Tool For Functional Analysis Research Articles

Some Results on Integer Solutions of Quadratic Polynomials in Two Variables

Although it is true that there are several articles that study quadratic equations in two variables, they do so in a general way. We focus on the study of natural numbers ending in one, because the other cases can be studied in a similar way. We have given the subject a different approach, that is why our bibliographic citations are few. In this work, using basic tools of functional analysis, we achieve some results in the study of integer solutions of quadratic polynomials in two variables that represent a given natural number. To determine if a natural number ending in one is prime, we must solve equations (i) <img src=image/13422919_01.gif>, (ii) <img src=image/13422919_02.gif>, (iii) <img src=image/13422919_03.gif>. If these equations do not have an integer solution, then the number P is prime. The advantage of this technique is that, to determine if a natural number p is prime, it is not necessary to know the prime numbers less than or equal to the square root of p. The objective of this work was to reduce the number of possibilities assumed by the integer variables <img src=image/13422919_04.gif> in the equation (i), (ii), (iii) respectively. Although it is true that this objective was achieved, we believe that the lower limits for the sums of the solutions of equations (i), (ii), (iii), were not optimal, since in our recent research we have managed to obtain limits lower, which reduce the domain of the integer variables <img src=image/13422919_04.gif> solve equations (i), (ii), (iii), respectively. In a future article we will show the results obtained. The methodology used was deductive and inductive. We would have liked to have a supercomputer, to build or determine prime numbers of many millions of digits, but this is not possible, since we do not have the support of our respective authorities. We believe that the contribution of this work to number theory is the creation of linear functionals for the study of integer solutions of quadratic polynomials in two variables, which represent a given natural number. The utility of large prime numbers can be used to encode any type of information safely, and the scheme shown in this article could be useful for this process.

Some Results on Number Theory and Differential Equations

In this work, using the basic tools of functional analysis, we obtain a technique that allows us to obtain important results, related to quadratic equations in two variables that represent a natural number and differential equations. We show the possible ways to write an even number that ends in six, as the sum of two odd numbers and we establish conditions for said odd numbers to be prime, also making use of a suitable linear functional <img src=image/13423281_01.gif> we obtain representations of natural numbers of the form <img src=image/13423281_02.gif> in order to obtain positive integer solutions of the equation quadratic <img src=image/13423281_03.gif> where <img src=image/13423281_04.gif> is a natural number given that it ends with one. And finally, we show with three examples the use of the proposed technique to solve some ordinary and partial linear differential equations. We believe that the third corollary of our first result of this investigation can help to demonstrate the strong Goldbach conjecture.

Establishing an efficient protoplast transient expression system for investigation of floral thermogenesis in aroids.

Floral thermogenesis is an important reproductive strategy for attracting pollinators. We developed essential biological tools for studying floral thermogenesis using two species of thermogenic aroids, Symplocarpus renifolius and Alocasia odora. Aroids contain many species with intense heat-producing abilities in their inflorescences. Several genes have been proposed to be involved in thermogenesis of these species, but biological tools for gene functional analyses are lacking. In this study, we aimed to develop a protoplast-based transient expression (PTE) system for the study of thermogenic aroids. Initially, we focused on skunk cabbage (Symplocarpus renifolius) because of its ability to produce intense as well as durable heat. In this plant, leaf protoplasts were isolated from potted and shoot tip-cultured plants with high efficiency (ca. 1.0 × 105/g fresh weight), and more than half of these protoplasts were successfully transfected. Using this PTE system, we determined the protein localization of three mitochondrial energy-dissipating proteins, SrAOX, SrUCPA, and SrNDA1, fused to green fluorescent protein (GFP). These three GFP-fused proteins were localized in MitoTracker-stained mitochondria in leaf protoplasts, although the green fluorescent particles in protoplasts expressing SrUCPA-GFP were significantly enlarged. Finally, to assess whether the PTE system established in the leaves of S. renifolius is applicable for floral tissues of thermogenic aroids, inflorescences of S. renifolius and another thermogenic aroid (Alocasia odora) were used. Although protoplasts were successfully isolated from several tissues of the inflorescences, PTE systems worked well only for the protoplasts isolated from the female parts (slightly thermogenic or nonthermogenic) of A. odora inflorescences. Our developed system has a potential to be widely used in inflorescences as well as leaves in thermogenic aroids and therefore may be a useful biological tool for investigating floral thermogenesis.

