NCERT Textbook: Introduction to Bioinformatics | Biotechnology for Class 11 - NEET PDF Download

 Page 1


Quantitative Biology 
and Bioinformatics
Unit IV
Chapter 9 
Introduction to Bioinformatics
Chapter 10 
Protein Informatics and 
Cheminformatics
Chapter 11 
Programming and Systems 
Biology
The recent advances in science have resulted in 
generation of enormous amount of data obtained 
from genome sequencing and functional genomics. 
Handling of such vast data obtained from diverse 
sources is beyond the scope of humans. This 
has consequently given rise to a whole new ??eld 
of science called bioinformatics. Chapter 9 
describes the various terminology and concepts of 
bioinformatics. The scope of utilisation of raw data 
in biology to retrieve important information about 
proteins of interest has been discussed in Chapter 
10. Chapter 11 deals with the accession, ??ltration 
and manipulation of biological data with the help of 
programming languages. 
Bioinformatics
Chapter 9.indd   233 09/01/2025   15:18:55
Reprint 2025-26
Page 2


Quantitative Biology 
and Bioinformatics
Unit IV
Chapter 9 
Introduction to Bioinformatics
Chapter 10 
Protein Informatics and 
Cheminformatics
Chapter 11 
Programming and Systems 
Biology
The recent advances in science have resulted in 
generation of enormous amount of data obtained 
from genome sequencing and functional genomics. 
Handling of such vast data obtained from diverse 
sources is beyond the scope of humans. This 
has consequently given rise to a whole new ??eld 
of science called bioinformatics. Chapter 9 
describes the various terminology and concepts of 
bioinformatics. The scope of utilisation of raw data 
in biology to retrieve important information about 
proteins of interest has been discussed in Chapter 
10. Chapter 11 deals with the accession, ??ltration 
and manipulation of biological data with the help of 
programming languages. 
Bioinformatics
Chapter 9.indd   233 09/01/2025   15:18:55
Reprint 2025-26
Margaret Oakley Dayhoff 
(1925-1983) 
Margaret Oakley Dayhoff (1925-
1983) was an American physical 
chemist and one of the most 
important ??gures in the ??eld of 
bioinformatics. She received her 
doctoral degree from Columbia 
University in the Department of 
Chemistry and dedicated her entire 
career to applying mathematics 
and computational methods to 
biochemistry.  In 1965, she published 
a comprehensive, open source 
collection of protein sequences— 
Atlas of Protein Sequence and 
Structure. It subsequently became 
a model for the sequence databases 
which later developed. She had also 
developed the one-letter codes for 
amino acids in an attempt to reduce 
the size of data ??les in computer 
applications.  
Chapter 9.indd   234 09/01/2025   15:18:57
Reprint 2025-26
Page 3


Quantitative Biology 
and Bioinformatics
Unit IV
Chapter 9 
Introduction to Bioinformatics
Chapter 10 
Protein Informatics and 
Cheminformatics
Chapter 11 
Programming and Systems 
Biology
The recent advances in science have resulted in 
generation of enormous amount of data obtained 
from genome sequencing and functional genomics. 
Handling of such vast data obtained from diverse 
sources is beyond the scope of humans. This 
has consequently given rise to a whole new ??eld 
of science called bioinformatics. Chapter 9 
describes the various terminology and concepts of 
bioinformatics. The scope of utilisation of raw data 
in biology to retrieve important information about 
proteins of interest has been discussed in Chapter 
10. Chapter 11 deals with the accession, ??ltration 
and manipulation of biological data with the help of 
programming languages. 
Bioinformatics
Chapter 9.indd   233 09/01/2025   15:18:55
Reprint 2025-26
Margaret Oakley Dayhoff 
(1925-1983) 
Margaret Oakley Dayhoff (1925-
1983) was an American physical 
chemist and one of the most 
important ??gures in the ??eld of 
bioinformatics. She received her 
doctoral degree from Columbia 
University in the Department of 
Chemistry and dedicated her entire 
career to applying mathematics 
and computational methods to 
biochemistry.  In 1965, she published 
a comprehensive, open source 
collection of protein sequences— 
Atlas of Protein Sequence and 
Structure. It subsequently became 
a model for the sequence databases 
which later developed. She had also 
developed the one-letter codes for 
amino acids in an attempt to reduce 
the size of data ??les in computer 
applications.  
