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The Retinoblastoma Gene

The Retinoblastoma Gene

Tumor suppressor genes are normal genes that can slow down cell division, repair DNA mistakes, or program the cells for apoptosis in case of irreparable damage. Hence, they play an essential role in preventing the proliferation of damaged cells.
The first-ever tumor suppressor gene called Rb was identified in retinoblastoma - a rare eye tumor in children. In inherited forms of the disease, a child inherits one defective copy of the Rb gene, which predisposes them to retinoblastoma. However,...

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Related Experiment Video

Updated: Dec 18, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Next-Generation Sequencing Data Analysis on Pool-Seq and Low-Coverage Retinoblastoma Data.

Gülistan Özdemir Özdoğan¹, Hilal Kaya²

¹Department of Computer Engineering, Ankara Yildirim Beyazit University, 06010, Ankara, Turkey.

Interdisciplinary Sciences, Computational Life Sciences

|June 11, 2020

Summary

New bioinformatics pipelines enhance the efficiency of low-coverage next-generation sequencing (NGS) and pool-seq data analysis. These methods improve variant calling accuracy for genomic research, especially in cancer studies.

Keywords:

Low-coverage sequencing NGS data analysis Pool-seq Retinoblastoma

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Last Updated: Dec 18, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

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Published on: March 12, 2021

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Published on: May 22, 2017

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Next-generation sequencing (NGS) has transformed genomic research, but data generation costs remain a challenge.
Pool-seq and low-coverage NGS strategies have emerged to mitigate costs.
The efficiency of these cost-saving NGS approaches requires rigorous evaluation.

Purpose of the Study:

To develop and validate a bioinformatics pipeline for analyzing pool-seq and low-coverage NGS data.
To assess the performance of the pipeline using retinoblastoma tumor data and other datasets.
To provide a guideline for effective NGS data analysis in cost-constrained studies.

Main Methods:

A specialized bioinformatics pipeline was applied to pool-seq and low-coverage retinoblastoma data.
The pipeline's performance was compared against a standard pipeline using three different datasets.
High-confidence variant calls from the Genome in a Bottle Consortium were used for validation.

Main Results:

The developed pipeline identified a higher number of variants compared to the standard pipeline across all tested datasets.
Improved recall and F-score values demonstrated the pipeline's superior accuracy.
Variant analysis included types and disease-related genes, with a focus on retinoblastoma.

Conclusions:

The study presents a validated bioinformatics pipeline for joint analysis of pool-seq and low-coverage NGS data.
The pipeline offers enhanced variant calling accuracy and efficiency.
For robust outcomes, utilizing cancer data with higher mutation rates and larger pools is recommended.