{"id":301,"date":"2023-08-15T19:12:53","date_gmt":"2023-08-16T02:12:53","guid":{"rendered":"https:\/\/dornsife.usc.edu\/fsun\/?page_id=301"},"modified":"2024-07-03T15:06:14","modified_gmt":"2024-07-03T22:06:14","slug":"software","status":"publish","type":"page","link":"https:\/\/dornsife.usc.edu\/fsun\/software\/","title":{"rendered":"Software"},"content":{"rendered":"\n\n \n \n\n\n\n\n\n\n\n
\n\n \n \n
\n\n \n
    \n
  1. \n
    DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and viruses<\/a><\/pre>\n<\/li>\n
  2. \n
    ImputeCC Enhances Integrative Hi-C-Based Metagenomic Binning Through Constrained Random-Walk-Based Imputation<\/a><\/pre>\n<\/li>\n
  3. \n
    DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data<\/a><\/pre>\n<\/li>\n
  4. \n
    MetaCC<\/span>\u00a0allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data<\/a><\/pre>\n<\/li>\n
  5. \n
    ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data<\/a><\/pre>\n<\/li>\n
  6. \n
    HiFine:\u00a0integrating Hi-c-based and shotgun-based methods to reFine binning of metagenomic contigs<\/a><\/pre>\n<\/li>\n
  7. \n
    ContigNet:\u00a0Phage\u2013bacterial contig association prediction with a convolutional neural network<\/a><\/pre>\n<\/li>\n
  8. \n
    MLR-OOD: a Markov chain based Likelihood Ratio method for Out-Of-Distribution detection of genomic sequences<\/a><\/pre>\n<\/li>\n
  9. \n
    ARG-SHINE: improve antibiotic resistance class prediction by integrating sequence homology, functional information and deep convolutional neural network<\/a><\/pre>\n<\/li>\n
  10. \n
    DeepLINK:\u00a0Deep Large-Scale Inference Using Knockoffs with Applications to Genomics<\/a><\/pre>\n<\/li>\n
  11. \n
    MicroPro:\u00a0using\u00a0metagenomic\u00a0unmapped reads to provide insights into human microbiota and disease associations<\/a><\/pre>\n<\/li>\n
  12. \n
    HiCBin:\u00a0Binning\u00a0metagenomic\u00a0contigs\u00a0and recovering metagenome-assembled genomes using Hi-C contact maps\u00a0<\/a><\/pre>\n<\/li>\n
  13. \n
    HiCzin:\u00a0Normalizing\u00a0metagenomic\u00a0Hi-C data and detecting spurious contacts using zero-inated\u00a0negative binomial regression<\/a><\/pre>\n<\/li>\n
  14. \n
    KIMI:\u00a0Knockoff Inference for Motif Identification from molecular sequences with controlled false discovery rate<\/a><\/pre>\n<\/li>\n
  15. \n
    DeepVirFinder:\u00a0Identifying viruses from\u00a0metagenomic\u00a0data using deep learning.<\/a><\/pre>\n<\/li>\n
  16. \n
    VirFinder:\u00a0VirFinder: a novel k-mer\u00a0based tool for identifying viral sequences from assembled\u00a0metagenomic\u00a0data.<\/a><\/pre>\n<\/li>\n
  17. \n
    VirHostMatcher-Net:\u00a0A network-based integrated framework for predicting virus\u2013prokaryote interactions<\/a><\/pre>\n<\/li>\n
  18. \n
    VirHostMatcher:\u00a0Alignment-free d2* oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences<\/a><\/span><\/pre>\n<\/li>\n
  19. \n
    CAFE:\u00a0aCcelerated<\/span>\u00a0Alignment-FrEe\u00a0sequence analysis<\/span><\/a><\/pre>\n<\/li>\n
  20. \n
    COCACOLA:\u00a0binning\u00a0metagenomic\u00a0contigs\u00a0using sequence\u00a0COmposition, read\u00a0CoverAge, CO-alignment, and paired-end read\u00a0LinkAge<\/span><\/a><\/pre>\n<\/li>\n
  21. \n
    D2sBin:\u00a0Improving\u00a0contig\u00a0binning of\u00a0metagenomic\u00a0data using\u00a0<\/span>d2S<\/span><\/i>\u00a0oligonucleotide frequency dissimilarity<\/span><\/a><\/pre>\n<\/li>\n
  22. \n
    Hetero-RP:\u00a0Towards enhanced and interpretable clustering\/<\/i>classification in integrative genomics<\/span><\/a><\/pre>\n<\/li>\n
  23. \n
    WeiSum:\u00a0Finding Genetic Overlaps among Diseases Based on Ranked Gene Lists<\/span><\/a><\/pre>\n<\/li>\n
