Rongting Huang

All Publications

• Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone. Nature communications Huang, R., Huang, X., Tong, Y., Yan, H. Y., Leung, S. Y., Stegle, O., Huang, Y. 2024; 15 (1): 6684

  Abstract

  Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

  View details for DOI 10.1038/s41467-024-51026-0

  View details for PubMedID 39107346

  View details for PubMedCentralID PMC11303794

• PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics. Computers in biology and medicine Tong, M., Liu, Z., Li, J., Wei, X., Shi, W., Liang, C., Yu, C., Huang, R., Lin, Y., Wang, X., Wang, S., Wang, Y., Huang, J., Wang, Y., Li, T., Qin, J., Zhan, D., Ji, Z. L. 2024; 174: 108391

  Abstract

  Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application.Here, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system.The source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis.PhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.

  View details for DOI 10.1016/j.compbiomed.2024.108391

  View details for PubMedID 38613887

• MQuad enables clonal substructure discovery using single cell mitochondrial variants. Nature communications Kwok, A. W., Qiao, C., Huang, R., Sham, M. H., Ho, J. W., Huang, Y. 2022; 13 (1): 1205

  Abstract

  Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, identifying informative mtDNA variants in noisy and sparse single-cell sequencing data is still challenging with few computation methods available. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA, DNA or ATAC sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrate its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution.

  View details for DOI 10.1038/s41467-022-28845-0

  View details for PubMedID 35260582

  View details for PubMedCentralID PMC8904442

• Crosstalk of intracellular post-translational modifications in cancer. Archives of biochemistry and biophysics Wu, Z., Huang, R., Yuan, L. 2019; 676: 108138

  Abstract

  Post-translational modifications (PTMs) have been reported to play pivotal roles in numerous cellular biochemical and physiological processes. Multiple PTMs can influence the actions of each other positively or negatively, termed as PTM crosstalk or PTM code. During recent years, development of identification strategies for PTMs co-occurrence has revealed abundant information of interplay between PTMs. Increasing evidence demonstrates that deregulation of PTMs crosstalk is involved in the genesis and development of various diseases. Insight into the complexity of PTMs crosstalk will help us better understand etiology and provide novel targets for drug therapy. In the present review, we will discuss the important functional roles of PTMs crosstalk in proteins associated with cancer diseases.

  View details for DOI 10.1016/j.abb.2019.108138

  View details for PubMedID 31606391

• Systematic characterization and prediction of post-translational modification cross-talk between proteins. Bioinformatics (Oxford, England) Huang, R., Huang, Y., Guo, Y., Ji, S., Lu, M., Li, T. 2019; 35 (15): 2626-2633

  Abstract

  Protein post-translational modifications (PTMs) regulate a wide range of cellular protein functions. Many PTM sites from the same (intra) or different (inter) proteins often cooperate with each other to perform a function, which is defined as PTM cross-talk. PTM cross-talk within proteins attracted great attentions in the past a few years. However, the inter-protein PTM cross-talk is largely under studied due to its large protein pair space and lack of a gold standard dataset, even though the PTM interplay between proteins is a key element in cell signaling and regulatory networks.In this study, 199 inter-protein PTM cross-talk pairs in 82 pairs of human proteins were collected from literature, which to our knowledge is the first effort in compiling such dataset. By comparing with background PTM pairs from the same protein pairs, we found that inter-protein cross-talk PTM pairs have higher sequence co-evolution at both PTM residue and motif levels. Also, we found that cross-talk PTMs have higher co-modification across multiple species and 88 human tissues or conditions. Furthermore, we showed that these features are predictive for PTM cross-talk between proteins, and applied a random forest model to integrate these features with achieving an area under the receiver operating characteristic curve of 0.81 in 10-fold cross-validation, prevailing over using any single feature alone. Therefore, this method would be a valuable tool to identify inter-protein PTM cross-talk at proteome-wide scale.A web server for prioritization of both intra- and inter-protein PTM cross-talk candidates is at http://bioinfo.bjmu.edu.cn/ptm-x/. Python code for local computer is also freely available at https://github.com/huangyh09/PTM-X.Supplementary data are available at Bioinformatics online.

  View details for DOI 10.1093/bioinformatics/bty1033

  View details for PubMedID 30590394

• Genome-wide characterization of intergenic polyadenylation sites redefines gene spaces in Arabidopsis thaliana. BMC genomics Wu, X., Zeng, Y., Guan, J., Ji, G., Huang, R., Li, Q. Q. 2015; 16 (1): 511

  Abstract

  Messenger RNA polyadenylation is an essential step for the maturation of most eukaryotic mRNAs. Accurate determination of poly(A) sites helps define the 3'-ends of genes, which is important for genome annotation and gene function research. Genomic studies have revealed the presence of poly(A) sites in intergenic regions, which may be attributed to 3'-UTR extensions and novel transcript units. However, there is no systematically evaluation of intergenic poly(A) sites in plants.Approximately 16,000 intergenic poly(A) site clusters (IPAC) in Arabidopsis thaliana were discovered and evaluated at the whole genome level. Based on the distributions of distance from IPACs to nearby sense and antisense genes, these IPACs were classified into three categories. About 70 % of them were from previously unannotated 3'-UTR extensions to known genes, which would extend 6985 transcripts of TAIR10 genome annotation beyond their 3'-ends, with a mean extension of 134 nucleotides. 1317 IPACs were originated from novel intergenic transcripts, 37 of which were likely to be associated with protein coding transcripts. 2957 IPACs corresponded to antisense transcripts for genes on the reverse strand, which might affect 2265 protein coding genes and 39 non-protein-coding genes, including long non-coding RNA genes. The rest of IPACs could be originated from transcriptional read-through or gene mis-annotations.The identified IPACs corresponding to novel transcripts, 3'-UTR extensions, and antisense transcription should be incorporated into current Arabidopsis genome annotation. Comprehensive characterization of IPACs from this study provides insights of alternative polyadenylation and antisense transcription in plants.

  View details for DOI 10.1186/s12864-015-1691-1

  View details for PubMedID 26155789

  View details for PubMedCentralID PMC4568572