The Use Of Bioinformatics Analysis On The Complexity Of Biological Information

Informatics is the science of the management of information. It includes different techniques on how the information is collected, stored, utilized and sent to other locations or transmitted. Informatics utilizes computers to process this information. Clinical informatics is also known as health informatics. Clinical informatics is the application and integration part of informatics. It takes the information that has been studied and learned and puts it into practice. (Coiera, 2015). Thus supporting best practices amongst organizations and improving the quality of care given. Informatics takes information studied and uses it to improve efficiency of care givers, eases quality improvement measures, and aids in decreasing liability risks (Aston, 2014).

Health informatics is the bridging of computer science, information and the health care field. This interdisciplinary field can be applied to a range of medical fields such as nursing, biomedicine, medicine and subspecialties such as immunology (immunoinformatics). Informatics not only has roles to play in day-to-day areas of immunology such as data storage/retrieval, decision support, standards and electronic health care records but also in research and education such as data mining and simulation systems (Coiera, 2002). Informatics and more specifically, health informatics first started being used in in the late 1950s with the rise of computers (Ho, 2010). Technologies such as computers allowed practitioners and researches

Informatics has two focus areas, public health informatics, and cancer surveillance informatics. According to the CDC (2015), “Public health informatics is the efficient and effective organization and management of data, information, and knowledge generated and used by public health professionals to fulfill the core functions of public health assessment, policy, and assurance.” The CDC also states (2013), “cancer surveillance informatics is the systematic application of information and computer science and technology to cancer surveillance practices, research, and

Informatics in nursing has realized success in data communication management of information in the health department. Technology has improved the process of monitoring the patients within and outside the hospital. Application of Informatics contributed in the integration of multiple information. The clinician can now access and retrieve information about specific the occurrence in the healthcare (Middleton, 2014). Additionally, it has facilitated the process of sharing knowledge among the experts in nursing profession (Kudyda, 2016). Follow up on the success of informatics can lead to more improvement. The new technology that emphasizes real time data is a necessary advancement. Data analytics can result in further improvement.

The Basic Local Alignment Search Tool (BLAST) was the final portion of this lab report. BLAST is provided courtesy of the National Center for Biotechnology Information (NCBI). After running the five chosen tests, students were given the two 16S rRNA sequences that were associated with their assigned culture. The BLAST results served to confirm or disprove the hypothesis of what the unknown may be. The BLAST program compared the given 16S rRNA sequence to a database of known sequences and searched for similarity. (10) This search tool is capable of comparing nucleotide or protein sequences to find statistical significance between the matches. A perfect match to the unknown sequence is indicated by a

This assignment is aimed to enable me identify an unknown sequence that has been provided. I have thereby used the DNA database, journals on cell division and medical publications in undertaking this assignment. To identify the sequence, application of theories and practical modelling is necessary for this kind of work. The sequence to be analysed is essentially the nucleotides present in cells sample under examination. Since each of the four typical nucleotides are available, chain extension continues typically until, by chance, DNA polymerase embeds a dideoxy- nucleotide which is indicated as hued letters rather than the ordinary de-oxynucleotide demonstrated as vertical lines. In the event that the proportion of typical nucleotide to the dideoxy forms is sufficiently high, some DNA strands will succeed in including a few hundred nucleotides before insertion of the dideoxy form stops the methodology.

These sequences are sourced from Subunit 1 of the Cytochrome Oxidase gene in the Mitochondria.

Every individuals response to drugs is based on their genetic makeup. Pharmacogenomic allows physicians to treat patients according to their genotypical treatment match. With a better understanding of an individual’s disease through pharmacogenomics, treatments can be mainly focus on patient’s disease and is likely to be effective due to prior testing. This will prevent patient’s from having many medication side effects, advert effect, and drug toxicity. It will help in making a better decision when it comes to drug therapy. Healthcare will be less costive for the patient because physicians will only use drug that works best for a person and avoiding the unnecessary

Genome sequencing – allows us to understand and be able to read the genes in organisms

DNA was extracted from blood and from tissue by DNA isolation kits (QIAamp DNA Minikit, QIAGEN GmbH, Hilden, Germany) according to the instructions of manufacturer.

