SUMMARY: The vast amount of publicly available genomic data requires analysis and visualization tools. Here, we present figeno, an application for generating publication-quality FIgures for GENOmics. Figeno particularly focuses on multi-region views across genomic breakpoints and on long reads with base modifications. In addition, we support epigenomic data including ATAC-seq, ChIP-seq or HiC, as well as whole genome sequencing data with copy numbers and structural variants. AVAILABILITY AND IMPLEMENTATION: Figeno is available as a python package with both a command line and graphical user interface. It can be installed via PyPI and the source code is available at https://github.com/CompEpigen/figeno.

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

DNA methylation analysis by sequencing is becoming increasingly popular, yielding methylomes at single-base pair and single-molecule resolution. It has tremendous potential for cell-type heterogeneity analysis using intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, systematic evaluation has not been performed yet. Here, we thoroughly benchmark six previously published methods: Bayesian epiallele detection, DXM, PRISM, csmFinder+coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman, as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation, thus each was individually assessed. With this elaborate evaluation, we aimed to establish which method achieves the highest performance in different scenarios of synthetic bulk samples. We found that cell-type deconvolution performance is influenced by different factors depending on the number of cell types within the mixture. Finally, we propose a best-practice deconvolution strategy for sequencing data and point out limitations that need to be handled. Array-based methods-both reference-based and reference-free-generally outperformed sequencing-based methods, despite the absence of read-level information. This implies that the current sequencing-based methods still struggle with correctly identifying cell-type-specific signals and eliminating confounding methylation patterns, which needs to be handled in future studies.

MOTIVATION: Whole-genome bisulfite sequencing (WGBS) measures DNA methylation at base pair resolution resulting in large bedGraph like coverage files. Current options for processing such files are hindered by discrepancies in file format specification, speed, and memory requirements. RESULTS: We developed methrix, an R package, which provides a toolset for systematic analysis of large datasets. Core functionality of the package includes a comprehensive bedGraph or similar tab-separated text file reader-which summarizes methylation calls based on annotated reference indices, infers and collapses strands and handles uncovered reference CpG sites while facilitating a flexible input file format specification. Additional optimized functions for quality control filtering, subsetting and visualization allow user-friendly and effective processing of WGBS results. Easy integration with tools for differentially methylated region (DMR) calling and annotation further eases the analysis of genome-wide methylation data. Overall, methrix enriches established WGBS workflows by bringing together computational efficiency and versatile functionality. AVAILABILITY AND IMPLEMENTATION: Methrix is implemented as an R package, made available under MIT license at https://github.com/CompEpigen/methrix and can be installed from the Bioconductor repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. RESULTS: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. CONCLUSION: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

DNA methylation is an epigenetic modification known to play a prime role in gene silencing and is an important topic in epigenetic research. However, due to technology-dependent errors there are inconsistencies between methylation measurements from different methods [ 1 ]. Incorrect methylation calls could result in the discovery of spurious associations between methylation patterns and specific phenotypes in epigenome-wide association studies (EWAS). We worked towards assigning a measure of confidence to individual CpGs to down-weigh or exclude positions with inconsistent measurements in such studies. We used methylation measurements from the Infinium HumanMethylation450 microarray (β450K) and whole genome bisulfite sequencing (βWGBS) to evaluate whether locus-specific measurement differences, Δβ = β450K − βWGBS, are predictable using statistical learning techniques. Methylation for Illumina WGBS data from HepaRGd7R2 was called with Bis-SNP [ 2 ], while methylation for Infinium 450K data from the same cell line was determined using RnBeads [ 3 ] and normalized with BMIQ [ 4 ]. For a uniform feature representation, we considered windows of reads overlapping with CpGs on the microarray (Figure 1 ). As predictors we examined sets of read sequences, their consensus sequences (with and without base frequencies), and non-sequence features such as base quality and depth of coverage. To obtain a predictive model independent of the methylation state, we masked CpG positions by introducing gaps or zeroing base frequencies. Data preprocessing . (1) Only reads overlapping with a CpG on the Infinium 450K chip are retained. (2) Windows are extended to the left and right of each CpG according to the maximum read length, yielding a uniform feature representation. (3) For each CpG, a consensus sequence is formed from its corresponding set of reads. Additionally, the position-specific frequency of each base is extracted. (4) Finally, CpG positions are masked by introducing gaps in the sequence or zeroing frequencies. To predict Δβ, we built support vector regression models based on Illumina WGBS data. Read similarity was measured with numerical, string [ 5 – 7 ], and set kernels [ 8 ]. We introduced the notion of hybrid string kernels to afford a similarity measure for both numeric and string input simultaneously. These kernels are based on scaling the motif similarity scores of two sequences according to the similarity of their base frequency profiles. For a read-based set kernel utilizing the weighted degree kernel with shifts [ 6 ], we found that the predicted values of Δβ correlated significantly with the observed outcomes (r = 0.37, p-value < 2.2 · 10−16). Furthermore, the hybrid weighted degree kernel (r = 0.234) outperformed the weighted degree kernel with shifts (r = 0.22) by also considering the frequencies of individual bases in addition to the consensus sequences. Non-sequence features were less predictive of the outcome than the sequence, e.g., RBF kernels on base quality and depth of coverage attained only correlations of r = 0.057 and r = 0.003 with the outcome, respectively. To our knowledge, this is the first approach indicating that differences between methylation measurements from bisulfite sequencing and the Infinium HumanMethylation450 microarray are predictable from the reads. The results suggest that features beside the sequence play only a minuscule role in the emergence of inconsistent methylation measurements. We were able to show that, in this scenario, set kernels and hybrid string kernels provide well-suited similarity measures. Further work is necessary to validate the model's generalizability for data from other cell lines and to evaluate its practical merit.

Motivated by an application in computational biology, we consider constrained low-rank matrix factorization problems with $\{0,1\}$-constraints on one of the factors. In addition to the the non-convexity shared with more general matrix factorization schemes, our problem is further complicated by a combinatorial constraint set of size $2^{m \cdot r}$, where $m$ is the dimension of the data points and $r$ the rank of the factorization. Despite apparent intractability, we provide $-$in the line of recent work on non-negative matrix factorization by Arora et al.~(2012)$-$ an algorithm that provably recovers the underlying factorization in the exact case with operations of the order $O(m r 2^r + mnr)$ in the worst case. To obtain that result, we invoke theory centered around a fundamental result in combinatorics, the Littlewood-Offord lemma.