InCHlib - interactive cluster heatmap for web applications

DendroX: multi-level multi-cluster selection in dendrograms

BACKGROUND

Cluster heatmaps are widely used in biology and other fields to uncover clustering patterns in data matrices. Most cluster heatmap packages provide utility functions to divide the dendrograms at a certain level to obtain clusters, but it is often difficult to locate the appropriate cut in the dendrogram to obtain the clusters seen in the heatmap or computed by a statistical method. Multiple cuts are required if the clusters locate at different levels in the dendrogram.

RESULTS

We developed DendroX, a web app that provides interactive visualization of a dendrogram where users can divide the dendrogram at any level and in any number of clusters and pass the labels of the identified clusters for functional analysis. Helper functions are provided to extract linkage matrices from cluster heatmap objects in R or Python to serve as input to the app. A graphic user interface was also developed to help prepare input files for DendroX from data matrices stored in delimited text files. The app is scalable and has been tested on dendrograms with tens of thousands of leaf nodes. As a case study, we clustered the gene expression signatures of 297 bioactive chemical compounds in the LINCS L1000 dataset and visualized them in DendroX. Seventeen biologically meaningful clusters were identified based on the structure of the dendrogram and the expression patterns in the heatmap. We found that one of the clusters consisting of mostly naturally occurring compounds is not previously reported and has its members sharing broad anticancer, anti-inflammatory and antioxidant activities.

CONCLUSIONS

DendroX solves the problem of matching visually and computationally determined clusters in a cluster heatmap and helps users navigate among different parts of a dendrogram. The identification of a cluster of naturally occurring compounds with shared bioactivities implicates a convergence of biological effects through divergent mechanisms.

Integrating statistical and visual analytic methods for bot identification of health-related survey data

OBJECTIVE

In recent years, we have increasingly observed issues concerning quality of online information due to misinformation and disinformation. Aside from social media, there is growing awareness that questionnaire data collected using online recruitment methods may include suspect data provided by bots. Issues with data quality can be particularly problematic in health and/or biomedical contexts; thus, developing robust methods for suspect data identification and removal is of paramount importance in informatics. In this study, we describe an interactive visual analytics approach to suspect data identification and removal and demonstrate the application of this approach on questionnaire data pertaining to COVID-19 derived from different recruitment venues, including listservs and social media.

METHODS

We developed a pipeline for data cleaning, pre-processing, analysis, and automated ranking of data to address data quality issues. We then employed the ranking in conjunction with manual review to identify suspect data and remove them from subsequent analyses. Last, we compared differences in the data before and after removal.

RESULTS

We performed data cleaning, pre-processing, and exploratory analysis on a survey dataset (N = 4,163) collected using multiple recruitment mechanins using the Qualtrics survey platform. Based on these results, we identified suspect features and used these to generate a suspect feature indicator for each survey response. We excluded survey responses that did not fit the inclusion criteria for the study (n = 29) and then performed manual review of the remaining responses, triangulating with the suspect feature indicator. Based on this review, we excluded 2,921 responses. Additional responses were excluded based on a spam classification by Qualtrics (n=13), and the percentage of survey completion (n=328), resulting in a final sample size of 872. We performed additional analyses to demonstrate the extent to which the suspect feature indicator was congruent with eventual inclusion, as well as compared the characteristics of the included and excluded data.

CONCLUSION

Our main contributions are: 1) a proposed framework for data quality assessment, including suspect data identification and removal; 2) the analysis of potential consequences in terms of representation bias in the dataset; and 3) recommendations for implementation of this approach in practice.

Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis

The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs), and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP, and NBN clusters and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands, and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders.

Increasing buffering capacity enhances rumen fermentation characteristics and alters rumen microbiota composition of high-concentrate fed Hanwoo steers

