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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.

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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Published on: May 9, 2017

NeatFreq: reference-free data reduction and coverage normalization for De Novo sequence assembly.

Jamison M McCorrison¹, Pratap Venepally², Indresh Singh³

¹Informatics Core Services, The J. Craig Venter Institute (JCVI), 9704 Medical Center Drive, Rockville, MD, 20850, USA. jmccorri@jcvi.org.

BMC Bioinformatics

|November 20, 2014

Summary

NeatFreq software normalizes deep sequencing data, improving genome assembly by handling coverage variations. This tool enhances the inclusion of unique genomic regions and reduces errors for more accurate results.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) generates vast genomic data but faces challenges with coverage variation and high error rates.
Techniques like multiple displacement amplification (MDA) and sequence independent single primer amplification (SISPA) enable sequencing of unculturable organisms but introduce amplification biases and coverage variability.
Accurate data interpretation, including mapping and de novo assembly, is hindered by these deep coverage variations and errors.

Purpose of the Study:

To introduce NeatFreq, a novel software tool designed to address deep coverage variations in sequencing data.
To improve the accuracy and efficiency of genome assembly by normalizing read coverage.
To enhance the incorporation of unique and low-coverage genomic regions into the final consensus sequence.

Main Methods:

NeatFreq utilizes a novel algorithm that clusters and selects reads based on median k-mer frequency (RMKF) and uniqueness.
The software incorporates methods for preferred selection of extremely low coverage regions and two-sided paired-end sequences.
It processes error-corrected data to increase the inclusion of unique, low-coverage segments.

Main Results:

NeatFreq application to bacterial, viral plaque, and single-cell sequencing data demonstrated increased inclusion of unique genomic reads.
The tool effectively reduced duplicative and erroneous contigs in the assembled consensus.
Coverage reduction using NeatFreq improved processing speed and reduced memory requirements for conventional bacterial assembly algorithms.

Conclusions:

Normalization of deep coverage spikes by NeatFreq facilitates the completion of High Throughput Sequencing (HTS) assembly projects with existing software.
The software enables consistent and reliable genome assembly by mitigating coverage variability.
NeatFreq is a free, open-source tool available for broader scientific application.