123
6.2.2 Transcriptomics
Transcriptomics for differential global gene expression studies depend upon the
reconstruction of the transcriptome of a given species. The transcriptome reconstruction mainly falls into two categories, depending on the usage of a reference
genome: the genome-guided transcriptome assembly and the de novo transcriptome
assembly. For organisms without reference genomes, only the second option is feasible, but for organisms with known reference genomes, both options are available.
D’Esposito et al. (2016) summarized that RNA-seq for transcriptome analysis of
non-model organisms is very efficient and cost-effective. Even so, the genomeguided approach is highly preferable, although de novo assembly can extend information on already existing genomes.
In Unix-based environment, a plethora of de novo transcriptome assemblers has
been developed, with the most common being Trinity (Haas et al. 2013), TransABySS (Robertson et al. 2010), SOAPdenovo-Trans (Xie et al. 2014), and Velvet/
Oases (Schulz et al. 2012). Beyond open-source software, which is extensively used
in research, commercial solutions have been developed accordingly. Differential
gene expression is quantified, in a holistic point of view, via counting “raw” reads
that map uniquely to each contig (transcript). In order to discover genes differentially expressed between two or more groups, expression values must be normalized. The normalization procedure is related with the appropriate statistical strategy
adopted (Dillies et al. 2013).
In many cases the understanding of the information acquired by microarrays or
RNA-seq data is far beyond a list of differentially expressed transcripts. Thus,
results from such experiments remain essentially unexplored. Toward this end, data
can be further explored with gene networks, through data reduction and clustering.
Gene ontology information can be useful in order to prioritize specific genes in gene
networks. Comparative analysis of transcription among different conditions was
primarily utilized through EST analysis (Reusch et al. 2008). Comparative EST
sequencing shed light to the pleiotropic effect of stress. A substantial proportion of
the sequences in a dataset remain unannotated (e.g., Kong et al. 2014). A database
available to download ESTs from Posidonia oceanica, Zostera marina, and
Nanozostera noltii is Dr. Zombo from the Institute for Evolution and Biodiversity at
the University of Münster (Wissler et al. 2009). Additionally, the complete transcriptome of Posidonia oceanica and the first gene catalogues for this plant
(D’Esposito et al. 2016) are a reliable tool to begin an assay in order to restrict
human-driven environmental changes on this Mediterranean endemic species.
Dattolo et al. (2013) studied on the in situ acclimation of P. oceanica to different
depths and revealed networks and pathways involved in response to depth gradients.
Seagrasses must cope with different levels of light irradiance at these different
depths. Furthermore, Dattolo et al. (2014) have also shown that light-associated
gene expression is connected with seagrass depth distribution; RuBisCO subunits in
P. oceanica were negatively regulated. Interestingly, Dattolo et al. (2013) observed
increased reactive oxygen species (ROS) in P. oceanica in low light conditions,
6 Abiotic Stress of Seagrasses
6.2.2 Transcriptomics
Transcriptomics for differential global gene expression studies depend upon the
reconstruction of the transcriptome of a given species. The transcriptome reconstruction mainly falls into two categories, depending on the usage of a reference
genome: the genome-guided transcriptome assembly and the de novo transcriptome
assembly. For organisms without reference genomes, only the second option is feasible, but for organisms with known reference genomes, both options are available.
D’Esposito et al. (2016) summarized that RNA-seq for transcriptome analysis of
non-model organisms is very efficient and cost-effective. Even so, the genomeguided approach is highly preferable, although de novo assembly can extend information on already existing genomes.
In Unix-based environment, a plethora of de novo transcriptome assemblers has
been developed, with the most common being Trinity (Haas et al. 2013), TransABySS (Robertson et al. 2010), SOAPdenovo-Trans (Xie et al. 2014), and Velvet/
Oases (Schulz et al. 2012). Beyond open-source software, which is extensively used
in research, commercial solutions have been developed accordingly. Differential
gene expression is quantified, in a holistic point of view, via counting “raw” reads
that map uniquely to each contig (transcript). In order to discover genes differentially expressed between two or more groups, expression values must be normalized. The normalization procedure is related with the appropriate statistical strategy
adopted (Dillies et al. 2013).
In many cases the understanding of the information acquired by microarrays or
RNA-seq data is far beyond a list of differentially expressed transcripts. Thus,
results from such experiments remain essentially unexplored. Toward this end, data
can be further explored with gene networks, through data reduction and clustering.
Gene ontology information can be useful in order to prioritize specific genes in gene
networks. Comparative analysis of transcription among different conditions was
primarily utilized through EST analysis (Reusch et al. 2008). Comparative EST
sequencing shed light to the pleiotropic effect of stress. A substantial proportion of
the sequences in a dataset remain unannotated (e.g., Kong et al. 2014). A database
available to download ESTs from Posidonia oceanica, Zostera marina, and
Nanozostera noltii is Dr. Zombo from the Institute for Evolution and Biodiversity at
the University of Münster (Wissler et al. 2009). Additionally, the complete transcriptome of Posidonia oceanica and the first gene catalogues for this plant
(D’Esposito et al. 2016) are a reliable tool to begin an assay in order to restrict
human-driven environmental changes on this Mediterranean endemic species.
Dattolo et al. (2013) studied on the in situ acclimation of P. oceanica to different
depths and revealed networks and pathways involved in response to depth gradients.
Seagrasses must cope with different levels of light irradiance at these different
depths. Furthermore, Dattolo et al. (2014) have also shown that light-associated
gene expression is connected with seagrass depth distribution; RuBisCO subunits in
P. oceanica were negatively regulated. Interestingly, Dattolo et al. (2013) observed
increased reactive oxygen species (ROS) in P. oceanica in low light conditions,
6 Abiotic Stress of Seagrasses