Genomic Regions Associated with Performance in Racing Line of Quarter Horses
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MICAL1 Mouse Monoclonal Antibody [Clone ID: OTI6G2] Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for TA501844 MICAL1 Mouse Monoclonal Antibody [Clone ID: OTI6G2] Product data: Product Type: Primary Antibodies Clone Name: OTI6G2 Applications: FC, IF, WB Recommended Dilution: WB 1:500, IF 1:100, FLOW 1:100 Reactivity: Human Host: Mouse Isotype: IgG1 Clonality: Monoclonal Immunogen: Full length human recombinant protein of human MICAL1 (NP_073602) produced in HEK293T cell. Formulation: PBS (PH 7.3) containing 1% BSA, 50% glycerol and 0.02% sodium azide. Concentration: 1 mg/ml Purification: Purified from mouse ascites fluids or tissue culture supernatant by affinity chromatography (protein A/G) Conjugation: Unconjugated Storage: Store at -20°C as received. Stability: Stable for 12 months from date of receipt. Predicted Protein Size: 117.7 kDa Gene Name: microtubule associated monooxygenase, calponin and LIM domain containing 1 Database Link: NP_073602 Entrez Gene 64780 Human Q8TDZ2 Background: May be a cytoskeletal regulator that connects NEDD9 to intermediate filaments Synonyms: MICAL; MICAL-1; NICAL This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 MICAL1 Mouse Monoclonal Antibody [Clone ID: OTI6G2] – TA501844 Product images: HEK293T cells were transfected with the pCMV6- ENTRY control (Left lane) or pCMV6-ENTRY MICAL1 ([RC208308], Right lane) cDNA for 48 hrs and lysed. Equivalent amounts of cell lysates (5 ug per lane) were separated by SDS-PAGE and immunoblotted with anti-MICAL1. -
Frontiers in Integrative Genomics and Translational Bioinformatics
BioMed Research International Frontiers in Integrative Genomics and Translational Bioinformatics Guest Editors: Zhongming Zhao, Victor X. Jin, Yufei Huang, Chittibabu Guda, and Jianhua Ruan Frontiers in Integrative Genomics and Translational Bioinformatics BioMed Research International Frontiers in Integrative Genomics and Translational Bioinformatics Guest Editors: Zhongming Zhao, Victor X. Jin, Yufei Huang, Chittibabu Guda, and Jianhua Ruan Copyright © òýÔ Hindawi Publishing Corporation. All rights reserved. is is a special issue published in “BioMed Research International.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Contents Frontiers in Integrative Genomics and Translational Bioinformatics, Zhongming Zhao, Victor X. Jin, Yufei Huang, Chittibabu Guda, and Jianhua Ruan Volume òýÔ , Article ID Þò ¥ÀÔ, ç pages Building Integrated Ontological Knowledge Structures with Ecient Approximation Algorithms, Yang Xiang and Sarath Chandra Janga Volume òýÔ , Article ID ýÔ ò, Ô¥ pages Predicting Drug-Target Interactions via Within-Score and Between-Score, Jian-Yu Shi, Zun Liu, Hui Yu, and Yong-Jun Li Volume òýÔ , Article ID ç ýÀç, À pages RNAseq by Total RNA Library Identies Additional RNAs Compared to Poly(A) RNA Library, Yan Guo, Shilin Zhao, Quanhu Sheng, Mingsheng Guo, Brian Lehmann, Jennifer Pietenpol, David C. Samuels, and Yu Shyr Volume òýÔ , Article ID âòÔçý, À pages Construction of Pancreatic Cancer Classier Based on SVM Optimized by Improved FOA, Huiyan Jiang, Di Zhao, Ruiping Zheng, and Xiaoqi Ma Volume òýÔ , Article ID ÞÔýòç, Ôò pages OperomeDB: A Database of Condition-Specic Transcription Units in Prokaryotic Genomes, Kashish Chetal and Sarath Chandra Janga Volume òýÔ , Article ID çÔòÔÞ, Ôý pages How to Choose In Vitro Systems to Predict In Vivo Drug Clearance: A System Pharmacology Perspective, Lei Wang, ChienWei Chiang, Hong Liang, Hengyi Wu, Weixing Feng, Sara K. -
Anti-ARL4A Antibody (ARG41291)