1B, they used a hexagonal binding routine to show where LHP1 and RNA pol II joined which gave the authors a good dimension of differentiation of overlapping points based on count. As shown on the figure the Y axis belongs to the H3K27me3 and the X-axis belongs to RNA pol II which helped them to compare the midpoints of the gene. Based on the result they identified 8,882 genes by LHP1 but most of them are not related to RNA pol II. However, in fig C, hexagonal binning of LHP1 and H3K27me3 revealed there is a strong association between the two. Points in fig. 1C, shows the distance from the midpoints of the gene to the nearest gene. Fig. 1D. shows the average CHIP-seq enrichment profiles of H3K27me3 and LHP1 in WT which is stratified by the gene length. They also did spin algorithm over genes and flanking 2kb region for

CsA concentration varied between rounds (Table 1). Library was ordered from Integrated DNA Technologies (IDT). N30 library (30 unknown nucleotide bases): 5 '-GGA GGC TCT CGG GAC GAC NNN NNN NNN NNN NNN NNN NNN NNN NNN NNN GTC GTC CCG ATG CTG CAA TCG TAA-3 '. Capture Sequence: GTC GTC CCG AGA GCC ATA/3BioTEG/. Primer_P1: GGAGGCTCTCGGGACGAC. Primer_P2: TTACGATTGCAGCATCGGGACG. P2_biotin: /5BiosG/TTACGATTGCAGCATCGGGACG. All oligo’s (primers, library, and capture-strand) were ordered from IDT . Bio-capture used: 5’ biotin from IDT. Two SELEX buffers were used: SELEX 1 buffer (10 mM HEPES, 150mM NaCl, 5mM KCl, and 2mM MgCl2) and SELEX 2 buffer (10mM HEPES, 150mM NaCl, 5mM KCl). The composition of the PCR mix was as follows: 10% 10X buffer, .5% Taq, 1% P1, 1% P2, 2% dTNP, and 80.5% water. All gels were run in .5X TBE buffer and 3% agarose gel. Columns used: Micro Bio-Spin™ Chromatography Columns. PCR polymerase used was Choice Taq from Denville Scientific. DNTP is also from Denville Scientific. Streptavidin Beads are from Sigma Aldrich.

After cell lysis and DNA extraction, 100µl of DNA was stored at -30oC prior to subsequent 16S rRNA PCR and gene sequencing. Universal primers 27F; AGAGTTTGATCCTGGCTCAG and 1492R; GGTTACCTTGTTACGACTT were used to target the 16S rRNA gene. PCR amplification was performed using a thermocycler with the following program: 25 cycles for 5 mins at 94oC, 30 secs at 94oC, 30 secs at 56oC and 90 secs at 72oC. The final elongation step was performed for 10 mins at 72oC. PCR products were analysed by agarose gel electrophoresis (Figure 1) to ensure the appropriate size of the amplification product. The PCR procedures were repeated in the similar manner except adding forward and reverse primer separately. Post PCR products were cleaned using Safodex G-50per the manufacturer’s instructions. Subsequently, the filtered PCR product was collected in fresh microtiter plates and heated at 90oC for 30 mins. The PCR product was submitted to the Ramaciotti DNA Sequencing Analysis Unit, at the School of Biotechnology and Biomolecular Sciences, UNSW for DNA

The current evolution of various techniques to sequence the genome of any species has led to newer heights of genome sequencing. Also, the annotation of data provides a more detailed explanation about the variations in the phenotypes. The study of these variations can help in identifying the normal and the diseased phenotypes. Generally the approaches have been limited to the DNA coding regions that causes disruption in the gene function but it has also been found that majority of the variations fall outside the coding regions and have been difficult to interpret. The ENCODE project, however, has mapped open chromatin and the protein binding regions for a

Incorporation of prior pathway knowledge into microarray data has become a popular research area in bioinformatics. Ever since the advent of microarray technology in the field of biomedical research, it led to the development of numerous analytic methods to analyse gene expression data from microarray. However, most of the methods are single gene based which unable to detect subtle but coordinated differentially expressed genes and these genes often dropped during feature selection by strict cut-off threshold [1,2]. In contrast, pathway-based microarray analysis consider a set of biologically related genes and help to detect these subtle changes in gene expression. Many discussion has been done by researchers regard pathway-based analysis such as reviews done regarding computational approaches proposed for pathway-based analysis by Curtis et al. [3] and also Misman et al. [4], enrichment-approach using various Kolmogorov-Smirnov by Subramanian et al. [5], random forest-based pathway analysis by Pang et al. [6], and Harris et al. [7] who proposed gene grouping based on gene ontology.