Material properties of N-10.
\r\n\tThe TSPO exact role in cellular metabolism is still enigmatic, and therefore, the understanding of its cellular function gradually opens new potential ways for therapeutic interventions, especially when cellular apoptosis, necrosis and energy utilization are involved.
\r\n\tThis book will present the overview on current knowledge of the TSPO functions in cells of different organs and aim to focus on the constantly ongoing research of cellular metabolism.
Lead zirconate titanate (PZT) ceramics in special electronic devices such as structural health monitoring systems of liquid rocket engines and microvalves for space applications are subjected to cryogenic temperatures. PZT ceramics are also used in active fuel injectors under severe environments (Senousy et al. 2009a; 2009b). In the application of the PZT stack actuators to hydrogen fuel injectors, the actuators are operated under electric fields at cryogenic temperatures. Hence, it is important to understand the cryogenic electromechanical response of the PZT actuators under electric fields.
In this chapter, we address the present state of piezomechanics in PZT stack actuators for fuel injectors at cryogenic temperatures. First, we discuss the cryogenic response of PZT stack actuators under direct current (DC) electric fields (Shindo et al. 2011). A thermodynamic model is used to predict a monoclinic phase around a morphotropic phase boundary (MPB). A shift in the boundary between the tetragonal and rhombohedral/monoclinic phases with decreasing temperature is determined, and the temperature dependent piezoelectric coefficients are evaluated. Temperature dependent coercive electric field is also predicted based on the domain wall energy. A finite element analysis (FEA) is then performed, considering the shift in the MPB and polarization switching, to calculate the electromechanical fields of the PZT stack actuators from room to cryogenic temperatures. In addition, experimental results on the DC electric field induced strain, which verify the model, are presented. Next, we discuss the dynamic response of PZT stack actuators under alternating current (AC) electric fields at cryogenic temperatures (Shindo et al. 2012). Dynamic electromechanical fields of the PZT stack actuators from room to cryogenic temperatures are simulated by the FEA with MPB shift and domain wall motion effects. Dynamic strain measurements of the PZT stack actuators under AC electric fields are also presented, and a comparison is made between calculations and measurements to validate the predictions. Moreover, a parametric study using FEA is performed to investigate the factors affecting the cryogenic response of PZT stack actuators and to provide a basis for selecting desirable design details.
Consider the orthogonal coordinate system with axes
where (
where (
The electric field components are related to the electric potential
Temperature dependent piezoelectric coefficient is outlined here. Figure 1 shows the phase diagram of PZT established in Jaffe et al. (1971) and Noheda et al. (2000). As the temperature
The MPB between the tetragonal and rhombohedral/monoclinic phases is the origin of the unusually high piezoelectric response of PZT, and this MPB is numerically predicted. For simplicity here, we ignore the octahedral tilt transition which differentiates the high temperature (HT) and low temperature (LT) rhombohedral phases, and the orthorhombic phase.
An energy function for the solid solution between the two end-members PbTiO3 and PbZrO3 is given by (Bell & Furman 2003)
where
In Eqs. (8) - (10),
PZT phase diagram
The thermodynamic equilibrium state can be determined via minimization of Δ
Cubic (C)
Tetragonal (T)
Rhombohedral (R)
Monoclinic (M)
The energies of the minima are then compared to define the stable state. In the simulation,
We now show a numerical example and comparison with experiments. Fig. 2 shows the
Calculated and experimental phase diagram of PZT
calculated PZT phase diagram. We also plot the experimental phase diagrams of PZT. The open square, open circle and solid circle are the results from Jeffe et al. (1971), Noheda et al. (2000) and Pandey et al. (2008). The simulation result reasonably agrees with the experimental data. It is noted that at room temperature, the MPB is located at about
where
High electromechanical fields lead to the polarization switching. We assume that the direction of a spontaneous polarization
where
For 90o switching in the
where
The constitutive equations after polarization switching are
where
In Eq. (23),
A domain wall displacement causes changes of strain and polarization (Cao et al. 1999). For simplicity, here the applied AC electric field
where all terms with Δ are the contributions from the domain wall motion, and
In Eq. (25),
Schematic drawing of many grains which in turn consist of domains and basic unit of a piezoelectric crystallite with a domain wall.