Product datasheet [email protected] ARG41291 Package: 100 μl anti-ARL4A antibody Store at: -20°C Summary Product Description Rabbit Polyclonal antibody recognizes ARL4A Tested Reactivity Hu, Ms, Rat Tested Application ICC/IF, IHC-P Host Rabbit Clonality Polyclonal Isotype IgG Target Name ARL4A Antigen Species Human Immunogen Recombinant fusion protein corresponding to aa. 121-200 of Human ARL4A (NP_001032241.1). Conjugation Un-conjugated Alternate Names ARL4; ADP-ribosylation factor-like protein 4A Application Instructions Application table Application Dilution ICC/IF 1:50 - 1:200 IHC-P 1:50 - 1:200 Application Note * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 23 kDa Properties Form Liquid Purification Affinity purified. Buffer PBS (pH 7.3), 0.02% Sodium azide and 50% Glycerol. Preservative 0.02% Sodium azide Stabilizer 50% Glycerol 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. 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. Note For laboratory research only, not for drug, diagnostic or other use. www.arigobio.com 1/2 Bioinformation Gene Symbol ARL4A Gene Full Name ADP-ribosylation factor-like 4A Background ADP-ribosylation factor-like 4A is a member of the ADP-ribosylation factor family of GTP-binding proteins. ARL4A is similar to ARL4C and ARL4D and each has a nuclear localization signal and an unusually high guaninine nucleotide exchange rate. -
Download Validation Data
PrimePCR™Assay Validation Report Gene Information Gene Name E3 ubiquitin-protein ligase PDZRN3 Gene Symbol Pdzrn3 Organism Rat Gene Summary Description Not Available Gene Aliases Not Available RefSeq Accession No. Not Available UniGene ID Rn.3111 Ensembl Gene ID ENSRNOG00000005632 Entrez Gene ID 312607 Assay Information Unique Assay ID qRnoCIP0047702 Assay Type Probe - Validation information is for the primer pair using SYBR® Green detection Detected Coding Transcript(s) ENSRNOT00000039201 Amplicon Context Sequence CAAAGATTCCTTCACTGGAGGATCCATCTTGATTGTCCACACAGGGCCGGCCAC CAATGATATTGAATCCCAGGGAGCCAGAGTCCCGATGCAGGACAAGAGTCACAC TTTTGGTCTCTTC Amplicon Length (bp) 91 Chromosome Location 4:198408551-198415479 Assay Design Intron-spanning Purification Desalted Validation Results Efficiency (%) 90 R2 0.9995 cDNA Cq 23.15 cDNA Tm (Celsius) 83.5 gDNA Cq 42.04 Specificity (%) 100 Information to assist with data interpretation is provided at the end of this report. Page 1/4 PrimePCR™Assay Validation Report Pdzrn3, Rat Amplification Plot Amplification of cDNA generated from 25 ng of universal reference RNA Melt Peak Melt curve analysis of above amplification Standard Curve Standard curve generated using 20 million copies of template diluted 10-fold to 20 copies Page 2/4 PrimePCR™Assay Validation Report Products used to generate validation data Real-Time PCR Instrument CFX384 Real-Time PCR Detection System Reverse Transcription Reagent iScript™ Advanced cDNA Synthesis Kit for RT-qPCR Real-Time PCR Supermix SsoAdvanced™ SYBR® Green Supermix Experimental Sample qPCR Reference Total RNA Data Interpretation Unique Assay ID This is a unique identifier that can be used to identify the assay in the literature and online. Detected Coding Transcript(s) This is a list of the Ensembl transcript ID(s) that this assay will detect. -
The Molecular Karyotype of 25 Clinical-Grade Human Embryonic Stem Cell Lines Received: 07 August 2015 1 1 2 3,4 Accepted: 27 October 2015 Maurice A
www.nature.com/scientificreports OPEN The Molecular Karyotype of 25 Clinical-Grade Human Embryonic Stem Cell Lines Received: 07 August 2015 1 1 2 3,4 Accepted: 27 October 2015 Maurice A. Canham , Amy Van Deusen , Daniel R. Brison , Paul A. De Sousa , 3 5 6 5 7 Published: 26 November 2015 Janet Downie , Liani Devito , Zoe A. Hewitt , Dusko Ilic , Susan J. Kimber , Harry D. Moore6, Helen Murray3 & Tilo Kunath1 The application of human embryonic stem cell (hESC) derivatives to regenerative medicine is now becoming a reality. Although the vast majority of hESC lines have been derived for research purposes only, about 50 lines have been established under Good Manufacturing Practice (GMP) conditions. Cell types differentiated from these designated lines may be used as a cell therapy to treat macular degeneration, Parkinson’s, Huntington’s, diabetes, osteoarthritis and other degenerative conditions. It is essential to know the genetic stability of the hESC lines before progressing to clinical