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Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Pancreatic Beta Cells Express a Diverse Set Ofhomeobox Genes
Proc. Nati. Acad. Sci. USA Vol. 91, pp. 12203-12207, December 1994 Biochemistry Pancreatic beta cells express a diverse set of homeobox genes (Lim motif/Lmx gene/Nkx gene/Alx gene/Vdx homeobox) ABRAHAM RUDNICK*t, THAI YEN LING*, HIROKI ODAGIRI*, WILLIAM J. RUTTER*t, AND MICHAEL S. GERMAN*t§ *Hormone Research Institute and Departments of tMedicine and tBiochemistry and Biophysics, University of California, San Francisco, CA 94143-0534 Contributed by William J. Rutter, August 22, 1994 ABSTRACT Homeobox genes, which are found in all RIPE3B element (16) and the P1 element (8) [also called CT1 eukaryotic organisms, encode transcriptional regulators in- (9)] lie on either side of the IEB1 element. The A/T elements volved in cell-type differentiation and development. Several and the E boxes function synergistically: none of the ele- homeobox genes encoding homeodomain proteins that bind and ments can function in isolation, but combination of an E box activate the insulin gene promoter have been described. In an and an A/T element results in dramatic activation of tran- attempt to identify additional beta-cell homeodomain proteins, scription (11, 16, 19). A number of complexes from beta-cell we designed primers based on the sequences of beta-cell nuclei bind to the A/T elements (6, 8-11, 16, 19). Some homeobox genes cdx3 and lmxl and the Drosophia homeodo- proteins in these complexes have been cloned, and they all main protein Antennapedia and used these primers to amplffy contain homeodomains. The A/T-binding proteins that have inserts by PCR from an insulinoma cDNA library. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
S1 Supplemental Materials Supplemental Methods Supplemental Figure 1. Immune Phenotype of Mcd19 Targeted CAR T and Dose Titratio
Supplemental Materials Supplemental Methods Supplemental Figure 1. Immune phenotype of mCD19 targeted CAR T and dose titration of in vivo efficacy. Supplemental Figure 2. Gene expression of fluorescent-protein tagged CAR T cells. Supplemental Figure 3. Fluorescent protein tagged CAR T cells function similarly to non-tagged counterparts. Supplemental Figure 4. Transduction efficiency and immune phenotype of mCD19 targeted CAR T cells for survival study (Figure 2D). Supplemental Figure 5. Transduction efficiency and immune phenotype of CAR T cells used in irradiated CAR T study (Fig. 3B-C). Supplemental Figure 6. Differential gene expression of CD4+ m19-humBBz CAR T cells. Supplemental Figure 7. CAR expression and CD4/CD8 subsets of human CD19 targeted CAR T cells for Figure 5E-G. Supplemental Figure 8. Transduction efficiency and immune phenotype of mCD19 targeted wild type (WT) and TRAF1-/- CAR T cells used for in vivo study (Figure 6D). Supplemental Figure 9. Mutated m19-musBBz CAR T cells have increased NF-κB signaling, improved cytokine production, anti-apoptosis, and in vivo function. Supplemental Figure 10. TRAF and CAR co-expression in human CD19-targeted CAR T cells. Supplemental Figure 11. TRAF2 over-expressed h19BBz CAR T cells show similar in vivo efficacy to h19BBz CAR T cells in an aggressive leukemia model. S1 Supplemental Table 1. Probesets increased in m19z and m1928z vs m19-musBBz CAR T cells. Supplemental Table 2. Probesets increased in m19-musBBz vs m19z and m1928z CAR T cells. Supplemental Table 3. Probesets differentially expressed in m19z vs m19-musBBz CAR T cells. Supplemental Table 4. Probesets differentially expressed in m1928z vs m19-musBBz CAR T cells. -
Anti-TERF2 / Trf2 Antibody (ARG59099)
Product datasheet [email protected] ARG59099 Package: 50 μg anti-TERF2 / Trf2 antibody Store at: -20°C Summary Product Description Rabbit Polyclonal antibody recognizes TERF2 / Trf2 Tested Reactivity Hu, Rat Tested Application IHC-P, WB Host Rabbit Clonality Polyclonal Isotype IgG Target Name TERF2 / Trf2 Antigen Species Human Immunogen Recombinant protein corresponding to A81-K287 of Human TERF2 / Trf2. Conjugation Un-conjugated Alternate Names Telomeric DNA-binding protein; TRF2; TTAGGG repeat-binding factor 2; TRBF2; Telomeric repeat- binding factor 2 Application Instructions Application table Application Dilution IHC-P 0.5 - 1 µg/ml WB 0.1 - 0.5 µg/ml Application Note IHC-P: Antigen Retrieval: By heat mediation. * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 60 kDa Properties Form Liquid Purification Affinity purification with immunogen. Buffer 0.9% NaCl, 0.2% Na2HPO4, 0.05% Sodium azide and 5% BSA. Preservative 0.05% Sodium azide Stabilizer 5% BSA Concentration 0.5 mg/ml Storage instruction For continuous use, store undiluted antibody at 2-8°C for up to a week. For long-term storage, aliquot and store at -20°C or below. Storage in frost free freezers is not recommended. Avoid repeated freeze/thaw cycles. Suggest spin the vial prior to opening. The antibody solution should be gently mixed before use. www.arigobio.com 1/3 Note For laboratory research only, not for drug, diagnostic or other use. Bioinformation Gene Symbol TERF2 Gene Full Name telomeric repeat binding factor 2 Background This gene encodes a telomere specific protein, TERF2, which is a component of the telomere nucleoprotein complex. -
