Comprehensive genomic landscape of antibiotic resistance in Staphylococcus epidermidis.

Staphylococcus epidermidis, a common commensal bacterium found on human skin, can cause infections in clinical settings, and the presence of antibiotic resistance genes (ARGs) impedes the treatment of S. epidermidis infections. However, studies characterizing the ARGs in S. epidermidis with regard to genomic and ecological diversities are limited. Thus, we performed a comprehensive and comparative analysis of 405 high-quality S. epidermidis genomes, including those of 35 environmental isolates from the Han River, to investigate the genomic diversity of antibiotic resistance in this pathogen. Comparative genomic analysis revealed the prevalence of ARGs in S. epidermidis genomes associated with multi-locus sequence types. The genes encoding dihydrofolate reductase (dfrC) and multidrug efflux pump (norA) were genome-wide core ARGs. ß-Lactam class ARGs were also highly prevalent in the S. epidermidis genomes, which was consistent with the resistance phenotype observed in river isolates. Furthermore, we identified chloramphenicol acetyltransferase genes (cat) in the plasmid-like sequences of the six river isolates, which have not been reported previously in S. epidermidis genomes. These genes were identical to those harbored by the Enterococcus faecium plasmids and associated with the insertion sequence 6 family transposases, homologous to those found in Staphylococcus aureus plasmids, suggesting the possibility of horizontal gene transfer between these Gram-positive pathogens. Comparison of the ARG and virulence factor profiles between S. epidermidis and S. aureus genomes revealed that these two species were clearly distinguished, suggesting genomic demarcation despite ecological overlap. Our findings provide a comprehensive understanding of the genomic diversity of antibiotic resistance in S. epidermidis. IMPORTANCE: A comprehensive understanding of the antibiotic resistance gene (ARG) profiles of the skin commensal bacterium and opportunistic pathogen Staphylococcus epidermidis needs to be documented from a genomic point of view. Our study encompasses a comparative analysis of entire S. epidermidis genomes from various habitats, including those of 35 environmental isolates from the Han River sequenced in this study. Our results shed light on the distribution and diversity of ARGs within different S. epidermidis multi-locus sequence types, providing valuable insights into the ecological and genetic factors associated with antibiotic resistance. A comparison between S. epidermidis and Staphylococcus aureus revealed marked differences in ARG and virulence factor profiles, despite their overlapping ecological niches.

Multi-layered knowledge graph neural network reveals pathway-level agreement of three breast cancer multi-gene assays.

Multi-gene assays have been widely used to predict the recurrence risk for hormone receptor (HR)-positive breast cancer patients. However, these assays lack explanatory power regarding the underlying mechanisms of the recurrence risk. To address this limitation, we proposed a novel multi-layered knowledge graph neural network for the multi-gene assays. Our model elucidated the regulatory pathways of assay genes and utilized an attention-based graph neural network to predict recurrence risk while interpreting transcriptional subpathways relevant to risk prediction. Evaluation on three multi-gene assays-Oncotype DX, Prosigna, and EndoPredict-using SCAN-B dataset demonstrated the efficacy of our method. Through interpretation of attention weights, we found that all three assays are mainly regulated by signaling pathways driving cancer proliferation especially RTK-ERK-ETS-mediated cell proliferation for breast cancer recurrence. In addition, our analysis highlighted that the important regulatory subpathways remain consistent across different knowledgebases used for constructing the multi-level knowledge graph. Furthermore, through attention analysis, we demonstrated the biological significance and clinical relevance of these subpathways in predicting patient outcomes. The source code is available at http://biohealth.snu.ac.kr/software/ExplainableMLKGNN.

Effectiveness of lumbar motion style acupuncture treatment on inpatients with acute low back pain: A pragmatic, randomized controlled trial.

BACKGROUND AND PURPOSE: This parallel, single-center, pragmatic, randomized controlled study aimed to investigate the effectiveness and safety of motion style acupuncture treatment (MSAT; a combination of acupuncture and Doin therapy) to reduce pain and improve the functional disability of patients with acute low back pain (aLBP) due to road traffic accidents. MATERIALS AND METHODS: Ninety-six patients with aLBP admitted to the Haeundae Jaseng Hospital of Korean Medicine in South Korea due to traffic accidents were treated with integrative Korean medicine (IKM) with additional 3-day MSAT sessions during hospitalization (MSAT group, 48 patients) or without (control group, 48 patients), and followed up for 90 days. RESULTS: The mean numeric rating scale (NRS) scores of low back pain (LBP) of the MSAT and control groups were both 6.7 (95% confidence interval [CI]: 6.3, 7.1) at baseline. After completing the third round of all applicable treatment sessions (the primary endpoint in this study), the mean NRS scores of the MSAT and control groups were 3.76 (95% CI: 3.54, 3.99) and 5.32 (95% CI: 5.09, 5.55), respectively. The difference in the mean NRS score between the two groups was 1.56 (95% CI: 1.25, 1.87). CONCLUSION: IKM treatment combined with MSAT can reduce pain and improve the range of motion of patients with aLBP. TRIAL REGISTRATION: This trial is registered at ClinicalTrial.gov (NCT04956458).

