Junbai Wang

1. Spin anisotropy for Excitation in Collisions between Two One-Electron atoms,
J. B. Wang , J. P. Hansen and A. Dubois
Physical Review Letters, 85, 1638, 2000

2. Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study,
Junbai Wang , Jan Delabie, Hans Christian Aasheim, Erlend Smeland and Ola Myklebost,
BMC Bioinformatics, 3, 36, 2002

3. MGraph: Graphical models for microarray data analysis, 
Junbai Wang, Ola Myklebost and Eivind Hovig
Bioinformatics, Vol. 19 no. 17, 2210, 2003

4. New probabilistic graphical models for genetic regulatory networks studies 
Junbai Wang, Leo Wang-kit Cheung and Jan Delabie.
Journal of Biomedical Informatics. Vol. 38/6 pp 443-455 2005

5. Hotspots of transcription factor colocalization in the genome of Drosophila melanogaster 
Moorman C*, Sun LV*, Wang JB*, Wit ED, Ward L, Lu XJ,Talhout W, Greil F, White KP, Bussemaker HJ and Steensel van B.
Proc. Natl. Acad. Sci. U S A. vol. 103| no.32| 12027-12032, August 8, 2006. (* co-first authors)

6. A new framework for identifying combinatorial regulation of transcription factors: a case study of the yeast cell cycle.
Junbai Wang.
Journal of Biomedical Informatics. Vol 40/6 pp 707-725, 2007.

7. BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors.
Junbai Wang, Morigen.
BMC Bioinformatics. 2009, 10:345.

8. Genome-wide analysis uncovers high frequency, strong differential chromosomal interactions and their associated epigenetic patterns in E2-mediated gene regulation. 
Wang J, Lan X, Hsu PY, Hsu HK, Huang K, Parvin J, Huang TH, Jin VX.
BMC Genomics. 2013 Jan 31;14(1):70.

9. Comprehensive genome-wide transcription factor analysis reveals that a combination of high affinity and low affinity DNA binding is needed for human gene regulation
Junbai Wang, Agnieszka Malecka, Gunhild Tr?d Jan Delabie
BMC Genomics 2015, 16(Suppl 7):S12.

10. BayesPI-BAR: a new biophysical model for characterization of regulatory sequence variations
Junbai Wang and Kirill Batmanov
Nucleic Acids Research 2015, July

11. Integrative whole-genome sequence analysis reveals roles of regulatory mutations in BCL6 and BCL2 in follicular lymphoma. 
Batmanov K, Wang W, Bjoras M, elabie J, Wang J. 
Sci Rep. 2017 Aug 1;7(1):7040. doi: 10.1038/s41598-017-07226-4.

12. Temporal dynamic reorganization of 3D chromatin architecture in hormone-induced breast cancer and endocrine resistance.
Zhou, Y*., Gerrard, D.L.*, Wang, JB., Li, T., Yang, Y., Fritz, A.J., Rajendran, M., Fu, X., Schiff, R., Lin, S., Frietze, S., Jin, V.X.
Nat Commun. 2019 Apr 3;10(1):1522. (* co-first authors)

13. Integrating whole genome sequencing, methylation, gene expression, topological associated domain information in regulatory mutation prediction: a study of follicular lymphoma. 
Amna Farooq, Gunhild Trøen, Jan Delabie, Junbai Wang.
Computational and Structural Biotechnology Journal. 2022 March. 

14. Identifying Functional Regulatory Mutation Blocks by Integrating Genome Sequencing and Transcriptome Data 
Mingyi Yang, Omer Ali, Magnar Bjørås, Junbai Wang.
iScience. 2023 July 2.

15. Integrated analysis of differential intra-chromosomal community interactions: A study of breast cancer.  
Yao Z, Fang K, Liu G, Bjørås M, Jin VX, Wang J. Artif Intell Med. 2025 Sep;167:103180. doi: 10.1016/j.artmed.2025.103180. Epub 2025 May 24.

• Yang, Mingyi; Liu, Gege; Bjørås, Magnar & Wang, Junbai (2026). Protocol for identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data. STAR Protocols. 7(3), p. 104642–104642. doi: 10.1016/j.xpro.2026.104642. Full text in Research Archive
• Liu, Gege; Bai, Baoyan & Wang, Junbai (2026). BayesPI-FLY: a Bayesian neural network approach for inferring feature weighted TF–DNA interaction. Briefings in Bioinformatics. ISSN 1467-5463. 27(2). doi: 10.1093/bib/bbag191. Full text in Research Archive Show summary

  Abstract Understanding how transcription factors (TFs) recognize DNA motifs is central to deciphering gene regulation. However, integrating multi-omics data, particularly DNA methylation, which can variably influence TF binding, remains a significant challenge. To address this, we developed BayesPI-Feature Learning Yard (BayesPI-FLY), a Bayesian neural network for de novo motif discovery that integrates DNA sequence information with DNA methylation status data. Building upon the classical biophysical model of TF–DNA interactions, BayesPI-FLY employs a two-layer inference architecture to jointly estimate model parameters and hyperparameters within a Bayesian framework. The core algorithms are implemented in C and parallelized through Python, ensuring computational efficiency. BayesPI-FLY quantitatively characterizes methylation effects at both single-nucleotide and motif levels, and generates position weight matrices and sequence logos to facilitate motif interpretation. Validation using synthetic and high-throughput sequencing datasets, including whole-genome bisulfite sequencing data, demonstrates that the framework can recapitulate known methylation-associated TF-binding patterns and infer strand-specific associations within the modeling framework. Collectively, BayesPI-FLY offers a versatile and extensible computational platform for characterizing methylation-related TF-DNA binding patterns across complex epigenetic contexts.

• Rønningen, Torunn; Zeng, Yong; Dahl, Mai Britt; Wang, Junbai; Visnovska, Martina & Tannæs, Tone Møller [Show all 18 contributors for this article] (2025). Global mapping of RNA N6-methyladenosine (m6A) in human subcutaneous and visceral adipose tissue reveals novel targets that correlate with clinical variables of obesity. Biomarker Research. 13(1). doi: 10.1186/s40364-025-00857-0. Full text in Research Archive Show summary

  Abstract Background Obesity is a major health challenge and fat accumulation in visceral depots is more strongly associated with metabolic comorbidities than deposition in subcutaneous depots. Epitranscriptomic regulation of gene expression by N 6 -methyladenosine (m 6 A) influences various aspects of RNA metabolism, however the m 6 A methylome in human adipose tissue and its relationship with fat distribution has not yet been investigated in detail. Methods In this study, we performed epitranscriptomic mapping of m 6 A in intra-individually paired samples of subcutaneous (SAT) and omental visceral adipose tissue (OVAT) from women with normal weight (BMI ≤25, n  = 3) and obesity (BMI ≥35, n  = 10) using meRIP-seq (discovery cohort). We further investigated differential m 6 A methylation for specific target genes in a larger cohort of individuals with obesity ( n  = 72, validation cohort) using meRIP-qPCR. meRIP-seq was performed for primary adipocytes from a subset of the patients ( n  = 4) to account for cell type specific differences. Results We here provide the first global map of m 6 A in human adipose tissue in paired samples of SAT and OVAT. We show an overall high overlap in m 6 A sites between individuals and depots, but also distinct depot-specific differences. We identify 339 target genes showing depot-specific m 6 A methylation. Depot-specific methylation was validated for selected sites in SEMA3A , SNAP47 and PPP1R9A in a larger validation cohort. We additionally identify differentially methylated targets between lean individuals and individuals with obesity, including TSC22D1 , FMNL2 and IL1R1 . By combining data from primary adipocytes with data from corresponding bulk adipose tissue, we identified a higher number of genes containing m 6 A in non-adipocyte cells in OVAT compared to SAT. Mechanistically, we show for selected targets that m 6 A affects RNA lifetime in pre-adipocyte cell culture models. Importantly, m 6 A methylation in selected targets correlates with clinically important variables related to obesity, fat distribution and glucose metabolism. Conclusions We identify a catalogue of novel targets showing adipose tissue depot specific m 6 A methylation, with potential as biomarkers in metabolic disease. Our findings underscore the regulatory role of m 6 A in obesity and provide valuable insights for future research. The datasets generated represent a significant resource for further insight in adipose tissue biology and its implications for metabolic health.

