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NGS sequencing technology
Sample processing is the major and most important phase in any metagenomic research. Hence, DNA extracted should represent all cells present in the sample and an adequate quantity of high-quality DNA must be acquired for consequent library production and sequencing. Exact procedures are required for each sample type and different robust methods of DNA extraction are presented by different researchers (Venter et al., 2004; Bruke et al., 2009; Delmont et al., 2011). Attempts have also been made to discover microbial diversity from different ecosystems using a single DNA extraction technology to ensure compatibility and a high level of precision.
Different sequencing technologies are now available, though Sanger sequencing is still considered the gold standard for sequencing, because of its low error rate, long read length (> 1,500 bp) and large insert sizes. These features will help improve assembly outcomes for shotgun data, which makes Sanger sequencing still appropriate for generating close to complete genomes in low-diversity environments (Goltsman et al., 2009). Of all the NGS technologies, the Roche 454, Illumina and Ion Torrent systems have been used extensively in metagenomic samples (Mardis, 2008; Metzker, 2010); however, the PacBio technology may replace them in the near future. Since the Roche 454 and Illumina technologies are mostly used in metagenomic research, it is of importance to describe their advantages and limitations in sequencing of metagenomics samples briefly (Oulas et al., 2015)
The chemistry of the 454 pyrosequencer relies on immobilisation of DNA fragments on DNA-capture beads in a water-oil emulsion and then using PCR to amplify the fixed fragments. The beads are placed on a PicoTiterPlate. DNA polymerase is also packed in the plate and pyrosequencing takes place (Ronaghi et al., 1998; Ronaghi, 2001). While Roche 454 pyrosequencing technology is considered highly reliable, it is associated with generation of several types of artefacts, which may affect the metagenomic data analysis and lead to biased results (Rosen et al., 2012). One problem consists in generation of artificial replicates of the same read that may cause an overestimation of species abundance or functional gene abundance in a sample. Amplification errors in the form of single base pair mismatches and improper sequencing of mononucleotide stretches of DNA may cause frame shifts in protein-coding genes (Rothberg and Leamon, 2008). Chimera sequences generated by an undesired end joining of two or more true sequences can also affect the results of metabarcoding based on amplified 16S rRNA with respect to the species richness (Bordin et al., 2013). The 454 pyrosequencing technology produces reads of up to 1 000 bp in length and >1 000 000 reads per run. The comparatively long reads length produced by this technology compared to other NGS technologies makes it more suitable for assembly genomes from shotgun metagenomic datasets and allows for better annotation accuracy (Wommack et al., 2008; Thomas et al., 2012)
CHAPTER 1: Literature Review
1.1 Sequencing technologies and advance in genomic studies
1.1.1 First-generation sequencing: Classical sequencing
1.1.2 Second-generation sequencing: Next-generation sequencing .
1.1.3 Third-generation sequencing: Insight into the near future
1.1.4 Fourth-generation sequencing: In situ sequencing
1.2 Metagenomics
1.2.1. Methods and approaches of metagenomics
1.2.2 NGS sequencing technology .
1.2.3 Assembly
1.2.4. Binning and binning algorithms
1.2.5 Taxonomy-dependent methods.
1.2.6 Taxonomy-independent or read clustering approaches
1.2.7 Strategies of validation of binning results
1.2.8 Annotation of metagenomic reads.
1.2.9 Sharing and storage of metagenomic data
1.3 Barcoding
1.4 Research aim and objectives
References
CHAPTER 2: Selection of reference genomes of microorganisms for case studies and design of the BarcodeGenerator: A novel software tool for generation of diagnostic barcode sequences
Abstract .
2.1 Introduction
2.2 Selection of microorganisms for case studies .
2.2.1 Bacillus
2.2.2 Escherichia coli and Shigella.
2.2.3 Lactobacillus
2.2.4 Mycobacteri
2.2.5 Prochlorococcus
2.2.6 Salmonella
2.2.7 Shewanella.
2.2.8 Streptococcus.
2.3 Design and implementation of a computer algorithm for the generation of diagnostic barcode sequences .
2.4 Conclusion.
CHAPTER 3 Program implemention for Barcoding 2.0
Abstract
3.1 Introduction
3.2 Methods and research design
3.3 Program implementation
CHAPTER 4: Barcoder web interface and case study of barcode-guided species detection
Abstract .
4.1 Introduction .
4.2 BarcodeGenerator.
4.3 Barcoding 2.0 command line interface for metagenome analysis and visualisation
4.4 Help and downloads .
4.4 SeqWord project.
4.6 Conclusion.
CHAPTER 5: Evaluation of the program Barcoding 2.0 by binning real metagenomic reads
Abstrac
5.1 Introduction
5.2 Program implementation
5.3. Identification of barcoded sequences in real metagenomes
5.4. Consistency of identification of taxonomic groups in real metagenomes .
5.5 Conclusion
References
CHAPTER 6: General conclusion.
Appendix 1
Appendix 2