Chapter 9.indd   234 09/01/2025   15:18:57
Reprint 2025-26
Introduction to 
Bioinformatics
9.1 The Utility of  Basic 
Mathematical and 
Statistical Concepts 
to Understand 
Biological Systems 
and Processes
9.2  Introduction
9.3 Biological 
Databases
9.4 Genome Informatics
9.5	 Role 	 of 	 Arti??cial 	
Intelligence (AI) in 
future
9.1 The UTili Ty of Basic Ma The Ma Tical and 
s Ta Tis Tical c oncep Ts To Unders Tand Biological 
s ys Te Ms and p rocesses The objective of this chapter is to explain the understanding 
of the basic concepts of mathematics and statistics is 
important to a biologist.
The outcome of any biological experiment is data. 
Previously, biologists used to generate and analyse data 
without the help of sophisticated software, computational 
tools and statistical tests. However, this is not the case 
anymore. With the advent of instruments like high-
throughput DNA sequencers, powerful microscopes and 
other imaging systems, and analytical instruments capable 
of generating large volumes of data, biologists can no 
longer deal with the data using their notebooks and excel 
sheets. Instead, they need computational and statistical 
tools to handle data. Large volumes of data often require 
quantitative analyses to interpret and generate biological 
meaning. Performing such analyses require one to have 
Chapter 9
Chapter 9.indd   235 09/01/2025   15:18:57
Reprint 2025-26
Page 4


Quantitative Biology 
and Bioinformatics
Unit IV
Chapter 9 
Introduction to Bioinformatics
Chapter 10 
Protein Informatics and 
Cheminformatics
Chapter 11 
Programming and Systems 
Biology
The recent advances in science have resulted in 
generation of enormous amount of data obtained 
from genome sequencing and functional genomics. 
Handling of such vast data obtained from diverse 
sources is beyond the scope of humans. This 
has consequently given rise to a whole new ??eld 
of science called bioinformatics. Chapter 9 
describes the various terminology and concepts of 
bioinformatics. The scope of utilisation of raw data 
in biology to retrieve important information about 
proteins of interest has been discussed in Chapter 
10. Chapter 11 deals with the accession, ??ltration 
and manipulation of biological data with the help of 
programming languages. 
Bioinformatics
Chapter 9.indd   233 09/01/2025   15:18:55
Reprint 2025-26
Margaret Oakley Dayhoff 
(1925-1983) 
Margaret Oakley Dayhoff (1925-
1983) was an American physical 
chemist and one of the most 
important ??gures in the ??eld of 
bioinformatics. She received her 
doctoral degree from Columbia 
University in the Department of 
Chemistry and dedicated her entire 
career to applying mathematics 
and computational methods to 
biochemistry.  In 1965, she published 
a comprehensive, open source 
collection of protein sequences— 
Atlas of Protein Sequence and 
Structure. It subsequently became 
a model for the sequence databases 
which later developed. She had also 
developed the one-letter codes for 
amino acids in an attempt to reduce 
the size of data ??les in computer 
applications.  
Chapter 9.indd   234 09/01/2025   15:18:57
Reprint 2025-26
Introduction to 
Bioinformatics
9.1 The Utility of  Basic 
Mathematical and 
Statistical Concepts 
to Understand 
Biological Systems 
and Processes
9.2  Introduction
9.3 Biological 
Databases
9.4 Genome Informatics
9.5	 Role 	 of 	 Arti??cial 	
Intelligence (AI) in 
future
9.1 The UTili Ty of Basic Ma The Ma Tical and 
s Ta Tis Tical c oncep Ts To Unders Tand Biological 
s ys Te Ms and p rocesses The objective of this chapter is to explain the understanding 
of the basic concepts of mathematics and statistics is 
important to a biologist.
The outcome of any biological experiment is data. 
