DOCSLIB.ORG

Mouse Armh3 Conditional Knockout Project (CRISPR/Cas9)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mouse Armh3 Conditional Knockout Project (CRISPR/Cas9) https://www.alphaknockout.com Mouse Armh3 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Armh3 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Armh3 gene (NCBI Reference Sequence: NM_198296.2 ; Ensembl: ENSMUSG00000039901 ) is located on Mouse chromosome 19. 26 exons are identified, with the ATG start codon in exon 2 and the TGA stop codon in exon 26 (Transcript: ENSMUST00000045396). Exon 2 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Armh3 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-333H17 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for a knock-out allele exhibit defects in chorion and trophoblast layer formation and complete embryonic lethality during organogenesis. Exon 2 starts from the start codon. The knockout of Exon 2 will result in frameshift of the gene. The size of intron 1 for 5'-loxP site insertion: 19703 bp, and the size of intron 2 for 3'-loxP site insertion: 3324 bp. The size of effective cKO region: ~602 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 2 26 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Armh3 Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the GC Content Distribution Window size: 300 bp Sequence 12 Summary: Full Length(7102bp) | A(25.53% 1813) | C(20.95% 1488) | T(32.27% 2292) | G(21.25% 1509) Note: The sequence of homologous arms and cKO region is analyzed to determine the GC content. No significant high GC-content region is found. So this region is suitable for PCR screening or sequencing analysis. Page 3 of 8 https://www.alphaknockout.com BLAT Search Results (up) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN -------------------------------------------------------------------------------------------------------------- browser details YourSeq 3000 1 3000 3000 100.0% chr19 - 45978952 45981951 3000 browser details YourSeq 216 180 886 3000 91.6% chr3 + 157381702 157393517 11816 browser details YourSeq 173 179 516 3000 92.0% chr11 + 82456065 82456477 413 browser details YourSeq 171 188 517 3000 88.1% chr10 + 70062030 70062397 368 browser details YourSeq 167 413 907 3000 91.2% chr10 - 12702650 12901627 198978 browser details YourSeq 166 188 517 3000 88.9% chr15 - 86204991 86205364 374 browser details YourSeq 162 180 528 3000 89.4% chr7 - 121627318 121627711 394 browser details YourSeq 159 180 519 3000 89.7% chr8 - 87320578 87320959 382 browser details YourSeq 159 180 522 3000 94.0% chr19 - 8561950 8562364 415 browser details YourSeq 155 195 904 3000 87.4% chr14 + 27783658 27784387 730 browser details YourSeq 150 182 520 3000 92.1% chr1 - 164847400 164847763 364 browser details YourSeq 150 180 516 3000 84.2% chr1 - 65097007 65097388 382 browser details YourSeq 149 188 522 3000 91.1% chr17 + 48794672 48795059 388 browser details YourSeq 149 179 522 3000 85.8% chr15 + 83909678 83910019 342 browser details YourSeq 146 191 502 3000 91.6% chr7 + 5282287 5282648 362 browser details YourSeq 140 202 515 3000 84.6% chr12 + 71816568 71816931 364 browser details YourSeq 137 180 523 3000 89.1% chrX - 70336841 70337194 354 browser details YourSeq 134 728 896 3000 95.9% chr9 + 100807288 100807468 181 browser details YourSeq 134 180 514 3000 87.0% chr13 + 81224375 81224693 319 browser details YourSeq 133 2507 2651 3000 94.5% chrX - 151185180 151185323 144 Note: The 3000 bp section upstream of Exon 2 is BLAT searched against the genome. No significant similarity is found. BLAT Search Results (down) QUERY SCORE