iBBiG: iterative binary bi-clustering of gene sets

Protocol to infer and analyze miRNA sponge modules in heterogeneous data using miRSM 2.0

MicroRNA (miRNA) sponges synergistically modulate physiological and pathological processes in the form of modules or clusters. Here, we present a protocol for inferring and analyzing miRNA sponge modules in heterogeneous data using the R package miRSM 2.0. We describe steps for identifying gene modules, inferring miRNA sponge modules at multi-sample and single-sample levels, and performing modular analysis. From the perspective of computational biology, miRSM 2.0 has the potential to advance our understanding of the role of miRNA sponges in diseases. For complete details on the use and execution of this protocol, please refer to Zhang et al.1,2,3.

EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma

The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.

Bioinformatic Analyzes of the Association Between Upregulated Expression of JUN Gene via APOBEC-Induced FLG Gene Mutation and Prognosis of Cervical Cancer

Globally, cervical cancer (CC) is the most common malignant tumor of the female reproductive system and its incidence is only second after breast cancer. Although screening and advanced treatment strategies have improved the rates of survival, some patients with CC still die due to metastasis and drug resistance. It is considered that cancer is driven by somatic mutations, such as single nucleotide, small insertions/deletions, copy number, and structural variations, as well as epigenetic changes. Previous studies have shown that cervical intraepithelial neoplasia is associated with copy number variants (CNVs) and/or mutations in cancer-related genes. Further, CC is also related to genetic mutations. The present study analyzed the data on somatic mutations of cervical squamous cell carcinoma (CESC) in the Cancer Genome Atlas database. It was evident that the Apolipoprotein B mRNA editing enzyme-catalyzed polypeptide-like (APOBEC)-related mutation of the FLG gene can upregulate the expression of the JUN gene and ultimately lead to poor prognosis for patients with CC. Therefore, the findings of the current study provide a new direction for future treatment of CC.

TERT mutations correlate with higher TMB value and unique tumor microenvironment and may be a potential biomarker for anti-CTLA4 treatment

Immune checkpoint inhibitors (ICIs) have recently changed therapeutic paradigms for patients across multiple cancer types. However, current biomarkers cannot accurately predict responses to ICIs. Telomerase reverse transcriptase (TERT) mutations lead to an aberrant upregulation of TERT expression, and ultimately allow telomere maintenance, thus supporting immortalization of cancer cells. This study aimed to investigate whether the TERT mutation is a potential predictor of ICI treatment across all cancer types. TERT mutations positively correlated with a higher tumor mutational burden (TMB) value, neoantigen load, and tumor purity. Lymphocyte infiltration, macrophage regulation, interferon‐gamma (IFN‐γ) response, and transforming growth factor‐β (TGF‐β) response which was representative immune‐expression signatures, all had higher signature scores in the TERT mutation group. Activated CD4 T cell, naïve B cell, activated dendritic cell, M0 macrophage, M1 macrophage, neutrophil, resting NK cell, and plasma cells all had relatively higher immune scores in the TERT mutation group, whereas Th series cells, memory B cell, resting mast cells, monocytes, and activated NK cells had lower immune scores. Notably, in the subgroup analysis of monotherapy and combination ICI treatment, only in the anti‐cytotoxic‐T‐lymphocyte‐associated antigen 4 (anti‐CTLA4) group, patients with TERT mutations had a better prognosis, especially for melanoma. Therefore, TERT mutations were closely related to a higher TMB value and unique tumor microenvironment, which may be the reason that TERT mutations may be a potential biomarker for anti‐CTLA4 treatment.

Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries

OBJECTIVE

Ageing is accompanied by deterioration of multiple bodily functions and inflammation, which collectively contribute to frailty. We and others have shown that frailty co-varies with alterations in the gut microbiota in a manner accelerated by consumption of a restricted diversity diet. The Mediterranean diet (MedDiet) is associated with health. In the NU-AGE project, we investigated if a 1-year MedDiet intervention could alter the gut microbiota and reduce frailty.

DESIGN

We profiled the gut microbiota in 612 non-frail or pre-frail subjects across five European countries (UK, France, Netherlands, Italy and Poland) before and after the administration of a 12-month long MedDiet intervention tailored to elderly subjects (NU-AGE diet).

RESULTS

Adherence to the diet was associated with specific microbiome alterations. Taxa enriched by adherence to the diet were positively associated with several markers of lower frailty and improved cognitive function, and negatively associated with inflammatory markers including C-reactive protein and interleukin-17. Analysis of the inferred microbial metabolite profiles indicated that the diet-modulated microbiome change was associated with an increase in short/branch chained fatty acid production and lower production of secondary bile acids, p-cresols, ethanol and carbon dioxide. Microbiome ecosystem network analysis showed that the bacterial taxa that responded positively to the MedDiet intervention occupy keystone interaction positions, whereas frailty-associated taxa are peripheral in the networks.

