of the American Chemical Society
● 217 papers in training set
Top 2%
1.8%
15
STAR Protocols
○ 18 papers in training set
Top 0.1%
1.8%
16
Nature Microbiology
○ 155 papers in training set
Top 2%
1.5%
17
Cell Chemical Biology
○ 94 papers in training set
Top 0.9%
1.4%
18
ACS Synthetic Biology
● 287 papers in training set
Top 2%
1.2%
19
Cell Reports
○ 1498 papers in training set
Top 22%
1.2%
20
RNA Biology
○ 78 papers in training set
Top 1%
0.9%
21
PLOS Pathogens
● 820 papers in training set
Top 9%
0.9%
22
Nature Chemical Biology
○ 119 papers in training set
Top 3%
0.9%
23
Molecular Cell
○ 350 papers in training set
Top 5%
0.9%
24
Biology Methods and Protocols
◐ 61 papers in training set
Top 3%
0.6%
25
ACS Omega
● 105 papers in training set
Top 4%
0.6%
26
Biochemistry
● 148 papers in training set
Top 3%
0.6%
27
SLAS Discovery
○ 25 papers in training set
Top 0.3%
0.6%
28
iScience
○ 1154 papers in training set
Top 39%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.