Software available at:
https://github.com/ChrisMaherLab/
https://sites.google.com/site/jinzhangwebsite
GAiN: An Integrative Tool Utilizing Generative Adversarial Neural Networks for Augmented Gene Expression Analysis
Waters MR, Inkman M, Jayachandran K, Kowalchuk RM, Robinson C, Schwarz JK, Swamidass SJ, Griffith OL, Szymanski JJ, Zhang J. GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis. Patterns. Patterns. 2024 Jan 8;5(2):100910. PMID: 38370125.
HPV-EM: an accurate HPV detection and genotyping tool
Inkman MJ, Jayachandran K, Ellis TM, Ruiz F, McLellan MD, Miller CA, Wu Y, Ojesina AI, Schwarz JK, Zhang J. HPV-EM: an accurate HPV detection and genotyping EM algorithm. Sci Rep. 2020 Aug 31;10(1):14340. doi: 10.1038/s41598-020-71300-7. PMID: 32868873; PMCID: PMC7459114.
INTEGRATE: A tool for calling gene fusions with exact fusion junctions and genomic breakpoints by combining RNA-Seq and WGS data.
Zhang J, White NM, Schmidt HK, Fulton RS, Tomlinson C, Warren WC, Wilson RK, Maher CA. INTEGRATE: gene fusion discovery using whole genome and transcriptome data. Genome Res. 2016 Jan;26(1):108-18. doi: 10.1101/gr.186114.114. Epub 2015 Nov 10. PubMed PMID: 26556708; PubMed Central PMCID: PMC4691743.
INTEGRATE-Neo: A tool for gene fusion neoantigen discovering tool using next-generation sequencing data.
Zhang J, Mardis ER, Maher CA. INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery. Bioinformatics. 2016 Oct 24. pii: btw674. [Epub ahead of print] PubMed PMID: 27797777.
INTEGRATE-Vis: A tool for comprehensive gene fusion visualization using next-generation sequencing data
Zhang J, Gao T, Maher CA. INTEGRATE-Vis: a tool for comprehensive gene fusion visualization. Sci Rep. 2017 Dec 19;7(1):17808. doi: 10.1038/s41598-017-18257-2. PMID: 29259323; PMCID: PMC5736641.
SVSeq2: Accurate and efficient detection of structural variations with low-coverage sequencing data
Zhang J, Wang J, Wu Y. An improved approach for accurate and efficient calling of structural variations with low-coverage sequence data. BMC Bioinformatics. 2012 Apr 19;13 Suppl 6(Suppl 6):S6. doi: 10.1186/1471-2105-13-S6-S6. PMID: 22537045; PMCID: PMC3358659.
SVseq: Detection of exact breakpoints with low-coverage sequencing data
Zhang J, Wu Y. SVseq: an approach for detecting exact breakpoints of deletions with low-coverage sequence data. Bioinformatics. 2011 Dec 1;27(23):3228-34. doi: 10.1093/bioinformatics/btr563. Epub 2011 Oct 12. PMID: 21994222.
HapReads: Haplotype inference from short sequence reads using a population genealogical history model
Zhang J, Wu Y. Haplotype inference from short sequence reads using a population genealogical history model. Pac Symp Biocomput. 2011:288-99. doi: 10.1142/9789814335058_0030. PMID: 21121056.
Book chapter on SVseq 1 and 2:
J Zhang, C Chu, Y Wu
Computational Methods for Next Generation Sequencing Data Analysis, 175-195
Book chapter on INTEGRATE tools:
Gene Fusion Discovery with INTEGRATE
J Zhang, C Maher
Methods in Molecular Biology, 41-68
PMID: 31728961
ICGC-TCGA DREAM Somatic Mutation Calling – RNA Challenge
https://www.synapse.org/#!Synapse:syn2813589
https://github.com/Sage-Bionetworks-Challenges/SMC-RNA-Challenge