Rotation Opportunities

Working in the Parker Lab

Below is a list of Bioinformatics affiliated faculty with rotation opportunities in their labs. Nearly all faculty are potential rotation mentors, however specific rotation opportunities are listed below. These research options are available for both first year graduate students and Master’s students. If there is a particular faculty member you would like to work with who is not listed, you are encouraged to contact them to learn of future 

For students interested in a lab rotation with a PIBS faculty member, a list of affiliated PIBS faculty and their respective research topics are available here:

Current Rotations Opportunities

Department of Computational Medicine and Bioinformatics (DCMB) 

Alan Boyle (Computational Medicine & Bioinformatics)


Contact Alan with interest ( I'd be happy to discuss possible rotation projects. A detailed description of my lab's research interests, and a list of recent publications, can be found on the Boyle lab web site:

  1. Specific projects include variations on:
  2. Analysis of variation in open chromatin in IPS cells
  3. Logic of gene regulatory control
  4. Machine learning tools for predicting the effect of variants on gene regulation
  5. Analysis of regulatory landscape in the Million Veterans program
  6. Regulatory effect on gene splicing and its effect on breast cancer
  7. Analysis of enhancer-promoter interactions
  8. Genome-wide screens of enhancers, promoters, silencers, and enhancer blockers"

Ivo Dinov (Computational Medicine & Bioinformatics/Nursing)


The Statistics Online Computational Resource (SOCR) develops, validates and shares resources for scientific computing, predictive big data analytics, statistical computing, and decision support. We welcome students, fellows, trainees and researchers interested in participating in SOCR projects ( and collaborating with faculty and researchers on advanced analytics, biomedical informatics, and high-throughput health information technologies.

Yuanfang Guan (Computational Medicine & Bioinformatics)


Potential projects includes:

  1. Developing tools to predict disease-related genes based on genomics data.
  2. Integrating data to study functional interactions between proteins (biological networks).
  3. Predicting pathways.

Ability to program is required; have to be interested to learn biology.

Alla Karnovsky (Computational Medicine & Bioinformatics)


Metabolomics is a rapidly developing field that uses a variety of analytical methods to detect small organic molecules and follow their changes in tissues and biofluids.

I am interested in developing new methods and tools for the analysis of metabolomics data and integrating them with other omics data. We recently developed a tool called Metscape for the analysis and visualisation of metabolomics data ( It allows users to build and analyze networks of genes and compounds, identify enriched pathways from expression profiling data, and visualize changes in metabolomics data. As more and more metabolomics data sets become available there is great interest in more sophisticated data analysis methods. Several possible projects in this area include developing new methods for metabolite enrichment analysis, predicting metabolite changes based on transcriptome and proteome, and building disease metabolite networks.

Jeffrey Kidd (Computational Medicine & Bioinformatics)


Several projects related to genomics research including:

  • structural and copy number variation in domestic dogs
  • retroelement insertion polymorphism in domestic dogs
  • assessment of genome assembly algorithms for determining rearrangement breakpoints
  • evolution of pseudo-autosomal regions in non-human primates
  • investigation of allele frequency data for disease genes in non-human primates
  • inference of human demographic history from full genome sequence data

Jacob Kitzman (Computational Medicine & Bioinformatics/Human Genetics)


Several rotation projects are available involving technology development for massively parallel sequencing and high-throughput functional analysis.

One major goal of our current work is to use massively parallel mutagenesis coupled with functional screens to systematically measure the effects of all possible mutations to genes implicated in cancer and other disorders. We then use these large-scale measurements, along with lists of known pathogenic and neutral variants, to train models to prospectively classify all other alleles as to their pathogenicity. (see e.g., Kitzman et al Nature Methods 2015, or Starita et al, Genetics, 2015).

Rotation students with either computational or experimental backgrounds (or both) are welcome. Please email Jacob ( or stop by (4811 Med Sci II) to learn more.

