acorde unravels functionally interpretable networks of isoform co-usage from single cell data.
acorde unravels functionally interpretable networks of isoform co-usage from single cell data.
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DOI:
10.1038/s41467-022-29497-w
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发表时间:
2022-04-05
影响因子:
16.6
通讯作者:
Conesa A
中科院分区:
文献类型:
--
作者:
Arzalluz-Luque A;Salguero P;Tarazona S;Conesa A
Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. However, the extent to which alternative isoforms establish co-expression networks that may be relevant in cellular function has not been explored yet. Here, we present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships. To achieve this, we develop and validate percentile correlations, an innovative approach that overcomes data sparsity and yields accurate co-expression estimates from single-cell data. Next, acorde uses correlations to cluster co-expressed isoforms into a network, unraveling cell type-specific alternative isoform usage patterns. By selecting same-gene isoforms between these clusters, we subsequently detect and characterize genes with co-differential isoform usage (coDIU) across cell types. Finally, we predict functional elements from long read-defined isoforms and provide insight into biological processes, motifs, and domains potentially controlled by the coordination of post-transcriptional regulation. The code for acorde is available at https://github.com/ConesaLab/acorde. Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. Here, the authors present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships.
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影响因子:
16.6
作者:
Jia G;Preussner J;Chen X;Guenther S;Yuan X;Yekelchyk M;Kuenne C;Looso M;Zhou Y;Teichmann S;Braun T
通讯作者:
Braun T
影响因子:
3
作者:
Chen S;Mar JC
通讯作者:
Mar JC
影响因子:
16.6
作者:
Byrne A;Beaudin AE;Olsen HE;Jain M;Cole C;Palmer T;DuBois RM;Forsberg EC;Akeson M;Vollmers C
通讯作者:
Vollmers C
DOI:
10.1007/s12064-015-0220-8
发表时间:
2016-06
期刊:
Theory in biosciences = Theorie in den Biowissenschaften
影响因子:
--
作者:
Erb I;Notredame C
通讯作者:
Notredame C
影响因子:
4.3
作者:
Hu, Yu;Wang, Kai;Li, Mingyao
通讯作者:
Li, Mingyao