Translational Research in the Garner Lab (Innovation Laboratory)

Laboratory Mission:
The Innovation Laboratory develops applied computational biology/bioinformatics applications focused to address important topics in biomedical research. The laboratory has constructed numerous computational tools and databases, which have led to a series of discoveries in basic and clinical science by the group and its large number of collaborators. The Innovation Laboratory staff is multi- disciplinary, including physicists, engineers, mathematicians, biologists and physicians. The Innovation Research and Development Laboratory personnel are engaged in a number of collaborations throughout the world, with academic and corporate institutions, supplying technology and conducting research in areas such as cancer, heart disease, drug discovery, biodefense, computer science and engineering. This laboratory has at any given time about 10-20 active collaborations and uses a team of computational biologists, programmers, and interpretive biologists that work with each researcher to provide a complete service. The researchers who interact with this group often themselves have a number of collaborations and frequently have a unique capability, such as high resolution mass spec systems or a unique biological model, and are in need of applied computational biology services. The Innovation Laboratory has a network of dedicated computer systems including several large Linux clusters, other dedicated servers, and over 25TB of on-line data with local copies of all the major public databases synchronized nightly (Medline, GenBank, PIR, etc.). This group offers many of its computational tools and databases on-line, many of which were inspired by the needs of their collaborators or emerged out of its independent research, and assists over 30,000 users each month. The group has developed many unique resources for its interpretive biologists to use for a given collaboration/dataset as well as many standard/commercial tools (GeneSpring and GeneTraffic for array databasing and analysis, for example). A sampling of some of its resources include: 1) text data mining and searching using IRIDESCENT and eTBLAST (try it on the web) for hypothesis generation to gene cluster analysis based on implicit links among biomedical metadata (gene lists, for example) derived from microarrays, 2) a high-throughput mass spec data analysis pipeline that bypasses traditional peak finding and yields significantly improved specificity and sensitivity for finding biomarkers in terabyte sized datasets, and 3) complete compilations of hot-spots for genetic or epigenetic variation in the sequenced genomes thus facilitating more directed experimental approaches for disease association and even drug development. The group also conducts research addressing experiment planning, statistics, and data unification from many high-throughput sources.

Unique resources contributing to Translational Research:
Computational: The computational biology group at UTSW has assembled the necessary infrastructure – hardware, software, databases and personnel – for archiving, analysis and interpretation of biomedical information. We currently operate a number of large compute and data servers/clusters on their own dedicated network and are continuously upgrading them as computational demand increases. Basic and clinical researchers wish to focus to advance their research, without having to become an expert in computer science. For most of the resources now available, a simple, straightforward web-based interface has been created, thus allowing researchers to analyze genetic/genomic/proteomic data, perform text data mining/searching. For those bioinformatics technologies with a fairly complex interface and methodology, group scientists perform analysis with our basic and clinical collaborators, and this is particularly true in large data set analysis, mutation prediction and hypothesis generation.

Interpretive Biology: One major bottleneck in contemporary science is the interpretation of the massive amount of data that can be quickly generated. To address this now universal need among basic and clinical research scientists, a new type of scientist has evolved in our laboratory, the ”interpretative biologists” These are scientists at the interface between the tools and international databases, the local data, and the researchers. These scientists are comfortable using the tools (custom and commercial) in the entire context of a particular experiment, spanning experiment planning, biostatistics, data acquisition, analysis, visualization, and ultimately the discovery of new biomedical knowledge and the definition of future directions. A typical dataset could consist of 10s to 100s of expression microarray experiments, each with ~40,000 values or 100s of high resolution mass spectra. A typical output would be a robust, reproducible set of candidate genes or biomarkers, and a manuscript-ready report along with suggestions for further experimentation, validation and pursuit.

Instrumentation Development: The multi-disciplinary group has also constructred a number of high-throughput technologies, most notably has been the development of a custom “Affymetrix-like” microarray system that has now been commercialized by Nimblegen, Inc. This emerged from our focus in the area of Light Biology, that is the application of photons through optical components for the production of DNA chips, new light sources, immune system modulation, tissue engineering, hyperspectral imaging, microscopy and cytogenetics. Some of our past work included the development of DNA sequencers, micro- and nano-volumeliquid handling and array spotting robotics and high-throughput oligo synthesizers. Much of this work was originally inspired by the needs of the human genome project or paradigm shift from reductionist driven to data driven science in the data-rich post-genomics era.