START END QSIZE IDENTITY CHROM STRAND START END SPAN ----------------------------------------------------------------------------------------------- browser details YourSeq 3000 1 3000 3000 100.0% chr19 - 45975350 45978349 3000 browser details YourSeq 403 151 2801 3000 91.1% chr8 - 72205131 72435723 230593 browser details YourSeq 382 154 2590 3000 91.2% chr11 + 20075212 20215176 139965 browser details YourSeq 369 163 2781 3000 90.5% chr1 - 170148467 170183879 35413 browser details YourSeq 169 146 646 3000 94.3% chr2 - 53196072 53196601 530 browser details YourSeq 169 6 323 3000 90.3% chr10 - 56455297 56455607 311 browser details YourSeq 164 161 334 3000 97.7% chr10 - 41301353 41301539 187 browser details YourSeq 162 151 323 3000 97.2% chr4 - 116325812 116325989 178 browser details YourSeq 160 4 321 3000 95.0% chr2 - 4920705 4921128 424 browser details YourSeq 160 153 395 3000 93.9% chr15 - 38534410 38534746 337 browser details YourSeq 160 151 323 3000 96.6% chr8 + 126978390 126978567 178 browser details YourSeq 157 153 323 3000 96.5% chr11 + 116721081 116721259 179 browser details YourSeq 157 161 324 3000 98.2% chr11 + 98122953 98123125 173 browser details YourSeq 157 161 323 3000 98.2% chr10 + 120974297 120974459 163 browser details YourSeq 156 150 321 3000 96.5% chr9 - 21218103 21218463 361 browser details YourSeq 156 162 334 3000 96.5% chr10 - 4345079 4345253 175 browser details YourSeq 155 151 323 3000 95.9% chr10 - 25337304 25337479 176 browser details YourSeq 155 154 322 3000 96.5% chr1 - 131162618 131162787 170 browser details YourSeq 155 161 321 3000 98.2% chr1 - 52882598 52882758 161 browser details YourSeq 155 163 323 3000 98.8% chrX + 8188651 8189191 541 Note: The 3000 bp section downstream of Exon 2 is BLAT searched against the genome. No significant similarity is found. Page 4 of 8 https://www.alphaknockout.com Gene and protein information: Armh3 armadillo-like helical domain containing 3 [ Mus musculus (house mouse) ] Gene ID: 71617, updated on 26-Jun-2020 Gene summary Official Symbol Armh3 provided by MGI Official Full Name armadillo-like helical domain containing 3 provided by MGI Primary source MGI:MGI:1918867 See related Ensembl:ENSMUSG00000039901 Gene type protein coding RefSeq status VALIDATED Organism Mus musculus Lineage Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Glires; Rodentia; Myomorpha; Muroidea; Muridae; Murinae; Mus; Mus Also known as AI431055; AW050122; 9130011E15Rik Expression Ubiquitous expression in heart adult (RPKM 5.9), whole brain E14.5 (RPKM 5.7) and 28 other tissues See more Orthologs human all Genomic context Location: 19; 19 C3 See Armh3 in Genome Data Viewer Exon count: 28 Annotation release Status Assembly Chr Location 108.20200622 current GRCm38.p6 (GCF_000001635.26) 19 NC_000085.6 (45817362..45998632, complement) Build 37.2 previous assembly MGSCv37 (GCF_000001635.18) 19 NC_000085.5 (45892634..46072978, complement) Chromosome 19 - NC_000085.6 Page 5 of 8 https://www.alphaknockout.com Transcript information: This gene has 3 transcripts Gene: Armh3 ENSMUSG00000039901 Description armadillo-like helical domain containing 3 [Source:MGI Symbol;Acc:MGI:1918867] Gene Synonyms 9130011E15Rik Location Chromosome 19: 45,817,364-45,998,488 reverse strand. GRCm38:CM001012.2 About this gene This gene has 3 transcripts (splice variants), 273 orthologues, is a member of 1 Ensembl protein family and is associated with 3 phenotypes. Transcripts Name Transcript ID bp Protein Translation ID Biotype CCDS UniProt Flags Armh3-201 ENSMUST00000045396.8 3676 689aa ENSMUSP00000048454.7 Protein coding CCDS50454 Q6PD19 TSL:5 GENCODE