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ISSN 2469-9853
Journal of Next Generation Sequencing & Applications

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Chloroplast Genome Sequencing: Some Reflections

Ibrar Ahmed*

Department of Biochemistry, Quaid-i-Azam University, Islamabad, 45320, Pakistan.

*Corresponding Author:
Ibrar Ahmed
Department of Biochemistry
Quaid-i- Azam University
Islamabad, 45320, Pakistan
Tel: +92 51 9064000
E-mail: [email protected]

Received date: September 28, 2015; Accepted date: October 01, 2015; Published date: October 05, 2015

Citation: Ahmed I (2015) Chloroplast Genome Sequencing: Some Reflections. Next Generat Sequenc & Applic 2:119. doi:10.4172/2469-9853.1000119

Copyright: © 2015 Ahmed I. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Chloroplast genome in plants-also known as a plastome-is a short, circular, double stranded DNA molecule of 83-292 kb in size. This haploid genome lacks recombination, and exhibits a uniparental inheritance which is predominantly maternal in angiosperms but paternal in gymnosperms.

Opinion

Chloroplast genome in plants-also known as a plastome-is a short, circular, double stranded DNA molecule of 83-292 kb in size [1]. This haploid genome lacks recombination, and exhibits a uniparental inheritance which is predominantly maternal in angiosperms but paternal in gymnosperms [2]. Sequence information contained in chloroplast DNA loci has been widely used in plant systematics, elucidating relationships from intraspecific through deep divergence [3-7]. In some species, chloroplast genomes have also been transformed with exogenous genes encoding important metabolites, including genes for production of vaccines against human diseases [8-10]. Selected chloroplast DNA loci have been used as barcodes for identifying plant species [11,12], although the concept of a universal barcode has inherent limitations [4]. Whole chloroplast genome sequence has been suggested as a super-barcode [13], but this idea might need to be reconsidered for out-crossing plant species that are, or have been living in sympatry.

Plastome sequencing has traditionally been reliant on cloning of DNA fragments to form DNA libraries followed by long-range PCR, or using a large set of DNA primers to amplify and sequence overlapping DNA fragments. Plastome sequencing appears to have benefited a little of recent advances in high-throughput sequencing technologies, as the number of plastomes currently available in public repositories is far little than anticipated. Even some researchers who use high-throughput sequencing technologies fail to appreciate that the plastome, rich in AT nucleotides, contains a number of mononucleotide microsatellite (As / Ts). They have used sequencing platforms such as Roche 454 [14-17] that inherently have high rates of insertion-deletion (indel) errors at mononucleotide microsatellites. Often such studies need to further validate their sequencing results by using additional platforms [14], or to fill-in the gaps at mononucleotide microsatellites by amplifying missing sequences through conventional PCR and Sanger Sequencing [17], which requires additional time and money. Instead, using an alternate platform (e.g. Illumina’s MiSeq) that produces short reads of exact length and avoids errors at mononucleotide microsatellites can give better results.

In a typical plastome sequencing run, about 5-10% of sequences belong to plastome; majority of the sequences come from nuclear genome [18]. The percentage of plastome reads can be increased by enriching chloroplasts before the organelle DNA extraction [19]. Alternatively, rolling circle amplification may be used to increase the proportion of the short reads, using oligonucleotide primers which preferentially amplify AT rich regions [20]. With increased proportion of chloroplast DNA, a better approach is to multiplex or combine several plastomes in a single run. Multiplexing can give ample sequence coverage depth while giving an output data file of suitable size (~1 GB per plastome sample), making downstream analyses easier on a desktop computer. If the size of output data file is bigger (usually 3-4 GB), high computation resources (such as a server) are required for subsequent analyses. Large output data files offer little extra advantage in terms of sequence quality and coverage depth.

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