where
Experimental studies on PZT ceramics have shown that 45-70% of dielectric and piezoelectric moduli values may originate from the extrinsic contributions (Luchaninov et al. 1989, Cao et al. 1991). The extrinsic dielectric constant Δ is approximately estimated as the two thirds of the bulk properties (Li et al. 1993). Here, the following equation (Narita et al. 2005) is utilized to describe Δ in terms of AC electric field amplitude
By substituting Eq. (28) into the sixth of Eq. (25),
Consider a PZT stack actuator with 300 PZT layers of width
In order to discuss the electromechanical fields near the internal electrode, the problem of the stack actuator is solved using the unit cell model (two layer piezoelectric composite with |
Schematic drawing of PZT stack actuator.
First, we consider the PZT stack actuator under DC electric fields. The electric potential on two electrode surfaces (-
Unit cell of the PZT stack actuator.
Each element in ANSYS is defined by eight-node 3-D coupled field solid for the PZT layers and eight-node 3-D structural solid for the coating layer.
where
The stack actuator is fabricated using 300 soft PZT N-10 layers (NEC/Tokin Co. Ltd., Japan) of width
Experimental setup.
Elastic compliance | Piezoelectric coefficient | Dielectric constant | Density | |||||||
(×10-12 m2/N) | (×10-12 m/V) | (×10-10 | (kg/m3) | |||||||
14.8 | 18.1 | 44.9 | -5.1 | -5.8 | -287 | 635 | 930 | 443 | 481 | 8000 |
Material properties of N-10.
coordinate system O-
The actuator is bonded to the test rig of a SUS304 stainless steel plate using epoxy bond, and DC voltage (0 Hz) and AC voltage (400 Hz) are applied using a power supply. Two strain gages are attached at the center of the
We first consider the PZT stack actuators under DC electric fields. Figure 7 shows the predicted normal strain
We next consider the PZT stack actuators under AC electric fields. Figure 10 shows the normal strain
Strain vs temperature of PZT stack actuators for X = 0.44 and 0.56 under DC electric field
Strain vs DC electric field of PZT stack actuators for X = 0.56 at 20 K.
Polarization switching zones at
Strain vs AC electric field of PZT stack actuators for X = 0.44 and 0.56 at 20 K.
Variation of normal stress
Variation of electric field
Numerical and experimental examination on the electromechanical response of PZT stack actuators at cryogenic temperatures is reported. It is found that the electric field induced strain decreases or increases with decreasing temperature depending on the mole fraction. That is, in the case of high performance PZTs for X = 0.44, the electric field induced strain is very high at room temperature, whereas in the case of PZTs for X = 0.56, the electric field induced strain at cryogenic temperatures will seem to be higher than at room temperature. It is also shown that the stress and electric field in the PZT layers are very high near the electrode tip for the PZT stack actuators with partial electrodes, although the electric field induced strains at the center of the surface for the partially and fully electroded PZT stack actuators have the same level. This study may be useful in designing high performance hydrogen fuel injectors.
Next-generation sequencing technologies have transformed our perception of diversity and microbial distribution in natural ecosystems and have contributed substantially to the discovery of totally new microbial landscapes in such distinctive environments as the gut of mammals, the vegetal rhizosphere, vascular tissues of higher plants, and even in volcanic lakes [1, 2, 3]. There are two general approaches to profile microbial communities through next-generation sequencing techniques: shotgun sequencing of total DNA isolated directly from the environment and sequencing of variable regions coming from SSU-rRNA genes (we know these approaches as metagenomics methods since all involve the culture-independent genomic analysis of microbiomes on a particular environment [4, 5]). Both approaches have been widely used to trace microbial diversity at increasingly fine taxonomic levels, either by capturing a representative fraction of the total gene content or by amplicon sequencing techniques like the popular bacterial 16S rRNA. Each method has advantages and disadvantages, and the selection depends on several factors like taxonomic level resolution, cost, sensitivity, and primer bias, among others. One of the challenges associated with metagenomics methods is the analysis of massively generated data. Both the sequencing of amplicons and environmental DNA produces millions of short DNA sequences (reads), which must undergo preprocessing and quality control, before they can be used to extract biologically useful information from them. One of the goals of massively sequencing data analysis is to obtain the patterns of phylogenetic diversity in ecological communities, an important trait in order to assess the classic ecological questions “Who is there?” or “What they are doing?” and provide better understandings into the phylogenetic relationships among microbial community taxa. Extracting phylogenetic information from massive sequencing reads is not a trivial task; however, it can be achieved with reasonable success by using several profiling tools adapted both to the analysis of amplicons of ribosomal genes and to the conserved genes between different domains [6, 7]. The microbial community structure has been approached mostly using the 16S SSU-rRNA gene as phylogenetic marker, mainly due to lower sequencing costs and an acceptable relation of specificity-resolution in taxonomic assignments [8], while methods that use single-copy markers obtained from shotgun sequencing reads or assembled samples are gaining relevance because they have demonstrated strain-level resolution [9, 10], a really hard issue when analyzing complex microbiomes.