Matthias Kretzler (Computational Medicine & Bioinformatics/Internal Medicine)


The research in Dr. Kretzler’s team focuses on the analysis of molecular mechanism of glomerular failure. Using integrated biology approaches the group defines transcriptional networks in human glomerular diseases and integrates them with complex clinical data sets and other large-scale data sets. The NEPTUNE network offers the unique opportunity to analyze a prospective cohort of glomerular disease patients with high-resolution clinical and molecular phenotyping. An international multi-disciplinary research team will enable large scale data integration across the genotype-phenotype continuum of glomerular failure with carefully monitored environmental exposures, genetic predispositions, epigenetic markers, transcriptional networks, proteomic profiles, metabolic fingerprints, digital histological biopsy archive and prospective clinical disease characterization.

Opportunities for bioinformatics graduate student rotations include integrative analyzes across species along the genotype-phenotype continuum in an interdisciplinary research team.

Possible projects include work on strategies for (1) integrating systems information into genetic association analyses, (2) identifying molecular subsystems affected by multiple candidate genetic variants, and (3) identifying shared mechanisms across tissues, species, or phenotype.

Expansion of the project into a Ph.D. thesis is possible and funds for support might be available.
Background in analysis of large-scale data sets is preferred; basic concepts of molecular biology, statistics and programming are required; and ability to function and interact in a multidisciplinary team is essential.

Jun Li (Computational Medicine & Bioinformatics/Human Genetics)


Professor Li's rotation opportunities focus on aspects of quantitative analysis in statistical genetics, bioinformatics, or cancer genomics. Current open projects (2016-2017) include: single-cell RNAseq data analysis, methodological and theoretical research in clustering, mutation patterns in the human genome, multi-omics data integration involving metabolomic, proteomic, and transcriptomic data, QTL mapping in rat models of metabolomic traits and emotional traits.

Rajasree Menon (Computational Medicine & Bioinformatics)


My main research is on the role of alternative splice variants in human cancers. Experience in some programming and statistics will be an advantage.

Kayvan Najarian (Computational Medicine & Bioinformatics)


Traumatic brain injury (TBI) is a major cause of disability and death and each year around two millions TBI occur in the United States with the approximately 3% of mortality across all TBI severities. About 50% of the deaths are within the first two hours after injury. Therefore, the speed and accuracy are vital in diagnosing the TBI for which a computer-aided trauma decision making system can help reduce mortality, long-term complications, and the associated costs. Developing such a system is challenging due to the inherent noise associated with images, quality of the images, different scales and capturing orientations of the images, variation in the size, shape and location of ventricles from patient to patient, etc. A fully-automated system to identify and assess traumatic brain injury and specially localize the damage would be beneficial in guiding real-time clinical diagnosis as well as quality assurance. The proposed project intends to design a fully-automated system to utilize advanced image processing and machine learning techniques to analyze CT brain images independent of human input. Our preliminary results show the promising results of the proposed system. We also intend to integrate and combine the information in CT images with other patient data (clinical, molecular, physiological, etc) to further improve the predictions / recommendations generated by the system.

Alexey Nesvizhskii (Computational Medicine & Bioinformatics/Pathology)


Alexey Nesvizhskii Lab has rotation opportunities in proteome informatics. Projects in the lab include 1) Development of computational methods for large-scale proteomics; 2) Analysis of protein-protein interaction data; 3) Integrative analysis across multiple omics datasets (RNA-seq transcriptomics, proteomics, etc.).

Stephen Parker (Computational Medicine & Bioinformatics)


Our research group uses an integrative approach in the general fields of computational biology and functional genomics. The major goal of the lab is to generate mechanistic knowledge about how disease susceptibility is genetically encoded in the non-coding portion of the genome, with a focus on type 2 diabetes (T2D). We accomplish this through an interdisciplinary combination of molecular/cellular and computational methods – we generate multiple high-throughput data sets on the genome, epigenome, transcriptome, and proteome across the human population and diverse species and in disease-relevant tissues/cells and use computational approaches to integrate and analyze this data.

