Rapid In-Plane Pattern Growth for Large-Area Inverse Replication Through Electrohydrodynamic Instability of Polymer Films.

Nanopatterning driven by electrohydrodynamic (EHD) instability can aid in the resolution of the drawbacks inherent in conventional imprinting or other molding methods. This is because EHD force negates the requirement of physical contact and is easily tuned. However, its potential has not examined owing to the limited size of the pattern replica (several to tens of micrometers). Thus, this study proposes a new route for large-area patterning through high-speed evolution of EHD-driven pattern growth along the in-plane axis. Through the acceleration of the in-plane growth, while selectively controlling a specific edge growth, the pattern replica area can be extended from the micro- to centimeter scale with high fidelity. Moreover, even in the case of nonuniform contact mode, the proposed rapid in-plane growth mode facilitates uniform large-scale replication, which is not possible in conventional imprinting or other molding methods.

Electrohydrodynamic Nanopatterning: A Novel Solvent-Assisted Technique for Unconventional Substrates.

Electrohydrodynamic (EHD)-driven patterning is a pioneering lithographic technique capable of replicating and modifying micro/nanostructures efficiently. However, this process is currently restricted to conventional substrates, as it necessitates a uniform and robust electric field over a large area. Consequently, the use of nontraditional substrates, such as those that are flexible, nonflat, or have high insulation, has been notably limited. In our study, we extend the applicability of EHD-driven patterning by introducing a solvent-assisted capillary peel-and-transfer method that allows the successful removal of diverse EHD-induced structures from their original substrates. Compared with the traditional route, our process boasts a success rate close to 100%. The detached structures can then be efficiently transferred to nonconventional substrates, overcoming the limitations of the traditional EHD process. Our method exhibits significant versatility, as evidenced by successful transfer of structures with engineered wettability and patterned structures composed of metals and metal oxides onto nonconventional substrates.

moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks.

BACKGROUND: Breast cancer is a highly heterogeneous disease that comprises multiple biological components. Owing its diversity, patients have different prognostic outcomes; hence, early diagnosis and accurate subtype prediction are critical for treatment. Standardized breast cancer subtyping systems, mainly based on single-omics datasets, have been developed to ensure proper treatment in a systematic manner. Recently, multi-omics data integration has attracted attention to provide a comprehensive view of patients but poses a challenge due to the high dimensionality. In recent years, deep learning-based approaches have been proposed, but they still present several limitations. RESULTS: In this study, we describe moBRCA-net, an interpretable deep learning-based breast cancer subtype classification framework that uses multi-omics datasets. Three omics datasets comprising gene expression, DNA methylation and microRNA expression data were integrated while considering the biological relationships among them, and a self-attention module was applied to each omics dataset to capture the relative importance of each feature. The features were then transformed to new representations considering the respective learned importance, allowing moBRCA-net to predict the subtype. CONCLUSIONS: Experimental results confirmed that moBRCA-net has a significantly enhanced performance compared with other methods, and the effectiveness of multi-omics integration and omics-level attention were identified. moBRCA-net is publicly available at https://github.com/cbi-bioinfo/moBRCA-net .

meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles.

BACKGROUND: Identification of the cancer subtype plays a crucial role to provide an accurate diagnosis and proper treatment to improve the clinical outcomes of patients. Recent studies have shown that DNA methylation is one of the key factors for tumorigenesis and tumor growth, where the DNA methylation signatures have the potential to be utilized as cancer subtype-specific markers. However, due to the high dimensionality and the low number of DNA methylome cancer samples with the subtype information, still, to date, a cancer subtype classification method utilizing DNA methylome datasets has not been proposed. RESULTS: In this paper, we present meth-SemiCancer, a semi-supervised cancer subtype classification framework based on DNA methylation profiles. The proposed model was first pre-trained based on the methylation datasets with the cancer subtype labels. After that, meth-SemiCancer generated the pseudo-subtypes for the cancer datasets without subtype information based on the model's prediction. Finally, fine-tuning was performed utilizing both the labeled and unlabeled datasets. CONCLUSIONS: From the performance comparison with the standard machine learning-based classifiers, meth-SemiCancer achieved the highest average F1-score and Matthews correlation coefficient, outperforming other methods. Fine-tuning the model with the unlabeled patient samples by providing the proper pseudo-subtypes, encouraged meth-SemiCancer to generalize better than the supervised neural network-based subtype classification method. meth-SemiCancer is publicly available at https://github.com/cbi-bioinfo/meth-SemiCancer .

Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations.

Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.

BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud.

MOTIVATION: Multi-omics data in molecular biology has accumulated rapidly over the years. Such data contains valuable information for research in medicine and drug discovery. Unfortunately, data-driven research in medicine and drug discovery is challenging for a majority of small research labs due to the large volume of data and the complexity of analysis pipeline. RESULTS: We present BioVLAB-Cancer-Pharmacogenomics, a bioinformatics system that facilitates analysis of multi-omics data from breast cancer to analyze and investigate intratumor heterogeneity and pharmacogenomics on Amazon Web Services. Our system takes multi-omics data as input to perform tumor heterogeneity analysis in terms of TCGA data and deconvolve-and-match the tumor gene expression to cell line data in CCLE using DNA methylation profiles. We believe that our system can help small research labs perform analysis of tumor multi-omics without worrying about computational infrastructure and maintenance of databases and tools. AVAILABILITY AND IMPLEMENTATION: http://biohealth.snu.ac.kr/software/biovlab_cancer_pharmacogenomics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

methCancer-gen: a DNA methylome dataset generator for user-specified cancer type based on conditional variational autoencoder.

BACKGROUND: Recently, DNA methylation has drawn great attention due to its strong correlation with abnormal gene activities and informative representation of the cancer status. As a number of studies focus on DNA methylation signatures in cancer, demand for utilizing publicly available methylome dataset has been increased. To satisfy this, large-scale projects were launched to discover biological insights into cancer, providing a collection of the dataset. However, public cancer data, especially for certain cancer types, is still limited to be used in research. Several simulation tools for producing epigenetic dataset have been introduced in order to alleviate the issue, still, to date, generation for user-specified cancer type dataset has not been proposed. RESULTS: In this paper, we present methCancer-gen, a tool for generating DNA methylome dataset considering type for cancer. Employing conditional variational autoencoder, a neural network-based generative model, it estimates the conditional distribution with latent variables and data, and generates samples for specified cancer type. CONCLUSIONS: To evaluate the simulation performance of methCancer-gen for the user-specified cancer type, our proposed model was compared to a benchmark method and it could successfully reproduce cancer type-wise data with high accuracy helping to alleviate the lack of condition-specific data issue. methCancer-gen is publicly available at https://github.com/cbi-bioinfo/methCancer-gen.

HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis.

BACKGROUND: Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples. RESULTS: HTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify "response order preserving DEGs" that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools. CONCLUSIONS: HTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.

Oncogenic ETS proteins mimic activated RAS/MAPK signaling in prostate cells.

The aberrant expression of an oncogenic ETS transcription factor is implicated in the progression of the majority of prostate cancers, 40% of melanomas, and most cases of gastrointestinal stromal tumor and Ewing's sarcoma. Chromosomal rearrangements in prostate cancer result in overexpression of any one of four ETS transcription factors. How these four oncogenic ETS genes differ from the numerous other ETS genes expressed in normal prostate and contribute to tumor progression is not understood. We report that these oncogenic ETS proteins, but not other ETS factors, enhance prostate cell migration. Genome-wide binding analysis matched this specific biological function to occupancy of a unique set of genomic sites highlighted by the presence of ETS- and AP-1-binding sequences. ETS/AP-1-binding sequences are prototypical RAS-responsive elements, but oncogenic ETS proteins activated a RAS/MAPK transcriptional program in the absence of MAPK activation. Thus, overexpression of oncogenic ETS proteins can replace RAS/MAPK pathway activation in prostate cells. The genomic description of this ETS/AP-1-regulated, RAS-responsive, gene expression program provides a resource for understanding the role of these ETS factors in both an oncogenic setting and the developmental processes where these genes normally function.

BiSpark: a Spark-based highly scalable aligner for bisulfite sequencing data.