  24. \n
    NGS-MC:\u00a0\u00a0Markovian Inference for Molecular Sequences Using NGS Data<\/span><\/a><\/pre>\n<\/li>\n
  25. \n
    multiAlignFree:\u00a0\u00a0Multiple Alignment-Free Sequence Comparison<\/span><\/a><\/pre>\n<\/li>\n
  26. \n
    d2Meta:\u00a0\u00a0Comparison of\u00a0Metagenomic\u00a0Samples Using Sequence Signatures<\/span><\/a><\/pre>\n<\/li>\n
  27. \n
    EM-SNP:\u00a0\u00a0A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms<\/span><\/a><\/pre>\n<\/li>\n
  28. \n
    GRAMMy:\u00a0\u00a0\u00a0Accurate genome relative abundance estimation based on shotgun\u00a0metagenomic\u00a0reads<\/span><\/a><\/pre>\n<\/li>\n
  29. \n
    eLSA:\u00a0\u00a0\u00a0Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates<\/span><\/a><\/pre>\n<\/li>\n
  30. \n
    D2NGS:\u00a0\u00a0\u00a0Alignment-Free Sequence Comparison Based on Next Generation Sequencing Reads<\/span><\/a><\/pre>\n<\/li>\n
  31. \n
    NGS-Motif-Power:\u00a0\u00a0Significance and Power of Pattern Occurrences in NGS reads<\/span><\/a><\/pre>\n<\/li>\n
  32. \n
    RD:\u00a0\u00a0A Program for Statistical Estimation of Isoform Expression Levels using RNA-Seq\u00a0Based on the Modeling of RNA Degradation<\/span><\/a><\/pre>\n<\/li>\n
  33. \n
    CEDER:\u00a0Accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq<\/span><\/a><\/pre>\n<\/li>\n
  34. \n
    D2, D2*, and D2S:\u00a0Alignment free sequence comparison (II): theoretical power of comparison statistics<\/span><\/a><\/pre>\n<\/li>\n
  35. \n
    D2, D2*, and D2S:\u00a0Alignment free sequence comparison (I): statistics and power<\/span><\/a><\/pre>\n<\/li>\n
  36. \n
    Motif_Power:\u00a0A Program for calculating The Power of Detecting Enriched Patterns: An HMM Approach<\/span><\/a><\/pre>\n<\/li>\n
  37. \n
    NePhe:\u00a0Network RNAi Phenotype (NePhe) Score<\/span><\/a><\/pre>\n<\/li>\n
  38. \n
    Sub-GSE:\u00a0A Program for Gene Set Enrichment Analysis by Testing Subsets of Genes<\/span><\/a><\/pre>\n<\/li>\n
  39. \n
    DynBin:\u00a0A Dynamic Programming Algorithm for Binning Microbial Community Profiles<\/span><\/a><\/pre>\n<\/li>\n
  40. \n
    LocSim:\u00a0Local Similarity Analysis for Microbial Community Profiles<\/span><\/a><\/pre>\n<\/li>\n
  41. \n
    CGI:\u00a0Prioritizing Genes by Combining Gene Expression and Protein-Protein Interaction Data<\/span><\/a><\/pre>\n<\/li>\n
  42. \n
    EM-NM:\u00a0An Expectation-Maximization (EM) Algorithm for Network Motif Identification<\/span><\/a><\/pre>\n<\/li>\n
  43. \n
    Int-Path:\u00a0An Integrative Approach for Causal Gene Inference and Pathway Identification<\/span><\/a><\/pre>\n<\/li>\n
  44. \n
    HapBlock\u00a0-\u00a0- The Dynamic Programming Algorithms for Haplotype Block Partitioning and Tag SNPs Selection by Haplotype Data and Genotype Data<\/span><\/a>.<\/pre>\n<\/li>\n<\/ol>\n\n\n\n<\/div>\n\n\n  <\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":407,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-301","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nSoftware - Dr. Fengzhu Sun<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dornsife.usc.edu\/fsun\/software\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Software - Dr. Fengzhu Sun\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dornsife.usc.edu\/fsun\/software\/\" \/>\n<meta property=\"og:site_name\" content=\"Dr. Fengzhu Sun\" \/>\n<meta property=\"article:modified_time\" content=\"2024-07-03T22:06:14+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/dornsife.usc.edu\/fsun\/software\/\",\"url\":\"https:\/\/dornsife.usc.edu\/fsun\/software\/\",\"name\":\"Software - 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