Bone SPECT/CT image fusion based on the discrete Hermite transform and sparse representation

Nuclear medicine imaging techniques have become fundamental modalities for metabolic evaluation of the human body. Single Photon Emission Computed Tomography (SPECT) is among the most used tools for functional image analysis. When combined with anatomical imaging such as Computed Tomography (CT), functional information can be analyzed with better performance since lesion localization can be substantially improved. In this paper, we present a transform-based fusion scheme applied to bone SPECT/CT image analysis. The method uses the discrete Hermite transform for image feature coding. It consists of a powerful mathematical tool that locally projects an input image onto the set of Hermite polynomials, providing low frequency and detail information. The image fusion is performed by using coefficients obtained from the transformation. Two different fusion strategies were designed based on coefficient content. Low frequency coefficients are fused through a sparse representation mechanism meanwhile coefficients with fine details are combined using local directional information and variance. Prior knowledge based on segmentation of bone tissues is included in the SR-based fusion rule. The final fused image is recovered by using the inverse transform from the resulting fused coefficients. The approach was evaluated with registered bone SPECT and CT images. It was also compared with other approaches of the state of art demonstrating its feasibility. We also present an evaluation of our method with brain tissues by using common images found in literature. Frequently used metrics were considered to quantitatively assess the performance of the proposed method.

Highly Efficient Microcell-Mediated Transfer of HACs Containing a Genomic Region of Interest into Mammalian Cells.

Human artificial chromosomes (HACs) are considered promising tools for gene delivery, functional analyses, and gene therapy. HACs have the potential to overcome many of the problems caused by the use of viral-based gene transfer systems, such as limited cloning capacity, lack of copy number control, and insertional mutagenesis during integration into host chromosomes. The recently developed alphoidtetO -HAC has an advantage over other HAC vectors because it can be easily eliminated from dividing cells by inactivation of its conditional kinetochore. This provides a unique control mechanism to study phenotypes induced by a gene or genes carried on the HAC. The alphoidtetO -HAC has a single gene acceptor loxP site that allows insertion of an individual gene of interest or a cluster of genes of up to several Mb in size in Chinese hamster ovary (CHO) hybrid cells. The HACs carrying chromosomal copies of genes can then be transferred from these donor CHO cells to different recipient cells of interest via microcell-mediated chromosome transfer (MMCT). Here, we describe a detailed protocol for loading a gene of interest into the alphoidtetO -HAC vector and for the subsequent transfer of the HAC to recipient cells using an improved MMCT protocol. The original MMCT protocol includes treatment of donor cells with colcemid to induce micronucleation, wherein the HAC becomes surrounded with a nuclear membrane. That step is followed by disarrangement of the actin cytoskeleton using cytochalasin B to help induce microcell formation. The updated MMCT protocol, described here, features the replacement of colcemid and cytochalasin B with TN16 + griseofulvin and latrunculin B, respectively, and the use of collagen/laminin surface coating to promote attachment of metaphase cells to plates during micronuclei induction. These modifications increase the efficiency of HAC transfer to recipient cells ten fold. The improved MMCT protocol has been successfully tested on several recipient cell lines, including human mesenchymal stem cells and mouse embryonic stem cells. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Insertion of a BAC containing a gene of interest into a single loxP loading site of alphoidtetO -HAC in hamster CHO cells Basic Protocol 2: Microcell-mediated chromosome transfer from donor hamster CHO cells to mammalian cells.

Cell-Permeable Nanobodies Allow Dual-Color Super-Resolution Microscopy in Untransfected Living Cells.