BACKGROUND: Bisulfite sequencing is one of the major high-resolution DNA methylation measurement method. Due to the selective nucleotide conversion on unmethylated cytosines after treatment with sodium bisulfite, processing bisulfite-treated sequencing reads requires additional steps which need high computational demands. However, a dearth of efficient aligner that is designed for bisulfite-treated sequencing becomes a bottleneck of large-scale DNA methylome analyses. RESULTS: In this study, we present a highly scalable, efficient, and load-balanced bisulfite aligner, BiSpark, which is designed for processing large volumes of bisulfite sequencing data. We implemented the BiSpark algorithm over the Apache Spark, a memory optimized distributed data processing framework, to achieve the maximum data parallel efficiency. The BiSpark algorithm is designed to support redistribution of imbalanced data to minimize delays on large-scale distributed environment. CONCLUSIONS: Experimental results on methylome datasets show that BiSpark significantly outperforms other state-of-the-art bisulfite sequencing aligners in terms of alignment speed and scalability with respect to dataset size and a number of computing nodes while providing highly consistent and comparable mapping results. AVAILABILITY: The implementation of BiSpark software package and source code is available at https://github.com/bhi-kimlab/BiSpark/ .

BioVLAB-mCpG-SNP-EXPRESS: A system for multi-level and multi-perspective analysis and exploration of DNA methylation, sequence variation (SNPs), and gene expression from multi-omics data.

Measuring gene expression, DNA sequence variation, and DNA methylation status is routinely done using high throughput sequencing technologies. To analyze such multi-omics data and explore relationships, reliable bioinformatics systems are much needed. Existing systems are either for exploring curated data or for processing omics data in the form of a library such as R. Thus scientists have much difficulty in investigating relationships among gene expression, DNA sequence variation, and DNA methylation using multi-omics data. In this study, we report a system called BioVLAB-mCpG-SNP-EXPRESS for the integrated analysis of DNA methylation, sequence variation (SNPs), and gene expression for distinguishing cellular phenotypes at the pairwise and multiple phenotype levels. The system can be deployed on either the Amazon cloud or a publicly available high-performance computing node, and the data analysis and exploration of the analysis result can be conveniently done using a web-based interface. In order to alleviate analysis complexity, all the process are fully automated, and graphical workflow system is integrated to represent real-time analysis progression. The BioVLAB-mCpG-SNP-EXPRESS system works in three stages. First, it processes and analyzes multi-omics data as input in the form of the raw data, i.e., FastQ files. Second, various integrated analyses such as methylation vs. gene expression and mutation vs. methylation are performed. Finally, the analysis result can be explored in a number of ways through a web interface for the multi-level, multi-perspective exploration. Multi-level interpretation can be done by either gene, gene set, pathway or network level and multi-perspective exploration can be explored from either gene expression, DNA methylation, sequence variation, or their relationship perspective. The utility of the system is demonstrated by performing analysis of phenotypically distinct 30 breast cancer cell line data set. BioVLAB-mCpG-SNP-EXPRESS is available at http://biohealth.snu.ac.kr/software/biovlab_mcpg_snp_express/.

BioVLAB-MMIA-NGS: microRNA-mRNA integrated analysis using high-throughput sequencing data.

MOTIVATION: It is now well established that microRNAs (miRNAs) play a critical role in regulating gene expression in a sequence-specific manner, and genome-wide efforts are underway to predict known and novel miRNA targets. However, the integrated miRNA-mRNA analysis remains a major computational challenge, requiring powerful informatics systems and bioinformatics expertise. RESULTS: The objective of this study was to modify our widely recognized Web server for the integrated mRNA-miRNA analysis (MMIA) and its subsequent deployment on the Amazon cloud (BioVLAB-MMIA) to be compatible with high-throughput platforms, including next-generation sequencing (NGS) data (e.g. RNA-seq). We developed a new version called the BioVLAB-MMIA-NGS, deployed on both Amazon cloud and on a high-performance publicly available server called MAHA. By using NGS data and integrating various bioinformatics tools and databases, BioVLAB-MMIA-NGS offers several advantages. First, sequencing data is more accurate than array-based methods for determining miRNA expression levels. Second, potential novel miRNAs can be detected by using various computational methods for characterizing miRNAs. Third, because miRNA-mediated gene regulation is due to hybridization of an miRNA to its target mRNA, sequencing data can be used to identify many-to-many relationship between miRNAs and target genes with high accuracy. AVAILABILITY AND IMPLEMENTATION: http://epigenomics.snu.ac.kr/biovlab_mmia_ngs/.