• Bordin, Diana Lilian; Lefol, Yohan Pierre; Lagartos, Maria Jose Donate; Montaldo, Nicola Pietro; Rolseth, Veslemøy & Tekin, Nuriye Basdag [Show all 18 contributors for this article] (2025). SMUG1 DNA glycosylase modulates reward behaviour through regulation of olfactory receptor gene expression. Nucleic Acids Research (NAR). ISSN 0305-1048. 53(14). doi: 10.1093/nar/gkaf722. Full text in Research Archive
• Yao, Zhihao; Fang, Kun; Liu, Gege; Bjørås, Magnar; Jin, Victor X. & Wang, Junbai (2025). Integrated analysis of differential intra-chromosomal community interactions: A study of breast cancer. Artificial Intelligence in Medicine. ISSN 0933-3657. 167. doi: 10.1016/j.artmed.2025.103180. Full text in Research Archive Show summary

  It is challenging to analyze the dynamics of intra-chromosomal interactions when considering multiple high-dimensional epigenetic datasets. A computational approach, differential network analysis in intra-chromosomal community interaction (DNAICI), was proposed here to elucidate these dynamics by integrating Hi-C data with other epigenetic data. DNAICI utilized a novel hyperparameter tuning method, for optimizing the network clustering, to identify valid intra-chromosomal community interactions at different resolutions. The approach was first trained on Hi-C data and other epigenetic data in an untreated and one hour estrogen (E2)-treated breast cancer cell line, MCF7, and uncovered two major types of valid intra-chromosomal community interactions (active/repressive) that resembles the properties of A/B compartments (or open/closed chromatin domains). It was further tested on the breast cancer cell line MCF7 and its corresponding tamoxifen-resistant (TR) derivative, MCF7TR, and identified 515 differentially interacting and expressed genes (DIEGs) within intra-chromosomal community interactions. In silico analysis of these DIEGs revealed that endocrine resistance is among the top biological pathways, suggesting an interacting/looping-mediated mechanism in regulating breast cancer tamoxifen resistance. This novel integrated network analysis approach offers a broad application in diverse biological systems for identifying a biological-context-specific differential community interaction.

• Dragland, Jenny Sofie; Liu, Gege; Nilsen, Hilde; Böttcher, Yvonne & Wang, Junbai (2025). EpiMapper: A new tool for analyzing high-throughput sequencing from CUT&Tag. Computers in Biology and Medicine. ISSN 0010-4825. 186. doi: 10.1016/j.compbiomed.2025.109692. Full text in Research Archive Show summary

  Since the invention of next-generation sequencing, new methods have been developed to understand the regulation of gene expression through epigenetic markers. Among these, CUT&Tag (Cleavage Under Targets and Tagmentation) analysis has emerged as an efficient epigenomic profiling technique with low input requirements, high sensitivity, and low background signals. Although wet-lab techniques are available, data analysis remains challenging for scientists without expert-level computational skills. Therefore, we developed EpiMapper, a new Python package that simplifies the data analysis of CUT&Tag sequencing and similar techniques, such as ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) or ChIP-seq (chromatin immunoprecipitation [ChIP] assays with sequencing), and allows biomedical scientists to easily interpret the results. This new package includes every necessary step, from quality control to annotation and differential peak analysis. In particular, EpiMapper has improved functionality (e.g., reproducibility assessment) compared to previous analysis protocols and visualization plots and provides new features, such as genome annotation and differential peak analysis. Using three case studies, two on CUT&Tag and one on ATAC-seq data, the EpiMapper Python package successfully reproduced previous results.

• Abentung, Andreas; Sakshaug, Teri; Dumaru, Rabina; Liabakk, Nina-Beate; Yang, Mingyi & Wang, Junbai [Show all 8 contributors for this article] (2025). DNA glycosylases Ogg1 and Mutyh influence gene expression of PRC2 targets associated with cognition. Cellular and Molecular Life Sciences (CMLS). ISSN 1420-682X. 82. doi: 10.1007/s00018-025-05730-9. Full text in Research Archive Show summary

  Base excision repair, initiated by DNA glycosylases, preserves genomic integrity by removing damaged bases. DNA glycosylases Ogg1 and Mutyh were shown to alter the hippocampal transcriptome independently of DNA damage repair. However, the role of DNA glycosylases as transcriptional regulators and their involvement in cognition remain unclear. Here, we combine transcriptomic and epigenomic analyses of the hippocampus in mice deficient in DNA glycosylases. We report that the combined deficiency of Ogg1 and Mutyh impairs spatial long-term memory. Mechanistically, Ogg1 and Mutyh modulate DNA methylation of polycomb repressive complex 2 (PRC2) target genes. PRC2 occupancy and associated histone post-translational modifications depend on Ogg1 and Mutyh in neurons and glia. These epigenetic changes correlate with cell-type specific differences in gene expression of PRC2 targets. Finally, human genetic data link DNA glycosylase-regulated genes to cognitive function, highlighting a novel role for Ogg1 and Mutyh in modulating the epigenome to control transcriptional responses relevant for brain-related diseases.

• Saeed, Sadia; Poulsen, Lars La Cour; Visnovska, Martina; Hoffmann, Anne; Ghosh, Adhideb & Wolfrum, Christian [Show all 18 contributors for this article] (2025). Chromatin landscape in paired human visceral and subcutaneous adipose tissue and its impact on clinical variables in obesity. EBioMedicine. 114. doi: 10.1016/j.ebiom.2025.105653. Full text in Research Archive Show summary

  Background Obesity is a global health challenge and adipose tissue exhibits distinct depot-specific characteristics impacting differentially on the risk of metabolic comorbidities. Methods Here, we integrate chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) data from intra-individually paired human subcutaneous (SAT) and omental visceral adipose tissue (OVAT) samples to unveil depot-specific regulatory mechanisms. Findings We identified twice as many depot-specific differentially accessible regions (DARs) in OVAT compared to SAT. SAT-specific regions showed enrichment for adipose tissue enhancers involving genes controlling extracellular matrix organization and metabolic processes. In contrast, OVAT-specific regions showed enrichment in promoters linked to genes associated with cardiomyopathies. Moreover, OVAT-specific regions were enriched for bivalent transcription start site and repressive chromatin states, suggesting a “lingering” regulatory state. Motif analysis identified CTCF and BACH1 as most significantly enriched motifs in SAT and OVAT-specific DARs, respectively. Distinct gene sets correlated with important clinical variables of obesity, fat distribution measures, as well as insulin, glucose, and lipid metabolism. Interpretation We provide an integrated analysis of chromatin accessibility and transcriptional profiles in paired human SAT and OVAT samples, offering new insights into the regulatory landscape of adipose tissue and highlighting depot-specific mechanisms in obesity pathogenesis.