Super‐resolution microscopy in living cells can be restricted by the availability of small molecule probes, which only exist against few targets and genetically encoded tags. Here, we expand the applicability of live‐cell STED by engineering cell‐permeable and highly fluorescent nanobodies as intracellular targeting agents. To ensure bright fluorescent signals at low concentrations we used the concept of intramolecular photostabilization by ligating a fluorophore along with the photostabilizer trolox to the nanobody using expressed protein ligation (EPL). Furthermore, these semi‐synthetic nanobodies are equipped with a cleavable cell‐penetrating peptide for efficient cellular entry, which enables super‐resolution imaging of GFP and mCherry, as well as two endogenous targets, nuclear lamins and the DNA replication and repair protein PCNA. We monitored cell division and DNA replication via confocal and STED microscopy thus demonstrating the utility of these new intracellular tools for functional analysis.

The relative fitness of the de novo variants in general Lithuanian population vs. in individuals with intellectual disability.

The effect of a variant on an organism is always multifaceted and can be considered from multiple perspectives-biochemical, medical, or evolutionary. However, the relationship between the effects of amino acid substitution on protein activity, human health, and an individual's evolutionary fitness is not trivial. We uncover that the general Lithuanian population is characterized by a "mirror reflection" of the de novo variant fitness effect, confirming the theory of neutrality. Meanwhile, in the group of individuals with intellectual disability, compared with the reference exome de novo variants significantly changed the composition of the amino acid. Therefore, it predicts that, both in terms of the number of amino acids and changes in their relative fitness, the structure of the proteins encoded by the studied amino acids undergo significant changes following the de novo variant, leading to possible changes in protein function associated with phenotypic traits. These results suggest that the analysis of relative fitness of exome sequences with de novo variants can predict the future phenotype. Therefore even in those cases, then only a few of all functional prediction analysis tools predict a variant as damaging, the negative relative fitness or even adaptability of the genome variant should be carefully evaluated considering both its direct function and the global background of the possible disease-associated mechanism regardless of the phenotype being studied.

Wavelet analysis of variance box plot

ABSTRACT Functional box plots satisfy two needs; visualization of functional data, and the calculation of important box plot statistics. Data visualization illuminates key characteristics of functional sets missed by statistical tests and summary statistics. The calculation of box plot statistics for functional sets permits a novel comparison more suited to functional data. The functional box plot uses a depth method to visualize and rank smooth functional curves in terms of a mean, box, whiskers, and outliers. The functional box plot improves upon other classic functional data analysis tools such as functional principal components and discriminant analysis for outlier detection. This research adds wavelet analysis as a generating mechanism along with depth for functional box plots to visualize functional data and calculate relevant statistics. The wavelet analysis of variance box plot tool gives competitive error rates in Gaussian test cases with magnitude outliers, and outperforms the functional box plot, for Gaussian test cases with shape outliers. Further, we show wavelet analysis is well suited at approximating irregular and noisy functional data and show the enhanced capability of WANOVA box plots to classify shape outliers which follow a different pattern than other functional data for both simulated and real data instances.

FLAME: A Web Tool for Functional and Literature Enrichment Analysis of Multiple Gene Lists.

Simple SummaryManipulation of multiple gene lists when going through a functional enrichment analysis is often a hefty task. In this article, we present FLAME, a web application that facilitates such multiple-list manipulations, enabling the construction of intersections and unions among multiple gene lists for further enrichment analysis. Reported results are presented via multiple interactive viewers such as heatmaps, barcharts, Manhattan plots, and networks.Functional enrichment is a widely used method for interpreting experimental results by identifying classes of proteins/genes associated with certain biological functions, pathways, diseases, or phenotypes. Despite the variety of existing tools, most of them can process a single list per time, thus making a more combinatorial analysis more complicated and prone to errors. In this article, we present FLAME, a web tool for combining multiple lists prior to enrichment analysis. Users can upload several lists and use interactive UpSet plots, as an alternative to Venn diagrams, to handle unions or intersections among the given input files. Functional and literature enrichment, along with gene conversions, are offered by g:Profiler and aGOtool applications for 197 organisms. FLAME can analyze genes/proteins for related articles, Gene Ontologies, pathways, annotations, regulatory motifs, domains, diseases, and phenotypes, and can also generate protein–protein interactions derived from STRING. We have validated FLAME by interrogating gene expression data associated with the sensitivity of the distal part of the large intestine to experimental colitis-propelled colon cancer. FLAME comes with an interactive user-friendly interface for easy list manipulation and exploration, while results can be visualized as interactive and parameterizable heatmaps, barcharts, Manhattan plots, networks, and tables.

