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)

E-Mail: [email protected]

We use modern genomics techniques and high-throughput experiments to explore biological systems. We can leverage wet lab and computational tools to help answer biological problems that were previously intractable. We aim to combine computational approaches with high-throughput biological assays to better understand the whole human transcriptional regulatory system. A detailed description of my lab's research interests, and a list of recent publications, can be found on the Boyle lab web site:
Specific projects include variations on:

  1.  Logic of gene regulatory control
  2.  Machine learning tools for predicting the effect of variants on gene regulation
  3.  Regulatory effect on gene splicing and its effect on breast cancer
  4.  Analysis of enhancer-promoter interactions
  5.  Genome-wide screens of enhancers, promoters, silencers, and enhancer blockers
  6.  Study of the efficiency of the CRISPR system

Marcin Cieslik (DCMB and Pathology)

Peter Freddolino (Computational Medicine & Bioinformatics/Biological Chemistry)

E-mail:  [email protected] 

We are interested in combining approaches from microbial genetics, biophysics, and computational biology to understand how and why regulatory networks function, and ultimately, to improve our ability to design synthetic biological systems for specific purposes. Some specific projects include:

More detailed information is available on the Freddolino lab website. Please contact Peter Freddolino ([email protected]) for further information on rotation options.

Jacob Kitzman (Computational Medicine & Bioinformatics/Human Genetics)

E-Mail: [email protected]

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 ([email protected]) or stop by (4811 Med Sci II) to learn more.

Ryan Mills (DCMB & Human Genetics)

Kayvan Najarian (Computational Medicine & Bioinformatics)

E-Mail: [email protected]

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.

Nambi Nallasamy (Ophthalmology and Visual Sciences & DCMB)

Matt O’Meara (DCMB)

Stephen Parker (Computational Medicine & Bioinformatics)

E-Mail: [email protected]

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 [email protected]

Elizabeth Speliotes (Gastroenterology, Internal Medicine & DCMB)

Josh Welch (Computational Medicine & Bioinformatics)

E-Mail: [email protected]

Our research focuses on enabling biological discovery by applying novel computational approaches to genomic data. We develop and apply algorithms, machine learning methods, and statistical models for analysis of single cell genomic data. Broadly, we seek to understand what genes define the complement of cell types and cell states within healthy tissue, how cells differentiate to their final fates, and how dysregulation of genes within specific cell types contributes to human disease.

Sample rotation topics include:

  1. Integrating single cell RNA-seq and singl cell epigenome data
  2. Deep learning methods for analysis of single cell RNA-seq data
  3. Comparing single cell RNA-seq data across species and tissues
  4. Scaling single cell RNA-seq analysis methods to datasets with millions of cells
  5. Predicting effects of perturbations from single cell RNA-seq data

This is just a sampling of project ideas – there is much exciting work in this area for those with a love for both computational method development and biological discovery!


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.

Charles Brooks (Biophysics and Chemistry)

E-mail: [email protected]

I have multiple opportunities for rotation students in the lab. These include:
Developing and applying free energy simulation methods to drug discovery and refinement
In this project the student will work with a team on the development of novel free energy simulation methods and analysis techniques that build on both statistical mechanics and foundational methods in modern statistics. Applications will be directed toward the refinement of small molecule inhibitors against protein-based targets, which include HIV-RT, menin-MLL, small molecular transcriptional activators, as well as other protein receptors associated with benchmarking and assessing evolving computational methods.

Novel in silico drug discovery through high-throughput flexible-ligand/flexible-receptor based docking on advanced computing hardware (GPUs)
The focus of this project is in hardening and refining in silico ligand discovery methods that utilize advanced computing platforms, such as GPUs, to achieve high-throughput and scalability in docking screens. Methods development will focus on establishment of optimal protocols for flexible-ligand/flexible-receptor docking as well as the use of machine learning techniques to improving the scoring of docked poses.

Utilizing machine learning algorithms based on auto-encoder/auto-decoder technology for exploration of small molecular and multiple sequence alignment protein sequence property analysis
This project will explore the development of auto-encoder/auto-decoder (AE/AD) machine learning methods to extract and interpolate properties associated with either small molecule collections of drug-like ligands or learning to predict evolutionary trends in proteins from AE/AD based analysis of multiple sequence alignments

Arul Chinnaiyan (Pathology)

E-mail: [email protected] 

The Michigan Center for Translational Pathology (MCTP), under the direction of Dr. Arul Chinnaiyan, employs genomic, proteomic, and bioinformatic approaches on clinical biospecimens to identify novel cancer biomarkers and therapeutic targets. The mission of the Center is to facilitate the discovery, validation, and implementation of candidate target genes/proteins in cancer diagnosis, prognosis, and therapy. The Center employs a multi-disciplinary approach, engaging talent from diverse disciplines ranging from medicine, pathology, bioinformatics, biostatistics, engineering, cytogenetics, and molecular therapeutics.

MCTP is seeking DCMB rotation students to work on the following projects:

  1. Comprehensive analysis of FOXA1 alterations in prostate cancer integrating whole genome data and transcriptome data. Primary paper is in press at Nature.
  2. Nomination of circular RNAs across various cancers that will serve as non-invasive cancer biomarkers. Follow up to Vo et al, Cell 2019.
  3. Defining immunogenomic signatures of cancer. Follow-up to Robinson et al, Nature 2017.

