[10] Clustering Microarray Data

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

Machine Learning-Based Analytical Systems: Food Forensics

Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.

Saturated Fatty Acids Engage an IRE1α-Dependent Pathway to Activate the NLRP3 Inflammasome in Myeloid Cells

Diets rich in saturated fatty acids (SFAs) produce a form of tissue inflammation driven by "metabolically activated" macrophages. We show that SFAs, when in excess, induce a unique transcriptional signature in both mouse and human macrophages that is enriched by a subset of ER stress markers, particularly IRE1α and many adaptive downstream target genes. SFAs also activate the NLRP3 inflammasome in macrophages, resulting in IL-1β secretion. We found that IRE1α mediates SFA-induced IL-1β secretion by macrophages and that its activation by SFAs does not rely on unfolded protein sensing. We show instead that the ability of SFAs to stimulate either IRE1α activation or IL-1β secretion can be specifically reduced by preventing their flux into phosphatidylcholine (PC) or by increasing unsaturated PC levels. Thus, IRE1α is an unrecognized intracellular PC sensor critical to the process by which SFAs stimulate macrophages to secrete IL-1β, a driver of diet-induced tissue inflammation.

Visual Assessment of Relative Apical Sparing Pattern Is More Useful Than Quantitative Assessment for Diagnosing Cardiac Amyloidosis in Borderline or Mildly Increased Left Ventricular Wall Thickness

BACKGROUND

Relative apical sparing pattern of longitudinal strain (RapSP-LS) was suggested in advanced cardiac amyloidosis (CA). It is unclear whether it is present in less advanced CA.

METHODS AND RESULTS

Patients with presumptive diagnosis of CA and mean left ventricular wall thickness (LVWT) ≤14 mm were recruited. Apart from RapSP-LS visually identified, relative apical longitudinal strain index (RapLSI) was defined as [average apical LS/(average basal LS+average mid-ventricle LS)]. Among 119 patients included, 47 were finally diagnosed with CA. RapLSI was higher in the CA group compared to other causes of increased mean LVWT (P<0.001), but with a significant range of overlap noted. In contrast, RapSP-LS visually assessed was noted in most CA patients (31/47, 66.0%) except in those with preserved LV ejection fraction, normal LVWT, and mildly decreased global LS, suggesting least advanced CA. On multivariate analysis of the added diagnostic role of RapSP-LS or RapLSI on top of clinical, electrocardiographic, and conventional echocardiographic parameters, addition of RapLSI produced only borderline increase in area under the curve of the multivariate model (P=0.05), whereas addition of RapSP-LS significantly increased it (P<0.001).

CONCLUSIONS

Visual identification of RapSP-LS is useful in terms of added diagnostic value compared with quantitative calculation of RapLSI. Its clinical application, however, should be used with caution in patients with less advanced CA.

biDCG: a new method for discovering global features of DNA microarray data via an iterative re-clustering procedure

Biclustering techniques have become very popular in cancer genetics studies, as they are tools that are expected to connect phenotypes to genotypes, i.e. to identify subgroups of cancer patients based on the fact that they share similar gene expression patterns as well as to identify subgroups of genes that are specific to these subtypes of cancer and therefore could serve as biomarkers. In this paper we propose a new approach for identifying such relationships or biclusters between patients and gene expression profiles. This method, named biDCG, rests on two key concepts. First, it uses a new clustering technique, DCG-tree [Fushing et al, PLos One, 8, e56259 (2013)] that generates ultrametric topological spaces that capture the geometries of both the patient data set and the gene data set. Second, it optimizes the definitions of bicluster membership through an iterative two-way reclustering procedure in which patients and genes are reclustered in turn, based respectively on subsets of genes and patients defined in the previous round. We have validated biDCG on simulated and real data. Based on the simulated data we have shown that biDCG compares favorably to other biclustering techniques applied to cancer genomics data. The results on the real data sets have shown that biDCG is able to retrieve relevant biological information.

