Research projects in Dr. Zhu's group include (1) development of bioinformatics algorithms and tools for integrative and interactive analysis of multi-omics data, (2) integrative analysis of multi-omics dataset for better understanding gene regulation and cancer etiology, and for biomarker discovery, and 3) development of algorithms and tools for designing effective gRNAs with minimal offtarget effects. Most recent relevant publications include Nature Methods 6(6):453-454. 2019. PMID: 31133757; Genome Res. 2019 PMID: 31201210; Nat Methods. 2018 Jan 03; 15(1):8-9. PMID: 29298290; BMC Genomics. 2018 03 01; 19(1):169. PMID: 29490630; Proc Natl Acad Sci 2019 PMID: 31068472; Cell. 2018 174(1):172-186.e21. PMID: 29958106. See profiles.umassmed.edu/display/129880 for details about the type of researches Dr. Zhu's group does. Here is the list of algorithms/tools her group has developed including a dozen Bioconductor packages available at bioconductor.org.
ChIPpeakAnno (BMC Bioinformatics 2010; Tilling Arrays, Methods in Molecular Biology 2013)
FlyFactorSurvey (a database web application. Nucleic Acids Res. 2011)
GeneNetworkBuilder (Tilling Arrays, Methods in Molecular Biology 2013)
cleanUpdTseq (Naïve Bayes classifier in a Bioconductor package, Bioinformatics 2013)
REDseq (BMC Genomics 2014)
CRISPRseek (PLoS One 2014; Front. Biol 2015)
GUIDEseq (BMC Genomics 2017)
motifStack (Nature Methods 2018)
ATACseqQC (BMC Genomics 2018)
trackViewer (Nature Methods 2019)
NADFinder (Genome Research 2019)
This is a collaborative project consisting of a multidisciplinary effort spanning multiple laboratories across UMass Medical School. The efforts will focus on the development, enhancement and benchmarking of algorithms/tools for the integrative and interactive analysis and visualization of multi-omics data including various types of NGS data such as ChIP-Seq, ATAC-Seq, RNA-Seq, scRNA-Seq, NAD-Seq, Hi-C and PAS-Aeq/PolyA-Aeq.
These efforts will expose the successful candidate to cutting-edge technologies and latest researches in the field of biology and bioinformatics. The successful candidate will also gain experience on analyzing different types of deep-sequencing data. In addition, the successful candidate will be involved in developing/enhancing Bioconductor packages, which will broaden his/her exposure to one of the most widely used bioinformatics frameworks and will increase his/her visibility to one of the most active bioinformatics communities. In addition to publishing his/her own research papers, the successful candidate will have ample opportunities to publish joint research findings with our collaborators.
A successful candidate should have a Ph.D. in bioinformatics, computational biology, statistics, computer sciences or a related field. In addition, the potential candidate must have strong oral and written communication skills and be eager to present research results at national meetings. The candidate should be highly motivated with strong interests in bioinformatics research, have excellent publication record (including at least one first author publication) in peer-reviewed journals, have outstanding technical skills on bioinformatics analysis and programming. Prior experience with R/Bioconductor package development, algorithm/database/web development, and workflow language is strongly preferred. Experience with analyzing genome wide NGS data is a plus.
Interested applicants are invited to send (via e-mail) a letter summarizing current research activity, the reason for applying to the group and a statement of research interests, the curriculum vitae, and a list of three references to Dr. Zhu (email@example.com).
UMass Medical School is committed to being an equal opportunity and affirmative action employer and recognizes the power of a diverse community. We encourage applications from protected veterans, individuals with disabilities and those with varied experiences, perspectives and backgrounds to consider UMass Medical School as their employer of choice.
Contact Dr. Zhu via email firstname.lastname@example.org