basic APPRIS P1 Armh3-202 ENSMUST00000235131.1 1942 No protein - Processed transcript - - - Armh3-203 ENSMUST00000236730.1 632 No protein - Retained intron - - - Page 6 of 8 https://www.alphaknockout.com 201.12 kb Forward strand 45.85Mb 45.90Mb 45.95Mb 46.00Mb Genes Gm50196-201 >antisense 4930505N22Rik-201 >bidirectional promoter lncRNA (Comprehensive set... 4930505N22Rik-202 >bidirectional promoter lncRNA Hps6-201 >protein coding Contigs AC131185.4 > < AC150685.2 Genes (Comprehensive set... < Kcnip2-202protein coding < Gm6813-201processed pseudogen<e Armh3-202processed transcript < Kcnip2-209protein coding < Armh3-203retained intron < Kcnip2-204retained intron < Kcnip2-203protein coding < Kcnip2-201protein coding < Kcnip2-208protein coding < Kcnip2-210protein coding < Armh3-201protein coding Regulatory Build 45.85Mb 45.90Mb 45.95Mb 46.00Mb Reverse strand 201.12 kb Regulation Legend CTCF Enhancer Open Chromatin Promoter Promoter Flank Transcription Factor Binding Site Gene Legend Protein Coding merged Ensembl/Havana Ensembl protein coding Non-Protein Coding processed transcript Bidirectional promoter lncRNA pseudogene Page 7 of 8 https://www.alphaknockout.com Transcript: ENSMUST00000045396 < Armh3-201protein coding Reverse strand 181.12 kb ENSMUSP00000048... Low complexity (Seg) Superfamily Armadillo-type fold SMART Domain of unknown function DUF1741 Pfam Domain of unknown function DUF1741 PANTHER Armadillo-like helical domain-containing protein 3-like All sequence SNPs/i... Sequence variants (dbSNP and all other sources) Variant Legend missense variant splice region variant synonymous variant Scale bar 0 60 120 180 240 300 360 420 480 540 600 689 We wish to acknowledge the following valuable scientific information resources: Ensembl, MGI, NCBI, UCSC. Page 8 of 8.
Load more

Recommended publications
  • Mouse Kcnip2 Conditional Knockout Project (CRISPR/Cas9)
    https://www.alphaknockout.com Mouse Kcnip2 Conditional Knockout Project (CRISPR/Cas9) Objective: To create a Kcnip2 conditional knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Kcnip2 gene (NCBI Reference Sequence: NM_145703 ; Ensembl: ENSMUSG00000025221 ) is located on Mouse chromosome 19. 10 exons are identified, with the ATG start codon in exon 1 and the TAG stop codon in exon 10 (Transcript: ENSMUST00000162528). Exon 4 will be selected as conditional knockout region (cKO region). Deletion of this region should result in the loss of function of the Mouse Kcnip2 gene. To engineer the targeting vector, homologous arms and cKO region will be generated by PCR using BAC clone RP23-98F2 as template. Cas9, gRNA and targeting vector will be co-injected into fertilized eggs for cKO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Mice homozygous for disruptions in this gene are susceptible to induced cardiac arrhythmias but are otherwise normal. Exon 4 starts from about 27.65% of the coding region. The knockout of Exon 4 will result in frameshift of the gene. The size of intron 3 for 5'-loxP site insertion: 574 bp, and the size of intron 4 for 3'-loxP site insertion: 532 bp. The size of effective cKO region: ~625 bp. The cKO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele gRNA region 5' gRNA region 3' 1 2 3 4 5 6 7 8 9 10 Targeting vector Targeted allele Constitutive KO allele (After Cre recombination) Legends Exon of mouse Kcnip2 Homology arm cKO region loxP site Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot Window size: 10 bp Forward Reverse Complement Sequence 12 Note: The sequence of homologous arms and cKO region is aligned with itself to determine if there are tandem repeats.