Comprehensive assessment of right ventricular function in patients with pulmonary hypertension with global longitudinal peak systolic strain derived from multiple right ventricular views

BACKGROUND

Right ventricular (RV) function is a strong predictor of mortality in pulmonary hypertension (PH), but two-dimensional (2D) echocardiography-derived assessments of RV function that could aid in risk assessment and management of patients with PH are of limited utility. RV longitudinal peak systolic strain (RVLS) derived from 2D speckle-tracking echocardiography is a relatively novel method for quantifying RV function but typically is derived from a single apical four-chamber view of the right ventricle and may have inherent limitations. The objective of this study was to determine the utility of regional and global RVLS calculated from multiple views of the right ventricle to comprehensively assess RV function in a cohort of patients with PH.

METHODS

Regional and global RVLS were obtained from multiple views of the right ventricle (centered on the right ventricle-focused apical position) in 40 patients with PH, defined as a mean pulmonary artery pressure ≥ 25 mm Hg, most of whom also had pulmonary capillary wedge pressures ≤ 15 mm Hg and were thus defined as having pulmonary arterial hypertension. This was compared with other 2D echocardiography-derived parameters of RV function and functional parameters.

RESULTS

Global RVLS calculated from multiple views had a superior correlation with 6-min walk distance compared with other parameters of RV function, including tricuspid annular plane systolic excursion, RV myocardial performance index, and fractional area change. Although global RVLS calculated from multiple views displayed a similar correlation with 6-min walk distance as global RVLS calculated from a single four-chamber view, analysis of regional strains provided by multiple views identified distinct patterns of RV dysfunction, consisting of global, free wall, or septal dysfunction, that were associated with specific clinical characteristics.

CONCLUSIONS

Global RVLS derived from multiple right ventricle-focused views yields a comprehensive quantitative assessment of regional and global RV function that correlates moderately with functional parameters and may be useful in the assessment of PH. Distinct patterns of regional RV dysfunction are associated with different clinical characteristics.

Intrinsic circannual regulation of brown adipose tissue form and function in tune with hibernation

Winter hibernators repeatedly cycle between cold torpor and rewarming supported by nonshivering thermogenesis in brown adipose tissue (BAT). In contrast, summer animals are homeotherms, undergoing reproduction, growth, and fattening. This life history confers variability to BAT recruitment and activity. To address the components underlying prewinter enhancement and winter activation, we interrogated the BAT proteome in 13-lined ground squirrels among three summer and five winter states. We also examined mixed physiology in fall and spring individuals to test for ambient temperature and seasonal effects, as well as the timing of seasonal transitions. BAT form and function differ circannually in these animals, as evidenced by morphology and proteome dynamics. This intrinsic pattern distinguished homeothermic groups and early vs. late winter hibernators. Homeothermic variation derived from postemergence delay in growth and substrate biosynthesis. The heterothermic proteome varied less despite extreme winter physiological shifts and was optimized to exploit lipids by enhanced fatty acid binding, β-oxidation, and mitochondrial protein translocation. Surprisingly, ambient temperature did not affect the BAT proteome during transition seasons; rather, the pronounced summer-winter shift preceded environmental changes and phenotypic progression. During fall transition, differential regulation of two fatty acid binding proteins provides further evidence of recruitment and separates proteomic preparation from successful hibernation. Abundance of FABP4 correlates with torpor bout length throughout the year, clarifying its potential function in hibernation. Metabolically active BAT is a target for treating human obesity and metabolic disorders. Understanding the hibernator's extreme and seasonally distinct recruitment and activation control strategies offers untapped potential to identify novel, therapeutically relevant regulatory pathways.

