Hi there, my github account did not notify me when there are issue. So if you are in a hurry, you can email me. [email protected]. I check email every day.

scTail was developed by using python 3.9. You can build a environment by using the following code at first.

conda create -n run_scTail python=3.9

You can install from this GitHub repository for latest (often development) version by following command line

pip install -U git+https://github.com/StatBiomed/scTail

In either case, add --user if you don't have the write permission for your Python environment.

You can download test file from figshare.

Here, you can download test data and also gene and PAS expression profiles for three dataset: human intestinal, mouse forelimb and ESCC.

Here are three steps in scTail : scTail-callPeak, scTail-peakMerge and scTail-count.

We set these three steps to speed up when running some large file (file size > 30G).

Please check your reads1 (the one that contains cellbarcode and UMI) at first before you run scTail to make sure the length of it more than 100bp. In the most situations, it is perfect that length of reads 1 is 150bp or 151bp.

scTail only support two species: mouse and human. Because classifier embedded in it only trains with sequence of mouse and human.

When you get fastq file, you should follow this instruction to run scTail step by step.

To identify differential alternative PAS usage, BRIE2 (Huang & Sanguinetti, 2021) is recommend to be used.

Here, we provide an example exploiting BRIE2 to detect differential PAS usage.

You can check it in our manual.

The full manual is here, including:

Preprocess

Run scTail

Detect alternative PAS

Hou R, Huang Y. scTail: precise polyadenylation site detection and its alternative usage analysis from reads 1 preserved 3'scRNA-seq data[J]. bioRxiv, 2024: 2024.07. 05.602174.