Memory-like HCV-specific CD8+ T cells retain a molecular scar after cure of chronic HCV infection.

In chronic hepatitis C virus (HCV) infection, exhausted HCV-specific CD8+ T cells comprise memory-like and terminally exhausted subsets. However, little is known about the molecular profile and fate of these two subsets after the elimination of chronic antigen stimulation by direct-acting antiviral (DAA) therapy. Here, we report a progenitor-progeny relationship between memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate subset. Single-cell transcriptomics implicated that memory-like cells are maintained and terminally exhausted cells are lost after DAA-mediated cure, resulting in a memory polarization of the overall HCV-specific CD8+ T cell response. However, an exhausted core signature of memory-like CD8+ T cells was still detectable, including, to a smaller extent, in HCV-specific CD8+ T cells targeting variant epitopes. These results identify a molecular signature of T cell exhaustion that is maintained as a chronic scar in HCV-specific CD8+ T cells even after the cessation of chronic antigen stimulation.

biotextgraph: graphical summarization of functional similarities from textual information.

SUMMARY: Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. AVAILABILITY AND IMPLEMENTATION: The package, documentation, and web server are available at: https://github.com/noriakis/biotextgraph.

Improve consensus partitioning via a hierarchical procedure.

Consensus partitioning is an unsupervised method widely used in high-throughput data analysis for revealing subgroups and assigning stability for the classification. However, standard consensus partitioning procedures are weak for identifying large numbers of stable subgroups. There are two major issues. First, subgroups with small differences are difficult to be separated if they are simultaneously detected with subgroups with large differences. Second, stability of classification generally decreases as the number of subgroups increases. In this work, we proposed a new strategy to solve these two issues by applying consensus partitioning in a hierarchical procedure. We demonstrated hierarchical consensus partitioning can be efficient to reveal more meaningful subgroups. We also tested the performance of hierarchical consensus partitioning on revealing a great number of subgroups with a large deoxyribonucleic acid methylation dataset. The hierarchical consensus partitioning is implemented in the R package cola with comprehensive functionalities for analysis and visualization. It can also automate the analysis only with a minimum of two lines of code, which generates a detailed HTML report containing the complete analysis. The cola package is available at https://bioconductor.org/packages/cola/.

rGREAT: an R/bioconductor package for functional enrichment on genomic regions.

SUMMARY: GREAT (Genomic Regions Enrichment of Annotations Tool) is a widely used tool for functional enrichment on genomic regions. However, as an online tool, it has limitations of outdated annotation data, small numbers of supported organisms and gene set collections, and not being extensible for users. Here, we developed a new R/Bioconductorpackage named rGREAT which implements the GREAT algorithm locally. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. AVAILABILITY AND IMPLEMENTATION: The package rGREAT is freely available from the Bioconductor project: https://bioconductor.org/packages/rGREAT/. The development version is available at https://github.com/jokergoo/rGREAT. Gene Ontology gene sets for more than 600 organisms retrieved from Ensembl BioMart are presented in an R package BioMartGOGeneSets which is available at https://github.com/jokergoo/BioMartGOGeneSets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pkgndep: a tool for analyzing dependency heaviness of R packages.

SUMMARY: Numerous R packages have been developed for bioinformatics analysis in the last decade and dependencies among packages have become critical issues to consider. In this work, we proposed a new metric named dependency heaviness that measures the number of dependencies that a parent uniquely brings to a package and we proposed possible solutions for reducing the complexity of dependencies by optimizing the use of heavy parents. We implemented the metric in a new R package pkgndep which provides an intuitive way for dependency heaviness analysis. Based on pkgndep, we additionally performed a global analysis of dependency heaviness on CRAN and Bioconductor ecosystems and we revealed top packages that have significant contributions of high dependency heaviness to their child packages. AVAILABILITY AND IMPLEMENTATION: The package pkgndep and documentations are freely available from the Comprehensive R Archive Network https://cran.r-project.org/package=pkgndep. The dependency heaviness analysis for all 22 076 CRAN and Bioconductor packages retrieved on June 8, 2022 are available at https://pkgndep.github.io/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Make Interactive Complex Heatmaps in R.

