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Related Concept Videos

Genome Annotation and Assembly03:36

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.
Evolutionary Relationships through Genome Comparisons02:54

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...
Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes02:16

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
Next-generation Sequencing03:00

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.
Genomics02:02

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...
RNA-seq03:21

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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Updated: May 14, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
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Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

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CGAL: computing genome assembly likelihoods.

Atif Rahman, Lior Pachter

    Genome Biology
    |January 31, 2013
    PubMed
    Summary
    This summary is machine-generated.

    We developed CGAL, a new likelihood-based method for assessing genome assembly quality without ground truth data. This approach improves upon traditional metrics like contig count and size for evaluating de novo assembly accuracy.

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    Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • De novo genome assembly is crucial for understanding genomic variation.
    • Current assembly quality assessment relies on simulated data or crude metrics (contig number/size).
    • Evaluating assembly accuracy without ground truth data presents a significant challenge.

    Purpose of the Study:

    • To introduce a novel, likelihood-based approach for assessing genome assembly quality.
    • To provide a more accurate method for evaluating assemblies in the absence of ground truth.
    • To enable optimization and comparison of different assembly algorithms.

    Main Methods:

    • Developed CGAL, a likelihood-based statistical framework for assembly assessment.
    • Applied CGAL to evaluate and compare various genome assembly algorithms.
    • Implemented the CGAL methodology into freely available software.

    Main Results:

    • Demonstrated that likelihood-based assessment is more accurate than traditional metrics.
    • Showcased the utility of CGAL for optimizing assembly parameters.
    • Provided evidence for CGAL's effectiveness in comparing assembly algorithm performance.

    Conclusions:

    • CGAL offers a robust and accurate method for de novo assembly evaluation.
    • Likelihood-based assessment represents a significant advancement over existing quality metrics.
    • The freely available CGAL software facilitates broader adoption and application in genomics research.