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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...

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.

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

Multi-species Conserved Sequences

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...

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...

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Related Experiment Video

Updated: Jun 28, 2026

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Evading the annotation bottleneck: using sequence similarity to search non-sequence gene data.

Michael J Gilchrist¹, Mikkel B Christensen, Richard Harland

¹The Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, CB2 1QN, UK. m.gilchrist@gurdon.cam.ac.uk

BMC Bioinformatics

|October 22, 2008

Summary

This study introduces a novel method for retrieving non-sequence gene data using sequence similarity, bypassing the limitations of text-based searches and gene annotation. This approach enhances data accessibility, particularly for cross-species comparisons and un-annotated genes.

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Novel Sequence Discovery by Subtractive Genomics

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Genomics
Bioinformatics
Data Science

Background:

Non-sequence gene data (e.g., images, literature) are often accessed via text-based methods relying on gene names.
Gene annotation completeness, consistency across organisms, and temporal stability pose challenges for text-based data retrieval, especially for cross-species searches.
Existing gene databases offer indirect access to non-sequence data through navigational links, limiting direct retrieval based on sequence similarity.

Purpose of the Study:

To develop and evaluate a novel method for retrieving non-sequence gene data based on sequence similarity.
To overcome the limitations of text-based searches and gene annotation for accessing diverse biological data.
To facilitate efficient cross-species comparisons and the handling of novel or un-annotated genes.

Main Methods:

Developed a method for non-sequence data retrieval utilizing sequence similarity searches.
Built three applications to demonstrate the method's utility for accessing image data, literature, and gene names.
Searches are initiated with a user's gene sequence, compared against a database of sequences linked to target data.

Main Results:

The sequence similarity method effectively retrieved non-sequence data, organized by similarity, directly to the user's browser.
Demonstrated high accuracy for image data management compared to text-based searches.
Facilitated high-relevance literature retrieval and provided insights into gene name variations within and between species.

Conclusions:

The proposed method offers a powerful alternative to text-based retrieval and curated gene lists for accessing gene data.
Significantly enhances cross-species comparative analysis and supports the inclusion of novel or un-annotated genes.
Applications are rapid to develop, require minimal data maintenance, and largely circumvent annotation-related obstacles in genomic data resource development.