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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...
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...
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.
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Genomic DNA in Eukaryotes00:58

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.
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: May 9, 2026

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

MGC: a metagenomic gene caller.

Achraf El Allali1, John R Rose

  • 1Department of Computer Science and Engineering, University of South Carolina, 315 Main Street, Columbia, SC 29208, USA. eachraf@gmail.com

BMC Bioinformatics
|August 2, 2013
PubMed
Summary

Metagenomics gene caller (MGC) improves gene prediction accuracy by using separate models for different GC-content regions and amino-acid features. This approach enhances the identification of genes from fragmented DNA sequences more effectively than previous methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Metagenomic sequencing presents challenges for gene finding due to fragmented data.
  • Existing algorithms struggle to accurately identify genes from incomplete DNA sequences.
  • Recent efforts focus on extracting and identifying open reading frames (ORFs) directly from short reads.

Purpose of the Study:

  • To introduce an improved metagenomics gene caller (MGC) that enhances gene and translation initiation site (TIS) prediction accuracy.
  • To address limitations of current gene-finding algorithms when applied to fragmented metagenomic data.
  • To demonstrate the benefit of using GC-content specific models and amino-acid features for improved gene prediction.

Main Methods:

  • Developed a metagenomics gene caller (MGC) improving upon the Orphelia algorithm.
  • Implemented a machine learning approach using separate models for distinct GC-content regions.
  • Incorporated two novel amino-acid usage features into the prediction model.

Main Results:

  • MGC demonstrates higher accuracy in predicting genes and translation initiation sites (TIS) compared to Orphelia.
  • Utilizing separate models based on local GC-content significantly reduces noise and improves prediction.
  • The inclusion of amino-acid features further enhances the overall accuracy of the gene caller.

Conclusions:

  • Training separate models for different GC-content regions improves neural network performance in gene finding.
  • Amino-acid usage features contribute to enhanced accuracy in metagenomic gene prediction.
  • MGC's advancements provide a foundation for future machine learning-based gene finders utilizing GC-content stratification.