Examples of Translational Research (Physicans/Medical students with whom we have collaborated are noted):

Informatics
Drug discovery (Michael DiMaio, Ralph Shohet, Shireen Guide, Robert Kumar, David Clark)

At UTSW we have established a new paradigm for drug development, which enables fast and cost efficient introduction of new therapeutics for the treatment of virtually any clinical indication, with an initial demonstrations being done in the area of cardiac disease. Using our code, IRIDESCENT, new uses for existing FDA approved compounds are hypothesized for various disease classes for which there are no available therapeutics or current drugs used in the clinic have significant side effects or safety profile issues. Since these drugs are currently prescribed, an accelerated clinical trials period is expected, enabling us to translate these findings to the clinic rapidly. IRIDESCENT, which analyzes all of the over 15 million Medline abstracts for direct and implicit connections for over 2.5 million biomedical objects (diseases, genes, drugs, chemicals, etc.), with its underlying biostatistical relevancy package suggests robust new hypotheses, in a way that emulates the way in which researchers go about conducting science. IRIDESCENT is used to develop a list of possible drugs with implicit connections that are indicative of a potential therapeutic or side effect for any given disease.Suggested drugs are prioritized by the code and subsequent inspection by teams of computational biologists and clinical experts in the field to select a subset of drugs, typically ~10, for testing in mouse models. With Drs. Dimaio and Shohet, we have succeeded in verifying in animal tests 4 new efficatious drugs for Cardiac Hypertrophy and 2 for Myocardial Infarction. With Dr. Guide, we are currently evaluating 4 broad classes of compounds on a Ptch +/- basal cell carcinoma mouse model, and anticipate our first results in a few months. With Dr. Kumar we have shown that the popular supplement, Melatonin, is not an effective treatment, although there is recent evidence that is reduces blood pressure a very small amount. The medical student, David Clark, has developed enhanced visualization for our text data mining process that enables more accurate identification of new hypotheses.

Disease and developmental biology (John Minna, Rhonda Bassel-Duby, R. Sanders Williams, Woodring Wright, Adi Gazdar, Gail Tomlinson, Ralph Shohet, Michael Lerman, Rama Ranganathan)
One major focus of our research is in the prediction of putative hot spots for high impact genetic variations that can result in phenotype, especially disease. Algorighms for the identification of potentially polymorphic microsatellite of single nucleotide sites were created, experimentally verified and now can be used to greatly influence the design, efficiency and success of causative variations in candidate genes, be they identified by linkage, RNA or protein expression, or other. In addition to being made available via the web, these have been applied to many studies in cancer, heart disease, developmental biology and evolution. Some examples include: 1) the identification and experimental validation of several microsatellite repeats within the coding regions of developmental genes (transcription factors) in a dog model which enabled us to explain the rapid diversification of the species into many breeds, for quantitative traits (length of the snout), 2) the development of a microsatellite linkage panel for ~100 loci in the 3p21 region that ultimately was used to identify a set of tumor suppressor genes for small cell lung carcinoma, 3) the identification of microsatellite repeats within oncogenes involved in colon cancer that were shown to be associated with microsatellite instability as a consequence of loss of miss match repair machinery. This computational work was recently married to a new custom array produced by the lab to monitor the status of all microsatellite motifs (mono- through hexa-) for numerous matched cell lines thus showing quantitatively that at least 10% of the over 800,000 microsatellites spread throughout the human genome undergo alterations in cancers classified as microsatellite unstable.

Trauma response (Grant O’Keefe)

In a study conducted with Dr. O’Keefe, a trauma surgeon, we conducted laboratory tests of SNP markers and reviewed the various studies to date to resolve why they have not agreed, largely due to poor experimental design.

Proteomics (Kevin Rosenblatt)

It is now possible to rapidly produce large, high-resolution mass spectra sets for sera from a variety of cohorts. To facilitate the rapid and reproducible analysis of those datasets to identify robust biomarkers we have developed a computational pipeline that can take terabyte-sized data sets (thousands of spectra) and using a novel method that bypasses peak calling. It has been applied to several cancers, a pre-mature labor and to Alzheimers, Parkinsons and other closely related neurological disorders and has produced for each of these new and verifiable biomarkers. This work was done with Dr. Rosenblatt.

