Metabolomics and drug discovery

Interesting article for those who are interested in the drug discovery using metabolomics approach. They have covered complete pipeline of drug discovery starting from the metaboloic data.

"Emerging applications of metabolomics in drug discovery and precision medicine" 

http://www.nature.com/nrd/journal/vaop/ncurrent/full/nrd.2016.32.html

Xenobiotics metabolism and gut microbes

The role of gut microbes in the metabolism of xenobiotics is known from almost three decades. It is one of the most important field now a days, as the activity and toxicity of xenobiotics is primarily dependent upon the gut microbial profile. The diverse and huge metabolic enzymes of gut microbes are the workhorses of this metabolic activity. The population specific differences in the gut microflora show well correlation with the activity of these gut microbes in modulating the biotransformation of these xenobiotics. 

Recently reports have shown the major impact of the gut microflora on the pharmacological properties of a drugs. Therefore, the gut metagenomic studies focusing on the gut metagenomic potential of these gut microbes will be crucial in the field of precision medicine. For more details you can go through the article published in nature reviews titled as "The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism". The article is freely available at http://www.nature.com/nrmicro/journal/vaop/ncurrent/full/nrmicro.2016.17.html .

Calculation of ROC using R script

Use the following script to calculate ROC and making plot of true positive rate vs false positive rate using your input training data for random forest

data <- read.csv (file = "INPUT training file", sep = ", or \t or  ")
pred = data[,1:20] # Number of columns or features used to describe each sequence
fac = data$Factor # Factor tag for each sequence this can be either positive or negative

library (randomForest)
library (ROCR)
library (pROC)

rf <- randomForest(pred, fac, mtry = 4, ntree = 500, do.trace = 100, na.action = na.fail, importance = TRUE, cv.fold = 10) # use mtry and ntree optimized parameters

OOB.votes <- rf$votes

print ("Area under the curve)
auc(data$Factor, predictions$Pos) #Here Pos is the tag for Positive data

OOB.pred <- OOB.votes[,2]

pred.obj <- prediction (OOB.pred,fac)

RP.perf <- performance(pred.obj, "rec","prec")
plot (RP.perf)

ROC.perf <- performance(pred.obj, "tpr","fpr")
plot (ROC.perf)


For more details please visit
https://cran.r-project.org/web/packages/randomForest/randomForest.pdf
https://cran.r-project.org/web/packages/ROCR/index.html
https://cran.r-project.org/web/packages/pROC/index.html

Relations of gut microbes with various metabolic disorders

Gut is a shelter for various microbes where they live symbiotically with the host. In this symbiotic relationship, these microbes utilizes the resources from the host to survive and in return regulates the metabolic machinery of the host. In hosts, exposure to these microbes begins at the time of birth and works as building blocks of infant gut microbiota and overall health in future. Several factors influences the colonization of microbes inside the infant gut, such as, mode of delivery, the diet of infant (selective substrates), treatment of antibiotics, enrichment of selective microbes and external environment.

The gut microbiota contribute to immune tolerance (by eliminating invading pathogens), intestinal homeostasis and healthy metabolism.  http://www.nature.com/nri/journal/v9/n5/execsumm/nri2515.html   http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337124/ 

Dysbiosis, means disruption of the regular gut microbial composition. This is associated with the various disorders such as:

1) Immune diseases: atrophy, asthma, multiple sclerosis, systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), experimental autoimmune encephalomyelitis and type -1 diabetes. For details read http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337124/   

2) Intestinal diseases: irritable bowel syndrome (IBS), inflammatory bowel diseases (IBD) (including ulcerative colitis and Crohn’s disease), necrotizing enterocolitis and colon cancer. For details read http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3667473/ 

3) Metabolic diseases: type-2 diabetes, atherosclerosis and obesity for details read  http://diabetes.diabetesjournals.org/content/62/10/3341.full   http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4483604/ 

4) Cardiovascular diseases: metabolic interaction between host and microbes in the development of cardiovascular diseases is the new field of study. For details read  http://www.nature.com/nm/journal/v18/n8/abs/nm.2895.html 

5) Liver diseases: alcoholic liver disease and hepatocarcinogenesis. For detail read  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4323444/ 

6) Blood diseases: haematopoiesis and hematologic disorders. For detail read  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4504946/   http://www.bloodjournal.org/content/126/3/311?sso-checked=true 

7) Brain diseases: role of gut microbiota in the regulation of anxiety, mood, cognition and pain has been discussed.  http://www.nature.com/nrn/journal/v13/n10/full/nrn3346.html   http://www.ncbi.nlm.nih.gov/pubmed/25390799  

The field is growing rapidly as facilitated by new, faster and cheaper sequencing technologies. Now, researchers are more interested and trying to find out how the specific microbes related with the disease phenotype? Understanding of host-microbes interactions using metabolomics approaches help us to understand the complete mechanism. 

Machine learning for metagenomics

This review covers most of the machine learning methods used till now for the analysis of metagenomic data. You can read the full text from the link below.
http://arxiv.org/pdf/1510.06621v1.pdf

Role of gut microbes in the metabolism of xenobiotics.

A complex and diverse microbial ecosystem associated with the several parts of human body plays an important role in the maintenance of human health. The large number (~trillion) of microbes resides within the gastrointestinal tract and they are significantly related with various metabolic disorders. It has been reported that the gut microbial community consists of ~1000 microbial species, from the bacterial and archaeal domains. Genomes of gut microbiota collectively encode for millions of genes that is more than 100 times of genes found in the human genome and hence, adding up to the increased metabolic variability of the human body. The unique protein-coding genes from the gut microbes can participate in the various metabolic functions. Several studies had proved that these symbiotic metabolic functions are crucial in deciding upon the actual metabolism of xenobiotics including drugs. This is the very important aspect in the field of pharmaceuticals as most of the drugs, administered via oral route are first encountered by gut microbes. These gut microbes can alter the overall activity and toxicity of a drug molecule via metabolizing it in the gastrointestinal tract. For example, A recent study revealed that the activity of a cardiovascular drug is dependent on the gut microbial composition of the individual. Until today, the complete understanding of the gut microbial metabolic potential towards the xeno-metabolism is very limited and seek for the advancement. 

Useful information for a computational biologist

If you want to be a computational biologist then you must read the article mentioned below. The article has covered the role of computational biologist, the approach before starting a project, use of software's, pipelines and scripts, and the most important thing is: "think like a Scientist not like a programmer/coder.  Interesting and must read.
http://www.nature.com/nbt/journal/v31/n11/full/nbt.2740.html