Prognostic scoring system for locoregional control among the patients with nasopharyngeal carcinoma treated by intensity-modulated radiotherapy

The prognostic value of T category for locoregional control in patients with nasopharyngeal carcinoma (NPC) has decreased with the extensive use of intensity-modulated radiotherapy (IMRT). We aimed to develop a prognostic scoring system (PSS) that incorporated tumor extension and clinical characteristics for locoregional control in NPC patients treated with IMRT. The magnetic resonance imaging scans and medical records of 717 patients with nonmetastatic NPC treated with IMRT at Sun Yat-sen University Cancer Center between January 2003 and January 2008 were reviewed. Age, pathologic classification, primary tumor extension, primary gross tumor volume (GTV-p), T and N categories, and baseline lactate dehydrogenase (LDH) level were analyzed. Hierarchical cluster analysis as well as univariate and multivariate analyses were used to develop the PSS. Independent prognostic factors for locoregional relapse included N2–3 stage, GTV-p ≥26.8 mL, and involvement of one or more structures within cluster 3. We calculated a risk score derived from the regression coefficient of each factor and classified patients into four groups: low risk (score 0), intermediate risk (score >0 and ≤1), high risk (score >1 and ≤2), and extremely high risk (score >2). The 5-year locoregional control rates for these groups were 97.4%, 93.6%, 85.2%, and 78.6%, respectively (P < 0.001). We have developed a PSS that can help identify NPC patients who are at high risk for locoregional relapse and can guide individualized treatments for NPC patients.

Integrative approaches for microarray data analysis

Microarrays were one of the first technologies of the genomic revolution to gain widespread adoption, rapidly expanding from a cottage industry to the source of thousands of experimental results. They were one of the first assays for which data repositories and metadata were standardized and researchers were required by many journals to make published data publicly available. Microarrays provide high-throughput insights into the biological functions of genes and gene products; however, they also present a "curse of dimensionality," whereby the availability of many gene expression measurements in few samples make it challenging to distinguish noise from true biological signal. All of these factors argue for integrative approaches to microarray data analysis, which combine data from multiple experiments to increase sample size, avoid laboratory-specific bias, and enable new biological insights not possible from a single experiment. Here, we discuss several approaches to integrative microarray analysis for a diverse range of applications, including biomarker discovery, gene function and interaction prediction, and regulatory network inference. We also show how, by integrating large microarray compendia with diverse genomic data types, more nuanced biological hypotheses can be explored computationally. This chapter provides overviews and brief descriptions of each of these approaches to microarray integration.

Transcriptome data analysis for cell culture processes

In the past decade, DNA microarrays have fundamentally changed the way we study complex biological systems. By measuring the expression levels of thousands of transcripts, the paradigm of studying organisms has shifted from focusing on the local phenomena of a few genes to surveying the whole genome. DNA microarrays are used in a variety of ways, from simple comparisons between two samples to more intricate time-series studies. With the large number of genes being studied, the dimensionality of the problem is inevitably high. The analysis of microarray data thus requires specific approaches. In the case of time-series microarray studies, data analysis is further complicated by the correlation between successive time points in a series.In this review, we survey the methodologies used in the analysis of static and time-series microarray data, covering data pre-processing, identification of differentially expressed genes, profile pattern recognition, pathway analysis, and network reconstruction. When available, examples of their use in mammalian cell cultures are presented.

Development of a human whole blood assay for prediction of cytokine release similar to anti-CD28 superagonists using multiplex cytokine and hierarchical cluster analysis