    [Show full text]
  • 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.
    [Show full text]
  • BIO4342 Exercise 2: Browser-Based Annotation and RNA-Seq Data
    BIO4342 Exercise 2: Browser-Based Annotation and RNA-Seq Data Jeremy Buhler March 15, 2010 This exercise continues your introduction to practical issues in comparative annotation. You’ll be annotating genomic sequence from the dot chromosome of Drosophila mojavensis using your knowledge of BLAST and some improved visualization tools. You’ll also consider how best to integrate information from high-throughput sequencing of expressed RNA. 1 Getting Started To begin, go to our local genome browser at http://gander.wustl.edu/. Select “Genome Browser” from the left-side menu and choose the “Improved Dot” assembly of D. mojavensis for viewing. Finally, hit submit to start looking at the sequence. The entire dot assembly is about 1.69 megabases in length; zoom out to see everything. This assembly is built from a set of overlapping fosmid clones prepared for the 2009 edition of BIO 4342. We’ve added a variety of information to the genome browser to help you annotate, such as: • gene-structure predictions from several different tools; • repeats annotated using the RepeatMasker program; • BLAST hits to D. melanogaster proteins; • RNA-Seq data, which we’ll describe in more detail later. Having all this evidence available at once is somewhat overwhelming. To keep the view to a manageable level, I’d suggest that you initially set all the gene prediction tracks (Genscan, Nscan, SNAP, Geneid), as well as the repeat tracks, to “dense” mode, so that each displays on a single line. Set the BLAST hit track (called “D. mel proteins”) to “pack” to see the locations of all BLAST hits, and set the “RNA-Seq Coverage” track to “full” and the “TopHat junctions” track to “pack” to get a detailed view of these results.
    [Show full text]
  • BLAT—The BLAST-Like Alignment Tool
    Resource BLAT—The BLAST-Like Alignment Tool W. James Kent Department of Biology and Center for Molecular Biology of RNA, University of California, Santa Cruz, Santa Cruz, California 95064, USA Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments. A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences. BLAT’s speed stems from an index of all nonoverlapping K-mers in the genome. This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly. BLAT has several major stages. It uses the index to find regions in the genome likely to be homologous to the query sequence. It performs an alignment between homologous regions. It stitches together these aligned regions (often exons) into larger alignments (typically genes). Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible. This paper describes how BLAT was optimized. Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches. BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications. http://genome.ucsc.edu hosts a web-based BLAT server for the human genome. Some might wonder why in the year 2002 the world needs sions on any number of perfect or near-perfect hits. Where another sequence alignment tool.
    [Show full text]
  • A Multithread Blat Algorithm Speeding up Aligning Sequences to Genomes Meng Wang and Lei Kong*
    Wang and Kong BMC Bioinformatics (2019) 20:28 https://doi.org/10.1186/s12859-019-2597-8 SOFTWARE Open Access pblat: a multithread blat algorithm speeding up aligning sequences to genomes Meng Wang and Lei Kong* Abstract Background: The blat is a widely used sequence alignment tool. It is especially useful for aligning long sequences and gapped mapping, which cannot be performed properly by other fast sequence mappers designed for short reads. However, the blat tool is single threaded and when used to map whole genome or whole transcriptome sequences to reference genomes this program can take days to finish, making it unsuitable for large scale sequencing projects and iterative analysis. Here, we present pblat (parallel blat), a parallelized blat algorithm with multithread and cluster computing support, which functions to rapidly fine map large scale DNA/RNA sequences against genomes. Results: The pblat algorithm takes advantage of modern multicore processors and significantly reduces the run time with the number of threads used. pblat utilizes almost equal amount of memory as when running blat. The results generated by pblat are identical with those generated by blat. The pblat tool is easy to install and can run on Linux and Mac OS systems. In addition, we provide a cluster version of pblat (pblat-cluster) running on computing clusters with MPI support. Conclusion: pblat is open source and free available for non-commercial users. It is easy to install and easy to use. pblat and pblat-cluster would facilitate the high-throughput mapping of large scale genomic and transcript sequences to reference genomes with both high speed and high precision.