\nTo date, several computational tools have been developed to carry out community profiling and phylogenetic inferences from next-generation sequencing data with considerable success. In this chapter we present a compendium of open-source tools and easy-to-use with modest hardware requirements, with the aim that they can be applied by biology non-specialists to study microbial diversity in a phylogenetic context. We show several practical examples explained step by step, in order to provide to the reader, the replication using their own data.
\nWe have selected tools for use on a local computer through the Unix command line, and tools are available from dedicated servers, with easy access and intuitive use. The examples described in the chapter were tested on a Dell Optiplex 7010 desktop, 6T6ZYV1 Series, Intel (R) Core (TM) i5-3550 CPU at 3.30 GHz, Memory 12 GiB.
\nWith the advent of massive DNA sequencing technologies, several methods have been developed to assign shotgun reads to microbial taxonomic categories. These methods aim to perform a microbial community profiling that infers its relative structure, and they are very important to understand how microbiomes work in nature, their phylogenetic composition, and even their dynamics and evolutionary history. The starting point for these analyzes is a set of reads obtained by massive sequencing whose length is variable (as little as 50–75 bp up to >1000 bp) depending on the platform used (Illumina, Ion Torrent, PacBio RS). We can understand by a read the sequence of bases from a single discrete molecule of DNA, obtained in a massively parallel manner [11]. However, currently most metagenomics studies use a range of a short-read sequencing instruments between 100 and 600 bp in order to maximize counting reads and lower costs. These short-reads contain the genomic, phylogenetic, and functional information of the microbiome into millions of discrete DNA fragments, which are sufficient to make a reliable estimate of the phylogenetic diversity present in a microbial sample (Figure 1).
\nGeneral overview of metagenomics analysis in a microbial sample by next-generation sequencing: (1) isolation of metagenomic DNA, (2) sequencing DNA library, (3) reads output text file, and (4) data analysis.
The taxonomic composition of a microbial community can be estimated from a set of short-reads by assigning each read to the most likely microbial lineage [12]. Historically, a single gene target approach has been the gold standard for assigning taxonomy in the Prokaryote domain, through the 16S ribosomal RNA gene. However, this presents important biases related to copy-number variations and significant intraspecific differences ~6%. In this sense, both clade-specific and universal single-copy phylogenetic markers genes have gained popularity among the scientific community since they are not subject to intragenomic diversity, are rarely subjects of horizontal transfer, and have proven robustness to delineate species and prokaryotic strains in multiple studies, because several genes can be combined to reconstruct phylogenies [13, 14]. Although each method selects its own set of clade-specific or universal markers, most of these genes encode proteins with functional relevance in housekeeping metabolism (Table 1). To make the analysis, the coding nucleotide sequences are generally used as they offer better resolution than amino acid sequences in closely related organisms [16]. This simplifies the computational analysis as the short-reads could be compared unambiguously without the need to translate them into proteins, which could generate artifacts given the small size of the reads.
\nOne of the most popular tools for microbial profiling based on clade-specific marker genes is the MetaPhlAn classifier [12, 17]. MetaPhlAn maps the experimental reads against a collection of 231 markers for species-level comparisons and >115,000 markers for higher taxonomic levels. Among the advantages of this classifier is that no preprocessing is required, so raw data can be uploaded and analyzed. The main disadvantage for non-specialists is that MetaPhlAn works through the command line in a Unix architecture.
