The document describes a method for identifying alternative polyadenylation (APA) outliers in RNA sequencing and whole genome sequencing data from the GTEx project. RNA sequencing data from over 17,000 samples across 49 tissues were analyzed. APA outliers were identified as individuals with extreme APA usage scores, either in a single tissue or across multiple tissues. The results can be queried and visualized using scatter plots, and data on outliers and watershed model predictions can be downloaded.

1. Web portal for human
APA outlier associated
rare variants atlas
A step-by-step guide

2. Data and methods
• We collected RNA-seq data and WGS data from the v8 release of the
GTEx project. The RNA-seq data contains 17,832 samples of 54
biological tissues from 838 donors. In the current study, we used 49 of
the tissues that with at least 70 samples. Original RNA-seq reads were
aligned with the human genome (hg38/GRCh38) using STAR v.2.5.2b.
The resulting sorted BAM files were converted into bedGraph formats
using BEDTools version 2.17.0 40.
• We called APA outlier (aOutlier) in a single tissue (single-tissue
aOutliers) and in multiple tissues (multitissue aOutliers). In brief, for
multitissue aOutliers, we calculated the median Z score on covariates
corrected APA usage for each APA event across all tissues for which
data were available, restricting to individuals with APA measurements in
at least five tissues. For each APA event, the multitissue aOutliers were
defined as individuals with an absolute median value of Z score greater
than 3. To account for situations where widespread aberrant APA might
occur in an individual due to non-genetic influences, we removed 11
individuals where the proportion of tested genes that were multitissue
outliers exceeded 1.5 times the interquartile range of the distribution of
proportion outlier genes across all individuals. The 11 individuals were
marked as global outliers. For single-tissue aOutlier calling, we
calculated a Z score for each APA event and defined single-tissue
aOutliers for each event in a single tissue as the individuals with the
absolute value of Z score greater than 3. The 11 individuals marked as
global outliers were also excluded in single-tissue aOutliers.

3. Part 1: Querying aOutliers/ipaOutliers of interest
Part 2: Downloading data of aOutliers and the
related data of the watershed model

4. Part 1 Querying
aOutliers/ipaOutliers of interest
Step 1:
Search a Gene ( e.g. SUGP1)

5. Step 2:
Select class of interest (e.g. 3’UTR)

6. Step 3:
Relevant results will
automatically show
A search function is also
available here.

7. Step 4:
Click “Scatter plot ”

8. Step 5:
Scatter plot for downloading

9. Part2 Downloading data of aOutliers
and the related data of the watershed
model Step 1: Click
“DOWNLOAD”

10. Step 2:
Click any filename for Visualizing
and Downloading