1. Identify driver genes and pathways in cancer sequencing studies (drgap, https://code.google.com/p/drgap/).

2. Calculate tumor-normal pair-matched depth of coverage in tumor sequencing studies (pairdoc, http://code.google.com/p/pairdoc/ )

3. Differential expression test for RNA-Seq data (deGPS, https://github.com/srnttt/degps-rna-seq).

  

4. RRBSsim is a simulator for benchmarking analysis of reduced representation bisulfite sequencing (RRBS) data (https://github.com/xwBio/RRBSsim).

  

5. DriverML is a supervised machine learning approach for scoring functional consequences of DNA sequence alterations to identify cancer driver genes (https://github.com/HelloYiHan/DriverML).

  

6. MDEHT: a Multivariate Approach for Detecting Differential Expression of MicroRNA Isoform Data in RNA Sequencing Studies. (https://github.com/amanzju/MDEHT).

  

7. Graphsort: a geometric deep learning algorithm for in silico dissecting cell compositions in bulk expression data. (https://github.com/HelloYiHan/GraphSort).

  

8. tRFseeker: a computational tool for de novo tRNA-derived fragments (tRFs) mining from sequencing libraries of small RNAs. (http://bioinformatics.zju.edu.cn/tRFseeker).

  

1. Circ2Disease: a manually curated database of experimentally validated circRNAs in human disease (http://bioinformatics.zju.edu.cn/Circ2Disease).

  

2. OncotRF is a valuable resource (including the detected tRFs, cancer super-driver tRFs and tRF tumor subtypes) for the research community to identify diagnostic and prognostic biomarkers, develop cancer therapy and study cancer pathogenesis (http://bioinformatics.zju.edu.cn/OncotRF/).