Interpretive biology:
Microarray data reduction and interpretation (Richard Gaynor, Jonathan Graff, C. Rick Lyons, Norikatsu Mizumoto, Akira Takashima, John Minna)

A major void exists in modern data-driven biomedical research – the analysis and interpretation of large data sets in the context of study, while utilizing the tremendous amount of ancillary meta-data now available from numerous sources to ultimately produce a broad understanding of the data, a model, additional hypotheses and directions for continued pursuit. A specially skilled person is required for this type of work, a biomedical scientist, rooted in basic and/or clinical research, who can work comfortably with advanced computational and biostatistical tools as a tool user to extract value from the data and combine that with their broad knowledge to non-the-less produce new deep and focused knowledge. This new acumen, interpretive biology, has been applied to a large number of studies, a few examples being: 1) the combination of expression and chromatin assays at a sub-gene resolution on a global scale to understand the response of model cancer cell lines when exposed to common therapeutic drugs, 2) the analysis of host response to different strains of anthraces (wild type and strains with various virulent factors deleted) using expression data, 3) variation in transcription factor activation in dendritic cells.


Instrument and methods development:
Custom microarray synthesis and applications (Rick Lyons, John Minna)

Our laboratory initiated microarray based research at UTSW, which ultimately became the microarray core, which now processes thousands of arrays annually for faculty. The infrastructure for that core, array spotters, array scanners and software were developed an operational before any commercial instruments were available. Since then, that facility has been completely renovated with all commercial spotted array associated systems as well as an Affymetrics GeneChip system. Our group does continue to offer analysis and interpretation support for all those desiring or needing it.
However, this work led to a new invention, the DOC custom array synthesizer, originally supported via an R21/R33 grant initiated in 2000. DOC, or Digital Optical Chemistry, array synthesizer utilizes Texas Instruments Digital Light Processing chips (the heart of DLP TVs and computer projectors) to illuminate a microscope slide with patterns of light in sequence with the introduction of photo-protected phosphoamidites, thus creating oligonucleotide microarrays that typically have a density of over 200,000 features (spots). With this technology, we have been able to develop special purpose microarrays (resequencing, expression, microsatellite content, genomic tiling for microRNA discovery, genomic annotation, gene methylation) for a variety of genomes (human, mouse, anthrax, pestis, etc.) in a large number of collaborations, many of which are ongoing and expected to produce manuscripts within weeks. Further, this technology was the basis of a university spin-out company, Light Biology, which was acquired by Nimblegen.


DNA sequencing and mapping (Glen Evans, Ralph Shohet, Mark Perlin, Bernhard Zabel, Lorie Romberg)

The human genome project was initiated without the technological base to accomplish its stated goals. This need was recognized and the genome centers, of which UTSW was one (Glen Evans, Director, Skip Garner, Co-Director). Out genome automation group developed several hardware and software components used by the center, many were propagated outside the center and some were and continue to be commercial products. Examples include: 1) a hyperspectral-based DNA sequencer, on which the concepts went on to become the heart of current ABI capillary sequencer detection systems, 2) a high-throughput oligo synthesizer, MerMade, currently in use in several genome centers internationally and many other laboratories (this device is still manufactured in Dallas, Texas, with typically 2 of these large robotic platforms being shipped monthly), 3) several software packages were written to facilitate DNA mapping and sequencing, including packages that correct problems associated with ABI DNA sequencers when used in a high-throughput setting (genome center) and software that automated the process of sequence closure using primers designed to amplify regions needing additional finishing reads.

Hyperspectral Imaging Microscope (Tom Nielsen, Jeff Zavaleta, Ranal Ruch, Kevin Rosenblatt, Lance Liotta)

Three medical students, Tom, Jeff and Ranal, one summer developed the hardware, software and applications demonstrations for this new type of microscope. In essence, we based this microscope and array scanner on the technology that NASA uses in their LandSat earth imaging system. The microscope has a moving X-Y stage, an imaging spectrograph and a image intensified cooled CCD camera. This allows us to collect the entire emission spectra for every pixel in the image, providing unparalleled new capabilities in pathology, cytometry and material science. We have demonstrated 5-12 depth multiplexing of antibody probes, and most recently, with Drs. Rosenblatt and Liotta quantum dot labeling. With NASA, we have now demonstrated the ability to digitally stain tissue samples, making it possible to now do immediate real time analysis, critical to decision making during surgery for tumor resection.

