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Highlights in BioScience      
ISSN: 2682-4043    
        

 

 
Highlights in BioScience                                   DOI: 10.36462/H.BioSci.20193                             October 2019 | Volume2 
http://highlightsin.org/index.php/bioscience/                                 

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Citation: Alsamman M.A. (2019) 
The Art of Bioinformatics Learning in 

Our Arabic World.  Highlights in 

BioScience, Volume 2. Article ID 

20193, dio:10.36462/ H.BioSci.20193 

 

 

 

 

 

 

Received:  August 19, 2019 

 

Accepted:  September 13, 2019 

 

Published: October 12, 2019 

 

 

Copyright: © 2019 Alsamman .This 

is an open access article distributed 

under the terms of the Creative 

Commons Attribution License, which 

permits unrestricted use, distribution, 

and reproduction in any medium, 

provided the original author and source 

are credited. 

 

 

 

Data Availability Statement: All 

relevant data are within the paper 

 

 

Funding: The authors have no support 

or funding to report. 

 

 

 

The Art of Bioinformatics Learning in Our 

Arabic World 
 

Alsamman M. Alsamman *
1 

 
1 

Molecular Genetics and Genome Mapping Lab. , Agricultural Genetic 

Engineering Research Institute, Agricultural Research Center, Egypt.  

* To whom correspondence should be addressed: smahmoud@ageri.sci.eg 

Abstract 
 

Bioinformatics became a significant field in life sciences that, 

draws a number of researchers and extends into a wide range of 

biological disciplines. Rendering bioinformatics analysis techniques 

are the most desirable skills in a variety of scholarship programs 

and academic positions. Teaching bioinformatics is very 

challenging since it is a multidisciplinary field, where most of the 

undergraduate programs in colleges provide only one area required 

for bioinformatics. Besides the regular education system, few 

bioinformatics training courses are offered and less are affordable 

to fresh graduates in countries most of which are categorized as 

developing countries. The high cost of learning, confusing 

education systems, and the complexity of bioinformatics science 

has made it very difficult to be taught and more challenging to be 

studied in Arab countries. This review provides possible solutions 

to most of these issues and offers the best practice to guide future 

Arab bioinformaticians to learn bioinformatics in a way that fits our 

social, financial and academic circumstances. Moreover, it 

discusses the key aspects that a bioinformatician needs to be aware 

of and the basic knowledge that must be gained. On the other side, 

it will illustrate how to start learning, to address some of these 

challenges and how to deal with some of the related social issues. 
 

Keywords: bioinformatics, Arab, programming, education, Linux, biological data.  

 

Introduction 

Bioinformatics analysis techniques are the most desirable skills in a wide 

range of scholarship programs and academic positions. As the study of 

bioinformatics and computational biology grows and evolves, it is essential to 

quantify the factors that contribute to the development of professionals in this field 

(1). Bioinformatics is an interdisciplinary research field where computational 

resources and techniques are used to interpret biological data through mathematical 

and statistical approaches.  

Perspective Review                                                                                      Open Access   



 
 

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Highlights in BioScience                                   DOI: 10.36462/H.BioSci.20193                             October 2019 | Volume2 
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The rapid acceleration in computing power and memory 

storage capacity have given rise to a new golden age in the 

biological data analysis (2). There is almost no biological 

sector in which bioinformatics has not yet been incorporated. 

Its techniques are used in microbiology to examine microbial 

diversity and species occurrence by identifying and 

quantifying the association of microbial communities among 

different biological samples (3). Throughout the 

pharmaceutical industry, bioinformatics offers analytical tools 

that can enhance drug target identification, drug candidate 

monitoring and drug optimization. In particular, it promotes 

the recognition of side effects and forecasts drug resistance 

(4). 

Recently, bioinformatics has proposed the idea of 

personalized medicine, where treatments will be adapted to the 

unique genotype of patients. Integrating the vast genetic 

information provided by the Genome Wide Association 

Studies (GWASs) is a valuable resource for mapping genetic 

traits with drug reactions and phenotypes, allowing individual 

characteristics of each patient to be monitored and their 

susceptibility to certain diseases to be considered (5). 

