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Protein structure alignment using a genetic algorithm

Joseph D. Szustakowski, Zhiping Weng
2000 Proteins: Structure, Function, and Bioinformatics  
A web-based server and additional supporting information are available at http://engpub1.bu.edu/ϳjosephs. Proteins 2000;38:428 -440.  ...  If they align well, we would expect d 12 A to be similar to d 12 B , d 13 A to be similar to d 13 B , and d 23 A to be similar to d B 23 .  ...  th residue and the j B th residue in protein B; d* ij is the average of d ij A and d ij B TABLE III .  ... 
doi:10.1002/(sici)1097-0134(20000301)38:4<428::aid-prot8>3.0.co;2-n pmid:10707029 fatcat:wlgrshjcovdo3clu7rdtkm35ba

Computational Identification of Operons in Microbial Genomes

Yu Zheng, Joseph D. Szustakowski, Lance Fortnow, Richard J. Roberts, Simon Kasif
2001 Genome Research  
Nodes with the labels A,B,C,D in the pathway graph and the genome (line) are matched enzymes.  ...  pneumoniae 24 1.2 75/190 0.39 3.1 4 0.007 B. burgdorferi 20 1.5 50/138 0.36 2.5 2 0.001 T. pallidum 15 1.2 44/152 0.29 2.9 5 0.03 Synechocystis 24 3.6 59/453 0.13 2.5 8 0.02 D.  ... 
doi:10.1101/gr.200602 pmid:12176930 pmcid:PMC186635 fatcat:pdxyu2uzofhexoubmxk4et4x24

Identification of novel pathway regulation during myogenic differentiation

Joseph D. Szustakowski, Jee-Hyung Lee, Christine A. Marrese, Penelope A. Kosinski, N.R. Nirmala, Daniel M. Kemp
2006 Genomics  
D  ...  -7543/$ -see front matter D 2005 Elsevier Inc.  ...  (D, E) Luciferase reporter gene assays showing the effects of differentiation on a MEF2-responsive promoter (D) and 2 kb of the PGC-1a gene promoter (E) in the absence or presence of the myogenic antagonist  ... 
doi:10.1016/j.ygeno.2005.08.009 pmid:16300922 fatcat:uatrjxctdvbz7cl4agntkfytni

Genome-Wide Association Analysis Identifies Genetic Correlates of Immune Infiltrates in Solid Tumors [article]

Nathan O Siemers, James L Holloway, Han Chang, Scott D Chasalow, Petra B Ross-MacDonald, Charles F Voliva, Joseph D Szustakowski
2017 bioRxiv   pre-print
C D 7 9 A , M S 4 A 1 B c e l l N K K I R 2 D L 1 , K I R 2 D L 3 , K I R 2 D L 4 , K I R 3 D L 1 , K I R 3 D L 2 , K I R 3 D L 3 , K I R 2 D S 4 N a t u r a l k i l l e r c e l l M o n o C D 8 6 , C S  ...  d .  ... 
doi:10.1101/106039 fatcat:5nlr4vnpzneknde5veu3vo3swq

The use of haplotype-specific transcripts improves sample annotation consistency

Nicole Hartmann, Evert Luesink, Edward Khokhlovich, Joseph D Szustakowski, Lukas Baeriswyl, Joshua Peterson, Andreas Scherer, Nirmala R Nanguneri, Frank Staedtler
2014 Biomarker Research  
Exact sample annotation in expression microarray datasets is essential for any type of pharmacogenomics research. Results: Candidate markers were explored through the application of Hartigans' dip test statistics to a publically available human whole genome microarray dataset. The marker performance was tested on 188 serial samples from 53 donors and of variable tissue origin from five public microarray datasets. A qualified transcript marker panel consisting of three probe sets for human
more » ... yte antigens HLA-DQA1 (2 probe sets) and HLA-DRB4 identified sample donor identifier inconsistencies in six of the 188 test samples. About 3% of the test samples require root-cause analysis due to unresolvable inaccuracies. Conclusions: The transcript marker panel consisting of HLA-DQA1 and HLA-DRB4 represents a robust, tissue-independent composite marker to assist control donor annotation concordance at the transcript level. Allele-selectivity of HLA genes renders them good candidates for "fingerprinting" with donor specific expression pattern.
doi:10.1186/2050-7771-2-17 pmid:25285214 pmcid:PMC4184161 fatcat:ax7y5bcpmrd25hwfws5wgfqb2i

Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors

Nathan O. Siemers, James L. Holloway, Han Chang, Scott D. Chasalow, Petra B. Ross-MacDonald, Charles F. Voliva, Joseph D. Szustakowski, Roger Chammas
2017 PLoS ONE  
(D) Relationship of TP53 mutation to CD8+ Tcell estimates in head and neck cancer.  ...  D: Mutual rank-based co-regulatory network around macrophage marker VSIG4 in TCGA. VSIG4, CD163, and MS4A4A were selected to create a signature to estimate macrophage content in tumors.  ... 
doi:10.1371/journal.pone.0179726 pmid:28749946 pmcid:PMC5531551 fatcat:wh7qjiupyvfnvkrovjl6suybye

Identification of Novel Genes and Pathways Regulating SREBP Transcriptional Activity

Sandipan Chatterjee, Joseph D. Szustakowski, Nirmala R. Nanguneri, Craig Mickanin, Mark A. Labow, Axel Nohturfft, Kumlesh K. Dev, Rajeev Sivasankaran, Aimin Xu
2009 PLoS ONE  
(D) Effect of the 176 selected activators and suppressors (grey points) on mutant SRE promoter.  ...  (B-D) Error bars indicate standard deviations (n = 3). Figure 2 . 2 Primary and secondary screen results.  ... 
doi:10.1371/journal.pone.0005197 pmid:19381295 pmcid:PMC2668173 fatcat:43m76qioljf4vihtoc2436ytxe

DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data

Ting Gong, Joseph D. Szustakowski
2013 Computer applications in the biosciences : CABIOS  
doi:10.1093/bioinformatics/btt090 pmid:23428642 fatcat:s43jbt65zjctrm2wvjwu34lciu

Pyruvate induces mitochondrial biogenesis by a PGC-1 α-independent mechanism

Leanne Wilson, Qing Yang, Joseph D. Szustakowski, P. Scott Gullicksen, Reza Halse
2007 American Journal of Physiology - Cell Physiology  
Szustakowski for the GeneChip data analysis, Daniel Kemp for the myogenic gene set, and Thomas Hughes for critical review of this manuscript.  ...  wildtype myoblasts, Mark Montminy for the shRNA PGC-1␣ adenovirus, Deborah Ahern-Ridlon and Akos Szilvasi for technical assistance with the confocal microscopy and FACS analyses, Nanguneri Nirmala and Joseph  ...  D: total cellular lysates were made from C2C12 myoblasts incubated in basal or SP test media at indicated time points.  ... 
doi:10.1152/ajpcell.00428.2006 pmid:17182725 fatcat:ayszzgx2jjbahggxzdxz2hchay

Uncovering mechanisms of transcriptional regulations by systematic mining of cis regulatory elements with gene expression profiles

Qicheng Ma, Gung-Wei Chirn, Joseph D Szustakowski, Adel Bakhtiarova, Penelope A Kosinski, Daniel Kemp, Nanguneri Nirmala
2008 BioData Mining  
Contrary to the traditional biology approach, where the expression patterns of a handful of genes are studied at a time, microarray experiments enable biologists to study the expression patterns of many genes simultaneously from gene expression profile data and decipher the underlying hidden biological mechanism from the observed gene expression changes. While the statistical significance of the gene expression data can be deduced by various methods, the biological interpretation of the data
more » ... sents a challenge. Results: A method, called CisTransMine, is proposed to help infer the underlying biological mechanisms for the observed gene expression changes in microarray experiments. Specifically, this method will predict potential cis-regulatory elements in promoter regions which could regulate gene expression changes. This approach builds on the MotifADE method published in 2004 and extends it with two modifications: up-regulated genes and down-regulated genes are tested separately and in addition, tests have been implemented to identify combinations of transcription factors that work synergistically. The method has been applied to a genome wide expression dataset intended to study myogenesis in a mouse C2C12 cell differentiation model. The results shown here both confirm the prior biological knowledge and facilitate the discovery of new biological insights. Conclusion: The results validate that the CisTransMine approach is a robust method to uncover the hidden transcriptional regulatory mechanisms that can facilitate the discovery of mechanisms of transcriptional regulation.
doi:10.1186/1756-0381-1-4 pmid:18822150 pmcid:PMC2553773 fatcat:hcibsqu7lrdpxeyzdzmtraem54