We contribute to multiple international consortia:

  • FUSION (Finland-United States Investigation of NIDDM genetics)
  • AMP-T2D (Accelerating Medicines Partnership for T2D)
  • TOPMed (Trans-Omics for Precision Medicine)
  • InsPIRE (Integrated Network for Systematic analysis of Pancreatic Islet RNA Expression)
  • MoTrPAC (Molecular Transducers of Physical Activity Consortium)

Projects spanning the lab and these consortia offer many exciting rotation opportunities.

Additional details can be found at the lab web site:
If you have any questions, or would like to discuss rotations, please feel free to contact Steve at

Indika Rajapakse (Computational Medicine & Bioinformatics)


I have potential projects related to:

  • Dynamics of the three dimensional structure of the human genome
  • The Biochronicity Program at the Defense Advanced Research Projects Agency.

If you're very creative and would like to work at the interface of mathematics and biology, please contact me, I would love to discuss potential projects.

Maureen Sartor (Computational Medicine & Bioinformatics)


We have multiple exciting rotation opportunities in our lab! The focus of my laboratory is developing bioinformatics methods and tools for the analysis and interpretation of high-throughput molecular biology data, with special focus on gene regulatory and epigenomics data from next generation sequencing experiments. The biological focus of my laboratory is cancer and epigenomics, in particular oropharyngeal/oral squamous cell carcinomas (OP/OSCC). Rotation projects are available working with OP/OSCC data, including CNV, RNA-seq, and DNA methylation data from two important subtypes (HPV associated and tobacco-use associated), working with ChIP-seq data from the ENCODE project, or developing software and methods for analysis of deep sequencing of bisulfite modified data. Projects may either be geared towards addressing a biological problem or developing a new method.

Other projects may be available in my lab - see Feel free to come talk with me if you're interested!

Jieping Ye (Computational Medicine & Bioinformatics)


Our project aims to develop novel machine learning and data mining methods to integrate and analyze high-dimensional genomes, connectomes, and multimodal brain images to discover diagnostic and prognostic markers for human brain diseases.

Yang Zhang(Computational Medicine & Bioinformatics)


  1. Structural prediction and ligand docking of G protein-couple receptors (GPCRs). GPCRs are integral membrane proteins embedded in cell surface and many human diseases involve the malfunction of these receptors. However, experimental solution of GPCR structure has been extremely difficult. This project is to develop computational methods for atomic-level structural modeling of human GPCRs and ligand-receptor interactions.
  2. Protein design. Protein design refers to the effort of designing new protein molecules of a desired 3D structure and function. It is a reversal procedure of protein structure prediction. This project is to develop novel computer methods for automated protein design. The methods are to be applied to a set of important diseases including cancer proteins with experimental validation by our collaborators.
  3. Ligand-protein docking and virtual screening. An important step towards structure-based drug discovery is the structural modeling of ligand-protein binding. However, most of the important drug targets have no experimental structure. This project seeks to develop new ligand-protein docking tools which are suitable for low-resolution protein structures predicted by computer. One aim is to combine the docking tools with protein structure predictions for reliable virtual ligand screening on any interested protein targets.

The projects are supported by National Institutes of Health (NIH R01GM083107 and NIH R01GM084222) and National Science Foundation (NSF DBI0746198).


Current Rotations Opportunities 

Center for Computational Medicine and Bioinformatics (CCMB) 

CCMB faculty members may serve mentors for Bioinformatics Program graduate students as well as providing research opportunities for students.

Joshua Berke (Psychology)


The Berke lab investigates how networks of individual neurons interact and process information during learning and adaptive decision-making. We are applying existing network analyses and also working with Michal Zochowski in UM Physics to develop new algorithms. Potential opportunities for BIOINF students involve working with large neural data sets to help uncover basic principles of brain computations underlying choice and motivation, and how these go awry in neurological and psychiatric disorders such as Parkinson's Disease and addiction.