\nNext we described the steps performed for profiling a microbial community capable of degrading the textile dye HC Blue no. 2. Also we show a graphical representation of the profiling phylogenetic metadata. This general strategy can be applied to profile any microbial community from short-reads obtained by massive sequencing. Symbol convention: Comments (#); executable commands ($). The raw data are available on [18].
\nYou can find a complete MetaPhlAn guide on the author’s site: https://bitbucket.org/biobakery/biobakery/wiki/metaphlan2.
\n#
# with an activated Bioconda channel in Linux, type the following command:
\n$
# this will install the software with all its dependencies
\n# Type the following command:
\n\n
# The output profile (called: textile_microbiome _profile.txt) contains the computed clade\'s abundances (Table 2).
\n\nClusters of orthologous groups of proteins (COG) | \nProtein name | \n
---|---|
COG0048 | \nRibosomal protein S12 | \n
COG0049 | \nRibosomal protein S7 | \n
COG0052 | \nRibosomal protein S2 | \n
COG0080 | \nRibosomal protein L11 | \n
COG0081 | \nRibosomal protein L1 | \n
COG0085 | \nDNA-directed RNA polymerase, beta subunit | \n
COG0087 | \nRibosomal protein L3 | \n
COG0088 | \nRibosomal protein L4 | \n
COG0090 | \nRibosomal protein L2 | \n
COG0091 | \nRibosomal protein L22 | \n
COG0092 | \nRibosomal protein S3 | \n
COG0093 | \nRibosomal protein L14 | \n
COG0094 | \nRibosomal protein L5 | \n
COG0096 | \nRibosomal protein S8 | \n
COG0097 | \nRibosomal protein L6P/L9E | \n
COG0098 | \nRibosomal protein S5 | \n
COG0099 | \nRibosomal protein S13 | \n
COG0100 | \nRibosomal protein S11 | \n
COG0102 | \nRibosomal protein L13 | \n
COG0103 | \nRibosomal protein S9 | \n
COG0124 | \nHistidyl-tRNA synthetase | \n
COG0172 | \nSeryl-tRNA synthetase | \n
COG0184 | \nRibosomal protein S15P/S13E | \n
COG0185 | \nRibosomal protein S19 | \n
COG0186 | \nRibosomal protein S17 | \n
COG0197 | \nRibosomal protein L16/L10E | \n
COG0200 | \nRibosomal protein L15 | \n
COG0201 | \nPreprotein translocase subunit SecY | \n
COG0202 | \nDNA-directed RNA polymerase, alpha subunit/40 kD subunit | \n
COG0215 | \nCysteinyl-tRNA synthetase | \n
Universal single-copy phylogenetic marker genes employed in metagenomics-based phylogenies for delineation of prokaryotic species (modified from [15]).
#Sample ID | \nAbundance MetaPHlAn2_analysis (%) | \n
---|---|
k__Bacteria | \n56.02708 | \n
k__Archaea | \n43.94783 | \n
k__Viruses | \n0.02509 | \n
k__Bacteria|p__Firmicutes | \n45.48396 | \n
k__Archaea|p__Euryarchaeota | \n43.94783 | \n
k__Bacteria|p__Proteobacteria | \n8.46518 | \n
k__Bacteria|p__Actinobacteria | \n2.07794 | \n
k__Viruses|p__Viruses_noname | \n0.02509 | \n
Features of the abundance table for a textile dye degrader microbiome profile.
The taxonomic levels are: Kingdom, k; and Phylum, p. The table was trimmed to show only up to the phylum level; to read complete report, see [7].
# In order to visualize microbial abundances on a phylogeny we\'ll use GraPhlAn tool [19].
\n# Installing GraPhlAn
\n# Type the following two commands:
\n\n
\n
# In order to know the installation directory type the following command:
\n$ which graphlan
\n# Type the following commands sequentially:
\n\n
\n
# Type the following commands sequentially:
\n\n
\n
Finally, you will obtain:
a cladogram called: merged_abundance.png
an annotation file called: merged_abundance_annot.png
a legend file called: merged_abundance_legend.png
You can change the format of the final results to pdf, just modifying the name:
Cladogram produced by GraPhlAn software with metadata from MetaPhlan community profiling to order level. Taxon abundance is proportional to the circle diameter.
