Biology subjects

Chang, J.-M.

Publications and source records attributed to Chang, J.-M..

5 recordsLinked to original sources

bioRxiv2026

GBactPro: cross-species bacterial promoter prediction using element-resolved promoter definitions

Accurate bacterial promoter prediction remains challenging across species, partly because conventional models often use fixed-length promoter windows that do not explicitly account for promoter architecture. Using promoter annotations derived from the Promoter Architecture Scanner (PAS), we found that training on sequences comprising the -35 element, spacer, and -10 element improved cross-species prediction compared with fixed-length promoter windows. Based on this element-resolved promoter definition, we developed GBactPro, a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) model jointly trained on curated promoter data from 43 bacterial species. The jointly trained model achieved a mean AUROC of 0.9943 on held-out test sequences. Under matched-length benchmarking, GBactPro outperformed Promotech, iPro-MP, Prompt, and iPro-WAEL by 0.066-0.314 in mean AUROC. Genome-wide scanning of Escherichia coli achieved an AUROC of 0.979 and an AUPRC of 0.160 under stringent one-to-one promoter-site matching, increasing to 0.997 and 0.323, respectively, with a 100-bp positional tolerance. Unmatched predictions were enriched for H-NS binding, and independent H-NS-knockout data further supported the possibility that some predicted promoter-like regions are transcriptionally repressed. Together, these findings highlight the importance of promoter-sequence definition for cross-species prediction and demonstrate the potential of GBactPro for genome-wide promoter identification and biological interpretation.

genomics↗

bioRxiv2026

HiCPEP: Efficient estimation of chromatin compartment PC1 from Hi-C covariance structure

Principal component analysis (PCA) of the Hi-C Pearson correlation matrix is the standard approach for identifying A/B chromatin compartments. Despite its widespread use, the relationship between the first principal component (PC1) and the underlying compartment structure remains insufficiently characterized, and computing PC1 can become computationally expensive for high-resolution Hi-C data. Here we investigate the role of the PC1 explained variance ratio in compartment analysis and show that chromosomes with strong compartment organization typically exhibit a dominant PC1 signal. Based on this observation, we propose HiCPEP, a heuristic algorithm that estimates the sign pattern and relative magnitude of PC1 directly from the Hi-C Pearson covariance matrix without performing explicit eigenvector decomposition. The method can operate from either a dense Pearson matrix for fast approximation or a sparse observed/expected (O/E) matrix to reduce memory usage. Furthermore, because many covariance columns exhibit PC1-like patterns when the compartment signal is strong, HiCPEP can be accelerated using random sampling without substantially reducing accuracy. Across multiple Hi-C datasets, HiCPEP consistently recovered compartment patterns with high similarity to reference PC1 vectors produced by standard PCA-based methods. Benchmark experiments show that HiCPEP achieves comparable accuracy while reducing computational cost in terms of runtime or memory usage. These results suggest that HiCPEP provides a practical alternative for efficient chromatin compartment analysis from large-scale Hi-C datasets. The HiCPEP implementation is freely available at https://github.com/ZhiRongDev/HiCPEP.

bioinformatics↗

bioRxiv2026

On the state of protein function prediction: a report on the fourth CAFA challenge

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). ResultsCAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced "partial knowledge" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.

bioinformatics↗

bioRxiv2022

Topological screen identifies hundreds of Cp190 and CTCF dependent Drosophila chromatin insulator elements

Drosophila insulators were the first DNA elements discovered to regulate gene expression by delimiting chromatin contacts. Remarkably, it is still unclear how many of them exist in the Drosophila genome and whether they have a pervasive impact on the genome folding. Contrary to vertebrates, there is no evidence that fly insulators block cohesin-mediated chromatin loop extrusion. Therefore, their mechanism of action remains an open question. To bridge these gaps, we mapped genomic contacts, transcriptomes and binding landscapes of insulator associated proteins in Drosophila cells deficient for CTCF and Cp190. With this approach, we discovered hundreds of chromatin insulator elements. Their study indicates that Drosophila insulators play a minor role in shaping the overall chromosome folding patterns but impact chromatin contacts locally at many individual loci. Our observations argue that Cp190 promotes co-binding of other insulator proteins and that the model, where Drosophila insulators block chromatin contacts by forming loops, needs revision. The extended catalogue of insulator elements presented here provides a significant new resource to study mechanisms that shape the folding of eukaryotic genomes.

genomics↗

bioRxiv2018

Targeting BMI1 and MCL1 for Lung Adenocarcinoma Treatment

Lung cancer is the leading cause of cancer-associated death worldwide. Early metastasis and the recurrence remain major challenges for lung cancer treatment in clinic. Targeting the cancer stemness could be a potential strategy to restrain tumor progression. In the current study, we found that in lung adenocarcinoma (LAC), BMI1 and MCL1 play crucial roles in invasion, chemo-resistance, and tumor initiation. JNK signaling is a link between oncogenic pathway or environment stress to cancer stemness. The activation of JNK, either by EGFR or chemotherapy agent, stabilized BMI1 and MCL1 proteins through suppressing the expression of E3-ubiquitin ligase HUWE1. In lung cancer patient samples, high level of BMI1 is correlated with poor survival, and the expression of BMI1 is positively correlated with MCL1. A novel small-molecule BI-44 was synthesized, which effectively suppressed BMI1/MCL1 expression and inhibited tumor formation and progression in preclinical models. Targeting BMI1/MCL1 provides the basis for a new therapeutic approach in the treatment of LAC.

cancer biology↗