开源软件

序列分析

1. EPGA, de novo assembly using the distributions of reads and insert size. The tool is publicly available at:

   https://github.com/bioinfomaticsCSU/EPGA

2. EPGA2, memory-efficient de novo assembler. The tool is publicly available at:

   https://github.com/bioinfomaticsCSU/EPGA2

3. BOSS, a novel scaffolding algorithm based on an optimized scaffold graph. The tool is publicly available at:

   https://github.com/bioinfomaticsCSU/BOSS

4. SCOP, a novel scaffolding algorithm based on contig classification and optimization. The tool is publicly available at:

   https://github.com/bioinfomaticsCSU/SCOP

5. Deepsignal, a deep-learning method for detecting DNA methylation state from Oxford Nanopore sequencing reads. The tool is publicly available at:

   https://github.com/bioinfomaticsCSU/deepsignal

6. NECAT, an error correction and de novo assembly tool designed to overcome complex errors in Nanopore reads. The tool is publicly available at:

   https://github.com/xiaochuanle/NECAT

7. DeepSignal-plant，a deep-learning method for detecting methylation state from Oxford Nanopore sequencing reads of plants. It applies BiLSTM to detect methylation from Nanopore reads and is built on Python3 and PyTorch. The tool is publicly available at: 

   https://github.com/PengNi/deepsignal-plant

8. MultiNanopolish，a refined grouping method for reducing redundant calculations in Nanopolish. It decomposes the whole process of iterative calculation in Nanopolish into small independent calculation tasks, making it possible to run this process in the parallel mode. The tool is publicly available at:

   https://github.com/BioinformaticsCSU/MultiNanopolish

9. NanoSNP，a novel deep learning-based SNP calling method that identifies the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. It uses a multi-step, multi-scale and haplotype-aware SNP detection pipeline. The method has been shown to outperform existing methods in terms of accuracy and sensitivity. The tool is publicly available at:

   https://github.com/huangnengCSU/NanoSNP

生物网络分析

1. Clusterviz, a Cytoscape app to detect clusters (highly interconnected regions, protein complexes or functional module) in a network using various clustering algorithms. The tool is publicly available at:

   http://apps.cytoscape.org/apps/clusterviz

2. CytoNCA, a Cytoscape app for network centrality analysis. This app supports eight typical centralities: Betweeness Centrality, Closeness Centrality, Degree Centrality, Eigenvector Centrality, Local Average. The tool is publicly available at:

   http://apps.cytoscape.org/apps/cytonca

3. C-DEVA, a scalable platform, in which a series of evaluation methods, such as recall, precision, sensitivity, specificity, p-value, and function enrichment, are implemented. Moreover, nine clustering algorithms, The tool is publicly available at:

   http://bioinformatics.csu.edu.cn/resources/softs/tangyu/CDEVA/README.html

4. HC-PIN, is a fast hierarchical clustering algorithm based on the local metric of edge clustering value which can be used both in the unweighted network and in the weighted network. The tool is publicly available at:

   http://bioinformatics.csu.edu.cn/resources/softs/limin/HC-PIN/index.html

5. IPCA, Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. The tool is publicly available at:

   http://bioinformatics.csu.edu.cn/resources/softs/limin/IPCA/index.htmlC

6. CytoCluster, a Cytoscape app for analysis and visualization of clusters from network and has been tested on Cytoscape 3.0.X. Three different graph clustering algorithms (HC-PIN, OH-PIN, IPCA) were implemented. The tool is publicly available at:

   http://apps.cytoscape.org/apps/cytocluster

7. CytoCtrlAnalyser, a convenient app called CytoCtrlAnalyser for network controllability analysis has been designed and achieved.This app supports nine algorithms in network controllability. The tool is publicly available at:

   http://apps.cytoscape.org/apps/cytoctrlanalyser

8. DyNetViewer, Construction, analysis, and visualization of dynamic networks in Cytoscape 3.0, The tool is publicly available at:

   http://apps.cytoscape.org/apps/dynetviewer

9. BiXGBoost, a bidirectional method that considers time information and integrates multi decision trees by the boosting method. The tool is publicly available at:

   https://github.com/zrq0123/BiXGBoost

10. CPPK & CEPPK, CPPK predicts new essential proteins based on network topology. CEPPK predicts new essential proteins by integrating network topology and gene expressions. The tool is publicly available at:

    http://bioinformatics.csu.edu.cn/resources/softs/limin/CPPK/index.html

11. TissueNexus， a platform for the analysis of spatially resolved transcriptomics data. The tool is publicly available at:

    www.diseaselinks.com/TissueNexus/

12. AlzCode， a platform dedicated to systematically assess whether given genes are functionally relevant to Alzheimer’s Disease (AD). The tool is publicly available at:

    http://www.alzcode.xyz/

医学影像分析

1. MIACSU, a multi-functional platform for neuroimage processing and visualization. The tool is publicly available at:

   http://www.miacsu.group/

2. AIMAFE, This method aims to achieve ASD identification based on multi-atlas deep feature representation and ensemble learning derived using resting-state functional magnetic resonance imaging (rs-fMRI) brain scans. The tool is publicly available at:

   https://github.com/wyf1995/AIMAFE

3. MTTU-Net，a fully automated multimodal MRI-based multi-task learning for glioma segmentation and IDH genotyping. The tool is publicly available at:

   https://github.com/miacsu/MTTU-Net

生物数据挖掘

1. DNRLMF-MDA, a novel algorithm based on dynamic neighborhood regularized logistic matrix factorization to predict miRNA-disease associations with integrating known miRNA-disease associations, functional similarity and Gaussian Interaction Profile.The tool is publicly available at:

   http://bioinformatics.csu.edu.cn/resources/softs/miRNA-diseases/DNRLMF-MDA/

2. SIMCLDA, an innovative computational algorithm, which utilizes inductive matrix completion based on extracted primary features of lncRNA and disease to predict potential lncRNA-disease associations. The tool is publicly available at:

   https://github.com/bioinfomaticsCSU/SIMCLDA

3. GMCLDA, a novel computational method, which uses geometric matrix completion based intrinsic structure of the association matrix to infer potential lncRNA-disease associations. The tool is publicly available at:

   https://github.com/bioinfomaticsCSU/GMCLDA

4. DMFCDA, a novel computational method, which utilizes deep matrix factorization to grasp the complex structure of data for predicting circRNA-disease associations. The tool is publicly available at:

   https://github.com/bioinfomaticsCSU/DMFCDA

5. OMC, a tool lies in constructing an efficient framework of incorporating multiple types of prior information in bilayer and tri-layer networks to predict potential associations between drugs and diseases. The tool is publicly available at:

   https://github.com/BioinformaticsCSU/OMC

6. DRRS, a novel computational drug repositioning method using low-rank matrix appropriation and randomized algorithm, is used for predicting drug indications by integrating related data sources and validated information of drugs and diseases. The tool is publicly available at:

   http://bioinformatics.csu.edu.cn/resources/softs/DrugRepositioning/DRRS/index.html

7. BNNR, a novel computational method, which utilizes Bounded Nuclear Norm Regularization algorithm to identify potential novel indications for known or new drugs. The tool is publicly available at:

   http://bioinformatics.csu.edu.cn/resources/softs/yangmengyun/BNNR/index.html

8. AttentionDTA, a deep learning-based model that predicts drug-target binding affinity. The model can be viewed as a regression problem of drug-target affinities which reflects how tightly the drug binds to the target. The tool is publicly available at:

   https://github.com/zhaoqichang/AttentionDTA_TCBB

9. gtftools, an open-source software package designed for working with gene annotation files in GTF (Gene Transfer Format) format. It provides a toolkit of tools and utilities that can be used for various tasks related to gene annotation, such as filtering, merging, conversion, and visualization. The tool is publicly available at:

   www.genemine.org/gtftools.php

10. SDPred, a similarity-based deep learning method that can predict the side effect frequencies of new drugs without any known drug-side effect information. It is an end-to-end deep learning method that can predict potential side effect frequencies of an approved drug and predict side effect frequencies of a new drug candidate. The tool is publicly available at:

    https://github.com/zhc940702/SDPred