Evaluation and optimization of sequence-based gene regulatory deep learning models.

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

A Prospective Sociocentric Study of 2 Entire Traditional Korean Villages: The Korean Social Life, Health, and Aging Project (KSHAP).

The Korean Social Life, Health, and Aging Project (KSHAP) was a multidisciplinary prospective study conducted in South Korea that measured various health biomarkers from blood, hair, and brain magnetic resonance imaging, and we examined their associations with sociocentric (global) social network data of older adults in 2 entire villages (or cohorts). Cohort K included participants aged 60 years or older, and cohort L included participants aged 65 years or older. We performed a baseline survey involving 814 of the 860 individuals (94.7% response rate) in cohort K in 2012 and 947 of the 1,043 individuals (90.8% response rate) in cohort L in 2017. We gathered longitudinal data for 5 waves in cohort K from 2011 to 2019 and 2 waves in cohort L from 2017 to 2022. Here, we describe for the first time the follow-up design of the KSHAP, the changes in social networks, and various biomarkers over a number of years. The data for cohort K are publicly available via the Korean Social Science Data Archive as well as the project website, and the data for cohort L will be shared soon.

Characterization of Mechanical Properties and Grain Size of Stainless Steel 316L via Metal Powder Injection Molding.

The metal powder injection molding process is completed by mixing a metal powder and a binder, performing an injection molding and degreasing process, and then performing a sintering process for high density. The disadvantage of metal powder injection molding is that defects occurring during the process affect mechanical properties, which are worse in mechanical properties than in products manufactured by cold-rolling. In this study, the mechanical properties and microstructure of stainless steel 316L manufactured by the metal powder injection molding process were analyzed. Mechanical properties such as density, tensile strength, and fatigue life were analyzed. The density was measured using Archimedes' principle, and a relative density of 94.62% was achieved compared to the theoretical density. The tensile strength was approximately 539.42 MPa and the elongation to fracture was approximately 92%. The fatigue test was performed at 80% of maximum tensile strength and a stress ratio of R = 0.1. The fatigue life was found in 55% (297 MPa) of maximum tensile strength that achieved 106 cycles. The microstructure was observed through scanning electron microscope after etching, and as a result, the average grain size was 88.51 µm. Using electron backscatter diffraction, inverse pole figure map, image quality map, and kernel average misorientation map of the specimen were observed in three different areas which were undeformed, uniformly deformed, and deformed. Based on these results, it is expected that research is needed to apply the metal powder injection molding process to the manufacture of agricultural machinery parts with complex shapes.

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.

Characterization of radiation-resistance mechanism in Spirosoma montaniterrae DY10T in terms of transcriptional regulatory system.

To respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance. In particular, we inferred gene regulatory network (GRN) and operons of radiation-resistance bacterium Spirosoma montaniterrae DY10[Formula: see text] and identified the major regulators for radiation-resistance. Our results showed that DNA repair and reactive oxygen species (ROS) scavenging mechanisms are key processes and Crp/Fnr family transcriptional regulator works as a master regulatory TF in early response to radiation.

Learning the histone codes with large genomic windows and three-dimensional chromatin interactions using transformer.

The quantitative characterization of the transcriptional control by histone modifications has been challenged by many computational studies, but most of them only focus on narrow and linear genomic regions around promoters, leaving a room for improvement. We present Chromoformer, a transformer-based, three-dimensional chromatin conformation-aware deep learning architecture that achieves the state-of-the-art performance in the quantitative deciphering of the histone codes in gene regulation. The core essence of Chromoformer architecture lies in the three variants of attention operation, each specialized to model individual hierarchy of transcriptional regulation involving from core promoters to distal elements in contact with promoters through three-dimensional chromatin interactions. In-depth interpretation of Chromoformer reveals that it adaptively utilizes the long-range dependencies between histone modifications associated with transcription initiation and elongation. We also show that the quantitative kinetics of transcription factories and Polycomb group bodies can be captured by Chromoformer. Together, our study highlights the great advantage of attention-based deep modeling of complex interactions in epigenomes.