Denise Kirschner (Microbiology and Immunology)

E-Mail: [email protected]

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.

Mats Ljungman (Radiation Oncology & Environmental Health Sciences)

E-Mail: [email protected]

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). We have generated a large nascent transcriptomic data set and as a newly funded ENCODE mapping center we will generate much more with great opportunities for novel bioinformatics analyses. Potential rotation projects would include:

  1.  Assessing transcriptional and post-transcriptional regulation of cell transitions from fibroblasts into iPSCs and then into neurons
  2.  Transcriptional and post-transcriptional regulation of cellular responses such as TGFb stimulation, heat-shock, hypoxia and DNA damage responses
  3.  Assessing transcription ongoing in repetitive DNA sequences in the genome across multiple cell lines and treatment conditions

Nambi Nallasamy (Ophthalmology and Visual Sciences)

E-Mail: [email protected]

Rotation positions are available in the Nallasamy Lab at University of Michigan's Kellogg Eye Center.

Our work focuses on the development and application of machine learning techniques for clinical problems in ophthalmology. We are currently developing a unified ophthalmology dataset for all of Kellogg Eye Center merging clinical data and imaging studies. One of our goals is to create a comprehensive database for clinical and imaging research in ophthalmology that can be queried interactively or with natural language rather than SQL queries.

Specific topics for which machine learning techniques are currently being developed and evaluated include 1) the selection of lens implant power for cataract surgery (the most commonly performed surgery in the US) based on preoperative measurements and 2) the diagnosis of cancers of the eye using specialized OCT imaging data, and 3) the diagnosis and monitoring of glaucoma through imaging studies.

Students joining the lab will be mentored by Dr. Nambi Nallasamy. Opportunities for joint mentorship between Dr. Nallasamy and Dr. Arvind Rao are also available. Those students with strong skill sets in machine learning and/or relational database design and an interest in high-impact clinical applications of machine learning are encouraged to contact Dr. Nallasamy at [email protected].

Brandon Ruotolo (Chemistry)

E-mail: [email protected]

We have two main opportunities for bioinformatics rotation students:

1) We are actively developing software (in Python) for the rapid analysis of protein-based therapeutics, where the stability assessments are based on a novel gas-phase protein unfolding technology that we call collision induced unfolding (CIU). We are actively developing and improving software that enables CIU signal processing, quantitative comparisons between CIU data, and rapid classification of unknown CIU datasets using machine learning (ML). CIU data is complex and multi-dimensional, containing many features that related to protein unfolding intermediates that can built in ML-classifiers in multiple ways. We are also working with highly multiplexed CIU datatypes, as well as extracting CIU information from highly complex mixtures for the purposes of drug discovery/development and protein quantification.

2) My group is actively building methodologies that leverage mass spectrometry (MS) data to build models of proteins and protein complexes that remain refractory to other structural biology workflows. In some cases, this requires the development of novel molecular dynamics (MD) simulation capabilities that focus on refining protein structures restrained/filtered using MS data. This project stream also has space for students interested in contributing to the development of software (Python) and scripts aimed at both utilizing and integrating multiple MS data types for the construction of such 3D protein models. Example datatypes include hydrogen-deuterium exchange (HDX) MS, chemical cross-linking (CXL)-MS, and ion mobility (IM)-MS. Applications vary widely, but include a number of applications toward protein complexes associated with cancer and Alzheimer's disease. 

Evan Snitkin (Microbiology and Immunology)

E-Mail: [email protected]

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

Andrzej Wierzbicki (MCDB)

E-Mail: [email protected]

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)

E-Mail: [email protected]

The Yang Lab studies biological oscillations and self-organization processes in cell-free systems and early zebrafish embryos. The local interactions in the form of mechanical and biochemical signals allow individual molecules and cells to generate collective spatiotemporal patterns. To pin down the physical mechanisms behind these processes, we integrate mathematical modeling, live-cell and super-resolution imaging, and microfluidics to study both water-in-oil droplet-based artificial cells and live embryos, and connect the understandings across the molecular, cellular, and embryonic levels. We look forward to recruiting students with a quantitative background and a strong interest in interdisciplinary research. The position is immediately available and the starting date is flexible. How to apply: Interested candidates should send a brief description of your interest and CV to Email: [email protected]. Dr. Qiong Yang.

Below are two current major research efforts in lab:

  1. Emergent phenomena in biological systems
    Investigating how self-organization behaviors, such as mitotic trigger waves in artificial cell-free systems and somite pattern formation in embryos, arise from complex interactive networks of cells and molecules through biochemical signals and mechanical forces.
  2. Method development for quantitative biology
    Development of an integrated interdisciplinary approach to study biological clock design, function, and coordination, e.g. statistical techniques to identify network motifs for clock function, combination of modeling, fluorescence imaging, and microfluidics, to quantitatively manipulate and analyze the oscillatory processes in artificial mitotic cells, live tissues and zebrafish embryos.

Jianzhi George Zhang (EEB)

E-Mail: [email protected]

Rotation options for evolutionary genetics, genomics and systems biology are available in my lab. 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 chromosome conformation, recombination rate variation, epistasis, pleiotropy, phenotypic 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 at