    [Show full text]
  • Homology & Alignment
    Protein Bioinformatics Johns Hopkins Bloomberg School of Public Health 260.655 Thursday, April 1, 2010 Jonathan Pevsner Outline for today 1. Homology and pairwise alignment 2. BLAST 3. Multiple sequence alignment 4. Phylogeny and evolution Learning objectives: homology & alignment 1. You should know the definitions of homologs, orthologs, and paralogs 2. You should know how to determine whether two genes (or proteins) are homologous 3. You should know what a scoring matrix is 4. You should know how alignments are performed 5. You should know how to align two sequences using the BLAST tool at NCBI 1 Pairwise sequence alignment is the most fundamental operation of bioinformatics • It is used to decide if two proteins (or genes) are related structurally or functionally • It is used to identify domains or motifs that are shared between proteins • It is the basis of BLAST searching (next topic) • It is used in the analysis of genomes myoglobin Beta globin (NP_005359) (NP_000509) 2MM1 2HHB Page 49 Pairwise alignment: protein sequences can be more informative than DNA • protein is more informative (20 vs 4 characters); many amino acids share related biophysical properties • codons are degenerate: changes in the third position often do not alter the amino acid that is specified • protein sequences offer a longer “look-back” time • DNA sequences can be translated into protein, and then used in pairwise alignments 2 Find BLAST from the home page of NCBI and select protein BLAST… Page 52 Choose align two or more sequences… Page 52 Enter the two sequences (as accession numbers or in the fasta format) and click BLAST.
    [Show full text]
  • Proteomic Expression Profile in Human Temporomandibular Joint
    diagnostics Article Proteomic Expression Profile in Human Temporomandibular Joint Dysfunction Andrea Duarte Doetzer 1,*, Roberto Hirochi Herai 1 , Marília Afonso Rabelo Buzalaf 2 and Paula Cristina Trevilatto 1 1 Graduate Program in Health Sciences, School of Medicine, Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba 80215-901, Brazil; [email protected] (R.H.H.); [email protected] (P.C.T.) 2 Department of Biological Sciences, Bauru School of Dentistry, University of São Paulo, Bauru 17012-901, Brazil; [email protected] * Correspondence: [email protected]; Tel.: +55-41-991-864-747 Abstract: Temporomandibular joint dysfunction (TMD) is a multifactorial condition that impairs human’s health and quality of life. Its etiology is still a challenge due to its complex development and the great number of different conditions it comprises. One of the most common forms of TMD is anterior disc displacement without reduction (DDWoR) and other TMDs with distinct origins are condylar hyperplasia (CH) and mandibular dislocation (MD). Thus, the aim of this study is to identify the protein expression profile of synovial fluid and the temporomandibular joint disc of patients diagnosed with DDWoR, CH and MD. Synovial fluid and a fraction of the temporomandibular joint disc were collected from nine patients diagnosed with DDWoR (n = 3), CH (n = 4) and MD (n = 2). Samples were subjected to label-free nLC-MS/MS for proteomic data extraction, and then bioinformatics analysis were conducted for protein identification and functional annotation. The three Citation: Doetzer, A.D.; Herai, R.H.; TMD conditions showed different protein expression profiles, and novel proteins were identified Buzalaf, M.A.R.; Trevilatto, P.C.
