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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.

Genomic DNA in Eukaryotes

Genomic DNA in Eukaryotes

Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.

Genomic DNA in Prokaryotes

Genomic DNA in Prokaryotes

The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
Genomic Diversity in Bacteria
Although bacterial genomes are much...

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Related Experiment Video

Updated: Mar 20, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Improving GENCODE reference gene annotation using a high-stringency proteogenomics workflow.

James C Wright¹, Jonathan Mudge¹, Hendrik Weisser¹

¹Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

Nature Communications

|June 3, 2016

Summary

This summary is machine-generated.

Researchers developed a stringent workflow to interpret proteogenomic data for human genome annotation. This method validates novel protein-coding genes, enhancing the GENCODE reference database.

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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

Area of Science:

Genomics
Proteomics
Bioinformatics

Background:

Accurate human genome annotation is crucial for medical research.
The GENCODE consortium manually annotates the genome using computational and experimental evidence.
Proteogenomics offers insights into gene translation but lacks standardized data integration guidelines.

Purpose of the Study:

To establish a stringent workflow for interpreting proteogenomic data for gene model refinement.
To provide guidelines for the annotation community to integrate novel proteogenomic evidence.
To identify and validate new protein-coding genes for the GENCODE database.

Main Methods:

Reprocessing of three large-scale human proteogenomic datasets.
Application of a conservative interpretation approach with stringent filtering.
Validation of peptide identifications against orthogonal supporting evidence.

Main Results:

A stringent workflow for proteogenomic data interpretation was successfully implemented.
Evidence supporting 16 novel protein-coding genes was identified for GENCODE addition.
Numerous peptide identifications in pseudogenes could not be annotated due to insufficient supporting evidence.

Conclusions:

A conservative, stringently filtered approach is necessary for reliable proteogenomic data interpretation.
The developed workflow facilitates the integration of proteogenomic evidence into genome annotation.
The study contributes to refining the human genome annotation by identifying novel protein-coding genes.