The buffering capacity of buffer agents and their effects on in vitro and in vivo rumen fermentation characteristics, and bacterial composition of a high-concentrate fed Hanwoo steers were investigated in this study. Treatments were comprised of CON (no buffer added), BC_0.3% (low buffering capacity, 0.3% buffer), BC_0.5% (medium buffering capacity, 0.5% buffer), and BC_0.9% (high buffering capacity, 0.9% buffer). Four Hanwoo steers in a 4 × 4 Latin square design were used for the in vivo trial to assess the effect of treatments. Results on in vitro experiment showed that buffering capacity, pH, and ammonia-nitrogen concentration (NH₃-N) were significantly higher in BC_0.9% and BC_0.5% than the other treatments after 24 h incubation. Individual and total volatile fatty acids (VFA) concentration of CON were lowest compared to treatment groups. Meanwhile, in vivo experiment revealed that Bacteroidetes were dominant for all treatments followed by Firmicutes and Proteobacteria. The abundances of Barnesiella intestinihominis, Treponema porcinum, and Vibrio marisflavi were relatively highest under BC_0.9%, Ruminoccocus bromii and Succiniclasticum ruminis under BC_0.5%, and Bacteroides massiliensis under BC_0.3%. The normalized data of relative abundance of observed OTUs' representative families have grouped the CON with BC_0.3% in the same cluster, whereas BC_0.5% and BC_0.9% were clustered separately which indicates the effect of varying buffering capacity of buffer agents. Principal coordinate analysis (PCoA) on unweighted UniFrac distances revealed close similarity of bacterial community structures within and between treatments and control, in which BC_0.9% and BC_0.3% groups showed dispersed community distribution. Overall, increasing the buffering capacity by supplementation of BC_0.5% and and BC_0.9% buffer agents enhanced rumen fermentation characteristics and altered the rumen bacterial community, which could help prevent ruminal acidosis during a high-concentrate diet.

The IMG/M data management and analysis system v.7: content updates and new features

The Integrated Microbial Genomes & Microbiomes system (IMG/M: https://img.jgi.doe.gov/m/) at the Department of Energy (DOE) Joint Genome Institute (JGI) continues to provide support for users to perform comparative analysis of isolate and single cell genomes, metagenomes, and metatranscriptomes. In addition to datasets produced by the JGI, IMG v.7 also includes datasets imported from public sources such as NCBI Genbank, SRA, and the DOE National Microbiome Data Collaborative (NMDC), or submitted by external users. In the past couple years, we have continued our effort to help the user community by improving the annotation pipeline, upgrading the contents with new reference database versions, and adding new analysis functionalities such as advanced scaffold search, Average Nucleotide Identity (ANI) for high-quality metagenome bins, new cassette search, improved gene neighborhood display, and improvements to metatranscriptome data display and analysis. We also extended the collaboration and integration efforts with other DOE-funded projects such as NMDC and DOE Biology Knowledgebase (KBase).

aTAP: automated transcriptome analysis platform for processing RNA-seq data by de novo assembly

RNA-seq is a sequencing technique that uses next-generation sequencing (NGS) to explore and study the entire transcriptome of a biological sample. NGS-based analyses are mostly performed via command-line interfaces, which is an obstacle for molecular biologists and researchers. Therefore, the higher throughputs from NGS can only be accessed with the help of bioinformatics and computer science expertise. As the cost of sequencing is continuously falling, the use of RNA-seq seems certain to increase. To minimize the problems encountered by biologists and researchers in RNA-seq data analysis, we propose an automated platform with a web application that integrates various bioinformatics pipelines. The platform is intended to enable academic users to more easily analyze transcriptome datasets. Our automated Transcriptome Analysis Platform (aTAP) offers comprehensive bioinformatics workflows, including quality control of raw reads, trimming of low-quality reads, de novo transcriptome assembly, transcript expression quantification, differential expression analysis, and transcript annotation. aTAP has a user-friendly graphical interface, allowing researchers to interact with and visualize results in the web browser. This project offers an alternative way to analyze transcriptome data, by integrating efficient and well-known tools, that is simpler and more accessible to research communities. aTAP is freely available to academic users at https://atap.psu.ac.th/.

REGENOMICS: A web-based application for plant REGENeration-associated transcriptOMICS analyses

In plants, differentiated somatic cells exhibit an exceptional ability to regenerate new tissues, organs, or whole plants. Recent studies have unveiled core genetic components and pathways underlying cellular reprogramming and de novo tissue regeneration in plants. Although high-throughput analyses have led to key discoveries in plant regeneration, a comprehensive organization of large-scale data is needed to further enhance our understanding of plant regeneration. Here, we collected all currently available transcriptome datasets related to wounding responses, callus formation, de novo organogenesis, somatic embryogenesis, and protoplast regeneration to construct REGENOMICS, a web-based application for plant REGENeration-associated transcriptOMICS analyses. REGENOMICS supports single- and multi-query analyses of plant regeneration-related gene-expression dynamics, co-expression networks, gene-regulatory networks, and single-cell expression profiles. Furthermore, it enables user-friendly transcriptome-level analysis of REGENOMICS-deposited and user-submitted RNA-seq datasets. Overall, we demonstrate that REGENOMICS can serve as a key hub of plant regeneration transcriptome analysis and greatly enhance our understanding on gene-expression networks, new molecular interactions, and the crosstalk between genetic pathways underlying each mode of plant regeneration. The REGENOMICS web-based application is available at http://plantregeneration.snu.ac.kr.

