1. MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data
  2. ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data
  3. HiFine: integrating Hi-c-based and shotgun-based methods to reFine binning of metagenomic contigs
  4. ContigNet: Phage–bacterial contig association prediction with a convolutional neural network
  5. MLR-OOD: a Markov chain based Likelihood Ratio method for Out-Of-Distribution detection of genomic sequences
  6. ARG-SHINE: improve antibiotic resistance class prediction by integrating sequence homology, functional information and deep convolutional neural network
  7. DeepLINK: Deep Large-Scale Inference Using Knockoffs with Applications to Genomics
  8. MicroPro: using metagenomic unmapped reads to provide insights into human microbiota and disease associations
  9. HiCBin: Binning metagenomic contigs and recovering metagenome-assembled genomes using Hi-C contact maps 
  10. HiCzin: Normalizing metagenomic Hi-C data and detecting spurious contacts using zero-inated negative binomial regression
  11. KIMI: Knockoff Inference for Motif Identification from molecular sequences with controlled false discovery rate
  12. DeepVirFinder: Identifying viruses from metagenomic data using deep learning.
  13. VirFinder: VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data.
  14. VirHostMatcher-Net: A network-based integrated framework for predicting virus–prokaryote interactions
  15. VirHostMatcher: Alignment-free d2* oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences
  16. CAFE: aCcelerated Alignment-FrEe sequence analysis
  17. COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment, and paired-end read LinkAge
  18. D2sBin: Improving contig binning of metagenomic data using d2S oligonucleotide frequency dissimilarity
  19. Hetero-RP: Towards enhanced and interpretable clustering/classification in integrative genomics
  20. WeiSum: Finding Genetic Overlaps among Diseases Based on Ranked Gene Lists
  21. NGS-MC:  Markovian Inference for Molecular Sequences Using NGS Data
  22. multiAlignFree:  Multiple Alignment-Free Sequence Comparison
  23. d2Meta:  Comparison of Metagenomic Samples Using Sequence Signatures
  24. EM-SNP:  A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms
  25. GRAMMy:   Accurate genome relative abundance estimation based on shotgun metagenomic reads
  26. eLSA:   Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates
  27. D2NGS:   Alignment-Free Sequence Comparison Based on Next Generation Sequencing Reads
  28. NGS-Motif-Power:  Significance and Power of Pattern Occurrences in NGS reads
  29. RD:  A Program for Statistical Estimation of Isoform Expression Levels using RNA-Seq Based on the Modeling of RNA Degradation
  30. CEDER: Accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq
  31. D2, D2*, and D2S: Alignment free sequence comparison (II): theoretical power of comparison statistics
  32. D2, D2*, and D2S: Alignment free sequence comparison (I): statistics and power
  33. Motif_Power: A Program for calculating The Power of Detecting Enriched Patterns: An HMM Approach
  34. NePhe: Network RNAi Phenotype (NePhe) Score
  35. Sub-GSE: A Program for Gene Set Enrichment Analysis by Testing Subsets of Genes
  36. DynBin: A Dynamic Programming Algorithm for Binning Microbial Community Profiles
  37. LocSim: Local Similarity Analysis for Microbial Community Profiles
  38. CGI: Prioritizing Genes by Combining Gene Expression and Protein-Protein Interaction Data
  39. EM-NM: An Expectation-Maximization (EM) Algorithm for Network Motif Identification
  40. Int-Path: An Integrative Approach for Causal Gene Inference and Pathway Identification
  41. HapBlock - - The Dynamic Programming Algorithms for Haplotype Block Partitioning and Tag SNPs Selection by Haplotype Data and Genotype Data.