APAview

** current version **: 0.1.1

Introduction

APAview is a web-based platform for alternative polyadenylation (APA) features exploration on APA quantification data from QAPA/DaPars in hematological tumors. Taking APA, gene/transcript expression, and clinical data as input, APAview could:

(1) provide correlation analysis between APA usage index and gene/transcript expression;

(2) provide differential analysis of APA usage index or other features among groups;

(3) identify genes with shortened/lengthened 3’UTR between groups;

(4) provide survival analysis based on APA usage index;

(5) annotate genes and APA sites using databases such as NCBI and polyA DB;

(6) visualize gene structures, APA sites, miRNA, RBP, and APA motifs in IGV plots.

Installation and Usage

1. Install Python 3.9 on Windows/Linux.

2. Create an environment for APAview running.

   Windows:

   python -m venv APAview_env
   .\APAview_env\Scripts\Activate.ps1 (using python)
   .\APAview_env\Scripts\activate.bat (using cmd)
   

   Linux:

   python3 -m venv APAview_env
   . ./APAview_env/bin/activate
   

3. Install dependency packages.

   Windows:

   python -m pip install --upgrade setuptools pip
   python -m pip install wheel
   python -m pip install flask statsmodels xarray h5netcdf plotly
   #########################
   Package         Version
   --------------- --------
   Flask           2.0.3
   h5netcdf        0.14.1
   pandas          1.4.1
   plotly          5.6.0
   statsmodels     0.13.2
   xarray          2022.3.0
   ##########################
   

   Linux:

   python3 -m pip install --upgrade setuptools pip
   python3 -m pip install wheel
   python3 -m pip install flask statsmodels xarray h5netcdf plotly
   #########################
   Package         Version
   --------------- --------
   Flask           2.0.3
   h5netcdf        0.14.1
   pandas          1.4.1
   plotly          5.6.0
   statsmodels     0.13.2
   xarray          2022.3.0
   ##########################
   

4. Download the latest development version of APAview and unzip the file to /pathway/APAview.

5. Run APAview in APAview_env.

   cd /pathway/APAview
   flask run
   #################
    * Environment: production
      WARNING: This is a development server. Do not use it in a production deployment.
      Use a production WSGI server instead.
    * Debug mode: off
    * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
    ################
   

6. Open the link (http://127.0.0.1:5000/) of APAview on the browser (e.g. Firefox).

Input data format

1. APA quantification data

   1.1 QAPA output (https://www.github.com/morrislab/qapa)

   Column	Description
   APA_ID	unique identifier in the format " _ _ <(P|D|S)>", P = proximal, D = distal, and S = single
   Transcript	one or more Ensembl Transcript IDs
   Gene	Ensembl Gene ID
   Gene_Name	gene symbol
   Chr	chromosome
   LastExon.Start	start coordinate of last exon
   LastExon.End	end coordinate of last exon
   Strand	"+" or "-"
   UTR3.Start	start coordinate of 3'UTR
   UTR3.End	end coordinate of 3'UTR
   Length	length of the 3′UTR
   Num_Events	number of PAS per gene
   .PAU	PolyA site Usage (PAU) estimate for sample1
   .TPM	TPM estimate for sample1

   1.2 DaPars output (http://bioinfo.szbl.ac.cn/DaPars2/DaPars2.html)

   Column	Description
   Gene	transcripts information in "RefSeq_transcript_ID| gene_symbol | chromosome | strand"
   Predicted_Proximal_APA	predicted proximal APA site by DaPars
   Loci	the 3'UTR region of the transcript
   Other columns	the Percentage of Distal APA site Usage Index (PDUI) values of each sample calculated by DaPars

2. gene/transcription data

   Rows: genes/transcripts using gene symbols/IDs as rows' names.

   Columns: expression values quantified in TPM format, each column represents a sample.

3. clinical data

   Rows: each row represents a sample.

   Columns: information like age, gender, disease types, survival data, and so on. The first column is sample IDs consistent with that in APA and expression data. Survival information should contain three columns including vital_status, days_to_last_followup, and days_to_death.

Data import

1. Put all imported files into a directory (e.g. source_dir).
2. Run import_data.sh source_dir/ in APAview_env.
3. Generate APA.sqlite and data.nc in the current directory. The two files store all input information.

Citation

APAview: a web-based platform for alternative polyadenylation analyses in hematological tumors

Contact

Emails for any comments, suggestions, questions, and bug reports of APAview are welcomed to send to Xiaoming Wu (wxm@mail.xjtu.edu.cn).

License

Copyright (c) 2022 Xiaoming Wu

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.