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Operations on Genomic Intervals and Genome Arithmetic
Published in Altuna Akalin, Computational Genomics with R, 2020
Import mouse (mm9 assembly) CpG islands and RefSeq transcripts for chr12 from the UCSC browser as GRanges objects using rtracklayer functions. HINT: Check chapter content and modify the code there as necessary. If that somehow does not work, go to the UCSC browser and download it as a BED file. The track name for Refseq genes is “RefSeq Genes” and the table name is “refGene”. [Difficulty: Beginner/Intermediate]
Gene Expression Profiling to Detect New Treatment Targets in Leukemia and Lymphoma: A Future Perspective
Published in Gertjan J. L. Kaspers, Bertrand Coiffier, Michael C. Heinrich, Elihu Estey, Innovative Leukemia and Lymphoma Therapy, 2019
Torsten Haferlach, Wolfgang Kern, Alexander Kohlmann
In addition to all the sequences represented on the HG-U133A and HG-U133B two-array set, the HG-U133 Plus 2.0 microarray also covers 9921 new probe sets representing approximately 6500 new genes. These gene sequences were selected from GenBank, dbEST, and RefSeq. Sequence clusters were created from the UniGene database (Build 159, January 25, 2003) and refined by analysis and comparison with a number of other publicly available databases, including the Washington University EST trace repository and the NCBI human genome assembly Build 31 (www.affymetrix.com). Thus, in using this comprehensive whole human genome expression array, an extensive coverage of the human genome is reached. HG-U133 Plus 2.0 microarrays are manufactured as standard format arrays with more than 54,000 probe sets of a feature size of 11 µm and use 11 probe pairs per sequence. The oligonucleotide length is 25 mer.
Laboratory Molecular Methodologies to Analyze DNA Methylation
Published in Cristina Camprubí, Joan Blanco, Epigenetics and Assisted Reproduction, 2018
Capture of targeted regions is performed by biotinylated RNA baits that are complementary to target sequences. These probes can be designed to genomic DNA with subsequent bisulfite treatment of the enriched sample (for example, using Agilent's SureSelect Human Methyl-Seq systems) that allow for more than 5.5 million CpGs to be interrogated. Other enrichment protocols allow for the capture of converted DNA where probes target all different possible methylated configurations associated with the regions of interest (as in the case of Roche SeqCap Epi Enrichment) (24). This protocol supports the enrichment of 84 Mb of sequence that contains ∼3.7 million CpG sites. Using standard DNA capture protocols in which bisulfite conversion occurs post-capture requires ∼3 μg of sample to compensate for loss of molecular complexity during library preparation and for damage caused by the harsh bisulfite treatment. However, by performing the bisulfite conversion prior to capture, the amount of input can be reduced to 1 μg of sample (Figure 5.3). These protocols are designed to target the same intervals as the HM450k/MethylationEPIC arrays (99% of RefSeq genes) and reveal methylated regions undetected by RRBS and meDIP-seq.
Site-specialization of human oral Gemella species
Published in Journal of Oral Microbiology, 2023
Julian Torres-Morales, Jessica L. Mark Welch, Floyd E. Dewhirst, Gary G. Borisy
All Gemella genomes were obtained from the National Center for Biotechnology Information (NCBI). We used the prokaryotes genome browser from NCBI to retrieve the genus report (https://www.ncbi.nlm.nih.gov/genome/browse/#!/prokaryotes/Gemella) as of March 1, 2021. Then, using the browser links (Strain, BioSample and BioProject), we searched for information (isolation host, isolation site, RefSeq status, isolation/metagenome-assembled genome, type strain, Human Microbiome Project reference, association to disease and submitter) for all 35 deposited genomes. Additionally, we used the GenBank Assembly ID to cross-check the genomes with HOMD genomes and added the Human Microbiome Taxon ID when available for a genome. This information can be found in SI Table S1. We used an internal script to download the assembly files of NCBI RefSeq genomes (n = 30) and simplify the files and header names to an alphanumeric code.
Molecular mechanisms underlying zinc oxide nanoparticle induced insulin resistance in mice
Published in Nanotoxicology, 2020
Hailong Hu, Qian Guo, Xingpei Fan, Xiangjuan Wei, Daqian Yang, Boya Zhang, Jing Liu, Qiong Wu, Yuri Oh, Yujie Feng, Kun Chen, Liping Hou, Ning Gu
To ensure unbiased analysis of tissue response, the total RNA was isolated from random sections (10–15 mg) of liver. The RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) and purified using an RNeasy Plus Mini kit (Qiagen, Mississauga, ON, Canada). Three livers of each group were sequenced on the Illumina Genome Analyzer II system (Illumina). The paired-end reads were mapped to the mouse genome (version GRCm38.75/mm10) using Tophat. The mapped reads were used to quantify the transcripts from the RefSeq reference database. For the functional annotation analysis of genes, the Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/home.jsp) online tool was used. The raw data deposited in gene expression omnibus (GEO), submission number GSE136174.
Identification of a CD8+ T-cell response to a predicted neoantigen in malignant mesothelioma
Published in OncoImmunology, 2020
Sophie Sneddon, Craig M. Rive, Shaokang Ma, Ian M. Dick, Richard J. N. Allcock, Scott D. Brown, Robert A. Holt, Mark Watson, Shay Leary, Y. C. Gary Lee, Bruce W. S. Robinson, Jenette Creaney
In order to examine the MM immune microenvironment and its relationship to predicted MM neoantigen loads, RNA was isolated and whole transcriptome sequencing performed as previously described18 on the whole cell population of the pleural effusion from time of sample collection. HTseq was used to collect raw alignment counts for the human (hg19) RefSeq reference transcriptome.46 CIBERSORT was used to deconvolute immune subsets within samples on a per sample basis.47 The CIBERSORT parameters used quantile normalization and 500 permutations to define immune types within samples. Expression values for CD8A and the geometric mean of expression values for GZMA and PRF148 were used as surrogate markers for level of CD8+ T cell and cytolytic activity, respectively. ESTIMATE software was used to determine tumor purity and to score the size of the immune and stromal compartments in each sample using a gene signature matrix to determine expression of tumor-, immune- and stroma-specific genes.21 Genes were considered to be expressed when they generated more than 0.5 counts per million, equivalent to 10 reads, in a given sample.