Estimating the taxonomic and phylogenetic diversity of a microbial community is also possible through sequencing and analysis of small ribosomal RNA subunit (16S rRNA) gene, whenever this sequence has been considered for a long time a stable marker, crucial in the microbial systematics of the last 30 years. 16S ribosomal ribonucleic acid is a key component of the small subunit of prokaryotic ribosomes, central player in the cellular biology of microorganism; it serves as a linker for the process of translating genetic information to proteins [20]. Because DNA is much easier to sequence than RNA, DNA segment coding for 16 rRNA is obtained for the purposes of sequencing (Figure 3). This gene fragment meets several features that have made it a “quasi-gold standard” for bacterial taxonomy:
It is a ubiquitous gene in the Bacteria and Archaea domains.
Within its ~1500 bp, it has discrete regions with enough variability to establish a phylogenetic signal among phyla and even genus.
It has conserved regions that allow the design of “universal primers,” a very useful feature in massive sequencing.
It has several databases enriched with sequences from almost all international projects where 16S sequences are obtained (Table 3). For example, the 16S ribosomal RNA (Bacteria and Archaea) database from the National Center for Biotechnology Information (NCBI) contains near to 20,831 curated records and more than 17 million of total records (consulted date: 2019/08/05: https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome).
Prokaryotic ribosome general representation and variable sequence regions used in microbial phylogenetic diversity estimations.
Database | \nAvailable SSU sequences | \nCurrent release | \nCitation/link | \n
---|---|---|---|
16S NCBI database | \n20831\na\n\n | \n2019 | \n[21]/https://blast.ncbi.nlm.nih.gov/\n | \n
SILVA rRNA database | \n23629\nb\n\n | \n2018 | \n[22]/https://www.arb-silva.de\n | \n
Ribosomal Database Project (RDP) | \n16277 | \nSeptember 2016 | \n[23]/https://rdp.cme.msu.edu\n | \n
EzBioCloud 16S database | \n13132\nc\n\n | \n2019 | \n[24]/https://www.ezbiocloud.net/\n | \n
Genomic-based 16S ribosomal RNA database (GRD) | \n13202 | \n2015 | \n\nhttps://metasystems.riken.jp/grd/download.html\n | \n
Most popular public databases for depositing and analyzing sequences of the 16S ribosomal gene.
Not redundant manually curated small (16S, SSU) subunit ribosomal RNA sequences.
The dataset contains 23,629 SSU sequences representing a single bacterial type strain up to June 2017.
Phylotypes with validly published names.
Microbial diversity is measured as a function that depends on the richness and abundance of distinct taxons among any community [25]. Obtaining representative DNA sequences from the entire community is essential to make valid inferences. Profiling a microbial community through 16S gene analysis generally consists of four steps (Figure 4). To date, several computational tools have been developed to analyze microbial communities through the 16S gene marker; however, estimating the total microbial diversity in any environment is a still a major challenge [6, 26, 27, 28], influenced by several factors, among them we want to mention two: (I) processing huge amounts of data moves within the limits of modern computing and (II) the need for some expertise that can cost years of training. Fortunately, many tools have been developed in recent years, aiming to make bioinformatics platforms dedicated to this type of analysis more human-friendly, and there are dedicated sites exclusively to deposit computational alternatives for almost all needs, for example, https://github.com/.
\nGeneral steps for profiling a microbial community through 16S gene analysis.
A good example of these multiplatforms to profile microbial communities is the Microbiome Taxonomic Profiling (MTP) pipeline from EzBioCloud site (https://www.ezbiocloud.net/contents/16smtp) [24]. Among its fundamental advantages are: it is free, knowledge of Linux environment is not needed to carry out the analyses, and several types of outputs such as functional profiles, taxonomic and phylogenetic structure, as well as on-demand comparison with other published microbiome data are fully available. New users of EzBioCloud will be required to open a local server account (https://www.ezbiocloud.net/signup?from=addMTP); after that you can upload up to 100,000 reads for sample and begin the analysis. We list general steps to perform a profiling on the platform (Box 1).
