54
Assembling the reads into contigs is nonobligatory and they can be mapped to
the reference genomes. The assembly process is computationally difficult and needs
a high standard sequenced dataset; thus, it has the prospect of discovering new facts
about mRNA expression that were not feasible earlier (Bashiardes et al. 2016a, b).
In experimental terms, deep sequencing is needed to perform the assembly, and thus
only the most abundant sequences can be organized from a larger set of reads
(Morgan and Huttenhower 2014). A step of assembly is needed in the case of limitation of reference genome and gene annotation platforms. In cases when the reference genome is unavailable, annotations for the sequenced transcripts are generally
acquired through sequence similarity searches to sequenced and annotated proteins.
In other terms, alignment is carried out between the assembled transcripts and large
annotated protein databases through software such as Blast2GO (Conesa et al.
2005), and when almost identical proteins are reported, then the parallel biological
function is typically concluded. Trinity-like graph-theoretic concept-based advanced
computational methods have been developed for reconstruction of a complete transcriptome (Grabherr et al. 2011).
Another critical problem in biological interpretation from metatranscriptomics
data is to link the sequenced RNA dataset with corresponding DNA sequences.
Simultaneous analysis of such datasets allows researchers to identify expressed
mRNAs from the total present mRNAs. With the presence of the step of assembly
for analysing the sequenced RNA and subsequent post-normalization, the data can
be transformed to the corresponding expressed gene value, which can be further
interpreted through statistically analysing the 16S-rRNA and metagenomic sequencing. This step can potentially reveal the expression level of mRNA, species richness,
and similarity percentage of the samples (Bashiardes et al. 2016a, b).
5.1.3 Metatranscriptomics and Soil Microbial Diversity
Soil microbial communities carry out crucial ecosystem functions such as decomposition and geochemical cycling that robustly affect physical characteristics of the
soils along with plant health and nutrition. Soils are complex and offer an enormous
diversity of habitats owing to their structural features such as size, shape, and pore
networks connectivity, with additional features such as the complication of
resources, physicochemical characteristics, and biological interactions (Carvalhais
et al. 2012). Metatranscriptomics allows in-depth information about the potential
expression of genes at the sampling time. As post-transcriptional and posttranslational gene expression performs protein synthesis regulation, control of gene
expression at the transcriptional level allows microbes to quickly adapt to varying
environmental conditions (Moran 2009). Thus, direct regulatory reactions to environmental changes may be better revealed by metatranscriptomics as compared to
metaproteomics (Moran 2009; Carvalhais et al. 2012). Metatranscriptomics is used
to study the diversity of soil microbes from different environmental samples such as
arctic peat soils (Tveit et al. 2014), sludge (Yu and Zhang 2012), rhizospheres
5 Metatranscriptomics and Metaproteomics for Microbial Communities Profiling
Assembling the reads into contigs is nonobligatory and they can be mapped to
the reference genomes. The assembly process is computationally difficult and needs
a high standard sequenced dataset; thus, it has the prospect of discovering new facts
about mRNA expression that were not feasible earlier (Bashiardes et al. 2016a, b).
In experimental terms, deep sequencing is needed to perform the assembly, and thus
only the most abundant sequences can be organized from a larger set of reads
(Morgan and Huttenhower 2014). A step of assembly is needed in the case of limitation of reference genome and gene annotation platforms. In cases when the reference genome is unavailable, annotations for the sequenced transcripts are generally
acquired through sequence similarity searches to sequenced and annotated proteins.
In other terms, alignment is carried out between the assembled transcripts and large
annotated protein databases through software such as Blast2GO (Conesa et al.
2005), and when almost identical proteins are reported, then the parallel biological
function is typically concluded. Trinity-like graph-theoretic concept-based advanced
computational methods have been developed for reconstruction of a complete transcriptome (Grabherr et al. 2011).
Another critical problem in biological interpretation from metatranscriptomics
data is to link the sequenced RNA dataset with corresponding DNA sequences.
Simultaneous analysis of such datasets allows researchers to identify expressed
mRNAs from the total present mRNAs. With the presence of the step of assembly
for analysing the sequenced RNA and subsequent post-normalization, the data can
be transformed to the corresponding expressed gene value, which can be further
interpreted through statistically analysing the 16S-rRNA and metagenomic sequencing. This step can potentially reveal the expression level of mRNA, species richness,
and similarity percentage of the samples (Bashiardes et al. 2016a, b).
5.1.3 Metatranscriptomics and Soil Microbial Diversity
Soil microbial communities carry out crucial ecosystem functions such as decomposition and geochemical cycling that robustly affect physical characteristics of the
soils along with plant health and nutrition. Soils are complex and offer an enormous
diversity of habitats owing to their structural features such as size, shape, and pore
networks connectivity, with additional features such as the complication of
resources, physicochemical characteristics, and biological interactions (Carvalhais
et al. 2012). Metatranscriptomics allows in-depth information about the potential
expression of genes at the sampling time. As post-transcriptional and posttranslational gene expression performs protein synthesis regulation, control of gene
expression at the transcriptional level allows microbes to quickly adapt to varying
environmental conditions (Moran 2009). Thus, direct regulatory reactions to environmental changes may be better revealed by metatranscriptomics as compared to
metaproteomics (Moran 2009; Carvalhais et al. 2012). Metatranscriptomics is used
to study the diversity of soil microbes from different environmental samples such as
arctic peat soils (Tveit et al. 2014), sludge (Yu and Zhang 2012), rhizospheres
5 Metatranscriptomics and Metaproteomics for Microbial Communities Profiling