Supplemental Information
Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig. -
Overexpressed WDR3 Induces the Activation of Hippo Pathway by Interacting with GATA4 in Pancreatic Cancer
Overexpressed WDR3 Induces the Activation of Hippo Pathway by Interacting with GATA4 in Pancreatic Cancer Wenjie Su Sichuan Provincial People's Hospital: Sichuan Academy of Medical Sciences and Sichuan People's Hospital Shikai Zhu Sichuan Provincial People's Hospital: Sichuan Academy of Medical Sciences and Sichuan People's Hospital Kai Chen Sichuan Provincial People's Hospital: Sichuan Academy of Medical Sciences and Sichuan People's Hospital Hongji Yang Sichuan Provincial People's Hospital: Sichuan Academy of Medical Sciences and Sichuan People's Hospital Mingwu Tian Sichuan Provincial People's Hospital: Sichuan Academy of Medical Sciences and Sichuan People's Hospital Qiang Fu Massachusetts General Hospital Ganggang Shi University of British Columbia School of Human Kinetics: The University of British Columbia School of Kinesiology Shijian Feng University of British Columbia School of Human Kinetics: The University of British Columbia School of Kinesiology Dianyun Ren Wuhan Union Hospital Xin Jin Wuhan Union Hospital Chong Yang ( [email protected] ) Sichuan Provincial People's Hospital: Sichuan Academy of Medical Sciences and Sichuan People's Hospital Page 1/34 Research Keywords: Pancreatic Cancer, WDR3, GATA4, YAP1, Hippo Signaling Pathway Posted Date: November 13th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-104564/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published on March 1st, 2021. See the published version at https://doi.org/10.1186/s13046-021-01879-w. Page 2/34 Abstract Background: WD repeat domain 3 (WDR3) is involved in a variety of cellular processes including gene regulation, cell cycle progression, signal transduction and apoptosis. -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
Analysis of Protein Complexes Through Model-Based Biclustering of Label-Free Quantitative AP-MS Data
Molecular Systems Biology 6; Article number 385; doi:10.1038/msb.2010.41 Citation: Molecular Systems Biology 6:385 & 2010 EMBO and Macmillan Publishers Limited All rights reserved 1744-4292/10 www.molecularsystemsbiology.com REPORT Analysis of protein complexes through model-based biclustering of label-free quantitative AP-MS data Hyungwon Choi1, Sinae Kim2, Anne-Claude Gingras3,4 and Alexey I Nesvizhskii1,5,* 1 Department of Pathology, University of Michigan, Ann Arbor, MI, USA, 2 Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA, 3 Samuel Lunenfeld Research Institute at Mount Sinai Hospital, Toronto, Ontario, Canada, 4 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada and 5 Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA * Corresponding author. Department of Pathology, University of Michigan, 1301 Catherine, 4237 MS1, Ann Arbor, MI 48109, USA. Tel.: þ 1 734 764 3516; Fax: þ 1 734 936 7361; E-mail: [email protected] Received 28.8.09; accepted 7.5.10 Affinity purification followed by mass spectrometry (AP-MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP-MS. We present a novel computational method, nested clustering, for biclustering of label-free quantitative AP-MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
Structural and Functional Characterization of Buffalo Oviduct-Specific Glycoprotein (OVGP1) Expressed During Estrous Cycle
Bioscience Reports (2019) 39 BSR20191501 https://doi.org/10.1042/BSR20191501 Research Article Structural and functional characterization of buffalo oviduct-specific glycoprotein (OVGP1) expressed during estrous cycle Suman Choudhary1, Jagadeesh Janjanam2, Sudarshan Kumar1, Jai K. Kaushik1 and Ashok K. Mohanty1 Downloaded from http://portlandpress.com/bioscirep/article-pdf/39/12/BSR20191501/862649/bsr-2019-1501.pdf by guest on 02 October 2021 1Animal Biotechnology Centre, National Dairy Research Institute, Karnal 132001, Haryana, India; 2Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, U.S.A. Correspondence: Ashok K. Mohanty ([email protected]) Oviduct-specific glycoprotein (OVGP1) is a high molecular weight chitinase-like protein belonging to GH18 family. It is secreted by non-ciliated epithelial cells of oviduct during estrous cycle providing an essential milieu for fertilization and embryo development. The present study reports the characterization of buffalo OVGP1 through structural modeling, carbohydrate-binding properties and evolutionary analysis. Structural model displayed the typical fold of GH18 family members till the boundary of chitinase-like domain further con- sisting of a large (β/α)8 TIM barrel sub-domain and a small (α+β) sub-domain. Two criti- cal catalytic residues were found substituted in the catalytic centre (Asp to Phe118, Glu to Leu120) compared with the active chitinase. The carbohydrate-binding groove in TIM bar- rel was lined with various conserved aromatic residues. Molecular docking with different sugars revealed the involvement of various residues in hydrogen-bonding and non-bonded contacts. Most of the substrate-binding residues were conserved except for a few replace- ments (Ser13, Lys48, Asp49, Pro50, Asp167, Glu199, Gln272 and Phe275) in compari- son with other GH18 members.