Fengzhu Sun


Affiliation: University of Southern California
Country: USA


  1. Tang K, Ren J, Cronn R, Erickson D, Milligan B, Parker Forney M, et al. Alignment-free genome comparison enables accurate geographic sourcing of white oak DNA. BMC Genomics. 2018;19:896 pubmed publisher
    ..The method provides a generalizable platform for the identification and sourcing of materials using a unified next generation sequencing and analysis framework. ..
  2. Li H, Sun F. Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences. Sci Rep. 2018;8:10032 pubmed publisher
    ..When alignment is difficult to achieve or highly time-consuming, alignment-free methods can be a promising substitute to predict the hosts of new viruses. ..
  3. Zhang W, Coba M, Sun F. Inference of domain-disease associations from domain-protein, protein-disease and disease-disease relationships. BMC Syst Biol. 2016;10 Suppl 1:4 pubmed publisher
    ..The Bayesian approach has the best performance for the inference of domain-disease relationships. The predicted landscape between domains and diseases provides a more detailed view about the disease mechanisms. ..
  4. Song K, Ren J, Reinert G, Deng M, Waterman M, Sun F. New developments of alignment-free sequence comparison: measures, statistics and next-generation sequencing. Brief Bioinform. 2014;15:343-53 pubmed publisher
  5. Xia L, Ai D, Cram J, Liang X, Fuhrman J, Sun F. Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains. BMC Bioinformatics. 2015;16:301 pubmed publisher
    ..The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa. ..
  6. Ren J, Ahlgren N, Lu Y, Fuhrman J, Sun F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 2017;5:69 pubmed publisher
    ..This innovative k-mer based tool complements gene-based approaches and will significantly improve prokaryotic viral sequence identification, especially for metagenomic-based studies of viral ecology. ..
  7. Bai X, Tang K, Ren J, Waterman M, Sun F. Optimal choice of word length when comparing two Markov sequences using a ? 2-statistic. BMC Genomics. 2017;18:732 pubmed publisher
    ..It is shown that the power loss is minimal for some of the estimators of the orders of Markov chains. Our studies provide guidelines on choosing the optimal word length for the comparison of Markov sequences. ..
  8. Tu Z, Wang L, Xu M, Zhou X, Chen T, Sun F. Further understanding human disease genes by comparing with housekeeping genes and other genes. BMC Genomics. 2006;7:31 pubmed
    ..We propose to classify them as a unique group for comparisons of disease genes with non-disease genes. This new way of classification and comparison enables us to have a clearer understanding of disease genes. ..
  9. Jiang B, Song K, Ren J, Deng M, Sun F, Zhang X. Comparison of metagenomic samples using sequence signatures. BMC Genomics. 2012;13:730 pubmed publisher
    ..The d2S dissimilarity measure is a good choice in all application scenarios. The optimal choice of tuple size depends on sequencing depth, but it is quite robust within a range of choices for moderate sequencing depths. ..

More Information


  1. Wang Y, Wang K, Lu Y, Sun F. Improving contig binning of metagenomic data using [Formula: see text] oligonucleotide frequency dissimilarity. BMC Bioinformatics. 2017;18:425 pubmed publisher
    ..The [Formula: see text] can be applied to any existing contig-binning tools for single metagenomic samples to obtain better binning results. ..
  2. Zhang M, Yang L, Ren J, Ahlgren N, Fuhrman J, Sun F. Prediction of virus-host infectious association by supervised learning methods. BMC Bioinformatics. 2017;18:60 pubmed publisher
    ..The maximum likelihood approach can be used to estimate the fraction of true infectious associated viruses in viral tagging experiments. ..