Anti-CD28 superagonist (SA) mediated cytokine release syndrome (CRS), an adverse event resulting in systemic release of cytokines, is an emergent issue in drug development. CRS is of potential concern for all monoclonal antibodies (mAbs) particularly those directed against cell surface targets on lymphocytes. Concern regarding patient safety requires development of novel methods to predict these adverse reactions. Due to the inability of animal studies to predict CRS, we have developed a whole blood in vitro screen to support First in Human studies and assess the potential for mAbs to cause anti-CD28 SA-like CRS. For this purpose we have immobilized marketed mAbs, whose potential for causing CRS and milder infusion reactions is known, on Protein A beads and used these beads to stimulate cytokine release. After culture, supernatants are harvested and frozen for later multiplex analysis of cytokines using Searchlight™ technology. We have employed hierarchicalluster analysis (HCA) to allow comparison of 12 different cytokine levels across numerous donors, treatments, and experiments. Results conclusively distinguish test mAb responses from an anti-CD28 superagonist mAb response. As part of a global analysis of preclinical data, the results of this assay can facilitate entry into First in Human clinical trials, help with selection of starting doses and may allow more rapid dose escalation using smaller cohorts.

Merged consensus clustering to assess and improve class discovery with microarray data

Background

One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a large number of methods available to perform clustering, but it is often unclear which method is best suited to the data and how to quantify the quality of the classifications produced.

Results

Here we describe an R package containing methods to analyse the consistency of clustering results from any number of different clustering methods using resampling statistics. These methods allow the identification of the the best supported clusters and additionally rank cluster members by their fidelity within the cluster. These metrics allow us to compare the performance of different clustering algorithms under different experimental conditions and to select those that produce the most reliable clustering structures. We show the application of this method to simulated data, canonical gene expression experiments and our own novel analysis of genes involved in the specification of the peripheral nervous system in the fruitfly, Drosophila melanogaster.

Conclusions

Our package enables users to apply the merged consensus clustering methodology conveniently within the R programming environment, providing both analysis and graphical display functions for exploring clustering approaches. It extends the basic principle of consensus clustering by allowing the merging of results between different methods to provide an averaged clustering robustness. We show that this extension is useful in correcting for the tendency of clustering algorithms to treat outliers differently within datasets. The R package, clusterCons, is freely available at CRAN and sourceforge under the GNU public licence.

Lung squamous cell carcinoma mRNA expression subtypes are reproducible, clinically important, and correspond to normal cell types

PURPOSE

Lung squamous cell carcinoma (SCC) is clinically and genetically heterogeneous, and current diagnostic practices do not adequately substratify this heterogeneity. A robust, biologically based SCC subclassification may describe this variability and lead to more precise patient prognosis and management. We sought to determine if SCC mRNA expression subtypes exist, are reproducible across multiple patient cohorts, and are clinically relevant.

EXPERIMENTAL DESIGN

Subtypes were detected by unsupervised consensus clustering in five published discovery cohorts of mRNA microarrays, totaling 382 SCC patients. An independent validation cohort of 56 SCC patients was collected and assayed by microarrays. A nearest-centroid subtype predictor was built using discovery cohorts. Validation cohort subtypes were predicted and evaluated for confirmation. Subtype survival outcome, clinical covariates, and biological processes were compared by statistical and bioinformatic methods.

RESULTS

Four lung SCC mRNA expression subtypes, named primitive, classical, secretory, and basal, were detected and independently validated (P < 0.001). The primitive subtype had the worst survival outcome (P < 0.05) and is an independent predictor of survival (P < 0.05). Tumor differentiation and patient sex were associated with subtype. The expression profiles of the subtypes contained distinct biological processes (primitive: proliferation; classical: xenobiotic metabolism; secretory: immune response; basal: cell adhesion) and suggested distinct pharmacologic interventions. Comparison with lung model systems revealed distinct subtype to cell type correspondence.

CONCLUSIONS

Lung SCC consists of four mRNA expression subtypes that have different survival outcomes, patient populations, and biological processes. The subtypes stratify patients for more precise prognosis and targeted research.