    [Show full text]
  • A Dissertation
    A Dissertation entitled Strategies for Membrane Protein Studies and Structural Characterization of a Metabolic Enzyme for Antibiotic Development by Buenafe T. Arachea Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Chemistry Dr. Ronald E. Viola, Committee Chair Dr. Max O. Funk, Committee Member Dr. Donald Ronning, Committee Member Dr. Marcia McInerney, Committee Member Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo August 2011 Copyright © 2011, Buenafe T. Arachea This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of Strategies for Membrane Protein Studies and Structural Characterization of a Metabolic Enzyme for Antibiotic Development by Buenafe T. Arachea Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Chemistry The University of Toledo August 2011 Membrane proteins are essential in a variety of cellular functions, making them viable targets for drug development. However, progress in the structural elucidation of membrane proteins has proven to be a difficult task, thus limiting the number of published structures of membrane proteins as compared with the enormous structural information obtained from soluble proteins. The challenge in membrane protein studies lies in the production of the required sample for characterization, as well as in developing methods to effectively solubilize and maintain a functional and stable form of the target protein during the course of crystallization. To address these issues, two different approaches were explored for membrane protein studies.
    [Show full text]
  • Tutorial 1: Exploring the UCSC Genome Browser
    Last updated: May 22, 2013 Tutorial 1: Exploring the UCSC Genome Browser Open the homepage of the UCSC Genome Browser at: http://genome.ucsc.edu/ In the blue bar at the top, click on the Genomes link. Change the drop down menus so that clade is set to Vertebrate, genome is set to Human and assembly is set to Feb. 2009 (GRCh37/hg19). Then type in “adam2” in the position or search term window and click submit. On the subsequent page, click on the top link under the UCSC Genes section. This should bring up a window that looks similar to that shown in Figure 1: Figure 1: ADAM2 as displayed in the UCSC genome browser with default tracks. The UCSC Genome browser is highly configurable. If your view looks much different than Fig. 1, try clicking the default tracks button below the graphic window. This is a complicated window, so we will go through it from top to bottom: 1. In the row labeled move, are buttons that allow you to move left to right on the chromosome as well as zoom in and out on the information. 2. The position window shows the genomic coordinates of the chromosome sequence displayed. You can edit this manually or use the move controls to zoom in and out or change positions. The entire chromosome can be viewed at once, though this takes a very long time to load. To the right of the jump and clear buttons, the size the genomic region being viewed is listed. The view is taken up by the ADAM2 gene, corresponding to about 94Kb in length, not atypical for a mammalian gene.
    [Show full text]
  • Genome-Wide DNA Methylation in Chronic Myeloid Leukaemia
    Genome-wide DNA Methylation in Chronic Myeloid Leukaemia Alexandra Bazeos Imperial College London Department of Medicine Centre for Haematology Thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy of Imperial College London 2015 1 Abstract Epigenetic alterations occur frequently in leukaemia and might account for differences in clinical phenotype and response to treatment. Despite the consistent presence of the BCR-ABL1 fusion gene in Philadelphia-positive chronic myeloid leukaemia (CML), the clinical course of patients treated with tyrosine kinase inhibitors (TKI) is heterogeneous. This might be due to differing DNA methylation profiles between patients. Therefore, a validated, epigenome-wide survey in CML CD34+ progenitor cells was performed in newly diagnosed chronic phase patients using array-based DNA methylation and gene expression profiling. In practice, the CML DNA methylation signature was remarkably homogeneous; it differed from CD34+ cells of normal persons and did not correlate with an individual patient’s response to TKI therapy. Using a meta-analysis tool it was possible to demonstrate that this signature was highly enriched for developmentally dynamic regions of the human methylome and represents a combination of CML-unique, myeloid leukemia- specific and pan-cancer sub-signatures. The CML profile involved aberrantly methylated genes in signaling pathways already implicated in CML leukaemogenesis, including TGF-beta, Wnt, Jak-STAT and MAPK. Furthermore, a core set of differentially methylated promoters were identified that likely have a role in modulating gene expression levels. In conclusion, the findings are consistent with the notion that CML starts with the acquisition of a BCR-ABL1 fusion gene by a haematopoietic stem cell, which then either causes or cooperates with a series of DNA methylation changes that are specific for CML.