Sally Camper (Human Genetics)


The focus of my laboratory is identifying the molecular basis for congenital birth defects using animal models of human disease. We have three NIH grants and a March of Dimes grant supporting active projects on the development of the neuroendocrine system (pituitary-hypothalamus), auditory system, and axial skeleton. There are at least three rotation projects that are appropriate for bioinformatics students. See the PIBS rotation pages for more details.

Heather Carlson (Medical Chemistry)


The Carlson group focuses on two areas: protein flexibility in computer-based drug design and protein-ligand structural databases. We are developing improved methods for mapping flexible protein surfaces in the drug design project. In the database project, we are mining our Binding MOAD (Mother of All Databases) set to investigate protein flexibility upon binding, amino acid propensities in binding sites, methods for identifying previously unknown binding sites on protein surfaces, and other properties of protein-ligand recognition.

Melissa Duhaime (EEB)


The Duhaime Lab uses (meta)genomic tools to investigate two realms of environmental microbiology and ecological genomics: (1) aquatic plastic-microbe associations and the role of microbes in the fate of marine plastics, and (2) the evolution and ecology of ocean viruses and their microbial hosts, at both the community and single isolate level. Fall and Winter rotations are available to study viruses of deep sea hydrothermal vents, with focus on developing bioinformatic applications to manage environmental sequence data, develop and apply sequence assembly techniques, and perform high-throughput functional annotations, with the end goal of incorporating ecological theory to interpret the genomic patterns discerned.

Oliver He (ULAM)


Integrative bioinformatics-based vaccine design
Global public health has dramatically increased due to the successful and effective implementation of immunization programs that utilize major infectious disease vaccines. New vaccines against various infectious diseases (e.g., HIV and tuberculosis) and non-infectious diseases (e.g., cancers and allergies) are also being developed and studied. In the post-genomic era, strategies of vaccine development have progressed dramatically from traditional experiment-to-experiment approaches to bioinformatics-to-experiment approches.

Reverse vaccinology is a cutting-edge vaccine design strategy that starts with bioinformatics analysis of microbial genomes. Dr. He's laboratory has developed Vaxign (, the first web-based vaccine design system based on reverse vaccinology. This pipeline includes prediction of various features, e.g., antigen sublocation, transmembrane domains, adhesin probability, epitope binding to MHC class I and class II, sequence conservation among microbial genomes, and sequence similarities to host proteomics. Vaxign has been proven effective and efficient in vaccine target prediction in many use case applications.

Trachette Jackson (Mathematics)


Mathematical Modeling of Endothelial Cell- Tumor Cell Cross Talk

In order to define and quantify the role of VEGF-mediated cross-talk between endothelial and tumor cells on tumor growth dynamics, we would like build two independent, experimentally based mathematical model modules that describe VEGF-mediated upregulation of Bcl-2 in endothelial and tumor cells, respectively. We will then link these modules to understand the impact of the mechanisms by which VEGF orchestrates a dynamic interplay, mediated by Bcl-2, between endothelial cells and tumor cells and to determine the influence of this cross-talk on tumor growth and therapeutic potential.

Required Skills: Basic knowledge of ordinary differential equations and MATLAB; an interest in mathematical/computational modeling

Denise Kirschner (Microbiology and Immunology)


We use mathematical and computational modeling tools to study the host-pathogen interaction dynamics for the pathogen Mycobacterium tuberculosis. We are currently focusing on antibiotic treatment and vaccine development. We use many different tools and collaborate with a BSL3 non-human primate center to provide data that guides the development, testing and validation of our work.