Computational processing of optical measurements of neuronal and synaptic activity in networks

Imaging of optical reporters of neural activity across large populations of neurones is a widely used approach for investigating the function of neural circuits in slices and in vivo. Major challenges in analysing such experiments include the automatic identification of neurones and synapses, extraction of dynamic signals, and assessing the temporal and spatial relationships between active units in relation to the gross structure of the circuit. We have developed an integrated set of software tools, named SARFIA, by which these aspects of dynamic imaging experiments can be analysed semi-automatically. Key features are image-based detection of structures of interest using the Laplace operator, determining the positions of units in a layered network, clustering algorithms to classify units with similar functional responses, and a database to store, exchange and analyse results across experiments. We demonstrate the use of these tools to analyse synaptic activity in the retina of live zebrafish by multi-photon imaging of SyGCaMP2, a genetically encoded synaptically localised calcium reporter. By simultaneously recording activity across tens of bipolar cell terminals distributed throughout the IPL we made a functional map of the ON and OFF signalling channels and found that these were only partially separated. The automated detection of signals across many neurones in the retina allowed the reliable detection of small populations of neurones generating “ectopic” signals in the “ON” and “OFF” sublaminae. This software should be generally applicable for the analysis of dynamic imaging experiments across hundreds of responding units.

AutoClass@IJM: a powerful tool for Bayesian classification of heterogeneous data in biology

Recently, several theoretical and applied studies have shown that unsupervised Bayesian classification systems are of particular relevance for biological studies. However, these systems have not yet fully reached the biological community mainly because there are few freely available dedicated computer programs, and Bayesian clustering algorithms are known to be time consuming, which limits their usefulness when using personal computers. To overcome these limitations, we developed AutoClass@IJM, a computational resource with a web interface to AutoClass, a powerful unsupervised Bayesian classification system developed by the Ames Research Center at N.A.S.A. AutoClass has many powerful features with broad applications in biological sciences: (i) it determines the number of classes automatically, (ii) it allows the user to mix discrete and real valued data, (iii) it handles missing values. End users upload their data sets through our web interface; computations are then queued in our cluster server. When the clustering is completed, an URL to the results is sent back to the user by e-mail. AutoClass@IJM is freely available at: http://ytat2.ijm.univ-paris-diderot.fr/AutoclassAtIJM.html.

Experimental malaria infection triggers early expansion of natural killer cells

In order to gain a better understanding of gene expression during early malaria infection, we conducted microarray analysis of early blood responses in mice infected with erythrocytic-stage Plasmodium chabaudi. Immediately following infection, we observed coordinated and sequential waves of immune responses, with interferon-associated gene transcripts dominating by 16 h postinfection, followed by strong increases in natural killer (NK) cell-associated and major histocompatibility complex class I-related transcripts by 32 h postinfection. We showed by flow cytometry that the observed elevation in NK cell-associated transcripts was the result of a dramatic increase in the proportion of NK cells in the blood during infection. Subsequent microarray analysis of NK cells isolated from the peripheral blood of infected mice revealed a cell proliferation expression signature consistent with the observation that NK cells replicate in response to infection. Early proliferation of NK cells was directly observed in studies with adoptively transferred cells in infected mice. These data indicate that the early response to P. chabaudi infection of the blood is marked by a primary wave of interferon with a subsequent response by NK cells.

Quantitative analysis of synaptic phosphorylation and protein expression

The postsynaptic density (PSD) signaling machinery contains proteins with diverse functions. Brain region-specific variations in PSD components mediate distinct physiological responses to synaptic activation. We have developed mass spectrometry-based methods to comprehensively compare both relative protein expression and phosphorylation status from proteins present in biochemical preparations of postsynaptic density. Using these methods, we determined the relative expression of 2159 proteins and 1564 phosphorylation sites in PSD preparations from murine cortex, midbrain, cerebellum, and hippocampus. These experiments were conducted twice using independent biological replicates, which allowed us to assess the experimental and biological variability in this system. Concerning protein expression, cluster analysis revealed that known functionally associated proteins display coordinated synaptic expression. Therefore, proteins identified as co-clustering with known protein complexes are prime candidates for assignment as previously unrecognized components. Concerning degree of phosphorylation, we observed more extensive phosphorylation sites on N-methyl-D-aspartate (NMDA) receptors than alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, consistent with the central role of N-methyl-D-aspartate receptors in processing synaptic transmission patterns. Average kinase and phosphatase levels were highest in the hippocampus, correlating with a higher overall phosphopeptide abundance present in this brain region. These findings suggest that the hippocampus utilizes reversible protein phosphorylation to a greater extent than other brain regions when modifying synaptic strength.