• Zhang, Xiaolu; li, jingwei; zhuang, zhengkai; Wang, Junbai; bu, zhaode & Lan, Xun (2024). Challenges and prospects of cell-free DNA in precision oncology. Medicine plus. ISSN 2950-3477. 1(4). doi: 10.1016/j.medp.2024.100059. Full text in Research Archive
• Wang, Junbai; Yang, Mingyi; Ali, Omer; Dragland, Jenny Sofie; Bjørås, Magnar & Farkas, Lorant (2024). Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of follicular lymphoma. Computers in Biology and Medicine. ISSN 0010-4825. 178, p. 1–15. doi: 10.1016/j.compbiomed.2024.108787. Full text in Research Archive Show summary

  Mutations in DNA regulatory regions are increasingly being recognized as important drivers of cancer and other complex diseases. These mutations can regulate gene expression by affecting DNA-protein binding and epigenetic profiles, such as DNA methylation in genome regulatory elements. However, identifying mutation hotspots associated with expression regulation and disease progression in non-coding DNA remains a challenge. Unlike most existing approaches that assign a mutation score to individual single nucleotide polymorphisms (SNP), a mutation block (MB)-based approach was introduced in this study to assess the collective impact of a cluster of SNPs on transcription factor-DNA binding affinity, differential gene expression (DEG), and nearby DNA methylation. Moreover, the long-distance target genes of functional MBs were identified using a new permutation-based algorithm that assessed the significance of correlations between DNA methylation at regulatory regions and target gene expression. Two new Python packages were developed. The Differential Methylation Region (DMR-analysis) analysis tool was used to detect DMR and map them to regulatory elements. The second tool, an integrated DMR, DEG, and SNP analysis tool (DDS-analysis), was used to combine the omics data to identify functional MBs and long-distance target genes. Both tools were validated in follicular lymphoma (FL) cohorts, where not only known functional MBs and their target genes (BCL2 and BCL6) were recovered, but also novel genes were found, including CDCA4 and JAG2, which may be associated with FL development. These genes are linked to target gene expression and are significantly correlated with the methylation of nearby DNA sequences in FL. The proposed computational integrative analysis of multiomics data holds promise for identifying regulatory mutations in cancer and other complex diseases.

• Yang, Mingyi; Kaarbø, Mari; Myhre, Vegard; Reims, Henrik Mikael; Karlsen, Tom Hemming & Wang, Junbai [Show all 13 contributors for this article] (2024). Altered Genome-Wide DNA Methylation in the Duodenum of Common Variable Immunodeficiency Patients. Journal of Clinical Immunology. ISSN 0271-9142. 44(6), p. 1–16. doi: 10.1007/s10875-024-01726-5. Full text in Research Archive Show summary

  Purpose A large proportion of Common variable immunodeficiency (CVID) patients has duodenal inflammation with increased intraepithelial lymphocytes (IEL) of unknown aetiology. The histologic similarities to celiac disease, lead to confusion regarding treatment (gluten-free diet) of these patients. We aimed to elucidate the role of epigenetic DNA methylation in the aetiology of duodenal inflammation in CVID and differentiate it from true celiac disease. Methods DNA was isolated from snap-frozen pieces of duodenal biopsies and analysed for differences in genome-wide epigenetic DNA methylation between CVID patients with increased IEL (CVID_IEL; n = 5) without IEL (CVID_N; n = 3), celiac disease (n = 3) and healthy controls (n = 3). Results The DNA methylation data of 5-methylcytosine in CpG sites separated CVID and celiac diseases from healthy controls. Differential methylation in promoters of genes were identified as potential novel mediators in CVID and celiac disease. There was limited overlap of methylation associated genes between CVID_IEL and Celiac disease. High frequency of differentially methylated CpG sites was detected in over 100 genes nearby transcription start site (TSS) in both CVID_IEL and celiac disease, compared to healthy controls. Differential methylation of genes involved in regulation of TNF/cytokine production were enriched in CVID_IEL, compared to healthy controls. Conclusion This is the first study to reveal a role of epigenetic DNA methylation in the etiology of duodenal inflammation of CVID patients, distinguishing CVID_IEL from celiac disease. We identified potential biomarkers and therapeutic targets within gene promotors and in high-frequency differentially methylated CpG regions proximal to TSS in both CVID_IEL and celiac disease.

• Zylla, Terese Elisabeth; Wang, Junbai & Gjerstad, Johannes (2024). Social stress in rats promotes transcriptional mitochondrial changes of the adrenal tissue. Endocrine and Metabolic Science. 16. doi: 10.1016/j.endmts.2024.100188. Full text in Research Archive Show summary

  Previous observations suggest that strong social stressors, that is, repeated social defeat, could initiate functional changes in the hypothalamic-pituitary-adrenal (HPA) axis in social mammals. Here, we examine whether exposure to such stress in rats causes persistent alterations in gene expression associated with hypo-and/or hyper-methylation in the adrenal tissue, i.e., the interface between the HPA axis and the circulating hormones. Our integrated analyses of RNA-sequencing (RNAseq) and whole genome bisulfite sequencing (WGBS), suggested that adrenal mitochondrial membrane transport necessary for corticosterone (CORT) synthesis, are affected by repeated social defeat. The increased CORT levels observed in blood in the stressed rats further suggested that corticosterone synthesis might be influenced by this form of social stress. The cellular mechanisms underlying these changes, i.e., the enriched gene ontology (GO)-terms and increased levels of circulating CORT, remain to be investigated.

• Yao, Zhihao; Jin, Suoqin; Zhou, Fuling; Wang, Junbai; Wang, Kai & Zou, Xiufen (2024). A novel multiscale framework for delineating cancer evolution from subclonal compositions. Journal of Theoretical Biology. ISSN 0022-5193. 582, p. 1–12. doi: 10.1016/j.jtbi.2024.111743. Full text in Research Archive Show summary

  Objective: Owing to the heterogeneity in the evolution of cancer, distinguishing between diverse growth patterns and predicting long-term outcomes based on short-term measurements poses a great challenge. Methods: A novel multiscale framework is proposed to unravel the connections between the population dynamics of cancer growth (i.e., aggressive, bounded, and indolent) and the cellular-subclonal dynamics of cancer evolution. This framework employs the non-negative lasso (NN-LASSO) algorithm to forge a link between an ordinary differential equation (ODE)-based population model and a cellular evolution model. Results: The findings of our current work not only affirm the impact of subclonal composition on growth dynamics but also identify two significant subclones within heterogeneous growth patterns. Moreover, the subclonal compositions at the initial time are able to accurately discriminate diverse growth patterns through a machine learning algorithm. Conclusion: The proposed multiscale framework successfully delineates the intricate landscape of cancer evolution, bridging the gap between long-term growth dynamics and short-term measurements, both in simulated and real-world data. This methodology provides a novel avenue for thorough exploration into the realm of cancer evolution.

• Yang, Mingyi; Ali, Omer; Bjørås, Magnar & Wang, Junbai (2023). Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data. iScience. 26(8), p. 1–23. doi: 10.1016/j.isci.2023.107266. Full text in Research Archive Show summary

  Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.

• Fang, Kun; Wang, Junbai; Liu, Lu & Jin, Victor X. (2022). Mapping nucleosome and chromatin architectures: A survey of computational methods. Computational and Structural Biotechnology Journal. 20, p. 3955–3962. doi: 10.1016/j.csbj.2022.07.037. Full text in Research Archive Show summary

  With ever-growing genomic sequencing data, the data variabilities and the underlying biases of the sequencing technologies pose significant computational challenges ranging from the need for accurately detecting the nucleosome positioning or chromatin interaction to the need for developing normalization methods to eliminate systematic biases. This review mainly surveys the computational methods for mapping the higher-resolution nucleosome and higher-order chromatin architectures. While a detailed discussion of the underlying algorithms is beyond the scope of our survey, we have discussed the methods and tools that can detect the nucleosomes in the genome, then demonstrated the computational methods for identifying 3D chromatin domains and interactions. We further illustrated computational approaches for integrating multi-omics data with Hi-C data and the advance of single-cell (sc)Hi-C data analysis. Our survey provides a comprehensive and valuable resource for biomedical scientists interested in studying nucleosome organization and chromatin structures as well as for computational scientists who are interested in improving upon them.

