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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

TSSi--an R package for transcription start site identification from 5' mRNA tag data.

C Kreutz¹, J S Gehring, D Lang

¹Institute for Physics, University of Freiburg, Germany. ckreutz@fdm.uni-freiburg.de

Bioinformatics (Oxford, England)

|April 20, 2012

Summary

This study introduces TSSi, an R package for identifying transcription start sites (TSSs) using 5' mRNA tag sequencing data. It offers a robust framework to accurately pinpoint TSSs by modeling data distributions and errors.

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Transcription Start Site Mapping Using Super-low Input Carrier-CAGE

Transcription Start Site Mapping Using Super-low Input Carrier-CAGE

Published on: June 26, 2019

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

High-throughput sequencing is crucial for studying transcriptional mechanisms.
Existing peak prediction methods are unsuitable for identifying transcription start sites (TSSs) due to distinct noise patterns in TSS data.
Accurate TSS identification is vital for understanding gene regulation.

Purpose of the Study:

To present the R package TSSi for heuristic identification of transcription start sites (TSSs).
To provide a framework adaptable to user-defined probabilistic assumptions for data distribution and systematic errors.
To offer a regularization procedure for noise reduction and improved TSS prediction accuracy.

Main Methods:

Development of the R package TSSi.
Utilizing 5' mRNA tag sequencing data for TSS identification.
Implementing a heuristic framework with probabilistic assumptions for data and error modeling.
Incorporating a regularization procedure for noise reduction.

Main Results:

The TSSi package provides a novel heuristic framework for TSS identification.
The framework allows user adaptation of probabilistic assumptions for data and error modeling.
A regularization procedure effectively reduces noise and minimizes false TSS predictions.
The package is available via Bioconductor.

Conclusions:

TSSi offers a specialized and adaptable solution for accurate transcription start site identification from 5' mRNA tag data.
The package enhances the analysis of transcriptional mechanisms by improving TSS prediction accuracy.
The heuristic framework and noise reduction capabilities make TSSi a valuable tool for researchers.