CONCLUSION

Collectively, our findings support the feasibility of improving the habitual diet to modulate the gut microbiota which in turn has the potential to promote healthier ageing.

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data

Integrative analysis aims to identify the driving factors of a biological process by the joint exploration of data from multiple cellular levels. The volume of omics data produced is constantly increasing, and so too does the collection of tools for its analysis. Comparative studies assessing performance and the biological value of results, however, are rare but in great demand. We present a comprehensive comparison of three integrative analysis approaches, sparse canonical correlation analysis (sCCA), non-negative matrix factorization (NMF) and logic data mining MicroArray Logic Analyzer (MALA), by applying them to simulated and experimental omics data. We find that sCCA and NMF are able to identify differential features in simulated data, while the Logic Data Mining method, MALA, falls short. Applied to experimental data, we show that MALA performs best in terms of sample classification accuracy, and in general, the classification power of prioritized feature sets is high (97.1-99.5% accuracy). The proportion of features identified by at least one of the other methods, however, is approximately 60% for sCCA and NMF and nearly 30% for MALA, and the proportion of features jointly identified by all methods is only around 16%. Similarly, the congruence on functional levels (Gene Ontology, Reactome) is low. Furthermore, the agreement of identified feature sets with curated gene signatures relevant to the investigated disease is modest. We discuss possible reasons for the moderate overlap of identified feature sets with each other and with curated cancer signatures. The R code to create simulated data, results and figures is provided at https://github.com/ThallingerLab/IamComparison.

runibic: a Bioconductor package for parallel row-based biclustering of gene expression data

Motivation

Biclustering is an unsupervised technique of simultaneous clustering of rows and columns of input matrix. With multiple biclustering algorithms proposed, UniBic remains one of the most accurate methods developed so far.

Results

In this paper we introduce a Bioconductor package called runibic with parallel implementation of UniBic. For the convenience the algorithm was reimplemented, parallelized and wrapped within an R package called runibic. The package includes: (i) a couple of times faster parallel version of the original sequential algorithm, (ii) much more efficient memory management, (iii) modularity which allows to build new methods on top of the provided one and (iv) integration with the modern Bioconductor packages such as SummarizedExperiment, ExpressionSet and biclust.

Availability and implementation

The package is implemented in R and is available from Bioconductor (starting from version 3.6) at the following URL http://bioconductor.org/packages/runibic with installation instructions and tutorial.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Immune Landscape of Cancer

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Biomarker Identification for Cancer Disease Using Biclustering Approach: An Empirical Study

This paper presents an exhaustive empirical study to identify biomarkers using two approaches: frequency-based and network-based, over seventeen different biclustering algorithms and six different cancer expression datasets. To systematically analyze the biclustering algorithms, we perform enrichment analysis, subtype identification and biomarker identification. Biclustering algorithms such as C&C, SAMBA and Plaid are useful to detect biomarkers by both approaches for all datasets except prostate cancer. We detect a total of 102 gene biomarkers using frequency-based method out of which 19 are for blood cancer, 36 for lung cancer, 25 for colon cancer, 13 for multi-tissue cancer and 9 for prostate cancer. Using the network-based approach we detect a total of 41 gene biomarkers of which 15 are from blood cancer, 12 from lung cancer, 6 from colon cancer, 7 from multi-tissue cancer and 1 from prostate cancer dataset. We further extend our network analysis over some biclusters and detect some gene biomarkers not detected earlier by both frequency-based or network-based approach. We expand our work on breast cancer miRNA expression data to evaluate the performance of the biclustering algorithms. We detect 19 breast cancer biomarkers by frequency-based method and 5 by network-based method for the miRNA dataset.

Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data

Motivation

Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment.

Results

We first demonstrate that detecting such ‘co-fit’ gene groups can be cast as a less well-studied problem in biclustering, i.e. constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype datasets for E. coli, proteobacteria and yeast.

Availability and Implementation

Our program is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx.

Supplementary information

Supplementary data are available at Bioinformatics online.

Semantic biclustering for finding local, interpretable and predictive expression patterns

Background

One of the major challenges in the analysis of gene expression data is to identify local patterns composed of genes showing coherent expression across subsets of experimental conditions. Such patterns may provide an understanding of underlying biological processes related to these conditions. This understanding can further be improved by providing concise characterizations of the genes and situations delimiting the pattern.

Results

We propose a method called semantic biclustering with the aim to detect interpretable rectangular patterns in binary data matrices. As usual in biclustering, we seek homogeneous submatrices, however, we also require that the included elements can be jointly described in terms of semantic annotations pertaining to both rows (genes) and columns (samples). To find such interpretable biclusters, we explore two strategies. The first endows an existing biclustering algorithm with the semantic ingredients. The other is based on rule and tree learning known from machine learning.