CRCView: a web server for analyzing and visualizing microarray gene expression data using model-based clustering

CRCView is a user-friendly point-and-click web server for analyzing and visualizing microarray gene expression data using a Dirichlet process mixture model-based clustering algorithm. CRCView is designed to clustering genes based on their expression profiles. It allows flexible input data format, rich graphical illustration as well as integrated GO term based annotation/interpretation of clustering results.

AVAILABILITY

http://helab.bioinformatics.med.umich.edu/crcview/.

TM4 microarray software suite

Powerful specialized software is essential for managing, quantifying, and ultimately deriving scientific insight from results of a microarray experiment. We have developed a suite of software applications, known as TM4, to support such gene expression studies. The suite consists of open-source tools for data management and reporting, image analysis, normalization and pipeline control, and data mining and visualization. An integrated MIAME-compliant MySQL database is included. This chapter describes each component of the suite and includes a sample analysis walk-through.

Gene expression omnibus: microarray data storage, submission, retrieval, and analysis

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information archives and freely distributes high-throughput molecular abundance data, predominantly gene expression data generated by DNA microarray technology. The database has a flexible design that can handle diverse styles of both unprocessed and processed data in a Minimum Information About a Microarray Experiment-supportive infrastructure that promotes fully annotated submissions. GEO currently stores about a billion individual gene expression measurements, derived from over 100 organisms, submitted by over 1500 laboratories, addressing a wide range of biological phenomena. To maximize the utility of these data, several user-friendly web-based interfaces and applications have been implemented that enable effective exploration, query, and visualization of these data at the level of individual genes or entire studies. This chapter describes how data are stored, submission procedures, and mechanisms for data retrieval and query. GEO is publicly accessible at http://www.ncbi.nlm.nih.gov/projects/geo/.

Random forests for microarrays

Random Forests is a powerful multipurpose tool for predicting and understanding data. If gene expression data come from known groups or classes (e.g., tumor patients and controls), Random Forests can rank the genes in terms of their usefulness in separating the groups. When the groups are unknown, Random Forests uses an intrinsic measure of the similarity of the genes to extract useful multivariate structure, including clusters. This chapter summarizes the Random Forests methodology and illustrates its use on freely available data sets.

Interpreting experimental results using gene ontologies

High-throughput experimental techniques, such as microarrays, produce large amounts of data and knowledge about gene expression levels. However, interpretation of these data and turning it into biologically meaningful knowledge can be challenging. Frequently the output of such an analysis is a list of significant genes or a ranked list of genes. In the case of DNA microarray studies, data analysis often leads to lists of hundreds of differentially expressed genes. Also, clustering of gene expression data may lead to clusters of tens to hundreds of genes. These data are of little use if one is not able to interpret the results in a biological context. The Gene Ontology Consortium provides a controlled vocabulary to annotate the biological knowledge we have or that is predicted for a given gene. The Gene Ontologies (GOs) are organized as a hierarchy of annotation terms that facilitate an analysis and interpretation at different levels. The top-level ontologies are molecular function, biological process, and cellular component. Several annotation databases for genes of different organisms exist. This chapter describes how to use GO in order to help biologically interpret the lists of genes resulting from high-throughput experiments. It describes some statistical methods to find significantly over- or underrepresented GO terms within a list of genes and describes some tools and how to use them in order to do such an analysis. This chapter focuses primarily on the tool GOstat (http://gostat.wehi.edu.au). Other tools exist that enable similar analyses, but are not described in detail here.