Previously, biologists used to generate and analyse data 
without the help of sophisticated software, computational 
tools and statistical tests. However, this is not the case 
anymore. With the advent of instruments like high-
throughput DNA sequencers, powerful microscopes and 
other imaging systems, and analytical instruments capable 
of generating large volumes of data, biologists can no 
longer deal with the data using their notebooks and excel 
sheets. Instead, they need computational and statistical 
tools to handle data. Large volumes of data often require 
quantitative analyses to interpret and generate biological 
meaning. Performing such analyses require one to have 
Chapter 9
Chapter 9.indd   235 09/01/2025   15:18:57
Reprint 2025-26
236
Biotechnology good working knowledge of computational and statistical 
concepts, for example; machine learning technologies, 
regression, variance, and correlation, etc. Mathematical 
and statistical concepts can only aid biologists to interpret 
their data and are not a replacement for asking the right 
questions and the biological acumen. The names of some 
of the commonly used statistical terms used in biology is 
provided in Box 1.
Let us examine with speci??c examples where both 
the knowledge of computing and statistics can help 
understand biological phenomena better. For example, 
we want to understand the association, if any, between 
blood pressure and heart rates in ten patients (Table 
9.1). As provided in the table below, a simple visual 
estimation (Fig.9.1) is not suf??cient to accurately 
determine the relationship (correlation) between the two 
variables. For that, one needs to draw a regression line. 
Correlation and regression are distinct, yet correlated. 
Correlation quanti??es how the variables are connected, 
but regression de??nes a statistical relationship between 
two or more variables where a change in one variable is 
Box 1
Box 1: Glossary of the commonly used statistical terms in biology
Null hypothesis— A statement that there is no relationship between two measured 
phenomena.
Statistical signi??cance— A result has statistical signi??cance when it is very 
unlikely to have occurred.
p-value— The probability of ??nding the observed results when the null hypothesis of 
a study question is true.
t-test —An analysis of two populations means through the use of statistical 
examination.
Multivariate analysis: A set of techniques used for analysis of data that contain more 
than one variable.
Regression analysis—A technique to investigate the relationship between a 
dependent and an independent variable.
Multiple testing correction— A statistical test that corrects for multiple tests to 
keep the overall error rate to less than or equal to the user-speci??ed P-value cutoff 
Analysis of Variance or ANOVA— A collection of statistical models used to analyse 
the differences among group means in a sample.
Chapter 9.indd   236 09/01/2025   15:18:58
Reprint 2025-26
Page 5


Quantitative Biology 
and Bioinformatics
Unit IV
Chapter 9 
Introduction to Bioinformatics
Chapter 10 
Protein Informatics and 
Cheminformatics
Chapter 11 
Programming and Systems 
Biology
The recent advances in science have resulted in 
generation of enormous amount of data obtained 
from genome sequencing and functional genomics. 
Handling of such vast data obtained from diverse 
sources is beyond the scope of humans. This 
has consequently given rise to a whole new ??eld 
of science called bioinformatics. Chapter 9 
describes the various terminology and concepts of 
bioinformatics. The scope of utilisation of raw data 
in biology to retrieve important information about 
proteins of interest has been discussed in Chapter 
10. Chapter 11 deals with the accession, ??ltration 
and manipulation of biological data with the help of 
programming languages. 
Bioinformatics
Chapter 9.indd   233 09/01/2025   15:18:55
Reprint 2025-26
Margaret Oakley Dayhoff 
(1925-1983) 
Margaret Oakley Dayhoff (1925-
1983) was an American physical 
chemist and one of the most 
important ??gures in the ??eld of 
bioinformatics. She received her 
doctoral degree from Columbia 
University in the Department of 
Chemistry and dedicated her entire 
career to applying mathematics 
and computational methods to 
biochemistry.  In 1965, she published 
a comprehensive, open source 
collection of protein sequences— 
Atlas of Protein Sequence and 
Structure. It subsequently became 
a model for the sequence databases 
which later developed. She had also 
developed the one-letter codes for 
amino acids in an attempt to reduce 
the size of data ??les in computer 
applications.  