• Ali, Omer; Farooq, Amna; Yang, Mingyi; Jin, Victor X.; Bjørås, Magnar & Wang, Junbai (2022). abc4pwm: affinity based clustering for position weight matrices in applications of DNA sequence analysis. BMC Bioinformatics. 23(1). doi: 10.1186/s12859-022-04615-z. Full text in Research Archive Show summary

  Background Transcription factor (TF) binding motifs are identified by high throughput sequencing technologies as means to capture Protein-DNA interactions. These motifs are often represented by consensus sequences in form of position weight matrices (PWMs). With ever-increasing pool of TF binding motifs from multiple sources, redundancy issues are difficult to avoid, especially when every source maintains its own database for collection. One solution can be to cluster biologically relevant or similar PWMs, whether coming from experimental detection or in silico predictions. However, there is a lack of efficient tools to cluster PWMs. Assessing quality of PWM clusters is yet another challenge. Therefore, new methods and tools are required to efficiently cluster PWMs and assess quality of clusters. Results A new Python package Affinity Based Clustering for Position Weight Matrices (abc4pwm) was developed. It efficiently clustered PWMs from multiple sources with or without using DNA-Binding Domain (DBD) information, generated a representative motif for each cluster, evaluated the clustering quality automatically, and filtered out incorrectly clustered PWMs. Additionally, it was able to update human DBD family database automatically, classified known human TF PWMs to the respective DBD family, and performed TF motif searching and motif discovery by a new ensemble learning approach. Conclusion This work demonstrates applications of abc4pwm in the DNA sequence analysis for various high throughput sequencing data using ~ 1770 human TF PWMs. It recovered known TF motifs at gene promoters based on gene expression profiles (RNA-seq) and identified true TF binding targets for motifs predicted from ChIP-seq experiments. Abc4pwm is a useful tool for TF motif searching, clustering, quality assessment and integration in multiple types of sequence data analysis including RNA-seq, ChIP-seq and ATAC-seq.

• Farooq, Amna; Trøen, Gunhild; Delabie, Jan & Wang, Junbai (2022). Integrating whole genome sequencing, methylation, gene expression, topologically associated domain information in regulatory mutation prediction: A study of follicular lymphoma. Computational and Structural Biotechnology Journal. 20, p. 1726–1742. doi: 10.1016/j.csbj.2022.03.023. Full text in Research Archive Show summary

  A major challenge in human genetics is of the analysis of the interplay between genetic and epigenetic factors in a multifactorial disease like cancer. Here, a novel methodology is proposed to investigate genome-wide regulatory mechanisms in cancer, as studied with the example of follicular Lymphoma (FL). In a first phase, a new machine-learning method is designed to identify Differentially Methylated Regions (DMRs) by computing six attributes. In a second phase, an integrative data analysis method is developed to study regulatory mutations in FL, by considering differential methylation information together with DNA sequence variation, differential gene expression, 3D organization of genome (e.g., topologically associated domains), and enriched biological pathways. Resulting mutation block-gene pairs are further ranked to find out the significant ones. By this approach, BCL2 and BCL6 were identified as top-ranking FL-related genes with several mutation blocks and DMRs acting on their regulatory regions. Two additional genes, CDCA4 and CTSO, were also found in top rank with significant DNA sequence variation and differential methylation in neighboring areas, pointing towards their potential use as biomarkers for FL. This work combines both genomic and epigenomic information to investigate genome-wide gene regulatory mechanisms in cancer and contribute to devising novel treatment strategies.

• Gatti, Francesca; Mia, Sobuj; Hammarström, Clara Louise; Frerker, Nadine; Fosby, Bjarte & Wang, Junbai [Show all 11 contributors for this article] (2021). Nuclear IL-33 restrains the early conversion of fibroblasts to an extracellular matrix-secreting phenotype. Scientific Reports. 11:108(1), p. 1–13. doi: 10.1038/s41598-020-80509-5. Full text in Research Archive Show summary

  Abstract Interleukin (IL)-33 is a cytokine that appears to mediate fibrosis by signaling via its receptor ST2 (IL-33R/IL1RL1). It is also, however, a protein that after synthesis is sorted to the cell nucleus, where it appears to affect chromatin folding. Here we describe a novel role for nuclear IL-33 in regulating the fibroblast phenotype in murine kidney fibrosis driven by unilateral ureteral obstruction. Transcriptional profiling of IL-33-deficient kidneys 24 h after ligation revealed enhanced expression of fibrogenic genes and enrichment of gene sets involved in extracellular matrix formation and remodeling. These changes relied on intracellular effects of IL-33, because they were not reproduced by treatment with a neutralizing antibody to IL-33 that prevents IL-33R/ST2L receptor signaling nor were they observed in IL-33R/ST2-deficient kidneys. To further explore the intracellular function of IL-33, we established transcription profiles of human fibroblasts, observing that knockdown of IL-33 skewed the transcription profile from an inflammatory towards a myofibroblast phenotype, reflected in higher levels of COL3A1, COL5A1 and transgelin protein, as well as lower expression levels of IL6 , CXCL8 , CLL7 and CCL8 . In conclusion, our findings suggest that nuclear IL-33 in fibroblasts dampens the initial profibrotic response until persistent stimuli, as enforced by UUO, can override this protective mechanism.

• Li, Jingwei; Fang, Kun; Choppavarapu, Lavanya; Yang, Ke; Yang, Yini & Wang, Junbai [Show all 9 contributors for this article] (2021). Hi-C profiling of cancer spheroids identifies 3D-growth-specific chromatin interactions in breast cancer endocrine resistance. Clinical Epigenetics. 13(175). doi: 10.1186/s13148-021-01167-6. Full text in Research Archive
• Naeini, Alireza Sahaf; Farooq, Amna; Bjørås, Magnar & Wang, Junbai (2020). IGAP-integrative genome analysis pipeline reveals new gene regulatory model associated with nonspecific TF-DNA binding affinity. Computational and Structural Biotechnology Journal. 18, p. 1270–1286. doi: 10.1016/j.csbj.2020.05.024. Full text in Research Archive
• Zhou, Yufan; Cheng, Xiaolong; Yang, Yini; Li, Tian; Li, Jingwei & Huang, Tim H.-M. [Show all 9 contributors for this article] (2020). Modeling and analysis of Hi-C data by HiSIF identifies characteristic promoter-distal loops. Genome Medicine. 12(12). doi: 10.1186/s13073-020-00769-8. Full text in Research Archive
• Farooq, Amna; Grønmyr, Sindre; Omer, Ali Avan; Rognes, Torbjørn; Scheffler, Katja & Bjørås, Magnar [Show all 7 contributors for this article] (2020). HMST-Seq-Analyzer: A new python tool for differential methylation and hydroxymethylation analysis in various DNA methylation sequencing data. Computational and Structural Biotechnology Journal. 18, p. 2877–2889. doi: 10.1016/j.csbj.2020.09.038. Full text in Research Archive Show summary

  DNA methylation (5mC) and hydroxymethylation (5hmC) are chemical modifications of cytosine bases which play a crucial role in epigenetic gene regulation. However, cost, data complexity and unavailability of comprehensive analytical tools is one of the major challenges in exploring these epigenetic marks. Hydroxymethylation-and Methylation-Sensitive Tag sequencing (HMST-seq) is one of the most costeffective techniques that enables simultaneous detection of 5mC and 5hmC at single base pair resolution. We present HMST-Seq-Analyzer as a comprehensive and robust method for performing simultaneous differential methylation analysis on 5mC and 5hmC data sets. HMST-Seq-Analyzer can detect Differentially Methylated Regions (DMRs), annotate them, give a visual overview of methylation status and also perform preliminary quality check on the data. In addition to HMST-Seq, our tool can be used on wholegenome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) data sets as well. The tool is written in Python with capacity to process data in parallel and is available at (https://hmst-seq.github.io/hmst/)

