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Introduction to Genomics
Published in Altuna Akalin, Computational Genomics with R, 2020
Many genomes and their associated data are available through genome browsers. A genome browser is a website or an application that helps you visualize the genome and all the available data associated with it. Via genome browsers, you will be able to see where genes are in relation to each other and other functional elements. You will be able to see gene structure. You will be able to see auxiliary data such as conservation, repeat content and SNPs. Here we review some of the popular browsers.
Molecular biology
Published in Maxine Lintern, Laboratory Skills for Science and Medicine, 2018
The University of California Santa Cruz Genome Bioinformatics website contains reference sequences and draft assemblies for a large collection of organisms.16 It is home to Genome Browser,17 which is a very user-friendly tool that allows you to scan across chromosomes and examine the annotations provided by the scientific community.
Molecular Diagnosis of Autosomal Dominant Polycystic Kidney Disease
Published in Jinghua Hu, Yong Yu, Polycystic Kidney Disease, 2019
Matthew Lanktree, Amirreza Haghighi, Xueweng Song, York Pei
After sequencing, FASTQ files from the sequencer undergo quality control, and are demultiplexed by assigning to the proper sample using the unique oligonucleotide barcode. The trimmed raw sequence is aligned to the human reference genome (hg19, NCBI build GRCh37) and PKD1 targeted region using the Burrows-Wheeler Aligner BWA-MEM alignment algorithm (BWA-0.7.12).26 BWA sequence alignments are converted into an analysis-ready binary alignment (BAM) file using SAMtools, and PCR duplicate reads are marked using Picard tools–1.123. Local realignment and base recalibration are performed using the Genome Analysis Tool Kit (GATK 3.6).27 Using the BAM file as input, single nucleotide variations and small insertion or deletions (InDels) are detected simultaneously using GATK HalotypeCaller 3.6, which produces a variant call format (VCF) file containing all the observed variation. For detecting mosaic or somatic variants, both HalotypeCaller 3.6 and FreeBayes caller v0.9.20-8-gfef284a (https://github.com/ekg/freebayes/) are employed. Freebayes has a tunable allele frequency setting, and we set the alternate allele fraction ≥5% for maximum sensitivity. To exclude false-positive calls, all variants are visually inspected on the Golden Helix Genome Browser (Golden Helix, Bozeman, Montana, USA), which vallows for observation of the variants at the level of the individual read. Poly-T, -C, -A, -G stretches, GC-rich areas and InDel regions may influence the mapping qualities or variant calls, creating false-positive calls. For assessment of mosaic or somatic variants with low alternate allele fraction (≤5%), the recurrent variants observed in multiple unrelated samples are considered sequencing artifacts and are excluded.
Long non-coding RNA NEAT1 promotes steatosis via enhancement of estrogen receptor alpha-mediated AQP7 expression in HepG2 cells
Published in Artificial Cells, Nanomedicine, and Biotechnology, 2019
Xiaohua Fu, Jing Zhu, Lin Zhang, Jing Shu
Total RNA of HepG2 cells was extracted using Trizol (Thermo Fisher Scientific, USA) according to the manufacturer’s protocol. Five micrograms of RNA in each group were used for library preparation using NEBNext Ultra Directional RNA Library Prep Kit for Illumina (NEB, USA) following manufacturer’s recommendations and were sequenced on an Illumina Hiseq platform. The raw data were trimmed adaptors and filter out low quality reads using Trimmomatic [13] and checked the quality of clean reads using Fastqc [14]. Next, clean reads were aligned to the latest human genome assembly hg38 using Hisat2 [15]. The transcripts were assembled and estimated the expression levels by FPKM values using the StringTie algorithm with default parameters [16]. Differential mRNA and lncRNA expression among the groups were evaluated using a R package Ballgown [17] and computed the significance of differences by the Benjamini & Hochberg (BH) p values adjustment method. Gene annotation is described using Ensembl genome browser database (http://www.ensembl.org/index.html). The R package ClusterProfiler was used to annotate the differential genes with gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [18].
Is carriership of a balanced translocation or inversion an indication for non-invasive prenatal testing?
Published in Expert Review of Molecular Diagnostics, 2018
Malgorzata I. Srebniak, Ida Vogel, Diane Van Opstal
When a robust test with a reliable resolution is available, the case selection by a clinician should be done carefully. To be able to advice an individual patient on whether NIPT is an option for them, the size of potential segmental imbalances should be estimated based on the translocation or inversion breakpoints. Since most of the balanced translocation carriers have been detected by conventional karyotyping, most of the imbalances would concern aberrations larger than 5–10 Mb. However, for the determination of the chromosomal breakpoints in particular families, the ones assessed by microarray on unbalanced offspring are preferred over those determined by karyotyping to decrease the risk of cryptic genomic imbalances. The individual family breakpoints should be evaluated and the sizes of potential imbalances can be assessed by using a genome browser. Roughly, the size of the distance between the chromosomal breakpoint and a telomere can be measured and in this way a clinician should be able to estimate whether a particular genome-wide NIPT test with a well-established resolution is appropriate for the individual family. For instance, if potential imbalances are expected to be >20 Mb, the freeware Wisecondor, may be an appropriate NIPT test [11].
HVEM has a broader expression than PD-L1 and constitutes a negative prognostic marker and potential treatment target for melanoma
Published in OncoImmunology, 2019
Nausicaa Malissen, Nicolas Macagno, Samuel Granjeaud, Clémence Granier, Vincent Moutardier, Caroline Gaudy-Marqueste, Nadia Habel, Marion Mandavit, Bernard Guillot, Christine Pasero, Eric Tartour, Robert Ballotti, Jean-Jacques Grob, Daniel Olive
To find genes whose expression correlates with HVEM expression in melanoma, data from the NCI-60 cancer cell line (GSE5846) was retrieved and processed using GEO2R at NCBI, then analyzed using Multiple Experiment Viewer. The NCI-60 panel was chosen in order to assess gene expression correlations in a wide panel of conditions, and on a wider dynamic range than restricting the analysis to melanoma only, aiming to increase the specificity of correlations with HVEM. Genes showing a Pearson correlation with HVEM higher than 0.8 were retained, resulting in a signature of 20 genes only overexpressed in melanoma cell lines: ACP5, CITED1, CPN1, DSTYK, GAB2, GNPTAB, GPNMB, GYPC, LYST, LZTS1, MITF, RXRG, S100A1, SNX10, ST6GAL1, ALX1, CAPN3, GAS7, MXI1, and SOX10 (data not shown). The dataset of the Cancer Cell Line Encyclopedia (CCLE, GSE36133) consortium was collected and used as external validation of the signature (data not shown). The CCLE gene expression also allowed the levels of HVEM and MITF expression to be assessed across various cellular types. Finally, the expression dataset from the SKCM project of TCGA consortium was collected to validate the signature in human samples of melanoma. The RNA-seq dataset (dataset ID: TCGA_SKCM_exp_HiSeqV2; version: 2015-02-24) was retrieved from the UCSC genome browser. In this dataset, gene-level transcription levels are estimated as RSEM normalized counts. The RSEM counts were log2 transformed and median centered, but not standardized, in order to retain a value similar to qRT-PCR cycles. Patient sample characteristics were downloaded from the UCSC genome browser. Methods and preprocessing are described at TCGA and UCSC websites.