trials. We evaluated the molecular karyotype of 25 clinical-grade hESC lines by whole-genome single nucleotide polymorphism (SNP) array analysis. A total of 15 unique copy number variations (CNVs) greater than 100 kb were detected, most of which were found to be naturally occurring in the human population and none were associated with culture adaptation. In addition, three copy-neutral loss of heterozygosity (CN-LOH) regions greater than 1 Mb were observed and all were relatively small and interstitial suggesting they did not arise in culture. The large number of available clinical-grade hESC lines with defined molecular karyotypes provides a substantial starting platform from which the development of pre-clinical and clinical trials in regenerative medicine can be realised. -
Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
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. -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
Association of Polymorphisms in the Telomere-Related Gene ACYP2 with Lung Cancer Risk in the Chinese Han Population
www.impactjournals.com/oncotarget/ Oncotarget, 2016, Vol. 7, (No. 52), pp: 87473-87478 Research Paper Association of polymorphisms in the telomere-related gene ACYP2 with lung cancer risk in the Chinese Han population Nanzheng Chen1,*, Xiaomei Yang2,*, Wen Guo3, Jiangtao You1, Qifei Wu1, Guangjian Zhang1, Haijun Li1, Donghong Geng4, Tianbo Jin5, Junke Fu1, Yong Zhang1 1Department of Thoracic Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China 2Hospital 521 of China’s Ordnance Industry Group, Xi’an 710065, Shaanxi, China 3Inner Mongolia Medical University, Hohhot 010050, Inner Mongolia, China 4School of Continuing Education of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, China 5School of Life Sciences, Northwest University, Xi’an 710069, Shaanxi, China *These authors have contributed equally to this work Correspondence to: Junke Fu, email: [email protected] Yong Zhang, email: [email protected] Keywords: association study, lung cancer, telomere-related gene, single nucleotide polymorphisms (SNPs) Received: August 26, 2016 Accepted: October 14, 2016 Published: December 10, 2016 ABSTRACT Single nucleotide polymorphisms (SNPs) in the telomere-associated gene ACYP2 are associated with increased lung cancer risk. We explored the correlation between ACYP2 SNPs and lung cancer susceptibility in the Chinese Han population. A total of 554 lung cancer patients and 603 healthy controls were included in this study. Thirteen SNPs in ACYP2 were selected. Odds ratios (ORs) and 95% confidence intervals -
Pan-Cancer Analysis Identifies Mutations in SUGP1 That Recapitulate Mutant SF3B1 Splicing Dysregulation
Pan-cancer analysis identifies mutations in SUGP1 that recapitulate mutant SF3B1 splicing dysregulation Zhaoqi Liua,b,c,1, Jian Zhangd,1, Yiwei Suna,c, Tomin E. Perea-Chambleea,b,c, James L. Manleyd,2, and Raul Rabadana,b,c,2 aProgram for Mathematical Genomics, Columbia University, New York, NY 10032; bDepartment of Systems Biology, Columbia University, New York, NY 10032; cDepartment of Biomedical Informatics, Columbia University, New York, NY 10032; and dDepartment of Biological Sciences, Columbia University, New York, NY 10027 Contributed by James L. Manley, March 2, 2020 (sent for review January 2, 2020; reviewed by Kristen Lynch and Gene Yeo) The gene encoding the core spliceosomal protein SF3B1 is the most also resulted in the same splicing defects observed in SF3B1 frequently mutated gene encoding a splicing factor in a variety of mutant cells (11). hematologic malignancies and solid tumors. SF3B1 mutations in- In addition to SF3B1, other SF-encoding genes have also been duce use of cryptic 3′ splice sites (3′ss), and these splicing errors found to be mutated in hematologic malignancies, e.g., U2AF1, contribute to tumorigenesis. However, it is unclear how wide- SRSF2, and ZRSR2. However, these SF gene mutations do not spread this type of cryptic 3′ss usage is in cancers and what is share common alterations in splicing (1, 3), suggesting that dif- the full spectrum of genetic mutations that cause such missplicing. ferent splicing patterns may contribute to different phenotypes To address this issue, we performed an unbiased pan-cancer anal- of cancers. Because SF3B1 is the most frequently mutated ysis to identify genetic alterations that lead to the same aberrant splicing gene, the splicing defects caused by mutant SF3B1 may SF3B1 splicing as observed with mutations. -