Jennifer Linderman (Chemical Engineering)


The focus of this lab is in developing mathematical and computational models that focus on the role of receptor dynamics and signaling pathways in disease. Particular project: Analysis of simulation data using machine learning tools to identify biological phenotypes relevant to the immune response to M. tuberculosis

Mats Ljungman (Radiation Oncology & Environmental Health Sciences)


We have developed a set of new techniques that we call Bru-seq that allows us to obtain very detailed molecular signatures of transcriptional and post-transcriptional regulation in cells. These techniques are based on bromouridine labeling and isolation of nascent RNA to assess genome-wide rates of transcription (Bru-seq), RNA splicing and turnover (BruChase-seq), identification of active enhancer elements (BruUV-seq) and transcription elongation rates (BruDRB-seq). These technologies have gone through an initial developmental stage and we are now in position to apply them to many biological questions.

Proc Natl Acad Sci U S A 110(6): 2240-2245, 2013.
PLoS One 8(10): e78190, 2013.
Methods 67(1): 45-54, 2014
Genome Res (in press)

Patrick Schloss (Microbiology and Immunology)


The Schloss lab develops and tests computational methods for analyzing microbial communities. The goal is to enable researchers to use data generated using next generation sequencing technology to test ecological theory with microbes and better understand how perturbations in these communities affects health. We are looking for curious and creative students interested in the interface between computational biology and microbial ecology.

  1. Analysis of the human microbiome:
  2. Heuristic clustering algorithms for clustering DNA sequences of unknown origin
  3. Development of metatranscriptomic analysis pipeline
  4. Statistical modeling of microbial communities and patient clinical data

Evan Snitkin (Microbiology and Immunology)


The Snitkin lab is interested in the application of genomics and bioinformatics approaches to study the evolution and epidemiology of hospital infections. Contact Evan to discuss specific rotation opportunities. In addition, an overview of the labs interests and past publications can be found at:

The types of projects available include:

  1. Mining large sets of healthcare-associated pathogen genomes to characterize the key evolutionary innovations associated with the success of pandemic lineages
  2. Applying phylogenetic approaches to large sets of healthcare-associated pathogen genomes to discern transmission patterns within and between healthcare facilities
  3. Analyzing sequencing data generated directly from patient samples to diagnose diseases of unknown infectious origin
  4. Mining patient health records to identify treatment protocols associated with the evolution of resistance
  5. Applying metabolic modeling approaches to make predictions regarding how GI pathogens colonize the host and compete with commensals
  6. Applying short-read sequencing approaches to characterize the genetic diversity of pathogen populations within individual patients

Chandra Sripada (Psychiatry)

My lab is uses functional neuroimaging to tackle the biomarker problem in psychiatry: the problem of constructing objective, quantitative measures for psychiatric disorders and clinically-relevant personality dimensions. Much of our recent work identifies graphical structure in “functional connectomes,” which are whole-brain maps of connectivity across thousands of brain regions. Students have opportunities to develop new analytic methods to quantify change in graphical structure over time (working with our collaborators in EECS and Statistics). We intend to use these methods to characterize aberrant change patterns in disordered populations, including adults with schizophrenia and children with attention dysfunction.

Scott Tomlins (Pathology)


Our lab uses next generation sequencing to profile the cancer genome and transcriptome with a focus on preciion medicine.

We focus on assessing routine tissue samples from patients with cancers from diverse organs using Ion Torrent instrumentation in our laboratory.

Rotations are available to work in a range of bioinformatics areas related to precision medicine from primary data analysis to clinical implementation.

Specific rotation projects including developing novel error models to minimize false positive/negative variant and copy number calls, developing methodology to automate data visualization and prioritize drivers, and integrate DNA and RNA based alterations from large cohorts to make biological insights."