Last but not the least, the integration of machine learning 

in bioinformatics analytical methods has opened a new era in 

which sample data and past experience could be used to 

improve the output criterion in computational algorithms, the 

optimized criteria could be the rating of reliability provided by 

the statistical model and the significance of the performance 

(6). Machine learning has therefore enabled what appears to be 

a breakthrough in biological research, where computer 

programs can solve complex biological problems efficiently 

and effectively. Through cancer diagnosis (7), neurooncology 

imaging (8) and drug design (9) in medicine and plant 

physiology (10), forecasting crop yields (11) and livestock 

production in agriculture (12), machine learning has been used 

to solve both challenging and basic tasks. 

The rapid development of life sciences and information 

technology requires the continuous development of 

bioinformatics learning programs in order to sustain their 

significance (1). The quality of the education systems in the 

Arab countries has gradually improved over the last 30 years 

in the Arab world. More work has been done by 

non-governmental and government agencies and organizations 

to improve the educational opportunities for Arab students, 

and to promote their independence and integration into their 

societies and to prepare them for future careers, taking into 

account current trends in the labor market (13). Few leading 

bioinformatics programs in Arab countries have been 

established over the last decade in Egypt, Lebanon, KSA, 

UAE, and Oman. Some of these programs have launched 

throughout the biotechnology and genetics departments of 

computer science, science and agriculture colleges in both 

special and governmental universities (14). So far, in Arab 

countries, neither colleges nor departments have been 

specifically founded to teach bioinformatics. 

As multidisciplinary science, bioinformatics is difficult 

to teach, most of the programs are designed for undergraduate 

students in colleges that provide only one field required for 

bioinformatics. These programs address these issues by 

borrowing a few courses from other colleges, for example, 

when bioinformatics programs are embraced by computer 

science colleges, students are expected to pursue a few courses 

in biology in science or agriculture colleges. These courses 

could take place in the second or last years, and could be 

acquired in the worst situations a few weeks before graduation. 

This system creates a gap in the experience of the participant, a 

hole that must be filled in the next few years of his life as a 

researcher or even as an employee. 

Beyond the regular education program, few 

bioinformatics training courses are offered and less are 

affordable to fresh graduates in countries most of which are 

classified as developing countries. The high cost of training, 

confusing education systems and the complexity of 

bioinformatics science has made it very difficult to teach and 

more difficult to study in Arab countries. Inducing 

bioinformatics education systems to move slowly toward 

improving the quality of life of Arab residents and addressing 

crucial food and drug issues. 

This review is written in order to offer possible solutions 

to most of these issues and to guide future Arab 

bioinformaticians throughout the best methods to study 

bioinformatics in a way that fits our social, financial and 

academic circumstances. It also addresses the key aspects that 

the bioinformatician needs to know about, such as how to 

begin learning the basic knowledge that needs to be learned, 

and how to overcome some of the social issues faced by young 

bioinformaticians. 

 

How to get started in bioinformatics 

Bioinformatics, as already stated, comprises of three 

different fields: genetics, computer science and mathematics. 

The purpose of these three domains is important for the 

organization, comprehension and interpretation of the 

different biological information that bioinformatician handles 

on a daily basis. The question of studying these three areas is 

very difficult and few places of education provide this 

knowledge in one location.  

You've certainly learned the fundamentals of one or two 

of these areas as biology, computer science or mathematics 

graduate, you only need one or two remaining knowledge to 

become a bioinformatician. As you begin to learn, my tips are: 

1) Learn the basics, and then you will know where to go 

next. 



 
 

                                                                                                              The Art of Bioinformatics Learning in Our Arabic World 

 

 

 
Highlights in BioScience                                   DOI: 10.36462/H.BioSci.20193                             October 2019 | Volume2 
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2) This is about knowledge, not certification; certification 

will not indicate that an individual has adequate 

knowledge of the subject area. Arab bioinformatics 

students are more worried about the credential than 

they are about the skills they need to acquire. 

3) Take what you need through free learning; this is your 

university and your home, attend those classes you need 

to learn, even if it is not your college. Many students 

overlook the fact that most university lectures are not 

closed to free learners as long as they do not need a 

credential.  

4) Take a basic course; most of the basic courses offered in 

Arabic countries cover weak bioinformatics skills such 

as primer design, sequence alignment, NCBI software 

and gene annotation using online tools. You may take 

one basic course if you don't understand the software, 

as long as you don't repeat courses with the same 

content. 

5) Ask as much as you can; there are many public forums 

for bioinformaticians that offer gatherings across all 

disciplines such as biology, mathematics, and computer 

science, where you can ask any question you want. 