Ubiquitin-Dependent Regulation of the WNT Cargo Protein EVI/WLS Handelt Es Sich Um Meine Eigenständig Erbrachte Leistung
DISSERTATION submitted to the Combined Faculty of Natural Sciences and Mathematics of the Ruperto-Carola University of Heidelberg, Germany for the degree of Doctor of Natural Sciences presented by Lucie Magdalena Wolf, M.Sc. born in Nuremberg, Germany Date of oral examination: 2nd February 2021 Ubiquitin-dependent regulation of the WNT cargo protein EVI/WLS Referees: Prof. Dr. Michael Boutros apl. Prof. Dr. Viktor Umansky If you don’t think you might, you won’t. Terry Pratchett This work was accomplished from August 2015 to November 2020 under the supervision of Prof. Dr. Michael Boutros in the Division of Signalling and Functional Genomics at the German Cancer Research Center (DKFZ), Heidelberg, Germany. Contents Contents ......................................................................................................................... ix 1 Abstract ....................................................................................................................xiii 1 Zusammenfassung .................................................................................................... xv 2 Introduction ................................................................................................................ 1 2.1 The WNT signalling pathways and cancer ........................................................................ 1 2.1.1 Intercellular communication ........................................................................................ 1 2.1.2 WNT ligands are conserved morphogens ................................................................. -
Organizing Knowledge to Enable Faster Data Interpretation in COVID
F1000Research 2021, 10(ELIXIR):703 Last updated: 01 SEP 2021 DATA NOTE Organizing knowledge to enable faster data interpretation in COVID-19 research [version 1; peer review: 2 approved with reservations] Joseph Hearnshaw, Marco Brandizi, Ajit Singh , Chris Rawlings , Keywan Hassani-Pak Rothamsted Research, Harpenden, AL5 2JQ, UK v1 First published: 30 Jul 2021, 10(ELIXIR):703 Open Peer Review https://doi.org/10.12688/f1000research.54071.1 Latest published: 30 Jul 2021, 10(ELIXIR):703 https://doi.org/10.12688/f1000research.54071.1 Reviewer Status Invited Reviewers Abstract Enormous volumes of COVID-19 research data have been published 1 2 and this continues to increase daily. This creates challenges for researchers to interpret, prioritize and summarize their own findings version 1 in the context of published literature, clinical trials, and a multitude of 30 Jul 2021 report report databases. Overcoming the data interpretation bottleneck is vital to help researchers to be more efficient in their quest to identify COVID- 1. Justin Reese , Lawrence Berkeley National 19 risk factors, potential treatments, drug side-effects, and much more. As a proof of concept, we have organized and integrated a Laboratory, Berkeley, USA range of COVID-19 and human biomedical data and literature into a knowledge graph (KG). Here we present the datasets we have 2. Keith Hall , Google Research, New York, integrated so far and the content of the KG which consists of 674,969 USA biological concepts and over 1.6 million relationships between them. The COVID-19 KG is available via KnetMiner, an interactive online Any reports and responses or comments on the platform for gene discovery and knowledge mining, or via RDF and article can be found at the end of the article.