Nishanth Marthandan
University of Texas Southwestern Medical Center
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Publication
Featured researches published by Nishanth Marthandan.
Nature Biotechnology | 2008
Chris F. Taylor; Dawn Field; Susanna-Assunta Sansone; Jan Aerts; Rolf Apweiler; Michael Ashburner; Catherine A. Ball; Pierre Alain Binz; Molly Bogue; Tim Booth; Alvis Brazma; Ryan R. Brinkman; Adam Clark; Eric W. Deutsch; Oliver Fiehn; Jennifer Fostel; Peter Ghazal; Frank Gibson; Tanya Gray; Graeme Grimes; John M. Hancock; Nigel Hardy; Henning Hermjakob; Randall K. Julian; Matthew Kane; Carsten Kettner; Christopher R. Kinsinger; Eugene Kolker; Martin Kuiper; Nicolas Le Novère
The Minimum Information for Biological and Biomedical Investigations (MIBBI) project aims to foster the coordinated development of minimum-information checklists and provide a resource for those exploring the range of extant checklists.
Chemical Communications | 2005
Shuwei Li; Nishanth Marthandan; Dawn Bowerman; Harold R. Garner; Thomas Kodadek
We report here a strategy for the photolithographic synthesis of diverse, spatially addressable arrays of cyclic peptides which employs a differential deprotection strategy for the combinatorial addition of side chains to a pre-fabricated cyclic core.
Human Molecular Genetics | 2010
David R. Karp; Nishanth Marthandan; Steven G. E. Marsh; Chul Ahn; Frank C. Arnett; David S. DeLuca; Alexander D. Diehl; Raymond Dunivin; Karen Eilbeck; Michael Feolo; Paula A. Guidry; Wolfgang Helmberg; Suzanna E. Lewis; Maureen D. Mayes; Christopher J. Mungall; Darren A. Natale; Bjoern Peters; Effie Petersdorf; John D. Reveille; Barry Smith; Glenys Thomson; Matthew Waller; Richard H. Scheuermann
We describe a novel approach to genetic association analyses with proteins sub-divided into biologically relevant smaller sequence features (SFs), and their variant types (VTs). SFVT analyses are particularly informative for study of highly polymorphic proteins such as the human leukocyte antigen (HLA), given the nature of its genetic variation: the high level of polymorphism, the pattern of amino acid variability, and that most HLA variation occurs at functionally important sites, as well as its known role in organ transplant rejection, autoimmune disease development and response to infection. Further, combinations of variable amino acid sites shared by several HLA alleles (shared epitopes) are most likely better descriptors of the actual causative genetic variants. In a cohort of systemic sclerosis patients/controls, SFVT analysis shows that a combination of SFs implicating specific amino acid residues in peptide binding pockets 4 and 7 of HLA-DRB1 explains much of the molecular determinant of risk.
Nature Immunology | 2017
Florian Rubelt; Christian E. Busse; Syed Ahmad Chan Bukhari; Jean-Philippe Bürckert; Encarnita Mariotti-Ferrandiz; Lindsay G. Cowell; Corey T. Watson; Nishanth Marthandan; William J. Faison; Uri Hershberg; Uri Laserson; Brian Corrie; Mark M. Davis; Bjoern Peters; Marie-Paule Lefranc; Jamie K. Scott; Felix Breden; Eline T. Luning Prak; Steven H. Kleinstein
High-throughput sequencing of B and T cell receptors is routinely being applied in studies of adaptive immunity. The Adaptive Immune Receptor Repertoire (AIRR) Community was formed in 2015 to address issues in AIRR sequencing studies, including the development of reporting standards for the sharing of data sets.
IEEE Transactions on Nanobioscience | 2008
Nishanth Marthandan; Stanley Klyza; Shuwei Li; Yong-Uk Kwon; Thomas Kodadek; Harold R. Garner
We have designed, constructed, and evaluated an automated instrument that has produced high-density arrays with more than 30 000 peptide features within a 1.5 area of a glass slide surface. These arrays can be used for high throughput library screening for protein binding ligands, for potential drug candidate molecules, or for discovering biomarkers. The device consists of a novel fluidics system, a relay control electrical system, an optics system that implements Texas Instrumentspsila digital micromirror device (DMD), and a microwave source for accelerated synthesis of peptide arrays. The instrument implements two novel solid phase chemical synthesis strategies for producing peptide and peptoid arrays. Biotin-streptavidin and DNP anti-DNP (dinitrophenol) models of antibody small molecule interactions were used to demonstrate and evaluate the instruments capability to produce high-density protein detecting arrays. Several screening assay and detection schemes were explored with various levels of efficiency and assays with sensitivity of 10 nM were also possible.
Standards in Genomic Sciences | 2011
Jie Huang; Daniel B. Mirel; Elizabeth W. Pugh; Chao Xing; Peter N. Robinson; Alexander Pertsemlidis; Lianghao Ding; Julia Kozlitina; Joseph F. Maher; Jonathan J. Rios; Michael D. Story; Nishanth Marthandan; Richard H. Scheuermann
Genotyping experiments are widely used in clinical and basic research laboratories to identify associations between genetic variations and normal/abnormal phenotypes. Genotyping assay techniques vary from single genomic regions that are interrogated using PCR reactions to high throughput assays examining genome-wide sequence and structural variation. The resulting genotype data may include millions of markers of thousands of individuals, requiring various statistical, modeling or other data analysis methodologies to interpret the results. To date, there are no standards for reporting genotyping experiments. Here we present the Minimum Information about a Genotyping Experiment (MIGen) standard, defining the minimum information required for reporting genotyping experiments. MIGen standard covers experimental design, subject description, genotyping procedure, quality control and data analysis. MIGen is a registered project under MIBBI (Minimum Information for Biological and Biomedical Investigations) and is being developed by an interdisciplinary group of experts in basic biomedical science, clinical science, biostatistics and bioinformatics. To accommodate the wide variety of techniques and methodologies applied in current and future genotyping experiment, MIGen leverages foundational concepts from the Ontology for Biomedical Investigations (OBI) for the description of the various types of planned processes and implements a hierarchical document structure. The adoption of MIGen by the research community will facilitate consistent genotyping data interpretation and independent data validation. MIGen can also serve as a framework for the development of data models for capturing and storing genotyping results and experiment metadata in a structured way, to facilitate the exchange of metadata.
Journal of the American Chemical Society | 2004
Shuwei Li; Dawn Bowerman; Nishanth Marthandan; Stanley Klyza; Kevin J. Luebke; Harold R. Garner; Thomas Kodadek
BioTechniques | 2004
Yuri Belosludtsev; Dawn Bowerman; Ryan Weil; Nishanth Marthandan; Robert P. Balog; Kevin J. Luebke; Jonathan N. Lawson; Stephen Albert Johnston; C. Rick Lyons; Kevin O'Brien; Harold R. Garner; Thomas F. Powdrill
pacific symposium on biocomputing | 2010
Glenys Thomson; Nishanth Marthandan; Jill A. Hollenbach; Steven J. Mack; Henry A. Erlich; Richard M. Single; Matthew Waller; Steven G.E. Marsh; Paula A. Guidry; David R. Karp; Richard H. Scheuermann; Susan D. Thompson; David N. Glass; Wolfgang Helmberg
Human Immunology | 2011
Steven J. Mack; Paula A. Guidry; Nishanth Marthandan; Thomas Smith; John M. Campbell; Patrick Dunn; David R. Karp; Richard M. Single; Glenys Thomson; Jeffrey Wiser; Richard H. Scheuermann; Henry A. Erlich