DegPack: a web package using a non-parametric and information theoretic algorithm to identify differentially expressed genes in multiclass RNA-seq samples.

Gene expression in the whole cell can be routinely measured by microarray technologies or recently by using sequencing technologies. Using these technologies, identifying differentially expressed genes (DEGs) among multiple phenotypes is the very first step to understand difference between phenotypes. Thus many methods for detecting DEGs between two groups have been developed. For example, T-test and relative entropy are used for detecting difference between two probability distributions. When more than two phenotypes are considered, these methods are not applicable and other methods such as ANOVA F-test and Kruskal-Wallis are used for finding DEGs in the multiclass data. However, ANOVA F-test assumes a normal distribution and it is not designed to identify DEGs where genes are expressed distinctively in each of phenotypes. Kruskal-Wallis method, a non-parametric method, is more robust but sensitive to outliers. In this paper, we propose a non-parametric and information theoretical approach for identifying DEGs. Our method identified DEGs effectively and it is shown less sensitive to outliers in two data sets: a three-class drought resistant rice data set and a three-class breast cancer data set. In extensive experiments with simulated and real data, our method was shown to outperform existing tools in terms of accuracy of characterizing phenotypes using DEGs. A web service is implemented at http://biohealth.snu.ac.kr/software/degpack for the analysis of multi-class data and it includes SAMseq and PoissonSeq methods in addition to the method described in this paper.

Comparative analysis using K-mer and K-flank patterns provides evidence for CpG island sequence evolution in mammalian genomes.

CpG islands are GC-rich regions often located in the 5' end of genes and normally protected from cytosine methylation in mammals. The important role of CpG islands in gene transcription strongly suggests evolutionary conservation in the mammalian genome. However, as CpG dinucleotides are over-represented in CpG islands, comparative CpG island analysis using conventional sequence analysis techniques remains a major challenge in the epigenetics field. In this study, we conducted a comparative analysis of all CpG island sequences in 10 mammalian genomes. As sequence similarity methods and character composition techniques such as information theory are particularly difficult to conduct, we used exact patterns in CpG island sequences and single character discrepancies to identify differences in CpG island sequences. First, by calculating genome distance based on rank correlation tests, we show that k-mer and k-flank patterns around CpG sites can be used to correctly reconstruct the phylogeny of 10 mammalian genomes. Further, we used various machine learning algorithms to demonstrate that CpG islands sequences can be characterized using k-mers. In addition, by testing a human model on the nine different mammalian genomes, we provide the first evidence that k-mer signatures are consistent with evolutionary history.

Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer.

Aberrant DNA methylation of CpG islands, CpG island shores and first exons is known to play a key role in the altered gene expression patterns in all human cancers. To date, a systematic study on the effect of DNA methylation on gene expression using high resolution data has not been reported. In this study, we conducted an integrated analysis of MethylCap-sequencing data and Affymetrix gene expression microarray data for 30 breast cancer cell lines representing different breast tumor phenotypes. As well-developed methods for the integrated analysis do not currently exist, we created a series of four different analysis methods. On the computational side, our goal is to develop methylome data analysis protocols for the integrated analysis of DNA methylation and gene expression data on the genome scale. On the cancer biology side, we present comprehensive genome-wide methylome analysis results for differentially methylated regions and their potential effect on gene expression in 30 breast cancer cell lines representing three molecular phenotypes, luminal, basal A and basal B. Our integrated analysis demonstrates that methylation status of different genomic regions may play a key role in establishing transcriptional patterns in molecular subtypes of human breast cancer.

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud.

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

Targeted Sequencing Revealed Distinct Mutational Profiles of Ocular and Extraocular Sebaceous Carcinomas.