• Pihlstrøm, Hege; Ueland, Thor; Michelsen, Annika; Aukrust, Pål; Gatti, Francesca & Hammarström, Clara Louise [Show all 15 contributors for this article] (2020). Exploring the potential effect of paricalcitol on markers of inflammation in de novo renal transplant recipients. PLOS ONE. 15:e0243759(12), p. 1–14. doi: 10.1371/journal.pone.0243759. Full text in Research Archive Show summary

  Following a successful renal transplantation circulating markers of inflammation may remain elevated, and systemic inflammation is associated with worse clinical outcome in renal transplant recipients (RTRs). Vitamin D-receptor (VDR) activation is postulated to modulate inflammation and endothelial function. We aimed to explore if a synthetic vitamin D, paricalcitol, could influence systemic inflammation and immune activation in RTRs. Newly transplanted RTRs were included in an open-label randomized controlled trial on the effect of paricalcitol on top of standard care over the first post-transplant year. Fourteen pre-defined circulating biomarkers reflecting leukocyte activation, endothelial activation, fibrosis and general inflammatory burden were analyzed in 74 RTRs at 8 weeks (baseline) and 1 year post-engraftment. Mean changes in plasma biomarker concentrations were compared by t-test. The expression of genes coding for the same biomarkers were investigated in 1-year surveillance graft biopsies (n = 60). In patients treated with paricalcitol circulating osteoprotegerin levels increased by 0.19 ng/ml, compared with a 0.05 ng/ml increase in controls (p = 0.030). In graft tissue, a 21% higher median gene expression level of TNFRSF11B coding for osteoprotegerin was found in paricalcitol-treated patients compared with controls (p = 0.026). Paricalcitol treatment did not significantly affect the blood- or tissue levels of any other investigated inflammatory marker. In RTRs, paricalcitol treatment might increase both circulating and tissue levels of osteoprotegerin, a modulator of calcification, but potential anti-inflammatory treatment effects in RTRs are likely very modest.

• Yang, Yini; Choppavarapu, Lavanya; Fang, Kun; Naeini, Alireza Sahaf; Nosirov, Bakhtiyor & Li, Jingwei [Show all 14 contributors for this article] (2020). The 3D genomic landscape of differential response to EGFR/HER2 inhibition in endocrine-resistant breast cancer cells. Biochimica et Biophysica Acta. Gene Regulatory Mechanisms. ISSN 1874-9399. 1863(11). doi: 10.1016/j.bbagrm.2020.194631. Full text in Research Archive
• Batmanov, Kirill; Delabie, Jan & Wang, Junbai (2019). BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts. Frontiers in Genetics. 10, p. 1–8. doi: 10.3389/fgene.2019.00282. Full text in Research Archive
• Zhou, Yufan; Gerrard, Diana L; Wang, Junbai; li, tian; Yang, Yini & Fritz, Andrew J [Show all 12 contributors for this article] (2019). Temporal dynamic reorganization of 3D chromatin architecture in hormone-induced breast cancer and endocrine resistance. Nature Communications. 10(1). doi: 10.1038/s41467-019-09320-9. Full text in Research Archive
• Gerrard, DL; Wang, Y; Gaddis, M; Zhou, Y; Wang, Junbai & witt, H [Show all 10 contributors for this article] (2018). Three-dimensional analysis reveals altered chromatin interaction by enhancer inhibitors harbors TCF7L2-regulated cancer gene signature. Journal of Cellular Biochemistry. ISSN 0730-2312. 120(3), p. 3056–3070. doi: 10.1002/jcb.27449. Full text in Research Archive
• Poulsen, Lars La Cour; Edelmann, Reidunn Jetne; Krüger, Stig; Diéguez-Hurtado, Rodrigo; Shah, Akshay & Stav-Noraas, Tor Espen [Show all 22 contributors for this article] (2018). Inhibition of endothelial NOTCH1 signaling attenuates inflammation by reducing cytokine-mediated histone acetylation at inflammatory enhancers. Arteriosclerosis, Thrombosis and Vascular Biology. ISSN 1079-5642. 38(4), p. 854–869. doi: 10.1161/ATVBAHA.117.310388. Full text in Research Archive Show summary

  Objective: Endothelial upregulation of adhesion molecules serves to recruit leukocytes to inflammatory sites and appears to be promoted by NOTCH1; however, current models based on interactions between active NOTCH1 and NF-κB components cannot explain the transcriptional selectivity exerted by NOTCH1 in this context. Approach and Results: Observing that Cre/Lox-induced conditional mutations of endothelial Notch modulated inflammation in murine contact hypersensitivity, we found that IL (interleukin)-1β stimulation induced rapid recruitment of RELA (v-rel avian reticuloendotheliosis viral oncogene homolog A) to genomic sites occupied by NOTCH1-RBPJ (recombination signal-binding protein for immunoglobulin kappa J region) and that NOTCH1 knockdown reduced histone H3K27 acetylation at a subset of NF-κB–directed inflammatory enhancers. Conclusions: Our findings reveal that NOTCH1 signaling supports the expression of a subset of inflammatory genes at the enhancer level and demonstrate how key signaling pathways converge on chromatin to coordinate the transition to an infla mmatory endothelial phenotype.

• Malecka, Agnieszka Maria; Trøen, Gunhild; Tierens, Anne; Østlie, Ingunn; Malecki, Jedrzej Mieczyslaw & Randen, Ulla [Show all 10 contributors for this article] (2017). Frequent somatic mutations of KMT2D (MLL2) and CARD11 genes in primary cold agglutinin disease. British Journal of Haematology. ISSN 0007-1048. 183(5), p. 838–842. doi: 10.1111/bjh.15063. Full text in Research Archive
• Li, Xiaoran; Lu, Jie; Kan, Quancheng; Li, Xiaoli; Fan, Qiong & Li, Yaqing [Show all 17 contributors for this article] (2017). Metabolic reprogramming is associated with flavopiridol resistance in prostate cancer DU145 cells. Scientific Reports. 7:5081, p. 1–20. doi: 10.1038/s41598-017-05086-6. Full text in Research Archive
• Batmanov, Kirill; Wang, Wei; Bjørås, Magnar; Delabie, Jan & Wang, Junbai (2017). Integrative whole-genome sequence analysis reveals roles of regulatory mutations in BCL6 and BCL2 in follicular lymphoma. Scientific Reports. 7(1). doi: 10.1038/s41598-017-07226-4. Full text in Research Archive Show summary

  The contribution of mutations in regulatory regions to tumorigenesis has been the subject of many recent studies. We propose a new framework for integrative analysis of genome-wide sequencing data by considering diverse genetic information. This approach is applied to study follicular lymphoma (FL), a disease for which little is known about the contribution of regulatory gene mutations. Results from a test FL cohort revealed three novel highly recurrent regulatory mutation blocks near important genes implicated in FL, BCL6 and BCL2. Similar findings were detected in a validation FL cohort. We also found transcription factors (TF) whose binding may be disturbed by these mutations in FL: disruption of FOX TF family near the BCL6 promoter may result in reduced BCL6 expression, which then increases BCL2 expression over that caused by BCL2 gene translocation. Knockdown experiments of two TF hits (FOXD2 or FOXD3) were performed in human B lymphocytes verifying that they modulate BCL6/BCL2 according to the computationally predicted effects of the SNVs on TF binding. Overall, our proposed integrative analysis facilitates non-coding driver identification and the new findings may enhance the understanding of FL.