Light therapy (Akira Takashima)

Using a digital light processor, the light modulation unit in computer projectors and DLP TVs, with its 800,000 micromirrors married to a light source and a light dispersive element (spectrograph), a new computer controlled light source was created. It is called the Variable Spectrum Synthesizer. This source can assemble ~800 independent light spectra (columns of micromirrors), where each of the 1,000 spectral components are selected digitally via a row of the micromirrors. Under computer control, the light spectra can be changed rapidly (50 microseconds and up) to create different rhythms as well. Although the VSS has applications in a number of physics and engineering disciplines, the control software and application described are tailored for measurement of wavelength-induced apoptosis/necrosis of skin cancer cells in the field of clinical photodynamic therapy. An experiment using psoralens coupled with UVA light and an experiment using visible light in photodynamic therapy were conducted. We found, for example, that cell cultures of PAM-212 cells with both UV and visible light irradiations exhibited very defined and wavelength-specific responses. Dr. Takashima’s group routinely uses this source for their experiments.


Peer reviewed publications originating from research involving clinicians and medical students (Physicians and med students denoted in bold)
  1. Elizabeth M. Flood, Robert S. Kumar, Rashmi Shah, Quinlan Amos, Jonathan D. Wren, Ralph V. Shohet, and Harold R. Garner, Melatonin administration does not affect isoproterenol-induced left ventricular hypertrophy in a mouse model, IEEE Engineering in Biology and Medicine, March, 2006.
  2. Deborah A. Ferguson, Matthew R. Muenster, Qun Zang, Jeffrey A. Spencer, Jeoffrey J. Schageman, Yun Lian, Harold R. Garner, Richard B. Gaynor, J. Warren Huff, Alexander Pertsemlidis, John Schorge, Carlos Becerra, Noelle S. Williams, Jonathan M. Graff, Selective Identification of Secreted and Transmembrane Breast Cancer Markers using Escherichia coli Ampicillin Secretion Trap, Cancer Res 2005; 65: (18). September 15, 2005
  3. David Geho, Nicholas Lahar, Prem Gurnani, Michael Huebschman, Paul Herrmann, Virginia Espina, Alice Shi, Julia Wulfkuhle, Harold Garner, Emanuel Petricoin III, Lance A. Liotta, and Kevin P.Rosenblatt, Pegylated, Steptavidin-Conjugated Quantum Dots Are Effective Detection Elements for Reverse Phase Protein Microarrays, ACS Journal Bioconjugate Chemistry, 2005 May-Jun;16(3):559-66
  4. Mizumoto N, Hui F, Edelbaum D, Weil MR, Wren JD, Shalhevet D, Matsue H, Liu L, Garner HR, Takashima A: Differential activation profiles of multiple transcription factors during dendritic cell maturation. J Invest Dermatol. 2005 Apr;124(4):718-24.
  5. Yuri Y Beloludtsev, Dawn Bowerman, Ryan Weil, Nishanth Marthandan, Robert Balog, Kevin Luebke, Jonathan Lawson, Stephen A Johnson, C Rick Lyons, Kevin O’Brien, Harold R Garner, PhD, Thomas F Powdrill, Organism Identification Using a Genome Sequence-Independent Universal Microarray Probe Set, Biotechniques. 2004 Oct;37(4):654-8, 660
  6. M.L. Huebschman, J. Hunt, B. Munjuluri, A. Takashima and H.R. Garner, Design and performance of a variable spectrum synthesizer, Journal of Review of Scientific Instruments, November, 2004, Volume 75, Issue 11, 4845-4855. Also appeared in The Virtual Journal of Biological Physics Research -- November 15, 2004, Volume 8, Issue 10
  7. M. Ryan Weil, Piotr Widlak, John D. Minna and Harold R. Garner, Global survey of chromatin accessibility using DNA microarrays, Genome Res. 2004 Jul;14(7):1374-81.
    8.
  8. Elizabeth M. Cronin, Frederick A. Thurmond, Rhonda Bassel-Duby, R. Sanders Williams, Woodring E. Wright, Kevin D. Nelson, Harold R. Garner, Protein coated poly (L-lactic acid) fibers provide a substrate for differentiation of human skeletal muscle cells, J Biomed Mater Res. 2004 Jun 1;69A(3):373-81.