The biological behavior of sebaceous carcinoma (SeC) is relatively indolent; however, local invasion or distant metastasis is sometimes reported. Nevertheless, a lack of understanding of the genetic background of SeC makes it difficult to apply effective systemic therapy. This study was designed to investigate major genetic alterations in SeCs in Korean patients. A total of 29 samples, including 20 ocular SeCs (SeC-Os) and 9 extraocular SeCs (SeC-EOs), were examined. Targeted next-generation sequencing tests including 171 cancer-related genes were performed. TP53 and PIK3CA genes were frequently mutated in both SeC-Os and SeC-EOs with slight predominance in SeC-Os, whereas the NOTCH1 gene was more commonly mutated in SeC-EOs. In clinical correlation, mutations in RUNX1 and ATM were associated with development of distant metastases, and alterations in MSH6 and BRCA1 were associated with inferior progression-free survival (all p < 0.05). In conclusion, our study revealed distinct genetic alterations between SeC-Os and SeC-EOs and some important prognostic molecular markers. Mutations in potentially actionable genes, including EGFR, ERBB2, and mismatch repair genes, were noted, suggesting consideration of a clinical trial in intractable cases.

Methylation and molecular profiles of ependymoma: Influence of patient age and tumor anatomic location.

Ependymomas are tumors of the central nervous system that can occur in patients of all ages. Guidelines from the World Health Organization (WHO) for the grading of ependymomas consider patient age, tumor resection range, tumor location and histopathological grade. However, recent studies have suggested that a greater focus on both tumor location and patient age in terms of transcriptomic, genetic, and epigenetic analyses may provide a more accurate assessment of clinical prognosis than the grading system proposed by WHO guidelines. The current study identified the differences and similarities in ependymoma characteristics using three different molecular analyses and methylation arrays. Primary intracranial ependymoma tissues were obtained from 13 Korean patients (9 adults and 4 children), after which whole-exome sequencing (WES), ion-proton comprehensive cancer panel (CCP) analysis, RNA sequencing, and Infinium HumanMethylation450 BeadChip array analysis was performed. Somatic mutations, copy number variations, and fusion genes were identified. It was observed that the methylation status and differentially expressed genes were significantly different according to tumor location and patient age. Several novel gene fusions and somatic mutations were identified, including a yes-associated protein 1 fusion mutation in a child with a good prognosis. Moreover, the methylation microarray revealed that genes associated with neurogenesis and neuron differentiation were hypermethylated in the adult group, whereas genes in the homeobox gene family were hypermethylated in the supratentorial (ST) group. The results confirmed the existence of significantly differentially expressed tumor-specific genes based on tumor location and patient age. These results provided valuable insight into the epigenetic and genetic profiles of intracranial ependymomas and uncovered potential strategies for the identification of location- and age-based ependymoma-related prognostic factors.

Comparative Gene Expression Profiles in Parathyroid Adenoma and Normal Parathyroid Tissue.

Parathyroid adenoma is the main cause of primary hyperparathyroidism, which is characterized by enlarged parathyroid glands and excessive parathyroid hormone secretion. Here, we performed transcriptome analysis, comparing parathyroid adenomas with normal parathyroid gland tissue. RNA extracted from ten parathyroid adenoma and five normal parathyroid samples was sequenced, and differentially expressed genes (DEGs) were identified using strict cut-off criteria. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using DEGs as the input, and protein-protein interaction (PPI) networks were constructed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and visualized in Cytoscape. Among DEGs identified in parathyroid adenomas (n = 247; 45 up-regulated, 202 down-regulated), the top five GO terms for up-regulated genes were nucleoplasm, nucleus, transcription DNA-template, regulation of mRNA processing, and nucleic acid binding, while those for down-regulated genes were extracellular exosome, membrane endoplasmic reticulum (ER), membrane, ER, and melanosome. KEGG enrichment analysis revealed significant enrichment of five pathways: protein processing in ER, protein export, RNA transport, glycosylphosphatidylinositol-anchor biosynthesis, and pyrimidine metabolism. Further, PPI network analysis identified a densely connected sub-module, comprising eight hub molecules: SPCS2, RPL23, RPL26, RPN1, SEC11C, SEC11A, RPS25, and SEC61G. These findings may be helpful in further analysis of the mechanisms underlying parathyroid adenoma development.