#macapana

Dr. Men Do Za looks so shocked here. Maybe this is the time when he found out that he gave the wrong papers to Mrs. Puno and Mrs. Cotta.

Here is the bootstrap consensus tree which was created using MEGA software:

The phylogenetic tree has showed that P. troglodytes is more related to H. sapiens compared to M. mulatta. The tree shows that P. troglodytes (Pana) is closer to  H.sapiens (Sapia) than M. mulatta (Maca) is. The values of the trees gives a 100% similarity between the clades of H.sapiens and P. troglodytes, and M. mulatta. However, this does not mean that they are equally similar. This value just refer to the DNA sequence of this organisms for the sickle cell.  P. troglodytes (Pana) and  H.sapiens (Sapia) are still the ones which share the most recent common ancestor. 

The significance of solving the phylogenetic tree of these organisms is learning the evolutionary adaptations its ancestors went through until it got to the descendants known today, which in our example are the humans and monkeys. Also, this tree reflects how organisms diverged and speciated through time. As for the sickle cell disease, this tree provides information about the development and evolution of the gene responsible for this disease through time, giving a hint of where it possibly started, how long ago has it been present to organisms, and how it is shared among organisms of different species. 

Through Bioinformatics, much is uncovered about the evolution of organisms using their genetic data. Also, this helps in tracing the source of certain traits, characteristics, and even genetically-related diseases. 

Here is a step-by-step procedure of how Maca Pana created their phylogenetic tree:

1. Go to google chrome and search homologene. 

2. Type the disease in the search tab and download.

 3. At homologene downloader, pick the genomic sequences in fasta format and remove the repeated species and wait for it to download. 

4 . In mega, click edit/build alignment under align.

5. Under select an option, press create a new alignment. 

6. Press DNA when asked about the sequence alignment. 

7. Find the text earlier that was downloaded. 

8. Press alignment and align by M7: ClustaIW. 

9. Wait for the pairwise and multiple alignment to load and minimize this tab. 

10. Remove the excess genes from the ends based on the model organism. 

11. Press models at Mega and find the best dna/protein models and use the currently active data of phyloanalysis.meg, press compute and wait for it to load. 

12. Under phylogeny, press construct/test maximum likelihood tree and use the hasagawa kishino yano model + gamma as the model/method as it is the best model for the study. 

13. For the rates among sites, press gamma distributed and compute. 

14. This would then lead us to the original and bootstrap consensus tree

They used BLAST, which is an online app, and MEGA which is a software, to create the phylogenetic tree. Try it, too. 

CAN’T WAIT FOR THE BIG REVELATION! ALSO, WATCH THEIR NEXT EPISODE WHEREIN THEY WILL FEATURE TUTORIAL ABOUT THIS.

Hi guy! I just want to share my realizations after doing background research about the plot of Maca Pana. Since sickle cell is a genetic disease, patients’ recovery from this disease is not really likely to happen (Amara, 2014). Treatment for this disease is not coming soon enough. This is where bioinformatics comes in. Using bioinformatics tools, DNA sequences can show how and where prototype mutants existed. With the early detection of such mutants, we (or scientists) will be able to avoid such built in serious genetic diseases (Amara, 2014)

Basically, the goal of Mana Pana is to resolve the phylogenetic tree for sickle cell disease. I found out that their drama will partially focus on the evolutionary relationship between Homo Sapiens, MACAca mulatta, and PAN troglodytes. 

This is so exciting! 

Welcome back guys! So, my entry today would be about sickle cell disease. Sickle cell disease or SCD refers to a group of inherited red blood cell disorders wherein people with this disease have abnormal hemoglobin, called hemoglobin S or sickle hemoglobin, in their red blood cells. These abnormal Sickle-shaped cells are "not flexible and can stick to vessel walls, causing a blockage that slows or stops the flow of blood" ("What Is Sickle Cell Disease?", 2017). This causes the insufficiency in the supply of oxygen to nearby tissues.

Here is an image of normal red blood cells flowing freely in a blood vessel  (A) and abnormal, sickled red blood cells blocking blood flow in a blood vessel (B) (Amara, 2014). 

An estimate of 5% of the world population comprises the population of gene carriers for relevant diseases of hemoglobin, one of which is SCD (Amara, 2014).

Here are my references: 

What Is Sickle Cell Disease? (2017). Retrieved December 02, 2017, from https://www.nhlbi.nih.gov/health/health-topics/topics/sca

Amara, A. A. (2014). The Need for Early Detection of the Prototype Mutants: Sickle Cell Anemia as a Case Study. Journal of Proteomics & Bioinformatics, S8. doi:10.4172/jpb.s8-006

Hi guys! So, this post will be all about bioinformatics. I hope I'll be able to give you the basics about it.

According to Lesk (2013), bioinformatics is "a hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific research."  It connects the fields of mathematics, computer and statistical science to biological research. The main goal of bioinformatics is to handle large amounts of data from biological research and use computer and statistical science to develop ways to store large amounts of data and enable an analysis of data in a faster and more efficient way (Sharma, 2015).

One of the studies which uses bioinformatics is phylogenetic analysis. Phylogenetic analysis is the "study of ancestral history of an organism" (Lesk, 2013). Based on the data from bioinformatics (such as DNA sequences), phylogenetic analysis relates an organism’s evolutionary history- its ancestors, related organisms, etc.

Here are my references for this post. I hope you’ve learned something :)

Lesk, A. M. (2013). Bioinformatics. Retrieved  from https://www.britannica.com/science/bioinformatics#Article-History

Sharma, R. K. (2015). Role of Bioinformatics in Various Aspects of Biological Research: A Mini Review. Retrieved from http://www.rroij.com/open-access/role-of-bioinformatics-in-various-aspects-of-biological-research-a-mini-review.php?aid=52670\