6) Engage the scientific society as a free bioinformatician; 

I know it's hard for some Arab students to work after 

graduation for 6-12 months without any kind of 

support. But it is about joining the research community 

, learning about the issues they face and seeking out 

how to manage it as a bioinformatician. The advantage 

of this would be that, if they don't pay they can't control 

you. You can choose to work on any type of data 

without any restrictions, the ability to attend any class 

outside the workplace, and if there is any other work 

opportunity you can apply. In fact, you should have a 

certificate of work experience and my recommendation 

is to look elsewhere if they don't offer you such a 

credential. Taking in advantage that, most of the 

scientific institutions in Arabic world can not offer 

payment for fresh graduates and they have limited 

funding, which made them in a big need for graduates 

who do not ask for payment.   

7) Read even when it is difficult to understand; 

bioinformatics is a science that is changing every day 

and few textbooks will cover all of its aspects so you 

need to keep reading. That you will be able to 

understand a few more every time you start reading new 

research, finally you will get a clear understanding of 

most of these articles. While you're reading, take care 

of the software that they have used and try to run it 

using sample data. Supplementary 1 contains some 

simple articles and reviews you can start with. 

8) Self-learning is not the full answer; while self-learning 

through online resources such as YouTube (15) and 

Academia (16) is very helpful in expanding your 

knowledge, you need to deal with real problems by 

hand in order to sharpen your abilities, and this will not 

happen without joining a scientific group. 

9) The way to learn more is to teach others; passing 

bioinformatics skills to others would open your eyes to 

different applications of the same tools and knowledge 

gaps, granting you more opportunities in the near 

future. 

 

Operating systems and bioinformatics 

Although most of the bioinformatics courses are 

interested in teaching software,I would suggest that it is not 

more about the tool than the environment in which you 

operate. Many scientists in our Arabic world use the Microsoft 

Windows Operating System every day to manage and evaluate 

their data. Microsoft's operating system is commercially 

closed and hackable (17). Such an environment is not a natural 

space for innovation and research, especially when dealing 

with mega-biological data. 

The normal bioinformatics operating system should be 

opened (could be programmatically modified), highly secured, 

free of charge, and compatible with all bioinformatics 

software. Of which only one operating system could have 

granted these privileges, Linux. Linux is a Unix-like and often 

POSIX-compliant operating system (OS) based on the design 

and distribution of free and open source code. The basic 

element of Linux is the Linux kernel, the very first operating 

system kernel introduced by Linus Torvalds on September 17, 

1991. The Free Software Foundation uses the name of 

GNU/Linux to describe a complex operating system (18). 

Most Arab researchers fear Linux operating system, 

depending on the false belief that Linux is only a black and 

complicated command line window. On the contrary, Linux 

can operate videos, games and handle all the types of data files 

you use in windows, and it has free writing programs like 

Libreoffice (19) and a lot of wonderful programs and tools that 

could change your life. Linux also gives users a bit of control 

about what occurs on their machine what does and doesn't 

changing. You could learn more about the advantages of Linux 

over Windows through a number of articles (20). 

Linux has different flavor distributions such as Ubuntu 

(21), Fedora (22), openSUSE (23), Red Hat (24) and many 

others. Mostly there is no difference between the various 

Linux distributions, although there is some variance between 

the software configuration , where it does not influence the 

core system performance or stability. Ubuntu is one of the 

most common Linux distributions (25), and the initial findings 

indicate that Ubuntu do’s not require technical assistance (26), 



 
 

                                                                                                              The Art of Bioinformatics Learning in Our Arabic World 

 

 

 
Highlights in BioScience                                   DOI: 10.36462/H.BioSci.20193                             October 2019 | Volume2 
http://highlightsin.org/index.php/bioscience/                                 

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has a defined and simple graphical user interface (GUI) and 

has been incorporated into some Arabic education systems 

(27). Ubuntu has a great and powerful version named 

Bio-Linux (28). In 2002, Dr. Dawn Field launched the 

Bio-Linux system under the NERC Environmental 

Bioinformatics Program (29). Bio-Linux release allows easy 

access to a versatile computing environment pre-loaded with 

bioinformatics software from basic data analysis tools to 

advanced analytic framework programming packages (21). 

You can download and install Bio-Linux in an ISO file format 

from the official website (http://environmentalomics.org/bio- 

linux/). 