    [Show full text]
  • Technologies for Genomic Medicine
    Technologies for Genomic Medicine CANVAS and AnnoBot, Solutions for Genomic Variant Annotation A RENCI TECHNICAL REPORT TR-14-04 CONTACT INFORMATION Christopher Bizon, PhD Telephone: 919.445.9600 Email: [email protected] List of Technical Terms and Websites The Team* 1000 Genomes Project, www.1000genomes.org Christopher Bizon, PhD, RENCI AnnoBot (Annotation Bot) Senior Informatics Scientist; Stanley Ahalt, PhD, RENCI Director and BioPython software,biopython.org/wiki/Main_Page Professor, Department of Computer BLAT (BLAST-like Alignment Tool), www.blat.net Science UNC Chapel Hill; Karamarie CANVAS (CAroliNa Variant Annotation Store) Fecho, PhD, Medical and Scientific Writer for RENCI; Nassib Nassar, ClinVar (Clinical Variants Resource database), www.ncbi.nlm.nih.gov/clinvar RENCI Senior Research Scientist; dbSNP (Single Nucleotide Polymorphism Database), www.ncbi.nlm.nih.gov/SNP Charles P. Schmitt, PhD, RENCI ESP (Exome Sequencing Project), evs.gs.washington.edu/EVS Chief Technical Officer and Director gbff (GenBank flat file) format, www.ncbi.nlm.nih.gov/Sitemap/samplerecord. of Informatics and Data Science; html. Erik Scott, RENCI Senior Research Software Developer; and Kirk C. HGNC (HUGO Gene Nomenclature Committee),www.genenames.org Wilhelmsen, MD, PhD, RENCI HGMD® (Human Gene Mutation Database), www.hgmd.cf.ac.uk/ac/index.php Director of Biomedical Research, PostgreSQL (Structured Query Language) database, www.postgresql.org RENCI Chief Domain Scientist pythonTM modules, www.python.org for Genomics, and Professor, Departments of Genetics and RefSeq (Reference Sequence Collection), www.ncbi.nlm.nih.gov/refseq Neurology, UNC Chapel Hill SQLite3 database, www.sqlite.org TR-14-04 in the RENCI Technical Report Series, May 2014 Introduction Renaissance Computing Institute enomic medicine holds great promise to transform the medical University of North Carolina profession and individualize health care.
    [Show full text]
  • BIOINFORMATICS Doi:10.1093/Bioinformatics/Bti1205
    Vol. 21 Suppl. 3 2005, pages iii20–iii30 BIOINFORMATICS doi:10.1093/bioinformatics/bti1205 Genome analysis Incorporation of splice site probability models for non-canonical introns improves gene structure prediction in plants Michael E. Sparks1 and Volker Brendel1,2,∗ 1Department of Genetics, Development and Cell Biology and 2Department of Statistics, Iowa State University, 2112 Molecular Biology Building, Ames, IA 50011-3260, USA Received on June 13, 2005; accepted on August 16, 2005 ABSTRACT pre-mRNA transcript prior to being translated into a functional Motivation: The vast majority of introns in protein-coding genes of protein. The 5 -terminus of an intron is commonly known as the higher eukaryotes have a GT dinucleotide at their 5-terminus and donor site, whereas the 3 -terminus is referred to as the acceptor an AG dinucleotide at their 3 end. About 1–2% of introns are non- site. These terms correlate with the roles of these sites in the bio- canonical, with the most abundant subtype of non-canonical introns chemical reactions underlying the process of splicing, as catalyzed being characterized by GC and AG dinucleotides at their 5- and 3- by the spliceosome. Most introns belong to the class of canonical termini, respectively. Most current gene prediction software, whether introns, characterized by a GT donor dinucleotide (first two bases based on ab initio or spliced alignment approaches, does not include of the intron) and an AG acceptor dinucleotide (last two bases of explicit models for non-canonical introns or may exclude their predic- the intron), and are processed by the U2-type splicing apparatus tion altogether.
    [Show full text]