METAnnotatorX2: a Comprehensive Tool for Deep and Shallow Metagenomic Data Set Analyses.

ABSTRACTThe use of bioinformatic tools for read-based taxonomic and functional analyses of metagenomic data sets, including their assembly and management, is rather fragmentary due to the absence of an accepted gold standard. Moreover, most currently available software tools need input of millions of reads and rely on approximations in data analysis in order to reduce computing times. These issues result in suboptimal results in terms of accuracy, sensitivity, and specificity when used either for the reconstruction of taxonomic or functional profiles through read analysis or analysis of genomes reconstructed by metagenomic assembly. Moreover, the recent introduction of novel DNA sequencing technologies that generate long reads, such as Nanopore and PacBio, represent a valuable data resource that still suffers from a lack of dedicated tools to perform integrated hybrid analysis alongside short read data. In order to overcome these limitations, here we describe a comprehensive bioinformatic platform, METAnnotatorX2, aimed at providing an optimized user-friendly resource which maximizes output quality, while also allowing user-specific adaptation of the pipeline and straightforward integrated analysis of both short and long read data. To further improve performance quality and accuracy of taxonomic assignment of reads and contigs, custom preprocessed and taxonomically revised genomic databases for viruses, prokaryotes, and various eukaryotes were developed. The performance of METAnnotatorX2 was tested by analysis of artificial data sets encompassing viral, archaeal, bacterial, and eukaryotic (fungal) sequence reads that simulate different biological matrices. Moreover, real biological samples were employed to validate in silico results.IMPORTANCE We developed a novel tool, i.e., METAnnotatorX2, that includes a number of new advanced features for analysis of deep and shallow metagenomic data sets and is accompanied by (regularly updated) customized databases for archaea, bacteria, fungi, protists, and viruses. Both software and databases were developed so as to maximize sensitivity and specificity while including support for shallow metagenomic data sets. Through extensive tests performed on Illumina and Nanopore artificial data sets, we demonstrated the high performance of the software to not only extract taxonomic and functional information from sequence reads but also to assemble and process genomes from metagenomic data. The robustness of these functionalities was validated using “real-life” data sets obtained from Illumina and Nanopore sequencing of biological samples. Furthermore, the performance of METAnnotatorX2 was compared to other available software tools for analysis of shotgun metagenomics data.

A Bayesian semi-parametric model for thermal proteome profiling

The thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets.

LjaFGD: Lonicera japonica functional genomics database

Lonicera japonica Thunb., a traditional Chinese herb, has been used for treating human diseases for thousands of years. Recently, the genome of L. japonica has been decoded, providing valuable information for research into gene function. However, no comprehensive database for gene functional analysis and mining is available for L. japonica. We therefore constructed LjaFGD (www.gzybioinformatics.cn/LjaFGD and bioinformatics.cau.edu.cn/LjaFGD), a database for analyzing and comparing gene function in L. japonica. We constructed a gene co-expression network based on 77 RNA-seq samples, and then annotated genes of L. japonica by alignment against protein sequences from public databases. We also introduced several tools for gene functional analysis, including Blast, motif analysis, gene set enrichment analysis, heatmap analysis, and JBrowse. Our co-expression network revealed that MYB and WRKY transcription factor family genes were co-expressed with genes encoding key enzymes in the biosynthesis of chlorogenic acid and luteolin in L. japonica. We used flavonol synthase 1 (LjFLS1) as an example to show the reliability and applicability of our database. LjaFGD and its various associated tools will provide researchers with an accessible platform for retrieving functional information on L. japonica genes to further biological discovery.

Inversion of Tridiagonal Matrices Using the Dunford-Taylor’s Integral

We show that using Dunford-Taylor’s integral, a classical tool of functional analysis, it is possible to derive an expression for the inverse of a general non-singular complex-valued tridiagonal matrix. The special cases of Jacobi’s symmetric and Toeplitz (in particular symmetric Toeplitz) matrices are included. The proposed method does not require the knowledge of the matrix eigenvalues and relies only on the relevant invariants which are determined, in a computationally effective way, by means of a dedicated recursive procedure. The considered technique has been validated through several test cases with the aid of the computer algebra program Mathematica©.