\n16S-based microbiome taxonomic profiling pipeline used in EzBioCloud.
The platform consists of a very intuitive and user-friendly presentation that guides the beginner user at every stage of the analysis. The first step is the uploading of the next-generation sequencing data (16S amplicon reads). After that, you can request for the MTP pipeline, and the analysis starts. In a relatively short time, you can access the result portal with the preprocessing results resumed in pre-filtered reads (by removing low-quality and chimeric amplicons), statistics about read lengths, and taxonomic read assignments at species level.
\nOther outputs in results portal are related with several diversity indices, taxonomic composition and hierarchy, and graphical implementations like Krona [29]. MTP implements seven different diversity indices; among them is the phylogenetic diversity index, a measure of biodiversity that considers phylogenetic difference between taxons and ponders several variables like taxonomic diversity and species abundances or distributions.
\nIn occasions, we do not have a set of DNA short-reads, but assembled composites in contiguous regions of variable size. Such is the case of genomes assembled from metagenomes or contigs from complex metagenomes. Inferring taxonomic diversity from this type of data usually requires other strategies. One of the most useful is to predict all the rRNA sequences contained in the assembly and cluster them according to their identity (this implies making a list of nonredundant sequences) to define operative taxonomic units. A simple way to address this problem is through the use of Barrnap software [27]; it works through the Unix command line and has the advantage of consuming few computational resources, so that several complex microbiomes can be analyzed in a personal computer for extraction of rRNA sequences. Barrnap gives us an output with all predicted sequences; this includes 5S, 16S, and 23S rRNA in the case of bacteria. The sequences can be saved on-demand in a text file and subsequently analyzed by a third-party phylogenetic processing software to establish evolutionary relationships between taxa. A suitable platform for this objective is SeaView [28], which contains sequence alignment and curing utilities, as well as a set of phylogenetic reconstruction methods, like PhyML, which uses maximum likelihood algorithms and seven different evolutionary models. It is also possible to use distance methods such as Neighbor Joining and BioNeighbor Joining, both with seven different methods to calculate distances between sequences. The platform is open access and has the advantage of being a graphical application that works on Unix and Windows, as well as being very intuitive.
\nGenome-based comparisons play an essential role in the current taxonomy and phylogenetic of Bacteria and Archaea domains and eventually will replace the single gene target approach ruled by 16S rRNA gene phylogeny. The exponential growth of complete genomes and genome drafts with significant completeness values and low contamination (<5%) in international databases has resulted in an approach to phylogenetic analysis where the whole information has become in a more conservative fingerprint of the taxonomic categories. The current challenges for science involve improving existing methods for data acquisition and processing, since comparative analysis, even among modest-sized microbial genomes, can be computationally expensive. Here we present a list of those open-source tools and easy-to-use and modest hardware requirements, with the aim that they can be applied by biologists to study microbial diversity in a phylogenetic context (Table 4).
\nSoftware | \nApplication | \nNGS data | \nLicense | \nEnvironment | \nReference | \n
---|---|---|---|---|---|
MetaPhlAn | \nMicrobial community profiling | \nShotgun sequencing data | \nOpen access | \nUnix command line | \n[17] | \n
FOCUS | \nTaxonomic profiling | \nShotgun unannotated sequencing reads | \nOpen access | \nUnix command line and Web implementation | \n[30] | \n
Kraken | \nAssigning taxonomic labels to metagenomic DNA sequences | \nShotgun unannotated sequencing reads | \nOpen access | \nUnix command line | \n[31] | \n
GraPhlAn | \nPhylogenetic analysis | \nMetadata from short-read community profiling | \nOpen access | \nUnix command line | \n[19] | \n
PICRUSt | \nPredictive functional profiling of microbial communities | \n16S amplicons | \nOpen access | \nUnix command line | \n[32] | \n
QIIME | \nTaxonomic and phylogenetic profiling | \n16S amplicons | \nOpen access | \nUnix command line and web implementation | \n[28] | \n
Mothur | \nTaxonomic and phylogenetic profiling | \n16S rRNA gene sequences | \nOpen access | \nUnix command line | \n[6] | \n
UBCG | \nPhylogenomic tree reconstruction | \nSet of bacterial genomes | \nOpen access | \nUnix command line | \n[33] | \n
GToTree | \nA user-friendly workflow for phylogenomics | \nSet of bacterial genomes | \nOpen access | \nUnix command line | \n[34] | \n
PhylOTU | \nIdentifies OTUs from rRNA sequence by phylogenetic profiles | \nPCR and shotgun sequenced SSU-rRNA markers | \nfreely available | \nUnix command line | \n[35] | \n
PhyloSift | \nPhylogenetic analysis of genomes and metagenomes | \nMetagenomic datasets generated by modern sequencing platforms | \nFreely available | \nUnix command line | \n[36] | \n
VITCOMIC2 | \nPhylogenetic representation based on 16S rRNA gene amplicons | \n16S amplicons | \nFreely | \nWeb server | \n[37] | \n
Barrnap | \nVery fast ribosomal RNA prediction | \nAssemblies from genomic or metagenomic data | \nFreely available | \nUnix command line | \n[38] | \n
SeaView | \nMultiplatform for phylogenetic inferences | \nDNA or protein sequences | \nFreely available | \nUnix and Windows environments | \n[39] | \n
Open-source software for metagenomics-based profiling and phylogenies.