The only downside is that Bio-Linux distributions are a 

way behind Ubuntu's latest updates, and most hardware drivers 

such as WIFI, computer mouse, or touch pad, are not installed 

which involve certain installation skills. In order to overcome 

this issue, my recommendation is to install the latest Ubuntu 

release and then download Bio-Linux packages via the Linux 

Synaptic Package Manager. Alternatively, you can download 

the edition of Bio-Linux and then request the system to be 

upgraded with simple commands. 

With the Ubuntu Software Center, you can quickly 

download and install thousands of Linux software without any 

complications. The Linux terminal is another way the software 

can be used (Supplementary 2). Although some tools require 

some command lines to be ready for analysis, most of them do 

not require installation skills. There are few Linux guidelines 

for fresh bioinformatics students: 

1) Install and boot from Bio-Linux using flash memory for 

a short period of time while you are in practice 

(Supplementary 2). 

2) Learn to use all the tools and software that Ubuntu has 

provided, even if they were really basic, such as 

LibreOffice and Calculator. This would eliminate the 

fear of using a new operating system to allow you more 

comfortable with the environment. 

3) Bio-Linux has a web page documentation, and sample 

data for most its bioinformatics software, try to open 

and use it through the Desktop panel. 

4) Try to write your own easy manual notes for all 

software built on Bio-Linux, explain the use, input, 

output and how you can use this tool in your future 

research. In Supplementary 2, I am explaining how to 

use a few of these tools. 

5) After a few weeks try installing the latest version of 

Ubuntu or Bio-Linux , update and lunch Jemboss (30) 

using the Ubuntu Software Manager 

6) Try to practice simple Linux commands and use as 

many tools as you can. 

Computer science and bioinformatics 

Most Arabic bioinformatics students prefer to start with 

programming languages such as Python for biological data 

analysis. Python has many advantages when it comes to 

bioinformatics and has several advantages as a computer 

language compared to languages such as PERL, which is also a 

famous bioinformatics computer language (31,32). The 

problem, though, is that most of these learners neglect and 

ignore the basic rule of computer language learning, which is 

the need to understand the basics of computing and its core 

structures, such as object orientation programming (OOP). On 

the other hand Java is a very common computer language in 

our Arabic country, and most commercial training companies 

offer paid courses to learn this language. My concern is that, 

although Java has a smaller relationship with bioinformatics 

compared to Python, few training companies offer python and 

fewer do so professionally in our Arab world compared to 

Java.  

In this regard, while you are learning computer language, 

you need to understand the fundamentals of computer science. 

These fundamentals involve algorithms that are a sequence of 

commands typically used to solve a problem or perform a 

computation, and can be expressed in a finite number of steps 

and a well-defined formal language (33). The best way to learn 

algorithms in your first step is to learn algorithms by 

programming languages that you have selected to learn such as 

Java (34), Python (35), and PERL (36). Using this method, in 

addition to learning the algorithm effectively, you will be able 

to sharpen your coding skills. 

After studying programming language in such a smarter 

way, you can easily add other programming languages to your 

set. Among these languages is R, which is a statistical 

programming language and common computational tool for 

data analysts, and is it has become one of the most commonly 

utilized programming languages in bioinformatics software. 

This is mainly due to its performance and the richness of the 

libraries that could be used for data manipulation and 

simulation (37). R is very simple, and there are large numbers 

of R libraries developed for bioinformatics. It might be 

difficult for biological researchers to learn coding and 

algorithms, and my recommendations for easy learning are: 

1) Practice basic codes as much as you can, I would prefer 

to write these codes a couple times by hand and then 

run it under the computer language compiler. 

2) Don't spend much of your time on graphical user 

interface (GUI) coding, most common bioinformatics 

software don't have a GUI, but instead focus on 

learning programming skills. 

3) Do not forget that, for the purpose of interpreting 

biological data, you are studying programming 

language, and you are not a computer scientist. Based 

on this, don't go deep into programming language 

http://environmentalomics.org/bio-%20linux/
http://environmentalomics.org/bio-%20linux/


 
 

                                                                                                              The Art of Bioinformatics Learning in Our Arabic World 

 

 

 
Highlights in BioScience                                   DOI: 10.36462/H.BioSci.20193                             October 2019 | Volume2 
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learning, instead of considering what it can give you to 

achieve your goal. 

4) After learning basics, you can search for programming 

codes that address simple biological issues such as 

DNA transcription and translation, reversing DNA 

sequence, and scripts which reflect the use of simple 

genetic concepts without the use of external libraries. 