Commentary: the first twelve years of the Journal of chemoinformatics

This commentary provides an overview of the publications in, and the citations to, the first twelve volumes of the Journal of Cheminformatics, covering the period 2009–2020. The analysis is based on the 622 articles that have appeared in the journal during that time and that have been indexed in the Clarivate Web of Science Core Collection database. It is clear that the journal has established itself as one of the most important publications in the field of cheminformatics: it attracts citations not only from other journals in its specialist field but also from biological and chemical journals more widely, and moreover from journals that are far removed in focus from it but that are still able to benefit from the articles that it publishes.

Examining stigma relating to substance use and contextual factors in social media discussions

Background

Stigma associated with substance use can have severe negative consequences for physical and mental health and serve as a barrier to treatment. Yet, research on stigma processes and stigma reduction interventions is limited.

Aim

We use a social media dataset to examine: 1) the nature of stigma-related experience related to substance use; and 2) salient affective and temporal factors in the use of three substances: alcohol, cannabis, and opioids.

Methods

We harvested several years of data pertaining to three substances - alcohol, cannabis, and opioids - from Reddit, a popular social networking platform. For Part I, we selected posts based on stigma-related keywords, performed content analysis, and rendered word clouds to examine the nature of stigma associated with these substances. In Part II, we employed natural language processing in conjunction with hierarchical clustering and visualization to explore temporal and affective factors.

Results

In Part I, internalized stigma was most commonly exhibited. Anticipated and enacted stigma were less common in posts relating to cannabis compared to the other two substances. Work, home, and school were important contexts in which stigma was observed. Part II showed that temporal markers were prominent; post authors shared stories of substance use journeys, and timelines of their experience with quitting and withdrawals. Shame, sadness, anxiety, and fear were common, with shame being more prominent in alcohol-related posts.

Conclusion

Our findings highlight the importance of contextual factors in substance use recovery and stigma reduction and offer directions for future interventions.

Computational Approach to the Systematic Prediction of Glycolytic Abilities: Looking Into Human Microbiota

Glycoside hydrolases are responsible for the enzymatic deconstruction of complex carbohydrates. Most of the families are known to conserve the catalytic machinery and molecular mechanisms. This work introduces a new method to predict glycolytic abilities in sequenced genomes and thus, gain a better understanding of how to target specific carbohydrates and identify potentially interesting sources of specialised enzymes. Genome sequences are aligned to those of organisms with expertly curated glycolytic abilities. Clustering of homology scores helps identify organisms that share common abilities and the most promising organisms regarding specific glycolytic abilities. The method has been applied to members of the bacterial families Ruminococcaceae (39 genera), Eubacteriaceae (11 genera) and Lachnospiraceae (59 genera), which hold major representatives of the human gut microbiota. The method predicted the potential presence of glycoside hydrolases in 1701 species of these genera. Here, the validity and practical usefulness of the method is discussed based on the predictions obtained for members of the genus Ruminococcus. Results were consistent with existing literature and offer useful, complementary insights to comparative genomics and physiological testing. The implementation of the Gleukos web portal (http://sing-group.org/gleukos) offers a public service to those interested in targeting microbial carbohydrate metabolism for biotechnological and health applications.

Nuclear Magnetic Resonance (NMR)-Based Quantification on Flavor-Active and Bioactive Compounds and Application for Distinguishment of Chicken Breeds