• Pihlstrøm, Hege; Gatti, Francesca; Hammarström, Clara Louise; Eide, Ivar Anders; Kasprzycka, Monika & Wang, Junbai [Show all 13 contributors for this article] (2017). Early introduction of oral paricalcitol in renal transplant recipients. An open-label randomized study. Transplant International. ISSN 0934-0874. 30(8), p. 827–840. doi: 10.1111/tri.12973. Full text in Research Archive Show summary

  In stable renal transplant recipients with hyperparathyroidism, previous studies have indicated that vitamin D agonist treatment might have anti-proteinuric effects. Animal studies indicate possible anti-fibrotic and anti-inflammatory effects. Early introduction of paricalcitol in de novo renal transplant recipients might reduce proteinuria and prevent progressive allograft fibrosis. We performed a single-center, prospective, randomized, open-label trial investigating effects of paricalcitol 2 mug/day added to standard care. Participants were included 8 weeks after engraftment and followed for 44 weeks. Primary end point was change in spot urine albumin/creatinine ratio. Exploratory microarray analyses of kidney biopsies at study end investigated potential effects on gene expression. Secondary end points included change in glomerular filtration rate (GFR), pulse wave velocity (PWV), and endothelial function measured by peripheral arterial tonometry as reactive hyperemia index (RHI). Seventy-seven de novo transplanted kidney allograft recipients were included, 37 receiving paricalcitol. Paricalcitol treatment lowered PTH levels (P = 0.01) but did not significantly reduce albuminuria (P = 0.76), change vascular parameters (PWV; P = 0.98, RHI; P = 0.33), or influence GFR (P = 0.57). Allograft gene expression was not influenced. To summarize, in newly transplanted renal allograft recipients, paricalcitol reduced PTH and was well tolerated without negatively affecting kidney function. Paricalcitol did not significantly reduce/prevent albuminuria, improve parameters of vascular health, or influence allograft gene expression.

• Olsen, Maria Belland; Hildrestrand, Gunn Annette; Scheffler, Katja; Vinge, Leif Erik; Alfsnes, Katrine & Palibrk, Vuk [Show all 29 contributors for this article] (2017). NEIL3-Dependent Regulation of Cardiac Fibroblast Proliferation Prevents Myocardial Rupture. Cell reports. 18, p. 82–92. doi: 10.1016/j.celrep.2016.12.009. Full text in Research Archive Show summary

  Myocardial infarction (MI) triggers a reparative response involving fibroblast proliferation and differentiation driving extracellular matrix modulation necessary to form a stabilizing scar. Recently, it was shown that a genetic variant of the base excision repair enzyme NEIL3 was associated with increased risk of MI in humans. Here, we report elevated myocardial NEIL3 expression in heart failure patients and marked myocardial upregulation of Neil3 after MI in mice, especially in a fibroblast-enriched cell fraction. Neil3−/− mice show increased mortality after MI caused by myocardial rupture. Genome-wide analysis of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) reveals changes in the cardiac epigenome, including in genes related to the post-MI transcriptional response. Differentially methylated genes are enriched in pathways related to proliferation and myofibroblast differentiation. Accordingly, Neil3−/− ruptured hearts show increased proliferation of fibroblasts and myofibroblasts. We propose that NEIL3-dependent modulation of DNA methylation regulates cardiac fibroblast proliferation and thereby affects extracellular matrix modulation after MI.

• Batmanov, Kirill & Wang, Junbai (2017). Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model. Genes. 8(9), p. E233–E233. doi: 10.3390/genes8090233. Full text in Research Archive
• Wang, Junbai; Wu, Qianqian; Hu, Xiaohua Tony & Tian, Tianhai (2016). An integrated approach to infer dynamic protein-gene interactions – A case study of the human P53 protein. Methods. ISSN 1046-2023. 110, p. 3–13. doi: 10.1016/j.ymeth.2016.08.001. Full text in Research Archive
• Li, Xiaoran; Zhong, Yali; Lu, Jie; Axcrona, Karol; Eide, Lars & Syljuåsen, Randi G. [Show all 13 contributors for this article] (2016). MtDNA depleted PC3 cells exhibit Warburg effect and cancer stem cell features. OncoTarget. 7(26), p. 40297–40313. doi: 10.18632/oncotarget.9610. Full text in Research Archive Show summary

  This article has been corrected. Correction in: Oncotarget. 2017; 8(4):7208-13. https://doi.org/10.18632/oncotarget.14800. The correction is attached. Reducing mtDNA content was considered as a critical step in the metabolism restructuring for cell stemness restoration and further neoplastic development. However, the connections between mtDNA depletion and metabolism reprograming-based cancer cell stemness in prostate cancers are still lack of studies. Here, we demonstrated that human CRPC cell line PC3 tolerated high concentration of the mtDNA replication inhibitor ethidium bromide (EtBr) and the mtDNA depletion triggered a universal metabolic remodeling process. Failure in completing that process caused lethal consequences. The mtDNA depleted (MtDP) PC3 cells could be steadily maintained in the special medium in slow cycling status. The MtDP PC3 cells contained immature mitochondria and exhibited Warburg effect. Furthermore, the MtDP PC3 cells were resistant to therapeutic treatments and contained greater cancer stem cell-like subpopulations: CD44+, ABCG2+, side-population and ALDHbright. In conclusion, these results highlight the association of mtDNA content, mitochondrial function and cancer cell stemness features.