5) Do not use libraries/modules/packages for simple tasks 

in your first steps; you need to learn how to use 

computer language to apply scientific principles in 

algorithmic analysis. 

6) Organize yourself; the first lines of your scripts should 

indicate the use of the script and its input and expected 

output data. You'll use these scripts in several 

occasions, and it's unfair to waste your time rewriting 

the same code. 

7) You can use the same script or tool differently; this 

depends on your imagination. 

8) Try to write clean and structured scripts where the 

overused programming functions can be found in well-

organized libraries. It would provide the ability to use 

the same collection of libraries for various uses in 

different projects. 

9) Backup your scripts every week.  

 

Mathematics and bioinformatics  

Most educational systems in our Arab world are 

concerned with teaching students without proper application 

of mathematics principles where student knowledge of these 

basics are not linked to their use. There is an easier way to 

teach mathematical rules in other countries by providing 

real-life experiments to learners (38). Most Arabic students 

have a decent mathematical background based on their high 

school education, which might be enough to start learning the 

science of bioinformatics, but my basic concern is that, they 

need to get missing knowledge through straightforward 

courses. 

The main target is a sub-science of mathematics, 

statistics. Statistics is a systematic and random analysis of 

variability. In many aspects of scientific research, statistical 

methods are valuable. They constitute the research of the right 

way to collect process and interpret data (40). The connection 

between statistical methods and bioinformatics is very critical; 

you might argue that most of the biological data analysis could 

not be conducted without a good statistical background (41). 

As a bioinformatician, you will continue to learn the 

fundamentals and implementations of this science in biology 

for the most part of your life. 

On the other hand, the basics of this science will need to 

be practiced in a simple way, where you can use R 

programming to study statistical methods. As I mentioned 

earlier, R is a programming language that has been written for 

statistical analysis in general, and studying basic statistics 

through R could provide an opportunity to learn a new 

language and to understand the basics of statistics in the main 

time. This doesn't mean that you don't have to solve statistical 

problems by hand, but it does mean that you have to 

understand both ways. 

 

Early publishing of students and graduates 

Although there are concerns about early scientific 

publishing in the Arabic education system, it is very important 

for bioinformatics students to publish their research, scripts, 

and pipelines (series-connected processing systems in which 

the output of one component is received from another) during 

their early years. Some of these articles would not include 

much, but this is aimed at improving their interest in research. 

It would also give them the ability to sharpen their writing 

skills, address the comments of reviewers and the international 

research society. This would add to their computing skills in 

early age more specificity and professionalism. You should try 

to write your publications for the tools, scripts, pipelines or 

methods you have developed with or without other colleges 

and you need some guidelines on how to publish in this regard: 

1) First of all, you need to know that most of the software 

articles are one or two pages long. It must include a 

brief introduction, a comprehensive methodology and 

some discussion. 

2) Don't extend your article beyond two pages, the more 

you do, the more unstable your sentences will be. 

3) Discuss the benefits of your tool, how you deal with the 

input, where adding a flowchart algorithm would be 

efficient. 

4) Use simple English.  

5) Cite previously published work by comparing your tool 

with others. It doesn't matter if your tool doesn't add 

much; the most important thing is that, it's your tool.  

6) Start with small publications. 

7) Search for free journals. In this regard, BioRxiv (42) is 

a non-profit, electronic archiving and distribution 

platform for the pre-printing of life science research 

papers. It was founded by the Cold Spring Harbor 

Laboratory, a scientific and academic organization. 

 

Learn new programs and use published scripts 

Hundreds of bioinformatics tools are released daily, 

specialized in the analysis of different data types, and written 

in a variety of programming languages. In order to use these 

tools, you need to take two steps: (1) test the configuration of 

the software using sample data and (2) read tutorials (if 

available) describing the different parameters and input data 



 
 

                                                                                                              The Art of Bioinformatics Learning in Our Arabic World 

 

 

 
Highlights in BioScience                                   DOI: 10.36462/H.BioSci.20193                             October 2019 | Volume2 
http://highlightsin.org/index.php/bioscience/                                 

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that this tool could handle, which would enable you to 

understand and analyze the outputs of these tools. Using 

sample data and reading tutorials will save you time and 

provide you with a simpler way to resolve the error messages. 