SUMMARY: Heatmap is a powerful visualization method on two-dimensional data to reveal patterns shared by subsets of rows and columns. In this work, we introduce a new R package InteractiveComplexHeatmap that brings interactivity to the widely used ComplexHeatmap package. InteractiveComplexHeatmap is designed with an easy-to-use interface where static complex heatmaps can be directly exported to an interactive Shiny web application only with one additional line of code. InteractiveComplexHeatmap also provides flexible functionalities for integrating interactive heatmap widgets to build more complex and customized Shiny web applications. AVAILABILITY AND IMPLEMENTATION: The InteractiveComplexHeatmap package and documentations are freely available from the Bioconductor project: https://bioconductor.org/packages/InteractiveComplexHeatmap/. A complete and printer-friendly version of the documentation can also be found in Supplementary File S1. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

spiralize: an R package for visualizing data on spirals.

SUMMARY: Spiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. Here, we present the R package spiralize that provides a general solution for visualizing data on spirals. spiralize implements numerous graphics functions so that self-defined high-level graphics can be easily implemented by users. The flexibility and power of spiralize are demonstrated by five examples from real-world datasets. AVAILABILITY AND IMPLEMENTATION: The spiralize package and documentations are freely available at the Comprehensive R Archive Network (CRAN) https://CRAN.R-project.org/package=spiralize. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The whole-genome landscape of medulloblastoma subtypes.

Current therapies for medulloblastoma, a highly malignant childhood brain tumour, impose debilitating effects on the developing child, and highlight the need for molecularly targeted treatments with reduced toxicity. Previous studies have been unable to identify the full spectrum of driver genes and molecular processes that operate in medulloblastoma subgroups. Here we analyse the somatic landscape across 491 sequenced medulloblastoma samples and the molecular heterogeneity among 1,256 epigenetically analysed cases, and identify subgroup-specific driver alterations that include previously undiscovered actionable targets. Driver mutations were confidently assigned to most patients belonging to Group 3 and Group 4 medulloblastoma subgroups, greatly enhancing previous knowledge. New molecular subtypes were differentially enriched for specific driver events, including hotspot in-frame insertions that target KBTBD4 and 'enhancer hijacking' events that activate PRDM6. Thus, the application of integrative genomics to an extensive cohort of clinical samples derived from a single childhood cancer entity revealed a series of cancer genes and biologically relevant subtype diversity that represent attractive therapeutic targets for the treatment of patients with medulloblastoma.

cola: an R/Bioconductor package for consensus partitioning through a general framework.

Classification of high-throughput genomic data is a powerful method to assign samples to subgroups with specific molecular profiles. Consensus partitioning is the most widely applied approach to reveal subgroups by summarizing a consensus classification from a list of individual classifications generated by repeatedly executing clustering on random subsets of the data. It is able to evaluate the stability of the classification. We implemented a new R/Bioconductor package, cola, that provides a general framework for consensus partitioning. With cola, various parameters and methods can be user-defined and easily integrated into different steps of an analysis, e.g., feature selection, sample classification or defining signatures. cola provides a new method named ATC (ability to correlate to other rows) to extract features and recommends spherical k-means clustering (skmeans) for subgroup classification. We show that ATC and skmeans have better performance than other commonly used methods by a comprehensive benchmark on public datasets. We also benchmark key parameters in the consensus partitioning procedure, which helps users to select optimal parameter values. Moreover, cola provides rich functionalities to apply multiple partitioning methods in parallel and directly compare their results, as well as rich visualizations. cola can automate the complete analysis and generates a comprehensive HTML report.

Quantitative proteomics reveals specific metabolic features of acute myeloid leukemia stem cells.

Acute myeloid leukemia is characterized by the accumulation of clonal myeloid blast cells unable to differentiate into mature leukocytes. Chemotherapy induces remission in the majority of patients, but relapse rates are high and lead to poor clinical outcomes. Because this is primarily caused by chemotherapy-resistant leukemic stem cells (LSCs), it is essential to eradicate LSCs to improve patient survival. LSCs have predominantly been studied at the transcript level, thus information about posttranscriptionally regulated genes and associated networks is lacking. Here, we extend our previous report on LSC proteomes to healthy age-matched hematopoietic stem and progenitor cells (HSPCs) and correlate the proteomes to the corresponding transcriptomes. By comparing LSCs to leukemic blasts and healthy HSPCs, we validate candidate LSC markers and highlight novel and potentially targetable proteins that are absent or only lowly expressed in HSPCs. In addition, our data provide strong evidence that LSCs harbor a characteristic energy metabolism, adhesion molecule composition, as well as RNA-processing properties. Furthermore, correlating proteome and transcript data of the same individual samples highlights the strength of proteome analyses, which are particularly potent in detecting alterations in metabolic pathways. In summary, our study provides a comprehensive proteomic and transcriptomic characterization of functionally validated LSCs, blasts, and healthy HSPCs, representing a valuable resource helping to design LSC-directed therapies.