Doubly stochastic matrices and the quantum channels

Abstract The main object of this paper is to study doubly stochastic matrices with majorization and the Birkhoff theorem. The Perron-Frobenius theorem on eigenvalues is generalized for doubly stochastic matrices. The region of all possible eigenvalues of n-by-n doubly stochastic matrix is the union of regular (n – 1) polygons into the complex plane. This statement is ensured by a famous conjecture known as the Perfect-Mirsky conjecture which is true for n = 1, 2, 3, 4 and untrue for n = 5. We show the extremal eigenvalues of the Perfect-Mirsky regions graphically for n = 1, 2, 3, 4 and identify corresponding doubly stochastic matrices. Bearing in mind the counterexample of Rivard-Mashreghi given in 2007, we introduce a more general counterexample to the conjecture for n = 5. Later, we discuss different types of positive maps relevant to Quantum Channels (QCs) and finally introduce a theorem to determine whether a QCs gives rise to a doubly stochastic matrix or not. This evidence is straightforward and uses the basic tools of matrix theory and functional analysis.

Using the GAL4-UAS System for Functional Genetics in Anopheles gambiae.

The bipartite GAL4-UAS system is a versatile and powerful tool for functional genetic analysis. The essence of the system is to cross transgenic 'driver' lines that express the yeast transcription factor GAL4 in a tissue specific manner, with transgenic 'responder' lines carrying a candidate gene/RNA interference construct whose expression is controlled by Upstream Activation Sequences (UAS) that bind GAL4. In the ensuing progeny, the gene or silencing construct is thus expressed in a prescribed spatiotemporal manner, enabling the resultant phenotypes to be assayed and gene function inferred. The binary system enables flexibility in experimental approaches to screen phenotypes generated by transgene expression in multiple tissue-specific patterns, even if severe fitness costs are induced. We have adapted this system for Anopheles gambiae, the principal malaria vector in Africa. In this article, we provide some of the common procedures used during GAL4-UAS analysis. We describe the An. gambiae GAL4-UAS lines already generated, as well as the cloning of new responder constructs for upregulation and RNAi knockdown. We specify a step by step guide for sexing of mosquito pupae to establish genetic crosses, that also includes screening progeny to follow inheritance of fluorescent gene markers that tag the driver and responder insertions. We also present a protocol for clearing An. gambiae embryos to study embryonic development. Finally, we introduce potential adaptions of the method to generate driver lines through CRISPR/Cas9 insertion of GAL4 downstream of target genes.

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning.

Simple SummaryBreast cancer (BrCa) is characterized by aberrant DNA methylation. We leveraged high-throughput methylation data from BrCa and normal breast tissues and identified 11,176 to 27,786 differentially methylated genes (DMGs) against clinically relevant end-points. Innovative automated machine learning was employed to construct three highly performing signatures for (1) the discrimination of BrCa patients from healthy individuals, (2) the identification of BrCa metastatic disease and (3) the early diagnosis of BrCa. Furthermore, functional analysis revealed that most genes selected in the signatures showed associations to BrCa, with regulation of transcription being the main biological process, the nucleus being the main cellular component and transcription factor activity and sequence-specific DNA binding being the main molecular functions. Overall, revisiting methylome datasets led to three high-performance signatures that are readily available for improving BrCa precision management and significant knowledge mining related to disease pathophysiology.DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982–1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921–1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920–1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

CRISPR/Cas9 Mediated High Efficiency Knockout of Myosin Essential Light Chain Gene in the Pacific Oyster (Crassostrea Gigas).

Pacific oyster (Crassostrea gigas) is one of the most widely cultivated shellfish species in the world. Because of its economic value and complex life cycle involving drastic changes from a free-swimming larva to a sessile juvenile, C. gigas has been used as a model for developmental, environmental, and aquaculture research. However, due to the lack of genetic tools for functional analysis, gene functions associated with biological or economic traits cannot be easily determined. Here, we reported a successful application of CRISPR/Cas9 technology for knockout of myosin essential light chain gene (CgMELC) in C. gigas. C. gigas embryos injected with sgRNAs/Cas9 contained extensive indel mutations at the target sites. The mutant larvae showed defective musculature and reduced motility. In addition, knockout of CgMELC disrupted the expression and patterning of myosin heavy chain positive myofibers in larvae. Together, these data indicate that CgMELC is involved in larval muscle contraction and myogenesis in oyster larvae.