On the other hand script archives such as GithHub are the 

most important source for software scripts with more than 10 

million repositories (43). Such websites offer you the ability to 

access open source software, script codes and pipelines in C, 

Python, PERL, R and other programming languages freely. 

You may be able to use such resources to know more about 

coding or bypass basic programming functions by using these 

codes as external libraries, but ethically you need to reference 

the published websites, articles or acknowledge the source of 

these scripts in your codes. It could be difficult to use these 

scripts and you need advice on how to handle it: 

1) Begin with basic codes; you can find several tutorial 

codes for programming language implementations in 

bioinformatics, which you will find useful in sharpening 

your programming skills. 

2) In order to understand any script, you need to divide the 

script parts according to what they do, use the print 

function to show the performance of the input 

processing stage after each step on the screen. 

3) To understand any code structural algorithm, block, 

remove and replace any line debug (run by compiler) 

and monitor performance changes. Furthermore, the 

key to understanding the overall algorithm could be to 

comprehend how to evaluate script errors when 

ignoring certain coding lines. 

4) Draw a basic flowchart that demonstrates your 

interpretation of how this program manages input data. 

Start learning cloud computing  

The management of large biological data requires 

sufficient computing power and storage capacity. Cloud 

computing can provide Arab research with large capabilities 

via open and often free websites. Some of these servers are 

Galaxy (44) and Cyverse (45). From my point of view, all 

these clouds and others provide computing resources and 

storage capacity to handle biological data, but I would prefer 

Cyverse more because it is more robust and offers a lot of 

software and simple GUI interfaces.  

Using such clouds can offer you the ability to manage big 

information and to conquer the poor and slow speed of the 

Internet which inhibits you from accessing those data; you can 

do even more than that. Such as: 

1) Cyverse gives you the ability to email your scripts to 

administration to handle or convert those scripts to a 

tool that you can use. 

2) Using the tools available to build your own pipeline.  

3) You can transfer or upload data from a private FTTP 

server such as NCBI (46), Ensemble (47) or from your 

computer. 

4) Choose to use the storage capacity provided by these 

platforms to store data instead of overfilling your 

computer memory.  

5) Such platforms have their own research communities, 

where you can submit your questions. 

 

Life as a bioinformatician in Arabic countries 

Many Arab countries do not support research (48) and 

fresh bioinformaticians may suffer from a lack of funding. My 

answer to this problem is to begin your career as a freelancer. 

Freelance employees are terms that are commonly used by a 

self-employed person who is not usually a long-term 

employer. Fresh bioinformatics graduates may offer their data 

analysis expertise to local and international research groups in 

exchange for money. In this manner, they will support their 

continuing self-education and their personal goals. 

Considering that, this path needs good research knowledge, 

free and low-cost services and communication skills. While 

freelance is a good way to start your life as a bioinformatician, 

some considerations are required: 

1) Don't manipulate output reports for those who want to 

finish their dissertations without having worked. 

2) Finish the work of each researcher without doing more 

than is necessary and not less than is essential. 

3) Note that freelancing is not a type of research, and you 

need to write any scientific papers for yourself in order 

to build your own scientific background. 

4) Once you have a successful and well-paid position, 

avoid freelancing and start sharing the credit for your 

work.  

Bioinformatics and communication skills 

Communication skills for bioinformatician are more than 

necessary and are very important for researchers, especially 

when working in scientific teams. Communicating with other 

bioinformaticians, biologists and researchers is critical to 

understanding, resolving and addressing daily bioinformatics 

issues. Such challenges could be software bugs, incorrect 

results and experimental methodologies. In most of our Arabic 

countries, there is a weak link between different scientific 

groups so, for order to overcome such difficulties, Arab 

bioinformaticians could use research communities to interact 

with each other and with international scientific teams. There 

are various advanced research, e.g. Ask Ubuntu (49), 

BIOINFORMATICS (50) and ResearchGate (51). Such 

groups will help young scientists start their careers and find 

new bioinformatics problems that they could address. 

 



 
 

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Highlights in BioScience                                   DOI: 10.36462/H.BioSci.20193                             October 2019 | Volume2 
http://highlightsin.org/index.php/bioscience/                                 

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The future for you  

Following basic learning, one or two programming 

languages and the handling of different biological data through 

a variety of analytical techniques, the expected next phases are 

as follows: 

1) Learn C programming language; C is faster and 

consumes less memory than other programming 

languages. Although learning C is difficult, most 

famous bioinformatics programs such as the BLAST 

(52) software package are written in C. The problem is 

that Python, PERL and R are slower than C when 

handling large data, and then when performing 

complicated processes and memory management are 

needed (53). You will need to write some of your codes 

in C in the future to make your program faster and more 

efficient, even if these scripts need more lines to 

express. Like C, FORTRAN is a very important 

programming language, particularly when it comes to 

statistical analysis, and some may argue that it is faster 

than C (54). 