Analysis of mutational signatures with yet another package for signature analysis.

Different mutational processes leave characteristic patterns of somatic mutations in the genome that can be identified as mutational signatures. Determining the contributions of mutational signatures to cancer genomes allows not only to reconstruct the etiology of somatic mutations, but can also be used for improved tumor classification and support therapeutic decisions. We here present the R package yet another package for signature analysis (YAPSA) to deconvolute the contributions of mutational signatures to tumor genomes. YAPSA provides in-built collections from the COSMIC and PCAWG SNV signature sets as well as the PCAWG Indel signatures and employs signature-specific cutoffs to increase sensitivity and specificity. Furthermore, YAPSA allows to determine 95% confidence intervals for signature exposures, to perform constrained stratified signature analyses to obtain enrichment and depletion patterns of the identified signatures and, when applied to whole exome sequencing data, to correct for the triplet content of individual target capture kits. With this functionality, YAPSA has proved to be a valuable tool for analysis of mutational signatures in molecular tumor boards in a precision oncology context. YAPSA is available at R/Bioconductor (http://bioconductor.org/packages/3.12/bioc/html/YAPSA.html).

Mutational patterns and regulatory networks in epigenetic subgroups of meningioma.

DNA methylation patterns delineate clinically relevant subgroups of meningioma. We previously established the six meningioma methylation classes (MC) benign 1-3, intermediate A and B, and malignant. Here, we set out to identify subgroup-specific mutational patterns and gene regulation. Whole genome sequencing was performed on 62 samples across all MCs and WHO grades from 62 patients with matched blood control, including 40 sporadic meningiomas and 22 meningiomas arising after radiation (Mrad). RNA sequencing was added for 18 of these cases and chromatin-immunoprecipitation for histone H3 lysine 27 acetylation (H3K27ac) followed by sequencing (ChIP-seq) for 16 samples. Besides the known mutations in meningioma, structural variants were found as the mechanism of NF2 inactivation in a small subset (5%) of sporadic meningiomas, similar to previous reports for Mrad. Aberrations of DMD were found to be enriched in MCs with NF2 mutations, and DMD was among the most differentially upregulated genes in NF2 mutant compared to NF2 wild-type cases. The mutational signature AC3, which has been associated with defects in homologous recombination repair (HRR), was detected in both sporadic meningioma and Mrad, but widely distributed across the genome in sporadic cases and enriched near genomic breakpoints in Mrad. Compared to the other MCs, the number of single nucleotide variants matching the AC3 pattern was significantly higher in the malignant MC, which also exhibited higher genomic instability, determined by the numbers of both large segments affected by copy number alterations and breakpoints between large segments. ChIP-seq analysis for H3K27ac revealed a specific activation of genes regulated by the transcription factor FOXM1 in the malignant MC. This analysis also revealed a super enhancer near the HOXD gene cluster in this MC, which, together with general upregulation of HOX genes in the malignant MC, indicates a role of HOX genes in meningioma aggressiveness. This data elucidates the biological mechanisms rendering different epigenetic subgroups of meningiomas, and suggests leveraging HRR as a novel therapeutic target.

Complex heatmaps reveal patterns and correlations in multidimensional genomic data.

UNLABELLED: Parallel heatmaps with carefully designed annotation graphics are powerful for efficient visualization of patterns and relationships among high dimensional genomic data. Here we present the ComplexHeatmap package that provides rich functionalities for customizing heatmaps, arranging multiple parallel heatmaps and including user-defined annotation graphics. We demonstrate the power of ComplexHeatmap to easily reveal patterns and correlations among multiple sources of information with four real-world datasets. AVAILABILITY AND IMPLEMENTATION: The ComplexHeatmap package and documentation are freely available from the Bioconductor project: http://www.bioconductor.org/packages/devel/bioc/html/ComplexHeatmap.html CONTACT: m.schlesner@dkfz.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HilbertCurve: an R/Bioconductor package for high-resolution visualization of genomic data.