    9.
  9. Jonathan D. Wren, Raffi Bekeredjian, Jelena A. Stewart, Ralph V. Shohet, Harold R. Garner, Implicit Relationship Analysis Predicts a Novel Effect for Chlorpromazine on Cardiac Hypertrophy, Bioinformatics. 2004 Feb 12;20(3):389-98.
    10.
  10. Jeoffrey J. Schageman, Deborah A. Ferguson, Qun Zang, Jeffrey A. Spencer, J. Warren Huff, Jonathan M. Graff, Yun Lian, Harold R. Garner, and Alexander Pertsemlidis, Reading the Fine Print of the Human Genome, IEEE Engineering in Biology and Medicine, 2003, Mar-Apr 22(2):105-8.
    11.
  11. Peters, D, Barber, R., Flood, E, Garner, H. and O’Keefe, G, Methodological Quality and Genotyping Reproducibility in Studies of the Tumor Necrosis Factor –308 G?A SNP and Bacterial Sepsis: Implications for Studies of Complex Traits, American Journal of Respiratory and Critical Care Medicine, Vol. 31, No. 6, 1691-1696, 2003.
    12.
  12. Robert P. Balog, Y. Emi Ponce de Souza, Hue M. Tang, Gina M. DeMasellis, Boning Gao, Adrian Avila, Desmond J. Gaban, David Mittelman, John D. Minna, Kevin J. Luebke, and Harold R. Garner, Parallel Assessment of CpG Methylation by Two-Color Hybridization with Oligonucleotide Arrays, Analytical Biochemistry, Vol. 309, 301-310, 2002.
    13.
  13. J. Schageman, M. Basit, T. Gallardo, R.V. Shohet and H.R. Garner, MarC-V: A Spreadsheet-Based Tool for Analysis, Normalization, and Visualization of Single cDNA Microarray Experiments, Biotechniques 32:338-344, Feb. 2002.
    14.
  14. Adel M. Talaat, Susan T. Howard, Walker Hale IV, Rick Lyons, Harold R. Garner and Stephen Albert Johnston, Genomic DNA Standards For Gene Expression Profiling of Mycobacterium tuberculosis, Nucleic Acids Research, Vol. 30, No. 20, 2002.
    15.
  15. Forgacs, E., Wren, J., Kamibayashi, C., Kondo, M., Xu, L., Markowitz, S., Tomlinson, G., Muller, C., Gazdar, A., Garner, H., and Minna, J. Searching for microsatellite mutations in coding regions in lung, breast, ovarian, and colorectal cancers, Oncogene 20:1005-1009, 2001.
    16.
  16. R. A. Schultz, T. Nielsen, J.R. Zavaleta, R. Ruch, R. Wyatt and H.R. Garner, Hyperspectral Imaging: A Novel Approach For Microscopic Analysis, Vol. 43, pgs. 239-247, Cytometry, 2001.
    17.
  17. Jonathan D. Wren, Eva Forgacs, John W. Fondon III, Alexander Pertsemlidis, Sandra Y. Cheng, Teresa Gallardo, R. S. Williams, Ralph V. Shohet, John D. Minna, Harold R. Garner, Repeat Polymorphisms Within Gene Regions: Phenotypic and Evolutionary Implications, American Journal of Human Genetics, Vol. 67, No. 2, 345-356, Aug. 2000.
    18.
  18. Kari A. Kukanskis, Zakir Siddiquee, Ralph V. Shohet, H. R. Garner, " A mix of sequencing technologies for sequence closure: An example," BioTechniques, Vol. 28, No. 4, 630-635, 2000.
    19.
  19. I.I. Wistuba, C. Behrens, A. K. Virmani, G. Mele, S. Milchgrub, L. Girard, J. Fondon III, H. R. Garner, B. McKay, F. Latif, M. I. Lerman, S. Lam, A. F. Gazdar and J. D. Minna, High Resolution Chromosome 3p Allelotyping of Human Lung Cancer and Preneoplastic/Preinvasive Bronchial Epithelium Reveals Multiple, Discontinuous Sites of 3p Allele Loss and Frequent Breakpoints, Cancer Research 60, 1949-1960, April, 2000.
    20.
  20. Pertsemlidis, B. Miller, A. Pande, P. Schilling, M. H. Wei, M. I. Lerman, J. D. Minna and H. R. Garner, "PANORAMA - An integrated web based sequence analysis tool and its role in gene discovery," Genomics 70, ppg 300-306, 2000.
    21.
  21. K. M. O’Brien, J. J. Schageman, T. H. Major, G. A. Evans and H.R. Garner, “Improving Read Lengths by Recomputing the Matrics of Model 377 DNA Sequencers,” BioTechniques, Vol. 24, No. 6, 1014-1016, 1998.
    22.