Chapter 9.indd   234 09/01/2025   15:18:57
Reprint 2025-26
Introduction to 
Bioinformatics
9.1 The Utility of  Basic 
Mathematical and 
Statistical Concepts 
to Understand 
Biological Systems 
and Processes
9.2  Introduction
9.3 Biological 
Databases
9.4 Genome Informatics
9.5	 Role 	 of 	 Arti??cial 	
Intelligence (AI) in 
future
9.1 The UTili Ty of Basic Ma The Ma Tical and 
s Ta Tis Tical c oncep Ts To Unders Tand Biological 
s ys Te Ms and p rocesses The objective of this chapter is to explain the understanding 
of the basic concepts of mathematics and statistics is 
important to a biologist.
The outcome of any biological experiment is data. 
Previously, biologists used to generate and analyse data 
without the help of sophisticated software, computational 
tools and statistical tests. However, this is not the case 
anymore. With the advent of instruments like high-
throughput DNA sequencers, powerful microscopes and 
other imaging systems, and analytical instruments capable 
of generating large volumes of data, biologists can no 
longer deal with the data using their notebooks and excel 
sheets. Instead, they need computational and statistical 
tools to handle data. Large volumes of data often require 
quantitative analyses to interpret and generate biological 
meaning. Performing such analyses require one to have 
Chapter 9
Chapter 9.indd   235 09/01/2025   15:18:57
Reprint 2025-26
236
Biotechnology good working knowledge of computational and statistical 
concepts, for example; machine learning technologies, 
regression, variance, and correlation, etc. Mathematical 
and statistical concepts can only aid biologists to interpret 
their data and are not a replacement for asking the right 
questions and the biological acumen. The names of some 
of the commonly used statistical terms used in biology is 
provided in Box 1.
Let us examine with speci??c examples where both 
the knowledge of computing and statistics can help 
understand biological phenomena better. For example, 
we want to understand the association, if any, between 
blood pressure and heart rates in ten patients (Table 
9.1). As provided in the table below, a simple visual 
estimation (Fig.9.1) is not suf??cient to accurately 
determine the relationship (correlation) between the two 
variables. For that, one needs to draw a regression line. 
Correlation and regression are distinct, yet correlated. 
Correlation quanti??es how the variables are connected, 
but regression de??nes a statistical relationship between 
two or more variables where a change in one variable is 
Box 1
Box 1: Glossary of the commonly used statistical terms in biology
Null hypothesis— A statement that there is no relationship between two measured 
phenomena.
Statistical signi??cance— A result has statistical signi??cance when it is very 
unlikely to have occurred.
p-value— The probability of ??nding the observed results when the null hypothesis of 
a study question is true.
t-test —An analysis of two populations means through the use of statistical 
examination.
Multivariate analysis: A set of techniques used for analysis of data that contain more 
than one variable.
Regression analysis—A technique to investigate the relationship between a 
dependent and an independent variable.
Multiple testing correction— A statistical test that corrects for multiple tests to 
keep the overall error rate to less than or equal to the user-speci??ed P-value cutoff 
Analysis of Variance or ANOVA— A collection of statistical models used to analyse 
the differences among group means in a sample.
Chapter 9.indd   236 09/01/2025   15:18:58
Reprint 2025-26
237
i ntroduction to Bioinformatics associated with a change in another. Therefore, in the 
example above a simple regression test will tell us if there 
is a direct relationship between heart rate and blood 
pressure. The output of a linear regression analysis is 
R
2
-value, a statistical measure to show as to how close 
the data is to the ??tted regression line. The R
2
 value 
ranges from 0 (no correlation between the variables) and 
1 (perfect correlation between the variables). As shown 
in Fig. 9.1, the R
2
 value suggests that there is a good 
correlation between the two variables. Therefore, the null 
hypothesis is rejected in this case.
Table 9.1: Heart rate and blood pressure recorded in 
ten patients
Patient Heart rate Blood pressure 
(cystolic)
1 112 189
2 83 140
3 92 153
4 121 192
5 85 147
6 111 178
7 94 135
8 88 143
9 102 177
10 111 189
Fig. 9.1: Correlation between the two variables with a 
simple linear regression line
R
2
Heart Rate
Chapter 9.indd   237 09/01/2025   15:18:58
Reprint 2025-26