| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T955 |
0-56 |
Sentence |
denotes |
Transcription factor activity after SARS-CoV-2 infection |
| T956 |
57-307 |
Sentence |
denotes |
To evaluate the effect of SARS-CoV-2 infection at the Transcription Factor (TF) level, we applied DoRothEA (Garcia-Alonso et al., 2019) to RNA-seq datasets of different human lung cell lines from a recent study (GSE147507) (Blanco-Melo et al., 2020). |
| T957 |
308-403 |
Sentence |
denotes |
DoRothEA is a comprehensive resource containing a curated collection of TF-target interactions. |
| T958 |
404-512 |
Sentence |
denotes |
Each TF-target interaction is associated with a confidence level based on the number of supporting evidence. |
| T959 |
513-710 |
Sentence |
denotes |
Here we selected the most reliable interactions (A, B, and C levels) and computed TF activities based on the normalized expression of their targets using the VIPER algorithm (Alvarez et al., 2016). |
| T960 |
711-949 |
Sentence |
denotes |
For the TF activity enrichment analysis, VIPER was executed with the Wald statistic resulting from the differential expression analysis at the gene level between controls and SARS-CoV-2 infected cells using the DESeq2 package (Love et al. |
| T961 |
950-956 |
Sentence |
denotes |
2014). |
| T962 |
957-1080 |
Sentence |
denotes |
In VIPER, we set the eset.filter parameter to FALSE and consider five as the minimum number of targets allowed per regulon. |