  22. J. W. Fondon III, G. M. Mele, D. Cummings, A. Pande, J. Wren, K. M. O’Brien, K. C. Kupfer, M. Lerman, J. D. Minna and H.R. Garner, “Computationally Assisted Polymorphic Marker Identification: Experimental validation and a predicted human polymorphism catalog”, Proc. Nat. Acad. Scie., 95:7514-7519, June 23, 1998.
    23.
  23. S. Rayner, S. Brignac, R. Bumeister, Y. Belodludtsev, T. Ward, O. Grant, K. O’Brien, G.A. Evans and H.R. Garner, “MerMade: A 2 x 96-Well Plate Oligo Synthesizer For High Throughput Production”, Genome Research, Vol. 8, 741-747, 1998.
    24.
  24. K. M. O'Brien, J. J. Schageman, G. A. Evans and H. R. Garner, “Reconstructing Fragmented Gel Files Created by Model 377 DNA Sequencers”, BioTechniques, Vol. 24, No. 6, 1004-1005, 1998.
    25.
  25. K. M. O'Brien, M. A. Ironside, M. C. Athanasiou, G. A. Evans and H. R. Garner, “Correcting Data Shifts in Gel Files Created by Model 377 DNA Sequencers”, BioTechniques, Vol. 24, No. 6, 1002-1003, 1998.
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  26. K. M. O’Brien, J. Wren, V. K. Dave, D. Bai, R. D. Anderson, S. Rayner, G. A. Evans, A. E. Dabiri, and H. R. Garner, “ASTRAL, a Hyperspectral Imaging DNA Sequencer,” Review of Scientific Instruments, Vol. 69, No. 5, May, 1998.
    27.
  27. P. Li, C. J. Davies, D. North, P. Schilling, G. A. Evans, and H. R. Garner, Supercomputing in genomic sequencing: Optimization of BLAST and other sequence algorithms for high speed parallel processing, Scientific Computing and Automation, November, 1997.
    28.
  28. K. O’Brien, T. Fondon, G. A. Evans and H. R. Garner, “Rescuing Corrupted ABI-type gel files,” BioTechniques, Vol. 22, No. 6, 1162-1163, 1997.
    29.
  29. P. Li, K. Kupfer, C. Davies, D. Burbee, G. A. Evans, and H. R. Garner, “PRIMO: A Primer Design Program that Applies Base Quality Statistics for Automated Large-Scale DNA Sequencing,” Genomics 40, 476-485, 1997.
    30.
  30. T.B. Shows, M. Alders, S. Bennett, D. Burbee, P. Cartwright, S. Chandrasekharappa, P. Cooper, A. Courseaux, C. Davies, M.-D. Devignes, P. Devilee, R. Elliott, G. Evans, J. Fantes, H. Garner, P. Gaudray, D.S. Gerhard, M. Gessler, M. Higgins, H. Hummerich, M. James, J. Lagercrantz, M. Litt, P. Little, M. Mannens, D. Munroe, N. Nowak, S. O’Brien, N. Parker, M. Perlin, L. Reid, C. Richard, M. Sawicki, D. Swallow, R. Thakker, V van Heyningen, E. van Schothorst, I. Vorechovsky, C. Wadelius, B. Weber, and B. Zabel, Report of the fifth international workshop on human chromosome 11 mapping, Cytogenetics and Cell Genetics, 74:1-56 (1996)
    31.
  31. J. Quackenbush, C. Davies, J. M. Baliks, J. V. Khristich, K. Diggle, Y. Marchuck, J. Tobin, S. P. Clark, A. Rodkins, S. Marcano, A. C. Churukian, J. S. Hutchinson, S. Probst, L. Romberg, Y. H. Wei, N. J. Nowak, H. R. Garner, M. W. Smith, L. Selleri, and G. A. Evans, “An STS Content Map of Human Chromosome 11: Localization of 910 YAC Clones and 109 Islands,” Genomics, 29, 512-525, 1995.

    32. Publications pending (submitted) from research involving clinicians and medical students:

  32. D. Bhattacharya, J. L. Mendoza, D. Clark, S. Guide and H. Garner, Computation and display of literature-based similarity networks, submitted, Bioinformatics

  33. M. Ryan Weil, Mark Burkart, David S. Shames, John D. Minna, and Harold Garner, 5-Aza-2'-deoxyCytidine induced chromatin modulation shows differential regulation at the sub-gene level, submitted, Genes, Chromosomes and Cancer

  34. J. Laidlaw, K. Ng, H.R. Garner, R. Ranganathan, J. Fondon, Slippery genomes: elevated repeat mutation rates contribute to canid variation, submitted, Nature.

  35. Jyoti K. Shah, Harold R. Garner , Michael A. White, David S. Shames and John D. Minna, sIR: siRNA Information Resource, a web-based tool for siRNA sequence design and analysis and an open source siRNA database, submitted, BioTechniques