DECODE The Script of LIFE

  GENETIC MUTATIONS Genetic mutations  are spontaneous changes occurring in the genetic material, (DNA) of an individual. Mutations can be passed to the next generations or may not, depending upon the type of cell in which they occur.       They can be harmful, beneficial or even neutral. Usually these are caused naturally but they can be caused by harmful radiations or chemicals or drugs etc. Somatic cell’s mutations do not pass to the offspring. While the mutations in germ line cells are hereditary. The term mutation was given by Devries in 1909. Types of Mutations Two most basic categories of mutations are: Point mutations These mutations are those occurring only at certain points in chromosomes or the genes if it. For example, insertion, deletion etc. Chromosomal mutation More sever class of mutations is chromosomal mutations. It includes deletion of one or set of chromosome, or may be more number of chromosomes is present in genome than normal numbers. They can be due to change in

 The concept of biology has undergone a tremendous change during the past few decades. It cannot be studied as an isolated subject. In order to understand life, a fair understanding of the two other natural sciences i.e. chemistry and physics is absolutely essential.  Apart from these two sciences, other branches of science like computer, electronics etc. have also been complementing the biology for the advancement in the living world. Following is a brief review of the interrelationship of biology with other sciences:             Biochemistry : The body of living organism is composed of several organic and inorganic compounds. The elements participating in these are not different from those which form nonliving matters.  The chemical reactions going inside the body of living organisms follow the general rules of chemistry.  The biological reactions are affected by change in pH, temperature and pressure just like the non-biological reactions.            Biophysics: The movement of s

Coursera Statistics Genomics Module #3 Statistical genomics 1. Load the example SNP data with the following code: library (snpStats) library (broom) data(for.exercise) use <- seq( 1 , ncol(snps.10), 10 ) sub.10 <- snps.10[,use] snpdata = sub.10@.Data status = subject.support$cc Fit a linear model and a logistic regression model to the data for the 3rd SNP. What are the coefficients for the SNP variable? How are they interpreted? (Hint: Don’t forget to recode the 0 values to NA for the SNP data) # recode 0 values to NA snp3 = as.numeric(snpdata[, 3 ]) snp3[snp3== 0 ] = NA # fit a linear model lm3 = lm(status ~ snp3) tidy(lm3) ## # A tibble: 2 x 5 ## term estimate std.error statistic p.value ## <chr> <dbl> <dbl> <dbl> <dbl> ## 1 (Intercept) 0.544 0.0549 9.91 3.75e-22 ## 2 snp3 -0.0394 0.0468 -0.842 4.00e- 1 # fit a logistic regression model glm3 = glm(status ~ snp3,family= "binomial&quo