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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.

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...

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Updated: Dec 28, 2025

Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

Chromatin Immunoprecipitation of Murine Brown Adipose Tissue

Published on: November 21, 2018

Statistical Mitogenome Assembly with RepeaTs.

Fahad Alqahtani^1,2, Ion I Măndoiu¹

¹Computer Science & Engineering Department, University of Connecticut, Storrs, Connecticut, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|February 13, 2020

Summary

This summary is machine-generated.

This study introduces the SMART pipeline for assembling mitochondrial genomes from whole-genome sequencing data. SMART improves the success rate of generating complete circular mitochondrial genomes, especially with low-coverage data.

Keywords:

bootstrapping de novo assembly maximum likelihood mitogenome repeats

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Next-generation sequencing (NGS) generates vast amounts of short DNA reads from nuclear and mitochondrial genomes.
Assembling mitochondrial genomes from whole-genome sequencing (WGS) data is challenging due to copy number differences, circularity, and repeats.
Existing de novo assemblers often struggle to produce high-quality mitochondrial genome sequences from WGS data.

Purpose of the Study:

To develop an automated pipeline for assembling complete mitochondrial genomes from WGS data.
To address challenges in mitochondrial genome assembly, including varying copy numbers, circularity, and repetitive elements.
To improve the accuracy and success rate of mitochondrial genome reconstruction.

Main Methods:

The Statistical Mitogenome Assembly with RepeaTs (SMART) pipeline was developed.
SMART employs a coverage-based filter for mtDNA-enriched read selection.
It utilizes BLAST searches and alignment-based filtering, followed by de novo assembly, scaffolding, and a maximum likelihood model for repeat resolution. Bootstrapping is used for annotation confidence.

Main Results:

The SMART pipeline successfully assembles complete circular mitochondrial genome sequences.
SMART demonstrates a higher success rate compared to current state-of-the-art tools.
Performance is particularly notable for low-coverage WGS datasets across various species.

Conclusions:

SMART provides an effective and automated solution for mitochondrial genome assembly from WGS data.
The pipeline overcomes common challenges, enabling more reliable reconstruction of mitochondrial genomes.
SMART is a valuable tool for genomic research, especially when dealing with limited sequencing depth.