• Huang, Ruixia; Wang, Junbai; Zhong, Yali; Liu, Yishan; Stokke, Trond & Tropé, Claes Gøran [Show all 8 contributors for this article] (2015). Mitochondrial DNA deficiency in ovarian cancer cells and cancer stem cell-like properties. Anticancer Research. ISSN 0250-7005. 35(7), p. 3743–3754. Full text in Research Archive
• Wang, Junbai & Batmanov, Kirill (2015). BayesPI-BAR: a new biophysical model for characterization of regulatory sequence variations. Nucleic Acids Research (NAR). ISSN 0305-1048. 43:e147(21), p. 1–13. doi: 10.1093/nar/gkv733. Full text in Research Archive
• Wang, Junbai; Malecka, Agnieszka Maria; Trøen, Gunhild & Delabie, Jan (2015). Comprehensive genome-wide transcription factor analysis reveals that a combination of high affinity and low affinity DNA binding is needed for human gene regulation. BMC Genomics. 16(Suppl7:S12), p. 1–15. doi: 10.1186/1471-2164-16-S7-S12. Full text in Research Archive
• Tangen, Jon-Magnus; Tierens, Anne; Caers, Jo; Binsfeld, Marilene; Olstad, Ole Kristoffer & Siebke, Anne-Marie [Show all 9 contributors for this article] (2015). Immunomodulatory Effects of the Agaricus blazei Murrill-Based Mushroom Extract AndoSan in Patients with Multiple Myeloma Undergoing High Dose Chemotherapy and Autologous Stem Cell Transplantation: A Randomized, Double Blinded Clinical Study. BioMed Research International. ISSN 2314-6133. doi: 10.1155/2015/718539. Full text in Research Archive
• Ward, Lucas D.; Wang, Junbai & Bussemaker, Harmen J. (2014). Characterizing a collective and dynamic component of chromatin immunoprecipitation enrichment profiles in yeast. BMC Genomics. 15(1). doi: 10.1186/1471-2164-15-494. Full text in Research Archive
• Wang, Junbai (2014). Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence. BMC Bioinformatics. 15(289). doi: 10.1186/1471-2105-15-289. Full text in Research Archive
• Wang, Junbai; Lan, Xun; Hsu, Pei-Yin; Hsu, Hang-Kai; Huang, Kun & Parvin, Jeffrey [Show all 8 contributors for this article] (2013). Genome-wide analysis uncovers high frequency, strong differential chromosomal interactions and their associated epigenetic patterns in E2-mediated gene regulation. BMC Genomics. 14. doi: 10.1186/1471-2164-14-70. Full text in Research Archive
• Pollheimer, Jürgen; Bodin, Johanna; Sundnes, Olav; Bekkeheien, Reidunn C; Skånland, Sigrid Strand & Sponheim, Jon [Show all 16 contributors for this article] (2013). Interleukin-33 drives a proinflammatory endothelial activation that selectively targets nonquiescent cells. Arteriosclerosis, Thrombosis and Vascular Biology. ISSN 1079-5642. 33(2), p. e47–e55. doi: 10.1161/ATVBAHA.112.253427. Full text in Research Archive
• Kim, Geoffrey; Davidson, Ben; Henning, Ryan; Wang, Junbai; Yu, Minshu & Annunziata, Christina [Show all 8 contributors for this article] (2012). Adhesion molecule protein signature in ovarian cancer effusions is prognostic of patient outcome. Cancer. ISSN 0008-543X. 118(6), p. 1543–1553. doi: 10.1002/cncr.26449. Full text in Research Archive
• Warsam, Abdirashid Ali; Delabie, Jan; Malecka, Agnieszka Maria; Wang, Junbai; Trøen, Gunhild & Tierens, Anne (2012). Monocytoid B cells: An Enigmatic B cell Subset Showing Evidence of Extrafollicular Immunoglobulin gene Somatic Hypermutation. Scandinavian Journal of Immunology. ISSN 0300-9475. 75(5), p. 500–509. doi: 10.1111/j.1365-3083.2012.02688.x. Full text in Research Archive
• Wang, Junbai & Tian, Tianhai (2012). Effective Non-linear Methods for Inferring Genetic Regulation from Time-Series Microarray Gene Expression Data. Methods in molecular biology. ISSN 1064-3745. 802, p. 235–246. doi: 10.1007/978-1-61779-400-1_15. Full text in Research Archive
• Gillet, Jean-Pierre; Wang, Junbai; Calcagno, Anna Maria; Green, Lisa J.; Varma, Sudhir & Elstrand, Mari Bunkholt [Show all 10 contributors for this article] (2011). Clinical relevance of multidrug resistance gene expression in ovarian serous carcinoma effusions. Molecular Pharmaceutics. ISSN 1543-8384. 8(6), p. 2080–2088. doi: 10.1021/mp200240a. Full text in Research Archive
• Wang, Junbai (2011). Computational study of associations between histone modification and protein-DNA binding in yeast genome by integrating diverse information. BMC Genomics. 12. doi: 10.1186/1471-2164-12-172. Full text in Research Archive
• Lan, Xun; Adams, Christopher J.; Landers, Mark; Dudas, Miroslav; Krissinger, Daniel & Marnellos, George [Show all 12 contributors for this article] (2011). High Resolution Detection and Analysis of CpG Dinucleotides Methylation Using MBD-Seq Technology. PLOS ONE. 6(7). doi: 10.1371/journal.pone.0022226. Full text in Research Archive
• Wang, Junbai (2010). The effect of prior assumptions over the weights in BayesPI with application to study protein-DNA interactions from ChIP-based high-throughput data. BMC Bioinformatics. 11. doi: 10.1186/1471-2105-11-412. Full text in Research Archive
• Tierens, Anne; Holte, Harald; Warsam, Abdirashid Ali; Ikonomou, Ida Merete Münster; Wang, Junbai & Chan, Wing C [Show all 7 contributors for this article] (2010). Low levels of monoclonal small B cells in the bone marrow of patients with diffuse large B-cell lymphoma of activated B-cell type but not of germinal center B-cell type. Haematologica. ISSN 0390-6078. 95(8), p. 1334–1341. doi: 10.3324/haematol.2009.014100. Full text in Research Archive
• Elgaaen, Bente Vilming; Foss, Kari Bente; Wang, Junbai; Olstad, Ole Kristoffer; Fortunati, Dario & Onsrud, Mathias [Show all 9 contributors for this article] (2010). POLD2 and KSP37 (FGFBP2) Correlate Strongly with Histology, Stage and Outcome in Ovarian Carcinomas. PLOS ONE. 5(11). doi: 10.1371/journal.pone.0013837. Full text in Research Archive
• Wang, Junbai & Tian, Tianhai (2010). Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53. BMC Bioinformatics. 11. doi: 10.1186/1471-2105-11-36. Full text in Research Archive
• Wang, Junbai & Morigen, M. (2009). BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors. BMC Bioinformatics. 10. doi: 10.1186/1471-2105-10-345. Full text in Research Archive
• Wang, Qing; He, Wei; Lu, CD; Wang, Zhihui; Wang, Junbai & Giercksky, Karl-Erik [Show all 8 contributors for this article] (2009). Oct3/4 and Sox2 Are Significantly Associated with an Unfavorable Clinical Outcome in Human Esophageal Squamous Cell Carcinoma. Anticancer Research. ISSN 0250-7005. 29(4), p. 1233–1241. Full text in Research Archive
• Wang, Junbai (2008). Computational biology of genome expression and regulation - A review of microarray bioinformatics. Journal of Environmental Pathology And Toxicology. ISSN 0731-8898. 27(3), p. 157–179. Full text in Research Archive
• Wang, Junbai (2007). A new framework for identifying combinatorial regulation of transcription factors: a case study of the yeast cell cycle. Journal of Biomedical Informatics. ISSN 1532-0464. doi: 10.1016/j.jbi.2007.02.003. Full text in Research Archive
• Berner, Jeanne-Marie; Muller, Christoph Rainer; Holden, Marit; Wang, Junbai; Hovig, Eivind & Myklebost, Ola (2006). Sampling effects on gene expression data from a human tumour xenograft. Scandinavian Journal of Laboratory Animal Science. ISSN 0901-3393. 33, p. 17–34. Full text in Research Archive
• Meza-Zepeda, L. A.; Kresse, Stine Henrichson; Barragan-Polania, AH; Bjerkehagen, Bodil; Ohnstad, Hege Oma & Namløs, Heidi Maria [Show all 9 contributors for this article] (2006). Array comparative genomic hybridization reveals distinct DNA copy number differences between gastrointestinal stromal tumors and leiomyosarcomas. Cancer Research. ISSN 0008-5472. doi: 10.1158/0008-5472.CAN-06-1972. Full text in Research Archive
• Moorman, C; Sun, LV; Wang, Junbai; wit, E de; Ward, LD & Lu, XJ [Show all 9 contributors for this article] (2006). Hotspots of transcription factor colocalization in the genome of Drosophila melanogaster. Proceedings of the National Academy of Sciences of the United States of America (PNAS). ISSN 0027-8424. doi: 10.1073/pnas.0605003103. Full text in Research Archive
• Wang, Junbai; Cheung, Leo Wang-Kit & Delabie, Jan (2005). New probabilistic graphical models for genetic regulatory networks studies. Journal of Biomedical Informatics. ISSN 1532-0464. doi: 10.1016/j.jbi.2005.04.003. Full text in Research Archive
• Hjortland, Geir Olav; Lillehammer, Trine; Somme, Stig; Wang, Junbai; Halvorsen, Thomas & Juell, Siri [Show all 9 contributors for this article] (2004). Plasminogen activator inhibitor-1 increases the expression of VEGF in human glioma cells. Experimental Cell Research. ISSN 0014-4827. 294, p. 130–139. doi: 10.1016/j.yexcr.10.013. Full text in Research Archive
• Wang, Junbai; Meza-Zepeda, LA; Kresse, Stine Henrichson & Myklebost, Ola (2004). M-CGH: analysing microarray-based CGH experiments. BMC Bioinformatics. doi: 10.1186/1471-2105-5-74. Full text in Research Archive Show summary

  Background Microarray-based comparative genomic hybridisation (array CGH) is a technique by which variation in relative copy numbers between two genomes can be analysed by competitive hybridisation to DNA microarrays. This technology has most commonly been used to detect chromosomal amplifications and deletions in cancer. Dedicated tools are needed to analyse the results of such experiments, which include appropriate visualisation, and to take into consideration the physical relation in the genome between the probes on the array. Results M-CGH is a MATLAB toolbox with a graphical user interface designed specifically for the analysis of array CGH experiments, with multiple approaches to ratio normalization. Specifically, the distributions of three classes of DNA copy numbers (gains, normal and losses) can be estimated using a maximum likelihood method. Amplicon boundaries are computed by either the fuzzy K-nearest neighbour method or a wavelet approach. The program also allows linking each genomic clone with the corresponding genomic information in the Ensembl database http://www.ensembl.org. Conclusions M-CGH, which encompasses the basic tools needed for analysing array CGH experiments, is freely available for academics http://www.uio.no/~junbaiw/mcgh, and does not require any other MATLAB toolbox. © 2004 Wang et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.