2) Boost your understanding of SHELL scripting. SHELL 

is the programming language used by the Linux system 

(55), improving your reading and writing skills in such 

a language will enhance the management of complex 

tasks. In some cases, you can create hybrid scripts 

where you compose an algorithm to manipulate data 

throughout different computer programming languages 

such as Python, C, Julia and R, in addition allowing you 

the flexibility to use what the Linux system could 

provide. 

3) Don't repeat your work; try to handle different types of 

data and use different methods of analysis to gain more 

knowledge and experience. 

4) Machine Learning (ML) is a natural outgrowth of a 

combination of computer science and statistics and 

answers the question of how to build machines that 

automatically learn from experience (56,57). 

Understanding ML is very essential for your career and 

could aid your scientific background. Python, R, Java 

and C have specific libraries for ML, but Python has 

the maximum attention (58). 

5) Begin the web programming learning process. Internet 

development is essential in order to make massive, 

definitive data available to the scientific community. In 

fact, you could create your own online software that 

would make it easier for scientists with poor 

programming skills to use. Python has its own web 

programming packages, such Django. 

Mental and social life of Arabic bioinformatician 

Research life is demanding, frustrating and highly 

competitive. Where postgraduates need high marks for their 

courses and a variety of publications under their belts if they 

want to earn excellent scholarships and research positions 

(59). It is important to take part in activities and social 

structures in order to promote the academic and social growth. 

Many current students in bioinformatics have been prepared to 

learn new research methodologies and programming 

languages and to ignore social life. Sadly, this kind of behavior 

is very risky with several articles on psychological science 

relating mental illness with creativity (60).  

As a bioinformatician, you need to socialize with others 

and participate in outdoor activities to boost self-efficiency 

and promote team harmony. These type of activities are highly 

recommended in the field of mental health through a number 

of scientific articles (61). In fact, physical activity has been 

shown to be related to mental health and can play a key role in 

sustaining moderate to severe mental health conditions, 

especially anxiety (62). First of all, there is a high rate of 

depression and work stress, especially for those working as 

researchers in developing countries who need to maintain their 

mental health in order to thrive and thrive. 

 

Conclusion 

Bioinformatician is a person who has the ability to take 

advantage of three different sciences, to think innovative and 

to reshape complexity into simplicity. His capabilities rely on 

a sound scientific experience, a high level of knowledge in 

addition to patience, enthusiasm and productivity. Our Arabic 

world needs this kind of expertise in translating biological 

knowledge into practice, preserving our natural ecological 

resources and boosting our standard of living. As citizens, we 

can not change the education system, the economic climate or 

our society, but as researchers, we can inform others how to 

deal with these conditions and achieve their goals with less 

sacrifice as possible. 

The secret of learning bioinformatics relies basically on 

the willingness of our students and researchers to acquire new 

sciences. Bioinformatics as a discipline is the most attractive 

and interesting field in which lack of resources, materials and 

equipment is not a concern, but requires additional knowledge 

and a high level of creativity that seems as easy to learn as it is 

difficult to obtain. My final advice is to be guided and to lead, 

both procedures could fill the gap in your scientific 

background, where opening your mind to new ideas would 

result in a better way, and deeper understanding and 

interaction with others would offer you multiple choices.  

Acknowledgement 

The author would like to thank Dr. Morad Mokhtar 

(Molecular Genetics and Genome Mapping Lab Agricultural 



 
 

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Genetic Engineering Research Institute, ARC, Egypt) and Dr. 

Abdulqader Jighly (Department of Economic Development, 

Jobs Transport and Resources Australia) for their valuable 

support in this research. 

 

 

Supplementary Files 

The Supplementary Material for this article can be found 

online at: http://supplementary.highlightsin.org/20193_Sup.zip   

  

Supplementary file 1: Articles and reviews on bioinformatics 

could be used to initiate the learning process in this field. 

Supplementary file 2: Short tutorial for some Bio-Linux 

basics. 

 

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