Nils Walter (Chemistry)


We have a diverse set of projects available that use bioinformatics, computational biophysics, systems biology and kinetic modeling approaches to interpret single molecule fluorescence microscopy data that illuminate how the recently discovered, ubiquitous universe of non-protein coding RNAs drives all aspects of gene expression and regulation in eukaryotes. Applications range from basic studies of RNA catalysis and interference, pre-mRNA splicing, and mRNA translation to understanding the onset of cancer in misregulated human cell lines. Here is a brief outline of possible rotation projects:

  1. Use bioinformatics and clustering to analyze complex single molecule signal traces
  2. Use atomistic molecular dynamics and coarse-grained simulations to understand the coupling of local and global motions in RNA enzymes
  3. Use Monte-Carlo simulations to understand complex diffusive behaviors of single molecules and nanodevices
  4. Use systems biology modeling to integrate vast datasets into mechanistic models of important cellular processes

Shaomeng Wang (Medicine, Pharmacology and Medicinal Chemistry)


Potential projects include:

  1. Deriving gene-signatures for predicting anticancer activity of novel drugs developed in our lab to guide future clinical trials.
  2. Developing new computational and bioinformatics tools and methods for drug design;
  3. Computational structure-based drug design.

Andrzej Wierzbicki (MCDB)


Our lab is focused on understanding non-coding regions of the genomes. We study how RNA produced from non-coding sequences (non-coding RNA) controls genome activity on the chromatin level. Chromatin-level gene regulation includes DNA methylation, histone modifications, nucleosome positioning and three-dimensional organization of chromosomes. We study these processes using genomic approaches and in depth bioinformatic analysis to gain mechanistic and quantitative understanding of genome regulation. Our work may be described as an interface of RNA biology and epigenomics.

Graduate students rotating in the lab would have the opportunity to work in one of the two following projects:

  1. Resolve the molecular mechanisms used by non-coding RNA to control chromatin structure. This project, funded by a recently awarded NIH grant, involves studying nucleosome positioning and higher level chromatin organization in various mutant backgrounds defective in non-coding RNA production and processing. This project involves establishment and application of a bioinformatic toolset suitable for high quality analysis of genome-wide nucleosome positioning data.
  2. Test if structures of non-coding RNA affect their functions and if these structures are actively modulated during RNA-mediated processes. This project, funded by an NSF grant, involves establishment of a toolset for genome-wide structural assays of non-coding RNA structures.

Qiong Yang (Biophysics)


Our lab is mainly focused on understanding how individual biological oscillators are designed to perform robust functions and how multiple oscillators are coordinated to generate spatiotemporal patterns in the early embryo development. We aim to connect the understanding at the single molecule level, to that of single cells and of the whole tissue. To achieve the goal, we combine cell-free extracts with live embryos of zebrafish. We integrate theoretical modeling with quantitative measurements using microfluidics and optical imaging.

Jianzhi George Zhang (EEB)


Rotation options for Evolutionary genetics, genomics and systems biology are available. They can be either experimental or computational. The experimental work is typically done in budding yeast. Current experimental and computational projects focus on position effects on gene expression level and noise, genome organization and 3D chromosomal conformation, recombination rate variation, epistasis, pleiotropy, plasticity, fitness landscape, and post-transcriptional modification, all in the context of evolution. I also encourage students to develop their own projects in the general area of evolutionary genetics/genomics. More information found on the lab website.

Xiang Zhou (Biostatistics)


Rotation opportunity is currently available in Dr. Xiang Zhou's lab in the Department of Biostatistics. The Zhou Lab is focused on developing statistical and computational methods to address interesting biological problems in genetic and genomic studies. These studies often involve large-scale and high-dimensional data sets, and examples include genome-wide association studies and functional genomic sequencing studies (RNAseq, ChIPseq etc.). Through developing novel analytic methods to extract important information from these data, we hope to advance our understanding of the genetic basis of phenotypic variation for various quantitative traits and complex diseases. Please refer to the website ( for more details. Current research projects include developing statistical methods for association tests with multiple correlated phenotypes, for integrating functional genomic studies with genome-wide association studies, for phenotype/risk prediction, for epistasis contribution and heritability estimation, and Bayesian methods for big data sets. Please contact Dr. Xiang Zhou ( if you are interested.