The purpose of this study was to use ¹H nuclear magnetic resonance (¹H NMR) to quantify taste-active and bioactive compounds in chicken breasts and thighs from Korean native chicken (KNC) [newly developed KNCs (KNC-A, -C, and -D) and commercial KNC-H] and white-semi broiler (WSB) used in Samgye. Further, each breed was differentiated using multivariate analyses, including a machine learning algorithm designed to use metabolic information from each type of chicken obtained using ¹H-¹³C heteronuclear single quantum coherence (2D NMR). Breast meat from KNC-D chickens were superior to those of conventional KNC-H and WSB chickens in terms of both taste-active and bioactive compounds. In the multivariate analysis, meat portions (breast and thigh) and chicken breeds (KNCs and WSB) could be clearly distinguished based on the outcomes of the principal component analysis and partial least square-discriminant analysis (R²=0.945; Q²=0.901). Based on this, we determined the receiver operating characteristic (ROC) curve for each of these components. AUC analysis identified 10 features which could be consistently applied to distinguish between all KNCs and WSB chickens in both breast (0.988) and thigh (1.000) meat without error. Here, both ¹H NMR and 2D NMR could successfully quantify various target metabolites which could be used to distinguish between different chicken breeds based on their metabolic profile.

Diet Transition from High-Forage to High-Concentrate Alters Rumen Bacterial Community Composition, Epithelial Transcriptomes and Ruminal Fermentation Parameters in Dairy Cows

Effects of changing diet on rumen fermentation parameters, bacterial community composition, and transcriptome profiles were determined in three rumen-cannulated Holstein Friesian cows using a 3 × 4 cross-over design. Treatments include HF-1 (first high-forage diet), HC-1 (first high-concentrate diet), HC-2 (succeeding high-concentrate diet), and HF-2 (second high-forage diet as a recovery period). Animal diets contained Klein grass and concentrate at ratios of 8:2, 2:8, 2:8, and 8:2 (two weeks each), respectively. Ammonia-nitrogen and individual and total volatile fatty acid concentrations were increased significantly during HC-1 and HC-2. Rumen species richness significantly increased for HF-1 and HF-2. Bacteroidetes were dominant for all treatments, while phylum Firmicutes significantly increased during the HC period. Prevotella, Erysipelothrix, and Galbibacter significantly differed between HF and HC diet periods. Ruminococcus abundance was lower during HF feeding and tended to increase during successive HC feeding periods. Prevotellaruminicola was the predominant species for all diets. The RNA sequence analysis revealed the keratin gene as differentially expressed during the HF diet, while carbonic-anhydrase I and S100 calcium-binding protein were expressed in the HC diet. Most of these genes were highly expressed for HC-1 and HC-2. These results suggested that ruminal bacterial community composition, transcriptome profile, and rumen fermentation characteristics were altered by the diet transitions in dairy cows.

Characteristic Metabolic Changes of the Crust from Dry-Aged Beef Using 2D NMR Spectroscopy

Two-dimensional quantitative nuclear magnetic resonance (2D qNMR)-based metabolomics was performed to understand characteristic metabolic profiles in different aging regimes (crust from dry-aged beef, inner edible flesh of dry-aged beef, and wet-aged beef striploin) over 4 weeks. Samples were extracted using 0.6 M perchlorate to acquire polar metabolites. Partial least squares-discriminant analysis showed a good cumulative explained variation (R² = 0.967) and predictive ability (Q² = 0.935). Metabolites of crust and aged beef (dry- and wet-aged beef) were separated in the first week and showed a completely different aspect in the second week via NMR-based multivariable analyses. Moreover, NMR-based multivariable analyses could be used to distinguish the method, degree, and doneness of beef aging. Among them, the crust showed more unique metabolic changes that accelerated proteolysis (total free amino acids and biogenic amines) and inosine 5′-monophosphate depletion than dry-aged beef and generated specific microbial catabolites (3-indoxyl sulfate) and γ-aminobutyric acid (GABA), while asparagine, glutamine, tryptophan, and glucose in the crust were maintained or decreased. Compared to the crust, dry-aged beef showed similar patterns of biogenic amines, as well as bioactive compounds and GABA, without a decrease in free amino acids and glucose. Based on these results, the crust allows the inner dry-aged beef to be aged similarly to wet-aged beef without microbial effects. Thus, 2D qNMR-based metabolomic techniques could provide complementary information about biochemical factors for beef aging.

Transcriptome and Network Analyses of Heterostyly in Turnera subulata Provide Mechanistic Insights: Are S-Loci a Red-Light for Pistil Elongation?