Efficient Cas9 multiplex editing using unspaced sgRNA arrays engineering in a Potato virus X vector.

SUMMARYSystems based on the clustered, regularly interspaced, short palindromic repeat (CRISPR) and CRISPR‐associated proteins (Cas) have revolutionized genome editing in many organisms, including plants. Most CRISPR‐Cas strategies in plants rely on genetic transformation using Agrobacterium tumefaciens to supply the gene editing reagents, such as Cas nucleases or the synthetic guide RNA (sgRNA). While Cas nucleases are constant elements in editing approaches, sgRNAs are target‐specific and a screening process is usually required to identify those most effective. Plant virus‐derived vectors are an alternative for the fast and efficient delivery of sgRNAs into adult plants, due to the virus capacity for genome amplification and systemic movement, a strategy known as virus‐induced genome editing. We engineered Potato virus X (PVX) to build a vector that easily expresses multiple sgRNAs in adult solanaceous plants. Using the PVX‐based vector, Nicotiana benthamiana genes were efficiently targeted, producing nearly 80% indels in a transformed line that constitutively expresses Streptococcus pyogenes Cas9. Interestingly, results showed that the PVX vector allows expression of arrays of unspaced sgRNAs, achieving highly efficient multiplex editing in a few days in adult plant tissues. Moreover, virus‐free edited progeny can be obtained from plants regenerated from infected tissues or infected plant seeds, which exhibit a high rate of heritable biallelic mutations. In conclusion, this new PVX vector allows easy, fast and efficient expression of sgRNA arrays for multiplex CRISPR‐Cas genome editing and will be a useful tool for functional gene analysis and precision breeding across diverse plant species, particularly in Solanaceae crops.

ResistoXplorer: a web-based tool for visual, statistical and exploratory data analysis of resistome data.

The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from metagenomic resistome studies. ResistoXplorer contains three modules—the ‘Antimicrobial Resistance Gene Table’ module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the ‘Integration’ module supports integrative exploratory analysis of resistome and microbiome abundance profiles derived from metagenomic samples; finally, the ‘Antimicrobial Resistance Gene List’ module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights. ResistoXplorer is publicly available at http://www.resistoxplorer.no.

RNA-Seq in Nonmodel Organisms.

RNA-Seq is nowadays an indispensable approach for comparative transcriptome profiling in model and nonmodel organisms. Analyzing RNA-Seq data from nonmodel organisms poses unique challenges, due to unavailability of a high-quality genome reference and to relative sparsity of tools for downstream functional analyses. In this chapter, we provide an overview of the analysis steps in RNA-Seq projects of nonmodel organisms, while elaborating on aspects that are unique to this analysis. These will include (1) strategic decisions that have to be made in advance, regarding sequencing technology and reference to use; (2) how to search for available draft genomes, and, if necessary, how to improve their gene prediction and annotation; (3) how to clean raw reads before de novo assembly; (4) how to separate the reads in RNA-Seq projects of symbiont organisms; (5) how to design and carry out a de novo transcriptome assembly that will be comprehensive and reliable; (6) how to assess transcriptome quality; (7) when and how to reduce redundancy in the transcriptome; (8) techniques and considerations in transcriptome functional annotation; (9) quantitating transcript abundance in the face of high transcriptome redundancy; and, most importantly, (10) how to achieve functional enrichment testing using available tools which either support a large range of species or enable a universal, non-species-specific analysis.Throughout the chapter, we will refer to a variety of useful software tools. For the initial analysis steps involving high-volume data, these will include Linux-based programs. For the later steps, we will describe both Linux and R packages for advanced users, as well as many user-friendly tools for nonprogrammers. Finally, we will present a full workflow for RNA-Seq analysis of nonmodel organisms using the NeatSeq-Flow platform, which can be used locally through a user-friendly interface.