Conclusions

The two alternatives are tested in experiments with two Drosophila melanogaster gene expression datasets. Both strategies are shown to detect sets of compact biclusters with semantic descriptions that also remain largely valid for unseen (testing) data. This desirable generalization aspect is more emphasized in the strategy stemming from conventional biclustering although this is traded off by the complexity of the descriptions (number of ontology terms employed), which, on the other hand, is lower for the alternative strategy.

Dimension reduction techniques for the integrative analysis of multi-omics data

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.

multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles

Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.

Targeted Sequencing and Meta-Analysis of Preterm Birth

Understanding the genetic contribution(s) to the risk of preterm birth may lead to the development of interventions for treatment, prediction and prevention. Twin studies suggest heritability of preterm birth is 36-40%. Large epidemiological analyses support a primary maternal origin for recurrence of preterm birth, with little effect of paternal or fetal genetic factors. We exploited an "extreme phenotype" of preterm birth to leverage the likelihood of genetic discovery. We compared variants identified by targeted sequencing of women with 2-3 generations of preterm birth with term controls without history of preterm birth. We used a meta-genomic, bi-clustering algorithm to identify gene sets coordinately associated with preterm birth. We identified 33 genes including 217 variants from 5 modules that were significantly different between cases and controls. The most frequently identified and connected genes in the exome library were IGF1, ATM and IQGAP2. Likewise, SOS1, RAF1 and AKT3 were most frequent in the haplotype library. Additionally, SERPINB8, AZU1 and WASF3 showed significant differences in abundance of variants in the univariate comparison of cases and controls. The biological processes impacted by these gene sets included: cell motility, migration and locomotion; response to glucocorticoid stimulus; signal transduction; metabolic regulation and control of apoptosis.

Composition and temporal stability of the gut microbiota in older persons

The composition and function of the human gut microbiota has been linked to health and disease. We previously identified correlations between habitual diet, microbiota composition gradients and health gradients in an unstratified cohort of 178 elderly subjects. To refine our understanding of diet–microbiota associations and differential taxon abundance, we adapted an iterative bi-clustering algorithm (iterative binary bclustering of gene sets (iBBiG)) and applied it to microbiota composition data from 732 faecal samples from 371 ELDERMET cohort subjects, including longitudinal samples. We thus identified distinctive microbiota configurations associated with ageing in both community and long-stay residential care elderly subjects. Mixed-taxa populations were identified that had clinically distinct associations. Microbiota temporal instability was observed in both community-dwelling and long-term care subjects, particularly in those with low initial microbiota diversity. However, the stability of the microbiota of subjects had little impact on the directional change of the microbiota as observed for long-stay subjects who display a gradual shift away from their initial microbiota. This was not observed in community-dwelling subjects. This directional change was associated with duration in long-stay. Changes in these bacterial populations represent the loss of the health-associated and youth-associated microbiota components and gain of an elderly associated microbiota. Interestingly, community-associated microbiota configurations were impacted more by the use of antibiotics than the microbiota of individuals in long-term care, as the community-associated microbiota showed more loss but also more recovery following antibiotic treatment. This improved definition of gut microbiota composition patterns in the elderly will better inform the design of dietary or antibiotic interventions targeting the gut microbiota.

Shifting-and-Scaling Correlation Based Biclustering Algorithm

The existence of various types of correlations among the expressions of a group of biologically significant genes poses challenges in developing effective methods of gene expression data analysis. The initial focus of computational biologists was to work with only absolute and shifting correlations. However, researchers have found that the ability to handle shifting-and-scaling correlation enables them to extract more biologically relevant and interesting patterns from gene microarray data. In this paper, we introduce an effective shifting-and-scaling correlation measure named Shifting and Scaling Similarity (SSSim), which can detect highly correlated gene pairs in any gene expression data. We also introduce a technique named Intensive Correlation Search (ICS) biclustering algorithm, which uses SSSim to extract biologically significant biclusters from a gene expression data set. The technique performs satisfactorily with a number of benchmarked gene expression data sets when evaluated in terms of functional categories in Gene Ontology database.

Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers

MOTIVATION

Sequence-specific transcription factors (TFs) regulate the expression of their target genes through interactions with specific DNA-binding sites in the genome. Data on TF-DNA binding specificities are essential for understanding how regulatory specificity is achieved.

RESULTS

Numerous studies have used universal protein-binding microarray (PBM) technology to determine the in vitro binding specificities of hundreds of TFs for all possible 8 bp sequences (8mers). We have developed a Bayesian analysis of variance (ANOVA) model that decomposes these 8mer data into background noise, TF familywise effects and effects due to the particular TF. Adjusting for background noise improves PBM data quality and concordance with in vivo TF binding data. Moreover, our model provides simultaneous identification of TF subclasses and their shared sequence preferences, and also of 8mers bound preferentially by individual members of TF subclasses. Such results may aid in deciphering cis-regulatory codes and determinants of protein-DNA binding specificity.

AVAILABILITY AND IMPLEMENTATION

Source code, compiled code and R and Python scripts are available from http://thebrain.bwh.harvard.edu/hierarchicalANOVA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.