Heterostyly employs distinct hermaphroditic floral morphs to enforce outbreeding. Morphs differ structurally in stigma/anther positioning, promoting cross-pollination, and physiologically blocking self-fertilization. Heterostyly is controlled by a self-incompatibility (S)-locus of a small number of linked S-genes specific to short-styled morph genomes. Turnera possesses three S-genes, namely TsBAHD (controlling pistil characters), TsYUC6, and TsSPH1 (controlling stamen characters). Here, we compare pistil and stamen transcriptomes of floral morphs of T. subulata to investigate hypothesized S-gene function(s) and whether hormonal differences might contribute to physiological incompatibility. We then use network analyses to identify genetic networks underpinning heterostyly. We found a depletion of brassinosteroid-regulated genes in short styled (S)-morph pistils, consistent with hypothesized brassinosteroid-inactivating activity of TsBAHD. In S-morph anthers, auxin-regulated genes were enriched, consistent with hypothesized auxin biosynthesis activity of TsYUC6. Evidence was found for auxin elevation and brassinosteroid reduction in both pistils and stamens of S- relative to long styled (L)-morph flowers, consistent with reciprocal hormonal differences contributing to physiological incompatibility. Additional hormone pathways were also affected, however, suggesting S-gene activities intersect with a signaling hub. Interestingly, distinct S-genes controlling pistil length, from three species with independently evolved heterostyly, potentially intersect with phytochrome interacting factor (PIF) network hubs which mediate red/far-red light signaling. We propose that modification of the activities of PIF hubs by the S-locus could be a common theme in the evolution of heterostyly.

Revisiting the Metabolic Capabilities of Bifidobacterium longum susbp. longum and Bifidobacterium longum subsp. infantis from a Glycoside Hydrolase Perspective

Bifidobacteria are among the most abundant microorganisms inhabiting the intestine of humans and many animals. Within the genus Bifidobacterium, several beneficial effects have been attributed to strains belonging to the subspecies Bifidobacterium longum subsp. longum and Bifidobacterium longum subsp. infantis, which are often found in infants and adults. The increasing numbers of sequenced genomes belonging to these two subspecies, and the availability of novel computational tools focused on predicting glycolytic abilities, with the aim of understanding the capabilities of degrading specific carbohydrates, allowed us to depict the potential glycoside hydrolases (GH) of these bacteria, with a focus on those GH profiles that differ in the two subspecies. We performed an in silico examination of 188 sequenced B. longum genomes and depicted the commonly present and strain-specific GHs and GH families among representatives of this species. Additionally, GH profiling, genome-based and 16S rRNA-based clustering analyses showed that the subspecies assignment of some strains does not properly match with their genetic background. Furthermore, the analysis of the potential GH component allowed the distinction of clear GH patterns. Some of the GH activities, and their link with the two subspecies under study, are further discussed. Overall, our in silico analysis poses some questions about the suitability of considering the GH activities of B. longum subsp. longum and B. longum subsp. infantis to gain insight into the characterization and classification of these two subspecies with probiotic interest.

MethMotif: an integrative cell specific database of transcription factor binding motifs coupled with DNA methylation profiles

Several recent studies have portrayed DNA methylation as a new player in the recruitment of transcription factors (TF) within chromatin, highlighting a need to connect TF binding sites (TFBS) with their respective DNA methylation profiles. However, current TFBS databases are restricted to DNA binding motif sequences. Here, we present MethMotif, a two-dimensional TFBS database that records TFBS position weight matrices along with cell type specific CpG methylation information computed from a combination of ChIP-seq and whole genome bisulfite sequencing datasets. Integrating TFBS motifs with TFBS DNA methylation better portrays the features of DNA loci recognised by TFs. In particular, we found that DNA methylation patterns within TFBS can be cell specific (e.g. MAFF). Furthermore, for a given TF, different DNA methylation profiles are associated with different DNA binding motifs (e.g. REST). To date, MethMotif database records over 500 TFBSs computed from over 2000 ChIP-seq datasets in 11 different cell types. MethMotif portal is accessible through an open source web interface (https://bioinfo-csi.nus.edu.sg/methmotif) that allows users to intuitively explore the entire dataset and perform both single, and batch queries.

eCALIBRATOR: A Comparative Tool to Identify Key Genes and Pathways for Eucalyptus Defense Against Biotic Stressors