Dynamic resolution of functionally related gene sets in response to acute heat stress

Joseph D Szustakowski, Penelope A Kosinski, Christine A Marrese, Jee-Hyung Lee, Stephen J Elliman, Nanguneri Nirmala, Daniel M Kemp
2007 BMC Molecular Biology  
(A-D) Transcriptional activity of the indicated genes was measured using Affymetrix GeneChips and data was analyzed using GeneSpring bioinformatics software.  ... 
doi:10.1186/1471-2199-8-46 pmid:17550601 pmcid:PMC1904231 fatcat:ivqm26swszgwtb27za72sxhu5i

Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural networks

Qicheng Ma, Gung-Wei Chirn, Richard Cai, Joseph D Szustakowski, N R Nirmala
2005 BMC Bioinformatics  
Let D i , i = 1,2, ... n. denote the protein sequences contained in Cluster D and let E j , j = 1,2, ..., m denote the protein sequence contained in Cluster E.  ...  The geometric mean distance G between Cluster D and Cluster E is defined as Equation 4: Equation 4: The hierarchical average linkage clustering works in an iterative process: it begins with each protein  ... 
doi:10.1186/1471-2105-6-242 pmid:16202129 pmcid:PMC1261163 fatcat:gz2cehmobredzbw3qcxkcnqllu

Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small Cell Lung Cancer [article]

Han Chang, Ariella Sasson, Sujaya Srinivasan, Ryan Golhar, Danielle M Greenawalt, William J Geese, George Green, Kim Zerba, Stefan Kirov, Joseph D Szustakowski
2019 bioRxiv   pre-print
Fabrizio D, Lieber D, Malboeuf C, Silterra J, White E, Coyne M, et al.  ...  Rizvi H, Sanchez-Vega F, La K, Chatila W, Jonsson P, Halpenny D, et al.  ... 
doi:10.1101/626143 fatcat:m77jhuidefcudge2kwl7fgkuve

Optimal Deconvolution of Transcriptional Profiling Data Using Quadratic Programming with Application to Complex Clinical Blood Samples

Ting Gong, Nicole Hartmann, Isaac S. Kohane, Volker Brinkmann, Frank Staedtler, Martin Letzkus, Sandrine Bongiovanni, Joseph D. Szustakowski, Magnus Rattray
2011 PLoS ONE  
Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent
more » ... statistical studies. We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently identifies the globally optimal solution while preserving non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell or tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. We tested our methods on several well controlled benchmark data sets with known mixing fractions of pure cell or tissue types and mRNA expression profiling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed. In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types (,10% total population). Furthermore, accurate changes in leukocyte trafficking associated with Fingolomid (FTY720) treatment were identified that were consistent with previous results generated by both cell counts and flow cytometry. These data suggest that our method can solve one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment.
doi:10.1371/journal.pone.0027156 pmid:22110609 pmcid:PMC3217948 fatcat:i5h37rlrize7hlmfkvz2pxuliu

Extending the pathway analysis framework with a test for transcriptional variance implicates novel pathway modulation during myogenic differentiation

Daniel M. Kemp, N. R. Nirmala, Joseph D. Szustakowski
2007 Computer applications in the biosciences : CABIOS  
., 2006; Szustakowski et al., 2006; Tian et al., 2005; Tomfohr et al., 2005; Zahn et al., 2006) .  ...  The LBF test first transforms the data according to Z ij ¼ X ij À median X i ð Þ , where X ij corresponds to the jth data point from the ith sample.  ... 
doi:10.1093/bioinformatics/btm116 pmid:17392327 fatcat:ehbl5245crhlpg3m25unu5ivsu
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