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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Genomics

Genomics

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...

Next-generation Sequencing

Next-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.

Genome Annotation and Assembly

Genome 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.

RNA-seq

RNA-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...

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Related Experiment Video

Updated: May 12, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Revising a personal genome by comparing and combining data from two different sequencing platforms.

Deokhoon Kim¹, Woo-Yeon Kim, Sun-Young Lee

¹Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon, Korea.

|April 18, 2013

Summary

Genome sequencing platforms have differences affecting results, with ~10% of single nucleotide variants (SNVs) being discordant or platform-specific. Merging data from multiple platforms improves genome callability and variant accuracy for robust genomic medicine.

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

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Last Updated: May 12, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

Genomic Medicine
Bioinformatics
Next-Generation Sequencing (NGS)

Background:

Genomic medicine requires compatible sequencing results across different technologies and algorithms.
Current genome sequencing is imprecise due to variations in chemistry, coverage, alignment, and variant-calling algorithms.
Platform-specific differences can impact the accuracy and reliability of identified genetic variations.

Purpose of the Study:

To compare sequencing results between two different genome sequencing platforms (SOLiD and Illumina).
To analyze concordant, discordant, and platform-specific single nucleotide variants (SNVs).
To evaluate the impact of merging data from multiple platforms on variant calling and genome coverage.

Main Methods:

Sequencing of the SJK genome using SOLiD and Illumina platforms.
Identification and comparison of single nucleotide variants (SNVs) between the two platforms.
Analysis of discordant and platform-specific SNVs, including their location and characteristics.
Merging of data from both platforms for joint variant calling.

Main Results:

Identified ~3.33 million SNVs (SOLiD) and ~3.62 million SNVs (Illumina).
Approximately 90% of SNVs were concordant; ~10% were discordant or platform-specific.
Platform-specific SNVs were often found in repetitive regions, influenced by differences in read length, insert size, and chemistry.
Merging data increased reference genome callability to 99.66%, a 1.43% improvement over individual platforms.

Conclusions:

Each sequencing platform possesses unique strengths and weaknesses, contributing to ~10% variation in SNV identification.
Merging data from complementary sequencing platforms enhances overall genome callability and SNV accuracy.
Utilizing combined data from multiple platforms offers a more robust and revised approach to genome re-sequencing.