To perform data-mining and analysis of the AIDS causing HIV.

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TP-B Each student is assigned a project exercise as indicated below. Develop the solution consistent with the project objectives indicated. Student Project Details sequenc e # Objective: To perform data-mining and analysis of the AIDS causing 1 & 24 HIV. No programming is needed, but processing a vast data and rationalizing the results expected: Steps: Obtain the genetic and protein data on HIV. Pick any complete sequence out of many strains. Gather info from protein data bank and other protein/enzyme databases regarding proteins made by this virus. Apply BLAST to align this sequence across other viruses Detect similarity/grafted regions between HIV and Hepatitis virus Create an evolutionary tree of the viruses and show the location of HIV Term Paper Assignments : TP-B You are required to supplement your answers with appropriate and relevant (state-of- the-art) details plus the particulars as needed. Each of your term-paper should include the following: One page Executive Summary An elaborate description of the topic assigned with relevant references. You may supplement your answers and augment your concepts with appropriate cross-references. All such references should be clearly identified and listed in a standard format such as IEEE journal publication format. Any Web page reference can be shown by its title and web-site address. You are encouraged to append the hard copies of such references with your solutions A Microsoft Word file plus a PDF file
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Please review the attached papers and let me whether they are okay

Running head: HIV DATA MINING AND ANALYSIS

HIV Data Mining and Analysis
Name
Institution

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HIV DATA MINING AND ANALYSIS

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Executive Summary
The primary goal of the study is to identify genes or proteins that are differentially
expressed and to measure the altered expression level for these genes that have been altered
during the infection of HIV, latency and replication which are the essential areas for enhancing
one's understanding for the dysfunction of T-Cell and HIV infection mechanisms. These studies
are very relevant in the development of effective plans and strategies for the eradication of the
virus from the body (Leech, Collins, & Onwuegbuzie, 2017). Strongly motivated by enrichment
and availability of data for gene expression when infected with HIV, in this current study, a
compendium novel termed HIVed is proposed.
The novel constitutes very detailed functional annotations of proteins, proteins that
their genes have been shown in HIV infection, replication and latency to be deregulated by the
various experimental measurement and designs. For functional and structural annotations of
the proteins entries in HIVed, some third-party databases were curated manually. Some
biological annotations were collected for all the HIVed entries, which include their expression
profile, secondary structure, ontology terms for Genes, pathway information, HIV-1
interaction and the necessary protein information with the primary aim of benefiting research
related to HIV.
Ideally hoping that the vast superior knowledge on protein-centric can help clarify the
gap between understanding genes that are differentially expressed and how their protein
products functions, then this will enable the development of the project hypotheses and creation
of strategies for the treatment to fight against the HIV disease.
Data mining process

HIV DATA MINING AND ANALYSIS

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To get the necessary experimental studies of expression of the gene during HIV
replication, latency and infection, literature about this was searched, and then several
published proteomic and genomic high-quality datasets got from a variety of experimental
conditions were extracted which was the essential step in data was mining. Of the eight
studies, six out of eight primarily examine the expression levels of a human gene during HIV
replication and infection and the remaining two major on a latency of HIV.

Experiments to represent this were conducted by use of different cell tissues or lines, this
includes the lymphatic tissue and CD4+ T-cells, through various techniques of experimenting
like genome-wide mRNA expression transcriptomic profiling/analysis and also deep RNAseq. As part of the data mining process, two large-scale experimental studies were selected to
involve the gene expression data. Also carried out some experiments to outline the pairwise
differential expression of transcripts during HIV latency and the later viral reactivation that
follows the treatment of models of CD4+ T-cell using various combinations of six agents
which include DISC, AZA, CD3, SAHA, DMSO and IL7. Another dataset selected mainly
covered the regulation of transcript in HIV latency; uninfected cells verses latently infect...


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