How I Think I Want to Use My Degree and Why

Hello!

For this week, I did more on UCSC. This time I looked at the gene that encodes my protein, which you will all recognize: CHIA!

GTEx Results

Description of Data: Homo sapiens chitinase acidic (CHIA), transcript variant 4, mRNA. (from RefSeq NM_201653)

Methods: Tissue samples were obtained using the GTEx standard operating procedures for informed consent and tissue collection, in conjunction with the National Cancer Institute Biorepositories and Biospecimen. All tissue specimens were reviewed by pathologists to characterize and verify organ source. Images from stained tissue samples can be viewed via the NCI histopathology viewer. The Qiagen PAXgene non-formalin tissue preservation product was used to stabilize tissue specimens without cross-linking biomolecules.

RNA-seq was performed by the GTEx Laboratory, Data Analysis and Coordinating Center (LDACC) at the Broad Institute. The Illumina TruSeq protocol was used to create an unstranded polyA+ library sequenced on the Illumina HiSeq 2000 and HiSeq 2500 platforms to produce 76-bp paired end reads with a coverage goal of 50M (median achieved was ~82M total reads).

Sequence reads were aligned to the hg38/GRCh38 human genome using STAR v2.5.3a assisted by the GENCODE 26 transcriptome definition. The alignment pipeline is available here.

Gene annotations were produced using a custom isoform collapsing procedure that excluded retained intron and read through transcripts, merged overlapping exon intervals and then excluded exon intervals overlapping between genes. Gene expression levels in TPM were called via the RNA-SeQC tool (v1.1.9), after filtering for unique mapping, proper pairing, and exon overlap. For further method details, see the GTEx Portal Documentation page.

UCSC obtained the gene-level expression files, gene annotations and sample metadata from the GTEx Portal Download page. Median expression level in TPM was computed per gene/per tissue.

Subject and Sample Characteristics: The scientific goal of the GTEx project required that the donors and their biospecimen present with no evidence of disease. The tissue types collected were chosen based on their clinical significance, logistical feasibility and their relevance to the scientific goal of the project and the research community. Summary plots of GTEx sample characteristics are available at the GTEx Portal Tissue Summary page.

These are the GTEx results. This image shows where the CHIA gene is expressed. As you can see, it is highly expressed in the stomach. If you look closely, you can also see it is less expressed in the lung, Esophagus- Mucosa, and Esophagus - Gastroesophageal Junction, and Adipose - Visceral.

GNF Atlas

The next section is for the GNF Atlas. There wasn't much information for this one. I don't think CHIA is expressed enough in these areas for there to be information. If you look REALLY CLOSELY at the screenshot below, you can see there is slight differences in color for each. I noticed when I highlighted the picture, it showed the colors easier, so I pasted that picture onto here also.

The only information available for GNF Atlas was a description, which is as follows:

Description: This track shows expression data from the GNF Gene Expression Atlas 2. This contains two replicates each of 79 human tissues run over Affymetrix microarrays. By default, averages of related tissues are shown. Display all tissues by selecting "All Arrays" from the "Combine arrays" menu on the track settings page. As is standard with microarray data red indicates overexpression in the tissue, and green indicates underexpression. You may want to view gene expression with the Gene Sorter as well as the Genome Browser.

This is the original screen shot.

This is the highlighted shot.

Questions:

If you wanted to search the transcriptome for an unknown sequence would you use microarray technology or RNA sequencing?

You would use RNA sequencing.

What is the major difference between microarray technology and RNA sequencing.

Microarrays require a predefined transcripts, whereas RNA sequencing lets you look at the whole transcriptome.

Citations

UCSC Genome Browser: Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genome browser at UCSC. Genome Res. 2002 Jun;12(6):996-1006. https://genome.cshlp.org/content/12/6/996.abstract

Track Data Hubs: Brian J. Raney, Timothy R. Dreszer, Galt P. Barber, Hiram Clawson, Pauline A. Fujita, Ting Wang, Ngan Nguyen, Benedict Paten, Ann S. Zweig, Donna Karolchik, W. James Kent, Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser, Bioinformatics, Volume 30, Issue 7, 1 April 2014, Pages 1003–1005, https://doi.org/10.1093/bioinformatics/btt637

UCSC Genome Browser on Human Dec. 2013 (GRCh38/hg38) Assembly. (n.d.). USCS Genome 

Browser. Retrieved November 17, 2020. http://genome.ucsc.edu/cgi-bin/hgchgsid=951431153_9K7qXxlLnleB1IIdLZDOYkxq7RIS&c=chr1&l=111290850&r=111321059&o=111290861&t=111320566&g=gtexGeneV8&i=CHIA

UCSC Genome Browser on Human Dec. 2013 (GRCh38/hg38) Assembly. (n.d.). USCS Genome Browser.

Retrieved November 17, 2020. http://genome.ucsc.edu/cgi-bin/hgc?hgsid=951431153_9K7qXxlLnleB1IIdLZDOY

kxq7RIS&c=chr1&l=111290850&r=111321059&o=111290861&t=111320557&g=gnfAtlas2&i=

220630_s_at&i2=220630_s_at