Related Experiment Video
Updated: May 11, 2026
06:40
G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
Published on: March 22, 2018
Genomic region operation kit for flexible processing of deep sequencing data.
Kristian Ovaska1, Lauri Lyly, Biswajyoti Sahu
1Biomedicine, Biochemistry and Developmental Biology, Biomedicum Helsinki, University of Helsinki, Helsinki, Finland.
Summary
This study introduces the Genomic Region Operation Kit (GROK), a tool simplifying complex deep sequencing (DS) data analysis. GROK uses set algebra for efficient computational analysis of genomic data, aiding research in areas like cancer.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Deep sequencing (DS) data analysis presents significant computational challenges due to large data volumes.
- Existing analysis methods often lack the flexibility required for diverse biomedical research questions.
- Translating biological queries into computational workflows for DS data is complex.
Purpose of the Study:
- To develop a mathematical formalism for simplifying common operations in deep sequencing data analysis.
- To implement a software tool, the Genomic Region Operation Kit (GROK), based on this formalism.
- To demonstrate GROK's utility in characterizing transcription factor roles in prostate cancer.
Main Methods:
- Developed a set algebra-based mathematical formalism for DS data operations.
- Implemented the Genomic Region Operation Kit (GROK) supporting preprocessing, filtering, file conversion, and sample comparison.
- Integrated GROK with R, Python, Lua, command line, and C++ APIs, supporting major genomic file formats and efficient data storage.
Main Results:
- GROK facilitates the translation of biomedical research questions into computational analyses for DS data.
- The tool supports diverse operations and file formats, utilizing efficient data structures like red-black trees and SQL databases.
- Demonstrated GROK's effectiveness by analyzing transcription factor roles in prostate cancer using 10 DS experiments.
Conclusions:
- GROK provides a flexible and efficient framework for computational analysis of deep sequencing data.
- The set algebra formalism simplifies complex operations, making DS data analysis more accessible.
- GROK is a valuable tool for genomic research, exemplified by its application in prostate cancer studies.
More Related Videos
09:34Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
13:24Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
Published on: April 11, 2016
Related Concept Videos
01:57Sanger Sequencing
DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
03:00Next-generation Sequencing
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
03:21RNA-seq
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
02:02Genomics
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
03:36Genome Annotation and Assembly
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.