Single-cell transcriptome analyses reveal distinct gene expression signatures of severe COVID-19 in the presence of clonal hematopoiesis.

Clonal hematopoiesis of indeterminate potential (CHIP), a common aging-related process that predisposes individuals to various inflammatory responses, has been reported to be associated with COVID-19 severity. However, the immunological signature and the exact gene expression program by which the presence of CHIP exerts its clinical impact on COVID-19 remain to be elucidated. In this study, we generated a single-cell transcriptome landscape of severe COVID-19 according to the presence of CHIP using peripheral blood mononuclear cells. Patients with CHIP exhibited a potent IFN-γ response in exacerbating inflammation, particularly in classical monocytes, compared to patients without CHIP. To dissect the regulatory mechanism of CHIP (+)-specific IFN-γ response gene expression in severe COVID-19, we identified DNMT3A CHIP mutation-dependent differentially methylated regions (DMRs) and annotated their putative target genes based on long-range chromatin interactions. We revealed that CHIP mutant-driven hypo-DMRs at poised cis-regulatory elements appear to facilitate the CHIP (+)-specific IFN-γ-mediated inflammatory immune response. Our results highlight that the presence of CHIP may increase the susceptibility to hyperinflammation through the reorganization of chromatin architecture, establishing a novel subgroup of severe COVID-19 patients.

Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection.

Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

Colistin-degrading proteases confer collective resistance to microbial communities during polymicrobial infections.

BACKGROUND: The increasing prevalence of resistance against the last-resort antibiotic colistin is a significant threat to global public health. Here, we discovered a novel colistin resistance mechanism via enzymatic inactivation of the drug and proposed its clinical importance in microbial communities during polymicrobial infections. RESULTS: A bacterial strain of the Gram-negative opportunistic pathogen Stenotrophomonas maltophilia capable of degrading colistin and exhibiting a high-level colistin resistance was isolated from the soil environment. A colistin-degrading protease (Cdp) was identified in this strain, and its contribution to colistin resistance was demonstrated by growth inhibition experiments using knock-out (Δcdp) and complemented (Δcdp::cdp) mutants. Coculture and coinfection experiments revealed that S. maltophilia carrying the cdp gene could inactivate colistin and protect otherwise susceptible Pseudomonas aeruginosa, which may seriously affect the clinical efficacy of the drug for the treatment of cystic fibrosis patients with polymicrobial infection. CONCLUSIONS: Our results suggest that Cdp should be recognized as a colistin resistance determinant that confers collective resistance at the microbial community level. Our study will provide vital information for successful clinical outcomes during the treatment of complex polymicrobial infections, particularly including S. maltophilia and other colistin-susceptible Gram-negative pathogens such as P. aeruginosa. Video abstract.

Optimum Urine Cotinine and NNAL Levels to Distinguish Smokers from Non-Smokers by the Changes in Tobacco Control Policy in Korea from 2008 to 2018.

INTRODUCTION: We examined the age- and sex-specific distributions of biomarkers of tobacco smoke exposure to determine the optimal cutoffs to distinguish smokers from non-smokers over the last 10 years in Korea, during which smoking prevalence and secondhand smoke (SHS) exposure declined due to changes in tobacco control policy. METHODS: We analyzed data from the Korea National Health and Nutrition Examination Survey on creatinine-adjusted urinary cotinine (2008-2018; 33 429 adults: 15 653 males and 17 776 females) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL; 2016-2018; 6337 adults: 3091 males and 3246 females). We determined the optimal cutoffs and confidence intervals (CIs) to distinguish smokers from non-smokers using receiver operator characteristic curve analysis and bootstrapping (1000 resamples). RESULTS: The optimal cutoff values of creatinine-adjusted urine cotinine and NNAL concentration were 20.9 ng/mg (95% CI: 20.8-21.0, sensitivity: 96.6%, specificity: 93.8%) and 8.9 pg/mg (95% CI: 8.8-8.9, sensitivity: 94.0%, specificity: 94.7%), respectively, in 2016-2018. The optimal cutoffs of both biomarkers increased with age and were higher in females than in males for NNAL concentration. In both sexes, the optimal cutoff of urine cotinine continuously declined over the study period. CONCLUSIONS: The optimal cotinine cutoff declined along with smoking prevalence and levels of SHS exposure due to enforcement of tobacco control policies, including smoke-free ordinances and tax increases. Monitoring of biomarkers of tobacco exposure appears necessary for verification of smoking status and regulatory use. IMPLICATIONS: Our results based on nationally representative data suggest that a large decrease in the optimal cutoff value of urine cotinine to distinguish smokers from non-smokers was caused by decreases in smoking prevalence and SHS exposure following enforcement of tobacco control policies over the last 10 years. We determined the optimal cutoff values of urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), which were not previously reported in representative population in Asia, to enable more accurate estimation of exposure to tobacco smoke and proper assessment of disease risks. Gender- and age-specific differences in the optimal cutoffs require further study. Monitoring of biomarkers of tobacco smoke exposure seems necessary for verification of smoking status and regulatory use.

Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells.

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

Eco-friendly erucamide-polydimethylsiloxane coatings for marine anti-biofouling.

Marine biofouling of ship hulls and ocean structures causes enormous economic losses due to increased frictional drag. Thus, efforts have been exerted worldwide to eliminate biofouling. In addition, a strong demand exists for the development of a cost-effective and eco-friendly anti-biofouling coating technology. Thus, erucamide-polydimethylsiloxane (EP) coating is proposed in this study. EP exhibits a hydrophobic surface as the erucamide content and drag reduction effect increase. In this study, the drag reduction effect of the EP 2.5 is better than that of glass and polydimethylsiloxane (PDMS) surfaces. Moreover, the proposed EP coatings are observed to prevent the biofouling induced by bacteria (E. coli) and brown algae (Cladosiphon sp.). In addition, through a marine field test, the anti-biofouling effect of the EP surface is found to be better than the previously studied oleamide-PDMS (OP) surface. In the marine field test, the EP 2.5 demonstrates superior anti-biofouling performance for 5.5 months under real marine environment. The proposed eco-friendly EP coating method could be applicable to marine vehicles that require effective drag reduction and anti-biofouling properties.

Impact of sulfamethoxazole on a riverine microbiome.

The continued emergence of bacterial pathogens presenting antimicrobial resistance is widely recognised as a global health threat and recent attention focused on potential environmental reservoirs of antibiotic resistance genes (ARGs). Freshwater environments such as rivers represent a potential hotspot for ARGs and antibiotic resistant bacteria as they are receiving systems for effluent discharges from wastewater treatment plants (WWTPs). Effluent also contains low levels of different antimicrobials including antibiotics and biocides. Sulfonamides are antibacterial chemicals widely used in clinical, veterinary and agricultural settings and are frequently detected in sewage sludge and manure in addition to riverine ecosystems. The impact of such exposure on ARG prevalence and diversity is unknown, so the aim of this study was to investigate the release of a sub-lethal concentration of the sulfonamide compound sulfamethoxazole (SMX) on the river bacterial microbiome using a flume system. This system was a semi-natural in vitro flume using river water (30 L) and sediment (6 kg) with circulation to mimic river flow. A combination of 'omics' approaches were conducted to study the impact of SMX exposure on the microbiomes within the flumes. Metagenomic analysis showed that the addition of low concentrations of SMX (<4 µg L-1) had a limited effect on the bacterial resistome in the water fraction only, with no impact observed in the sediment. Metaproteomics did not show differences in ARGs expression with SMX exposure in water. Overall, the river bacterial community was resilient to short term exposure to sub-lethal concentrations of SMX which mimics the exposure such communities experience downstream of WWTPs throughout the year.

Chemoenzymatic valorization of agricultural wastes into 4-hydroxyvaleric acid via levulinic acid.

Given that (i) levulinic acid (LA) is one of the most significant platform chemicals derived from biomass and (ii) 4-hydroxyvaleric acid (4-HV) is a potential LA derivative, the aim of this study is to achieve chemoenzymatic valorization of LA, which was obtained from agricultural wastes, to 4-HV. The thermochemical process utilized agricultural wastes (i.e., rice straw and corncob) as feedstocks and successfully produced LA, ranging from 25.1 to 65.4 mM. Additionally, formate was co-produced and used as a hydrogen source for the enzymatic hydrogenation of LA. Finally, engineered 3-hydroxybutyrate dehydrogenase from Alcaligenes faecalis (eHBDH) was applicable for catalyzing the conversion of agricultural wastes-driven LA, resulting in a maximum concentration of 11.32 mM 4-HV with a conversion rate of 48.2%. To the best of our knowledge, this is the first report describing the production of 4-HV from actual biomass, and the results might provide insights into the valorization of agricultural wastes.

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.

Inferring transcriptomic cell states and transitions only from time series transcriptome data.

Cellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/ .

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches.

The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.