Profiling microbial communities from massive sequencing data constitutes a breaking point in the understanding of population structure and dynamics, their ecological functions and the complex relationships established between non-cultivable microorganisms. Through technological developments such as next-generation sequencing and the developing of hundreds of open-access platforms, we have been able to better understand the role of the microbial world in natural ecosystems. This chapter intends to bring the use of computational biology tools to professionals in biological sciences with different expertise, interested in the world of metagenomics analysis. We have started with the basics of microbial community profiling through shotgun sequencing data and its processing using MetaPhlAn software (the reader will notice that there are other tools perhaps more appropriate to their conditions, an interesting option is the FOCUS software that works through a Web server). MetaPhlAn has the advantage of being fully integrated with the GraPhlan phylogenetic reconstruction tools. We dedicate a complete section to the 16S gene-based communities profiling; we illustrate the EzBioCloud platform, a useful tool to obtain ecological and phylogenetic information of microbiomes. An alternative approach to process assembled data is the use of Barrnap software, which is very fast and efficient to extract ribosomal sequences in assembled data, which can be subsequently clustered and processed with phylogenetic construction tools such as SeaView. Finally, we present a list of software that can serve as a guide for the analysis of microbiomes from their taxonomic characterization to the study of phylogenetic relationships between taxa.
\nWe thank the supercomputing resources and services of the Dirección General de Cómputo y de Tecnologías de Información y Comunicación (DGTIC) from Universidad Nacional Autónoma de México, through the project: LANCAD-UNAM-DGTIC-371.
\nThe authors declare no conflict of interest.
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\\n\\nCURRENT PROJECTS
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\n\nQUALITY CONTENT
\n\nOver the years we have learned what is important. What makes a difference to the researchers that work with us, what they value. Something that is very high not only on their lists, but our own, is the quality of the published content.
\n\nOur books contain scientific content written by two Nobel Prize winners, two Breakthrough Prize winners and 73 authors who are in the top 1% Most Cited.
\n\nWith regular submission for coverage in the single most important database, the Book Citation Index in the Web of Science™ Core Collection (BKCI), and no rejected submissions to date, over 43% of all Open Access books indexed in the BKCI are IntechOpen published books.
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\n\nYOUR WORK, YOUR COPYRIGHT
\n\nThe utilization of CC licenses allow researchers to retain copyright to their work. Researchers are free to use, adapt and share all content they publish with us. You will never have to pay permission fees to reuse a part of an experiment that you worked so hard to complete and are free to build upon your own research and the research of others. The Edited Volume helps bring together research from all over the world and compiles that research into one book - accessible for all. The research presented in chapter one can inspire the author of chapter three to take his or her research to the next level. It is about sharing ideas, insights and knowledge.
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\n\nPUBLISHING PROCESS STEPS
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\n\nCURRENT PROJECTS
\n\nTo view current Open Access book projects that are Open for Submissions visit us here.
\n\nNot sure if this is the right publishing option for you? Feel free to contact us at book.department@intechopen.com.
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