• Wang, Junbai; Myklebost, Ola & Hovig, Eivind (2003). MGraph: graphical models for microarray data analysis. Bioinformatics. ISSN 1367-4803. 19, p. 2210–2211. Full text in Research Archive
• Tierens, Anne; Delabie, Jan; Malecka, Agnieszka Maria; Wang, Junbai; Gruszka-Westwood, A & Catovsky, D [Show all 7 contributors for this article] (2003). Splenic marginal zone lymphoma with villous lymphocytes shows on-going immunoglobulin gene mutations. American Journal of Pathology. ISSN 0002-9440. doi: 10.1016/S0002-9440(10)63862-X. Full text in Research Archive
• Wang, Junbai; Myklebost, Ola; Hovig, Eivind; Henriksen, Jørn; Mælandsmo, Gunhild Mari & Lothe, Ragnhild A [Show all 14 contributors for this article] (2003). Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data. BMC Bioinformatics. 4, p. 60–60. Full text in Research Archive
• Wang, Junbai; Delabie, Jan; aasheim, Hans-Christian; Smeland, Erlend B & Myklebost, Ola (2002). Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study. BMC Bioinformatics. Full text in Research Archive
• Wang, Junbai; Delabie, Jan; Åsheim, Hans-Christian; Smeland, Erlend B. & Myklebost, Ola (2002). Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study. BMC Bioinformatics. doi: 10.1186/1471-2105-3-36. Full text in Research Archive Show summary

  Background A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. Because of the complexity and the high dimensionality of microarray gene expression profiles, the dimensional reduction of raw expression data and the feature selections necessary for, for example, classification of disease samples remains a challenge. To solve the problem we propose a two-level analysis. First self-organizing map (SOM) is used. SOM is a vector quantization method that simplifies and reduces the dimensionality of original measurements and visualizes individual tumor sample in a SOM component plane. Next, hierarchical clustering and K-means clustering is used to identify patterns of gene expression useful for classification of samples. Results We tested the two-level analysis on public data from diffuse large B-cell lymphomas. The analysis easily distinguished major gene expression patterns without the need for supervision: a germinal center-related, a proliferation, an inflammatory and a plasma cell differentiation-related gene expression pattern. The first three patterns matched the patterns described in the original publication using supervised clustering analysis, whereas the fourth one was novel. Conclusions Our study shows that by using SOM as an intermediate step to analyze genome-wide gene expression data, the gene expression patterns can more easily be revealed. The "expression display" by the SOM component plane summarises the complicated data in a way that allows the clinician to evaluate the classification options rather than giving a fixed diagnosis.

• Dubois, A; Nielsen, S E; Hansen, Jan Petter & Wang, Junbai (2000). Left/right capture asymmetry in He²⁺-Na(3p_-1) collisions: classical versus quantal collision dynamics. Journal of Physics B: Atomic, Molecular and Optical Physics. ISSN 0953-4075. 33(6). doi: 10.1088/0953-4075/33/6/101. Full text in Research Archive
• Kamber, E Y; Abdallah, M A; Cocke, C L; Stöckli, M; Wang, Junbai & Hansen, Jan Petter (2000). Experimental evidence of transfer excitation in Ar⁶⁺-He collisions. Journal of Physics B: Atomic, Molecular and Optical Physics. ISSN 0953-4075. 33(5). doi: 10.1088/0953-4075/33/5/104. Full text in Research Archive
• Wang, Junbai; Hansen, Jan Petter & Dubois, Anna (2000). Neutralization and charge transfer in H⁺-H^-and H-H collisions. Journal of Physics B: Atomic, Molecular and Optical Physics. ISSN 0953-4075. 33(2), p. 249–249. doi: 10.1088/0953-4075/33/2/309. Full text in Research Archive
• Hansen, JP; Kocbach, Ladislav; Synnes, SA & Wang, Junbai (1998). Origin of the structures in the excitation cross sections in ion-atom collisions, Physical Review A. Atomic, Molecular, and Optical Physics (PRA). ISSN 1050-2947. doi: 10.1103/PhysRevA.57.R4086. Full text in Research Archive

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• Wang, Junbai; Tian, Tianhai & Tan, Aik Choon (2012). Next Generation Microarray Bioinformatics. Humana Press. ISBN 9781617793998. 401 p. Full text in Research Archive

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• Wang, Junbai (2022). Integrated analysis of genome and epigenome to understanding cancer. BestPractice nordic. Full text in Research Archive
• Zhou, Yufan; Gerrard, Diana L.; Wang, Junbai; Li, Tian; Yang, Yini & Fritz, Andrew J. [Show all 13 contributors for this article] (2020). Author Correction: Temporal dynamic reorganization of 3D chromatin architecture in hormone-induced breast cancer and endocrine resistance (Nature Communications, (2019), 10, 1, (1522), 10.1038/s41467-019-09320-9). Nature Communications. 11(1). doi: 10.1038/s41467-020-15789-6. Full text in Research Archive
• Li, Xiaoran; Zhong, Yali; Lu, Jie; Axcrona, Karol; Eide, Lars & Syljuåsen, Randi G. [Show all 13 contributors for this article] (2017). Correction: MtDNA depleted PC3 cells exhibit Warburg effect and cancer stem cell features. OncoTarget. 8(4), p. 7208–7213. doi: 10.18632/oncotarget.14800. Full text in Research Archive
• Shen, Bairong; Ma, Jian; Wang, Jiajun & Wang, Junbai (2014). Biomedical informatics and computational biology for high-throughput data analysis. Scientific World Journal. 2014. doi: 10.1155/2014/398181. Full text in Research Archive
• Wang, Junbai; Nygaard, Vegard; Smith-Sørensen, Birgitte; Hovig, Eivind & Myklebost, M. (2002). MARRAY:analysing single, replicated or reversed microarray experiments. Bioinformatics. ISSN 1367-4803. 8, p. 1139–1140. Full text in Research Archive
• Dragland, Jenny Sofie; Wang, Junbai & Hvidsten, Torgeir Rhoden (2023). CutAndTagAnalyzer : a new Python package for CUT&Tag data analysis. Norges miljø- og biovitenskapelige universitet (NMBU). Full text in Research Archive Show summary

  Ever since the invention of next-generation sequencing, new methods for understanding gene expression and control through epigenetics marks have been created. Among these, CUT&Tag analysis has emerged to become an efficient epigenomic profiling technique with low input requirements, high sensitivity, and lower background signals. Even though the wet-lab techniques are in place, analyzing the data is still a challenge for scientists with less computational skills, such as biologists. Therefore, this master’s thesis presents a new Python package that not only simplifies the data analysis of CUT&Tag sequencing but also allows biomedical scientists to easily interpret the results. The new pipeline package is based on the original CUT&Tag innovation team ́s data analysis protocol. It includes every step necessary from quality control to annotation and differential peak analysis. The package also fixed a few bugs, (e.g., reproducibility assessment) from the original protocol, and added new visualization plots, and features like genome annotation and differential peak analysis. In a demonstration run on a real CUT&Tag data set, the new package successfully recreated the plots from the original study. Additionally, function annotation analysis on annotated genes revealed predictions supporting current literature on the target proteins.

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