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Welcome to Bytes of Bio, a podcast where we explain Bioinformatics, an effective combination between Computer Science and Biology. We're May and Allamanda or Allie for short. Welcome to our...
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Welcome to Bytes of Bio, a podcast where we explain Bioinformatics, an effective combination between Computer Science and Biology. We're May and Allamanda or Allie for short. Welcome to our show! So glad to have you here! We upload every 3 weeks on Sundays. Hope to see you then!
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Bytes of Bio
Bytes of Bio
Episode 5 - Stimulate the Simulation
30 DEC 2018 · Welcome to the 5th episode of Bytes of Bio, Stimulate the Simulation!
In between making babies with the Reaper and burning people alive, the Sims can actually teach you Bioinfo?
Let’s go through the main points we have discussed!
- Goal: Use simulation created by bioinformatics to study interactions between drugs and viruses. By doing so, we can determine the efficiency of drugs.
- Important definition:
Docking: how one molecule fits into another and interact with each other (binding energy, binding position, etc.)
Active sites: sites that make the virus active, or “brain” of the virus → the place the drug should bind to in order to inactivate the virus.
Peak: The location with the highest binding energy → signal the drug where the active site is.
- Steps:
Prep phase: prepare the “environment” for interaction
Molecular Dynamics: carry out simulation and record results.
Steepest descent algorithm: analyze the results using specific algorithms.
Partial least-squares regression: double-check results.
Mutual Info: filter out data to clarify and give better results.
Reference links mentioned throughout the podcast:
- Detailed procedure of prep phase (http://dock.compbio.ucsf.edu/DOCK_6/tutorials/struct_prep/prepping_molecules.htm)
- Mutual Info (https://imgur.com/a/Tuet7RK)
Additional Information:
What is the reason behind we have to add hydrogen atoms during the protein optimization for molecular docking?
Answer: The addition of hydrogen helps to find the hydrogen bond interactions and more favorable to us to find binding affinity of ligand against protein.
If you like this episode please leave a rating or a review!
Episode 4 - The activation and deactivation of genes
9 DEC 2018 · Welcome to the 4th episode of Bytes of Bio, Regulatory Sequence!
Can Italian food help you study biology? Probably. But not here it won't. First out of many terrible metaphors to come.
Also, it might sound like we recorded in a dishwasher at the beginning. But it gets better, I swear. (/ ; ^ ; )/
Let’s go through the main points we have discussed!
- Goal: Locate regulatory sequences, such as the promoters or enhancers, within the gene using histone methylation.
- Definitions:
Regulatory sequences: genetic sequences that regulate the expression of genes.
Histone methylation: a method to regulate genes, using methyl-groups that bind to histone proteins.
- Mapping epigenetic features:
Identify sites of specific histone methylation to locate regulatory sequence.
Extract the aforementioned histones from the genome, and the located regulatory sequence along with it.
Create a map of regulatory sequence locations.
- Predicting promoters based on TSSs and enhancers based on epigenetic features: utilize the results of the map to locate the sequences.
Reference links mentioned throughout the podcast:
Mapping epigenetic feature process (https://imgur.com/a/tUS07iY)
Research on the relation between histone methylation and regulatory sequence (https://academic.oup.com/bioinformatics/article/22/4/392/183869)
ChIP-sequencing (https://en.wikipedia.org/wiki/ChIP-sequencing)
If you like this episode please leave a rating or a review!
Episode 3 - Genealogy of the holy war
18 NOV 2018 · Welcome to the 3rd episode of Bytes of Bio, Gene Enrichment!
Sigurd deserved better TT ^ TT
Also, can you tell that the placeholder title made it through?
Let’s go through the main points we have discussed!
-Goal: calculate the possibilities that unknown genes have a particular function.
-Database: the training set for the program to match the function to the gene.
+Gene Ontology: a graph showing relationships between biological phrases.
+KEGG pathway: demonstrates high-level functions of the biological systems.
-Tests for enrichment: use statistics to calculate the possibilities.
+Hypergeometric Test.
+Chi-square Test.
+Use statistics to code the programs.
-Factors affecting accuracy:
+Combine various methods other than statistics.
+Narrow down data input range.
+Use p-values as a ranking figure.
Reference links mentioned throughout the podcast:
Case studies for this episode:
+Gene ontology & hypergeometric test, lecture by Simon Rasmussen (http://www.cbs.dtu.dk/chipcourse/biosys/Lectures/GO_BioSys08.pdf)
+Gene List and Enrichment Analysis, lecture by George Bell (http://barc.wi.mit.edu/education/hot_topics/enrichment/Gene_list_enrichment_Mar10.pdf)
-Gene Ontology example 1 (https://media.springernature.com/lw785/springer-static/image/chp%3A10.1007%2F978-1-4939-3743-1_3/MediaObjects/330277_1_En_3_Fig1_HTML.gif)
-Gene Ontology example 2 (https://www.frontiersin.org/files/Articles/137043/fninf-10-00014-HTML-r1/image_m/fninf-10-00014-g004.jpg)
-KEGG Pathway example (https://www.genome.jp/kegg/pathway/hsa/hsa04150.png)
If you like this episode please leave a rating or a review!
Episode 2 - Identify the Identifier!
28 OCT 2018 · Welcome to the 2nd episode of Bytes of Bio, Identify the Identifier!
Tadaa~ It's the TATA box. Boy, if you found the first episode hard, wait until you listen to this one. Statistically, I can confirm that 50% of the hosts on the show didn't understand 80% of it.
Let’s go through the main points we have discussed!
Goal: Identify the start of a genetic sequence (TATA box) to better study it.
TATA box: a non-coding sequence that helps the RNA polymerase identify where to start transcription.
Naive Bayes (in detecting TATA box): takes into account the characteristics of a TATA box and calculate the possibility that a string of TAs is actually a TATA box.
Improvements to Naive Bayes:
Improve the prediction accuracy by perturbing the computed prior probabilities.
Take into account dependency between factors that determine a TATA box.
Reference links mentioned throughout the podcast:
Case study for this episode, Extensions of Naive Bayes and Their Applications to Bioinformatics, study by Raja Loganantharaj, University of Louisiana, Lafayette (https://drive.google.com/file/d/1QXQHr8iCwDOTdy-u055MvBDARYNjdESY/view)
TATA box (http://bio1151.nicerweb.com/Locked/media/ch17/17_08TranscripInitiation.jpg)
Explanation of Naive Bayes (https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/)
Limited dependency (https://cdn-images-1.medium.com/max/800/1*cdvfzvpkJkUudDEryFtCnA.png)
If you like this episode please leave a rating or a review!
It gets easier... I promise... Just, strap yourself in for next week's...
Episode 1 - The big launch
7 OCT 2018 · Where May fangirls about her life long love of money and Allie randomly segues into a rant for no apparent reason.
A taste of what's to come, perhaps?
Terrible metaphors, corny jokes, and long monologues about random science subjects all included!
Welcome to Bytes of Bio, a podcast where we explain Bioinformatics, an effective combination between Computer Science and Biology. We're May and Allamanda or Allie for short. Welcome to our...
show more
Welcome to Bytes of Bio, a podcast where we explain Bioinformatics, an effective combination between Computer Science and Biology. We're May and Allamanda or Allie for short. Welcome to our show! So glad to have you here! We upload every 3 weeks on Sundays. Hope to see you then!
show less
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