UNLABELLED: : Hilbert curves enable high-resolution visualization of genomic data on a chromosome- or genome-wide scale. Here we present the HilbertCurve package that provides an easy-to-use interface for mapping genomic data to Hilbert curves. The package transforms the curve as a virtual axis, thereby hiding the details of the curve construction from the user. HilbertCurve supports multiple-layer overlay that makes it a powerful tool to correlate the spatial distribution of multiple feature types. AVAILABILITY AND IMPLEMENTATION: The HilbertCurve package and documentation are freely available from the Bioconductor project: http://www.bioconductor.org/packages/devel/bioc/html/HilbertCurve.html CONTACT: m.schlesner@dkfz.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Environment-induced epigenetic reprogramming in genomic regulatory elements in smoking mothers and their children.

Epigenetic mechanisms have emerged as links between prenatal environmental exposure and disease risk later in life. Here, we studied epigenetic changes associated with maternal smoking at base pair resolution by mapping DNA methylation, histone modifications, and transcription in expectant mothers and their newborn children. We found extensive global differential methylation and carefully evaluated these changes to separate environment associated from genotype-related DNA methylation changes. Differential methylation is enriched in enhancer elements and targets in particular "commuting" enhancers having multiple, regulatory interactions with distal genes. Longitudinal whole-genome bisulfite sequencing revealed that DNA methylation changes associated with maternal smoking persist over years of life. Particularly in children prenatal environmental exposure leads to chromatin transitions into a hyperactive state. Combined DNA methylation, histone modification, and gene expression analyses indicate that differential methylation in enhancer regions is more often functionally translated than methylation changes in promoters or non-regulatory elements. Finally, we show that epigenetic deregulation of a commuting enhancer targeting c-Jun N-terminal kinase 2 (JNK2) is linked to impaired lung function in early childhood.

gtrellis: an R/Bioconductor package for making genome-level Trellis graphics.

BACKGROUND: Trellis graphics are a visualization method that splits data by one or more categorical variables and displays subsets of the data in a grid of panels. Trellis graphics are broadly used in genomic data analysis to compare statistics over different categories in parallel and reveal multivariate relationships. However, current software packages to produce Trellis graphics have not been designed with genomic data in mind and lack some functionality that is required for effective visualization of genomic data. RESULTS: Here we introduce the gtrellis package which provides an efficient and extensible way to visualize genomic data in a Trellis layout. gtrellis provides highly flexible Trellis layouts which allow efficient arrangement of genomic categories on the plot. It supports multiple-track visualization, which makes it straightforward to visualize several properties of genomic data in parallel to explain complex relationships. In addition, gtrellis provides an extensible framework that allows adding user-defined graphics. CONCLUSIONS: The gtrellis package provides an easy and effective way to visualize genomic data and reveal high dimensional relationships on a genome-wide scale. gtrellis can be flexibly extended and thus can also serve as a base package for highly specific purposes. gtrellis makes it easy to produce novel visualizations, which can lead to the discovery of previously unrecognized patterns in genomic data.

circlize Implements and enhances circular visualization in R.

SUMMARY: Circular layout is an efficient way for the visualization of huge amounts of genomic information. Here we present the circlize package, which provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of this package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, circlize gives users more convenience and freedom to design figures for better understanding genomic patterns behind multi-dimensional data. AVAILABILITY AND IMPLEMENTATION: circlize is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/circlize/

CePa: an R package for finding significant pathways weighted by multiple network centralities.

SUMMARY: CePa is an R package aiming to find significant pathways through network topology information. The package has several advantages compared with current pathway enrichment tools. First, pathway node instead of single gene is taken as the basic unit when analysing networks to meet the fact that genes must be constructed into complexes to hold normal functions. Second, multiple network centralities are applied simultaneously to measure importance of nodes from different aspects to make a full view on the biological system. CePa extends standard pathway enrichment methods, which include both over-representation analysis procedure and gene-set analysis procedure. CePa has been evaluated with high performance on real-world data, and it can provide more information directly related to current biological problems. AVAILABILITY: CePa is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/CePa/