65
comparison with other short read assembly programs, Genovo access additional nucleotides bases and identifies/predicts more genes and provides a
higher score for gene assembly (Laserson et al. 2011).
(ii) khmer: khmer is a memory-efficient graph representation for analysis of the
k-mer analysis of metagenomically processed samples that overcomes the
limitation of high-end-memory necessities for de novo assembly of shortreads sequenced via shotgun methods. Over a soil metagenome assembly, this
method obtains highly efficiency from memory perspectives for assembly
(Pell et al. 2012).
(iii) Meta-IDBA is a useful asset to assemble the diverse metagenomic reads possessing numerous genomes from various species. Meta-IDBA toolkit is available from http://www.cs.hku.hk/~alse/metaidba (Peng et al. 2011).
(iv) metAMOS: MetAMOS is a fully automatized and powerful tool to assemble
the reads, produces a valuable scaffold, intron–exon identification, and complete annotation. MetAMOS is available from https://github.com/treangen/
MetAMOS (Treangen et al. 2013).
(v) MetaVelvet: MetaVelvet provides an advanced version of Velvet with the
power to assemble complex short reads of various sources. MetaVelvet is also
able to reconstruct comparatively poor coverage reads (Namiki et al. 2012).
(vi) MOCAT: MOCAT is an extremely flexible and rapid platform for quality control; mapping and assembly of Illumina-based single or paired-end reads.
MOCAT can be accessed on http://www.bork.embl.de/mocat/ (Kultima et al.
2012).
(vii) SOAPdenovo: SOAPdenovo is a cheaper approach for assembling of the large
genomes via the de novo method (Li et al. 2010).
(viii) MetaORFA: Metagenomic ORFome Assembly (MetaORFA) facilitates
metagenomic data analysis in three steps. In the first step, reads from the
metagenomics dataset are annotated with putative protein-encoding regions.
Then, the predicted regions are assembled through EULER assembly
approach into a group of peptides. In the third, the processed proteins provide
homologues and successive diversity with the help of the online databases
(Ye and Tang 2009).
(ix) FragGeneScan: FragGeneScan is a Markov model-based improved gene prediction tool help in identification of the translated region of the genome from
small reads (Rho et al. 2010).
(x) MetaGeneAnnotator (MGA): MetaGeneAnnotator (MGA) is again a potential tool for unicellular prokaryote gene identification (Noguchi et al. 2008).
(xi) Orphelia: Orphelia is also a gene prediction tool applicable in small reads
that can be found on http://orphelia.gobics.de. (Hoff et al. 2009).
(xii) SILVA: Ribosomal RNA (rRNA) genes sequencing is at present a highly
accepted method for assessing the nature of unculturable microbes. SILVA is
a central all-inclusive web resource for most recent, quality controlled databases of aligned highly conserved sequences from all prokaryotes as well as
complex microorganisms (Pruesse et al. 2007).
6.1 Tools for Assembly and Annotation
comparison with other short read assembly programs, Genovo access additional nucleotides bases and identifies/predicts more genes and provides a
higher score for gene assembly (Laserson et al. 2011).
(ii) khmer: khmer is a memory-efficient graph representation for analysis of the
k-mer analysis of metagenomically processed samples that overcomes the
limitation of high-end-memory necessities for de novo assembly of shortreads sequenced via shotgun methods. Over a soil metagenome assembly, this
method obtains highly efficiency from memory perspectives for assembly
(Pell et al. 2012).
(iii) Meta-IDBA is a useful asset to assemble the diverse metagenomic reads possessing numerous genomes from various species. Meta-IDBA toolkit is available from http://www.cs.hku.hk/~alse/metaidba (Peng et al. 2011).
(iv) metAMOS: MetAMOS is a fully automatized and powerful tool to assemble
the reads, produces a valuable scaffold, intron–exon identification, and complete annotation. MetAMOS is available from https://github.com/treangen/
MetAMOS (Treangen et al. 2013).
(v) MetaVelvet: MetaVelvet provides an advanced version of Velvet with the
power to assemble complex short reads of various sources. MetaVelvet is also
able to reconstruct comparatively poor coverage reads (Namiki et al. 2012).
(vi) MOCAT: MOCAT is an extremely flexible and rapid platform for quality control; mapping and assembly of Illumina-based single or paired-end reads.
MOCAT can be accessed on http://www.bork.embl.de/mocat/ (Kultima et al.
2012).
(vii) SOAPdenovo: SOAPdenovo is a cheaper approach for assembling of the large
genomes via the de novo method (Li et al. 2010).
(viii) MetaORFA: Metagenomic ORFome Assembly (MetaORFA) facilitates
metagenomic data analysis in three steps. In the first step, reads from the
metagenomics dataset are annotated with putative protein-encoding regions.
Then, the predicted regions are assembled through EULER assembly
approach into a group of peptides. In the third, the processed proteins provide
homologues and successive diversity with the help of the online databases
(Ye and Tang 2009).
(ix) FragGeneScan: FragGeneScan is a Markov model-based improved gene prediction tool help in identification of the translated region of the genome from
small reads (Rho et al. 2010).
(x) MetaGeneAnnotator (MGA): MetaGeneAnnotator (MGA) is again a potential tool for unicellular prokaryote gene identification (Noguchi et al. 2008).
(xi) Orphelia: Orphelia is also a gene prediction tool applicable in small reads
that can be found on http://orphelia.gobics.de. (Hoff et al. 2009).
(xii) SILVA: Ribosomal RNA (rRNA) genes sequencing is at present a highly
accepted method for assessing the nature of unculturable microbes. SILVA is
a central all-inclusive web resource for most recent, quality controlled databases of aligned highly conserved sequences from all prokaryotes as well as
complex microorganisms (Pruesse et al. 2007).
6.1 Tools for Assembly and Annotation