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MAAPER

Model-based analysis of APA using 3' end-linked reads

https://github.com/vivianstats/maaper

Science Score: 33.0%

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Keywords

alternative-polyadenylation bioinformatics-tool rna-seq
Last synced: 9 months ago · JSON representation

Repository

Model-based analysis of APA using 3' end-linked reads

Basic Info
Statistics
  • Stars: 10
  • Watchers: 1
  • Forks: 4
  • Open Issues: 7
  • Releases: 0
Topics
alternative-polyadenylation bioinformatics-tool rna-seq
Created over 5 years ago · Last pushed almost 5 years ago
Metadata Files
Readme

README.md

MAAPER: Model-based analysis of alternative polyadenylation using 3’

end-linked reads

Wei Vivian Li, Bin Tian 2021-08-13

Latest News

2021/08/13:

  • Version 1.1.1 released!

2021/06/15:

  • Version 1.1.0 released!

Introduction

MAAPER is a computational method for model-based analysis of alternative polyadenylation using 3’ end-linked reads. It uses a probabilistic model to predict polydenylation sites (PASs) for nearSite reads with high accuracy and sensitivity, and examines different types of alternative polyadenylation (APA) events, including those in 3’UTRs and introns, using carefully designed statistics.

Any suggestions on the package are welcome! For technical problems, please report to Issues. For suggestions and comments on the method, please contact Vivian (vivian.li@rutgers.edu).

Installation

You can install MAAPER from CRAN with:

r install.packages("MAAPER")

Quick start

maaper requires three input files:

  • The GTF file of the reference genome;
  • The BAM files of the 3’ sequencing data (nearSite reads). The BAM file should be sorted and the index BAI file should be present in the same directory as the BAM file;
  • The PAS annotation file whose version matches the reference genome. We have prepared PolyA_DB annotation files for MAAPER, and they can be downloaded from this page.

The final output of mapper are two text files named “gene.txt” and “pas.txt”, which contain the predicted PASs and APA results.

Below is a basic example which shows how to use the maaper function. The bam and gtf files used in this example can be downloaded here. To save computation time, we are providing a toy example dataset of chr19. In real data application, we do not recommend dividing the files into subsets by chromosomes.

``` r library(MAAPER)

pas_annotation = readRDS("./mouse.PAS.mm9.rds") gtf = "./gencode.mm9.chr19.gtf"

bam file of condition 1 (could be a vector if there are multiple samples)

bamc1 = "./NTchr19_example.bam"

bam file of condition 2 (could be a vector if there are multiple samples)

bamc2 = "./AS4hchr19example.bam"

maaper(gtf, # full path of the GTF file pasannotation, # PAS annotation outputdir = "./", # output directory bamc1, bamc2, # full path of the BAM files read_len = 76, # read length ncores = 12 # number of cores used for parallel computation ) ```

Please note the following options in the mapper function:

  • By default, maaper users the unpaired test. Please set paired = TRUE in order to use the paired test. We recommend only using the paired test when samples are paired and sample size is relatively large.
  • If you would like to obtain bedGraph files corresponding to estimated APA profiles for visualization with UCSC or IGV genome browser, please set bed = TRUE. It is set to FALSE by default.

Please refer to the package manual for a full list of arguments and detailed usage.

Citation

Li, W. V., Zheng, D., Wang, R., & Tian, B. (2021). MAAPER: model-based analysis of alternative polyadenylation using 3’end-linked reads. Genome Biology, in press. Link

Owner

  • Name: Vivian Li Lab
  • Login: Vivianstats
  • Kind: user
  • Company: University of California, Riverside

Statistical and bioinformatics methods developed by the Wei Vivian Li Lab.

GitHub Events

Total
  • Issues event: 2
  • Watch event: 3
  • Issue comment event: 5
  • Fork event: 1
Last Year
  • Issues event: 2
  • Watch event: 3
  • Issue comment event: 5
  • Fork event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 9
  • Total Committers: 2
  • Avg Commits per committer: 4.5
  • Development Distribution Score (DDS): 0.333
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Wei Vivian Li l****w@u****u 6
Wei Vivian Li v****i@r****u 3
Committer Domains (Top 20 + Academic)
rutgers.edu: 1 ucla.edu: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 11
  • Total pull requests: 0
  • Average time to close issues: about 2 months
  • Average time to close pull requests: N/A
  • Total issue authors: 10
  • Total pull request authors: 0
  • Average comments per issue: 3.45
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 2.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • AidenSb (2)
  • sonurdogan (1)
  • kjHuoo (1)
  • jung233 (1)
  • rbarbieri86 (1)
  • chentongouc (1)
  • Venteen123 (1)
  • ODiacone (1)
  • irenemei (1)
  • josoga2 (1)
Pull Request Authors
Top Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 244 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: MAAPER

Analysis of Alternative Polyadenylation Using 3' End-Linked Reads

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 244 Last month
Rankings
Forks count: 12.8%
Stargazers count: 21.1%
Dependent packages count: 29.8%
Average: 35.2%
Dependent repos count: 35.5%
Downloads: 77.0%
Maintainers (1)
vivian.li@rutgers.edu
Last synced: 10 months ago

Dependencies

DESCRIPTION cran
  • GenomeInfoDb * imports
  • GenomicAlignments * imports
  • GenomicFeatures * imports
  • GenomicRanges * imports
  • IRanges * imports
  • MASS * imports
  • Rsamtools * imports
  • parallel * imports
  • stats * imports
  • utils * imports
  • knitr * suggests
  • rmarkdown * suggests