Many pests and pathogens threaten Eucalyptus plantations. The study of defense responses in this economically important wood and fiber crop enables the discovery of novel pathways and genes, which may be adopted to improve resistance. Various functional genomics experiments have been conducted in Eucalyptus-biotic stress interactions following the availability of the Eucalyptus grandis genome, however, comparisons between these studies were limited largely due to a lack of comparative tools. To this end, we developed eCALIBRATOR http://ecalibrator.bi.up.ac.za, a tool for the comparison of Eucalyptus biotic stress interaction. The tool, which is not limited to Eucalyptus, allows the comparison of various datasets, provides a visual output in the form of Venn diagrams and clustering and extraction of lists for gene ontology enrichment analyses. We also demonstrate the usefulness of the tool in revealing pathways and key gene targets to further functionally characterize. We identified 708 differentially expressed E. grandis genes in common among responses to the insect pest Leptocybe invasa, oomycete pathogen Phytophthora cinnamomi and fungus Chrysoporthe austroafricana. Within this set of genes, one of the Gene Ontology terms enriched was "response to organonitrogen compound," with NITRATE TRANSPORTER 2.5 (NRT2.5) being a key gene, up-regulated under susceptible interactions and down-regulated under resistant interactions. Although previous functional genetics studies in Arabidopsis thaliana support a role in nitrate acquisition and remobilization under long-term nitrate starvation, the importance of NRT2.5 in plant defense is unclear. The T-DNA mutants of AtNRT2.5 were more resistant to Pseudomonas syringae pv. tomato pv tomato DC3000 inoculation than the wild-type counterpart, supporting a direct role for NRT2.5 in plant defense. Future studies will focus on characterizing the Eucalyptus ortholog of NRT2.5.

Haplotype-resolved genomes of geminivirus-resistant and geminivirus-susceptible African cassava cultivars

Background

Cassava is an important food crop in tropical and sub-tropical regions worldwide. In Africa, cassava production is widely affected by cassava mosaic disease (CMD), which is caused by the African cassava mosaic geminivirus that is transmitted by whiteflies. Cassava breeders often use a single locus, CMD2, for introducing CMD resistance into susceptible cultivars. The CMD2 locus has been genetically mapped to a 10-Mbp region, but its organization and genes as well as their functions are unknown.

Results

We report haplotype-resolved de novo assemblies and annotations of the genomes for the African cassava cultivar TME (tropical Manihot esculenta), which is the origin of CMD2, and the CMD-susceptible cultivar 60444. The assemblies provide phased haplotype information for over 80% of the genomes. Haplotype comparison identified novel features previously hidden in collapsed and fragmented cassava genomes, including thousands of allelic variants, inter-haplotype diversity in coding regions, and patterns of diversification through allele-specific expression. Reconstruction of the CMD2 locus revealed a highly complex region with nearly identical gene sets but limited microsynteny between the two cultivars.

Conclusions

The genome maps of the CMD2 locus in both 60444 and TME3, together with the newly annotated genes, will help the identification of the causal genetic basis of CMD2 resistance to geminiviruses. Our de novo cassava genome assemblies will also facilitate genetic mapping approaches to narrow the large CMD2 region to a few candidate genes for better informed strategies to develop robust geminivirus resistance in susceptible cassava cultivars.

Electronic supplementary material

The online version of this article (10.1186/s12915-019-0697-6) contains supplementary material, which is available to authorized users.

ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data

SUMMARY

Clustering is one of the most common techniques used in data analysis to discover hidden structures by grouping together data points that are similar in some measure into clusters. Although there are many programs available for performing clustering, a single web resource that provides both state-of-the-art clustering methods and interactive visualizations is lacking. ClusterEnG (acronym for Clustering Engine for Genomics) provides an interface for clustering big data and interactive visualizations including 3D views, cluster selection and zoom features. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides clustering tutorials that demonstrate potential pitfalls of each algorithm. The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner.

AVAILABILITY

ClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.

CONTACT

songi@illinois.edu.

Cell-Based Reporter System for High-Throughput Screening of MicroRNA Pathway Inhibitors and Its Limitations

MicroRNAs (miRNAs) are small RNAs repressing gene expression. They contribute to many physiological processes and pathologies. Consequently, strategies for manipulation of the miRNA pathway are of interest as they could provide tools for experimental or therapeutic interventions. One of such tools could be small chemical compounds identified through high-throughput screening (HTS) with reporter assays. While a number of chemical compounds have been identified in such high-throughput screens, their application potential remains elusive. Here, we report our experience with cell-based HTS of a library of 12,816 chemical compounds to identify miRNA pathway modulators. We used human HeLa and mouse NIH 3T3 cell lines with stably integrated or transiently expressed luciferase reporters repressed by endogenous miR-30 and let-7 miRNAs and identified 163 putative miRNA inhibitors. We report that compounds relieving miRNA-mediated repression via stress induction are infrequent; we have found only two compounds that reproducibly induced stress granules and relieved miRNA-targeted reporter repression. However, we have found that this assay type readily yields non-specific (miRNA-independent) stimulators of luciferase reporter activity. Furthermore, our data provide partial support for previously published miRNA pathway modulators; the most notable intersections were found among anthracyclines, dopamine derivatives, flavones, and stilbenes. Altogether, our results underscore the importance of appropriate negative controls in development of small compound inhibitors of the miRNA pathway. This particularly concerns validation strategies, which would greatly profit from assays that fundamentally differ from the routinely employed miRNA-targeted reporter assays.

Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data

Most tools developed to visualize hierarchically clustered heatmaps generate static images. Clustergrammer is a web-based visualization tool with interactive features such as: zooming, panning, filtering, reordering, sharing, performing enrichment analysis, and providing dynamic gene annotations. Clustergrammer can be used to generate shareable interactive visualizations by uploading a data table to a web-site, or by embedding Clustergrammer in Jupyter Notebooks. The Clustergrammer core libraries can also be used as a toolkit by developers to generate visualizations within their own applications. Clustergrammer is demonstrated using gene expression data from the cancer cell line encyclopedia (CCLE), original post-translational modification data collected from lung cancer cells lines by a mass spectrometry approach, and original cytometry by time of flight (CyTOF) single-cell proteomics data from blood. Clustergrammer enables producing interactive web based visualizations for the analysis of diverse biological data.

shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics

BACKGROUND

Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets, especially across single-cell sequencing studies. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps.

RESULTS

We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Also, shinyheatmap features a built-in high performance web plug-in, fastheatmap, for rapidly plotting interactive heatmaps of datasets as large as 105-107 rows within seconds, effectively shattering previous performance benchmarks of heatmap rendering speed.

CONCLUSIONS

shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap. Users can access fastheatmap directly from within the shinyheatmap web interface, and all source code has been made publicly available on Github: https://github.com/Bohdan-Khomtchouk/fastheatmap.

Interactive Toxicogenomics: Gene set discovery, clustering and analysis in Toxygates

Toxygates was originally released as a user-friendly interface to enhance the accessibility of the large-scale toxicogenomics database, Open TG-GATEs, generated by the Japanese Toxicogenomics Project. Since the original release, significant new functionality has been added to enable users to perform sophisticated computational analysis with only modest bioinformatics skills. The new features include an orthologous mode for data comparison among different species, interactive clustering and heatmap visualisation, enrichment analysis of gene sets, and user data uploading. In a case study, we use these new functions to study the hepatotoxicity of peroxisome proliferator-activated receptor alpha (PPARα) agonist WY-14643. Our findings suggest that WY-14643 caused hypertrophy in the bile duct by intracellular Ca²⁺ dysregulation, which resulted in the induction of genes in a non-canonical WNT/Ca²⁺ signalling pathway. With this new release of Toxygates, we provide a suite of tools that allow anyone to carry out in-depth analysis of toxicogenomics in Open TG-GATEs, and of any other dataset that is uploaded.

Unboxing cluster heatmaps

Background

Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks.

Results

We developed an approach to “unbox” the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results.

Conclusions

The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps.

IMG-ABC: new features for bacterial secondary metabolism analysis and targeted biosynthetic gene cluster discovery in thousands of microbial genomes

Secondary metabolites produced by microbes have diverse biological functions, which makes them a great potential source of biotechnologically relevant compounds with antimicrobial, anti-cancer and other activities. The proteins needed to synthesize these natural products are often encoded by clusters of co-located genes called biosynthetic gene clusters (BCs). In order to advance the exploration of microbial secondary metabolism, we developed the largest publically available database of experimentally verified and predicted BCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc/). Here, we describe an update of IMG-ABC, which includes ClusterScout, a tool for targeted identification of custom biosynthetic gene clusters across 40 000 isolate microbial genomes, and a new search capability to query more than 700 000 BCs from isolate genomes for clusters with similar Pfam composition. Additional features enable fast exploration and analysis of BCs through two new interactive visualization features, a BC function heatmap and a BC similarity network graph. These new tools and features add to the value of IMG-ABC's vast body of BC data, facilitating their in-depth analysis and accelerating secondary metabolite discovery.

MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps

Background

Heatmaps are an indispensible visualization tool for examining large-scale snapshots of genomic activity across various types of next-generation sequencing datasets. However, traditional heatmap software do not typically offer multi-scale insight across multiple layers of genomic analysis (e.g., differential expression analysis, principal component analysis, gene ontology analysis, and network analysis) or multiple types of next-generation sequencing datasets (e.g., ChIP-seq and RNA-seq). As such, it is natural to want to interact with a heatmap’s contents using an extensive set of integrated analysis tools applicable to a broad array of genomic data types.

Results

We propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network analysis.

Conclusions

MicroScope is hosted online as an R Shiny web application based on the D3 JavaScript library: http://microscopebioinformatics.org/. The methods are implemented in R, and are available as part of the MicroScope project at: https://github.com/Bohdan-Khomtchouk/Microscope.

How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity

Background

Hierarchical cluster analysis (HCA) is a widely used classificatory technique in many areas of scientific knowledge. Applications usually yield a dendrogram from an HCA run over a given data set, using a grouping algorithm and a similarity measure. However, even when such parameters are fixed, ties in proximity (i.e. two equidistant clusters from a third one) may produce several different dendrograms, having different possible clustering patterns (different classifications). This situation is usually disregarded and conclusions are based on a single result, leading to questions concerning the permanence of clusters in all the resulting dendrograms; this happens, for example, when using HCA for grouping molecular descriptors to select that less similar ones in QSAR studies.

Results

Representing dendrograms in graph theoretical terms allowed us to introduce four measures of cluster frequency in a canonical way, and use them to calculate cluster frequencies over the set of all possible dendrograms, taking all ties in proximity into account. A toy example of well separated clusters was used, as well as a set of 1666 molecular descriptors calculated for a group of molecules having hepatotoxic activity to show how our functions may be used for studying the effect of ties in HCA analysis. Such functions were not restricted to the tie case; the possibility of using them to derive cluster stability measurements on arbitrary sets of dendrograms having the same leaves is discussed, e.g. dendrograms from variations of HCA parameters. It was found that ties occurred frequently, some yielding tens of thousands of dendrograms, even for small data sets.

Conclusions

Our approach was able to detect trends in clustering patterns by offering a simple way of measuring their frequency, which is often very low. This would imply, that inferences and models based on descriptor classifications (e.g. QSAR) are likely to be biased, thereby requiring an assessment of their reliability. Moreover, any classification of molecular descriptors is likely to be far from unique. Our results highlight the need for evaluating the effect of ties on clustering patterns before classification results can be used accurately.Graphical abstract

Disease tolerance mediated by microbiome E. coli involves inflammasome and IGF-1 signaling

Infections and inflammation can lead to cachexia and wasting of skeletal muscle and fat tissue by as yet poorly understood mechanisms. We observed that gut colonization of mice by a strain of Escherichia coli prevents wasting triggered by infections or physical damage to the intestine. During intestinal infection with the pathogen Salmonella Typhimurium or pneumonic infection with Burkholderia thailandensis, the presence of this E. coli did not alter changes in host metabolism, caloric uptake, or inflammation but instead sustained signaling of the insulin-like growth factor 1/phosphatidylinositol 3-kinase/AKT pathway in skeletal muscle, which is required for prevention of muscle wasting. This effect was dependent on engagement of the NLRC4 inflammasome. Therefore, this commensal promotes tolerance to diverse diseases.

An Interactive Cluster Heat Map to Visualize and Explore Multidimensional Metabolomic Data

Heat maps are a commonly used visualization tool for metabolomic data where the relative abundance of ions detected in each sample is represented with color intensity. A limitation of applying heat maps to global metabolomic data, however, is the large number of ions that have to be displayed and the lack of information provided about important metabolomic parameters such as m/z and retention time. Here we address these challenges by introducing the interactive cluster heat map in the data-processing software XCMS Online. XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process, statistically evaluate, and visualize mass-spectrometry based metabolomic data. An interactive heat map is provided for all data processed by XCMS Online. The heat map is clickable, allowing users to zoom and explore specific metabolite metadata (EICs, Box-and-whisker plots, mass spectra) that are linked to the METLIN metabolite database. The utility of the XCMS interactive heat map is demonstrated on metabolomic data set generated from different anatomical regions of the mouse brain.

A cross reactive sensor array to probe divalent metal ions

A simple sensing ensemble was designed to discriminate structurally similar divalent metal chlorides utilizing multivariate data analysis.