Copyright © 2021 pubrica. All rights reserved 1

Current Developments in Bioinformatics and Computational Biology: A

Systematic Review

Dr. Nancy Agnes, Head,

Technical Operations, Pubrica

sales@pubrica.com

In-Brief

Bioinformatics includes information

technology and computers to large

molecular biology data sets. Bioinformatics

is anticipated as a cutting-edge branch of

the biotechnology sector, which help in

drug innovation and personalized medical

treatments. The field closely associates

artificial intelligence and computer science

with microbiology and genomics.

Keywords: systematic review services,

conducting a systematic review, systematic

review paper, systematic review writing

service, Systematic Review writing help,

systematic review writing, systematic review

writing help

I. INTRODUCTION

Bioinformatics is an interdisciplinary field

of life sciences that develops methods and

software tools that help understand

biological data, particularly when the data

sets are large. Its application and research

comprise genomics data analysis, genome

annotation, molecular folding, gene/protein

prediction and molecular sequence. It also

includes developing databases and data

management systems, software and analysis

tools.

Bioinformatics education and training are

necessary to understand the basics and

specifics of bioinformatics and create new

generation specialists and scientists with

integrated, interdisciplinary, and

multilingual knowledge to use recent

bioinformatics sources powered with

advanced operating systems, software, and

database/networking technologies.

A systematic review paper helps create a

review that formulates a question that

uses systematic and reproducible methods to

identify, select and critically appraise all

relevant research, and gather and analyze

data from the analysis included in

the Review.

II. SYSTEMATIC REVIEW WRITING

Bioinformatics encourages prospects for

novel discoveries and reveals pathways for

biological experiments with the support of

data analysis. Research in this field mainly

depends on high-quality databases to offer

precise outcomes. However, most biological

databases have obtainable mistakes, such as

data wrongly classified or incomplete

information. These mistakes may be

problematic. Recent data mining algorithms

may use the filter data. However, sometimes

these algorithms cannot treat these faults,

leading to severe problems for analysis.

Manual data curation and extracted data

from literature is a solution to this problem.

For example, a database of mutations effects

on protein-ligand affinities, composed of

1,000 mutations, was generated through a

deep search in the literature. Nevertheless,

the manual analysis may be address bias

risk, which may not collect all possible data

necessary for a determined study.

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Manual curation also permits the

identification of mistakes and, when

possible, corrects them. Therefore,

systematic literature reviews used to collect

data for bioinformatics analysis. Systematic

Review is a technique used to discover,

estimate and summarize the state-of-the-art

of a precise theme in the literature.

Systematic Review writing helps gather

literature information restrictively that

allows a rigorous methodological analysis

with lower prejudice than the traditional

reviews. Conducting a systematic review,

the objective is to build a general vision of a

specific question and give it a reasonable

summary of the literature. To perform

systematic reviews is essential to follow a

pre-established and well-defined protocol.

Following the distinct systematic steps may

guarantee the reproducibility of the study.

Some monitors have been proposed to help

commentators to construct Systematic

Literature Review. Also, estimation

approaches of Systematic Literature Review

quality, such as the PRISMA declaration, is

which PRISMA (Preferred Reporting Items

for Systematic Reviews and MetaAnalyses)

describes a set of items to help authors

advance the reporting of Systematic

Literature Review.

III. STRUCTURAL BIOINFORMATICS:

MOLECULAR FOLDING, MODELLING,

AND DESIGN

One of the commonly used applications of

bioinformatics is the identification of three-

dimensional protein molecular modelling,

structure, and folding to forecast the

possible function of proteins or model

behaviour of molecules, other molecular

structures, fold the molecule to its natural

biologically functional three-dimensional

structure and help in creating biomedical

drugs for various complicated human

diseases.

It is used in designing the structuring of

biological molecules and enzymes. There

are few examples where they are widely in

use de novo protein design, protein-peptide

interaction, protein-ligand/drug docking,

enzyme design, macromolecular complexes,

and structure prediction of biological

macromolecules. It supports the prediction

that the structure of a protein depends on the

known structure of a homologous protein(s).

Apart from that, the discovery of secondary,

tertiary, and quaternary structures of

proteins is significant to understand proteins'

function. The exact three-dimensional

structure is necessary for correct function,

and a failure to fold into native construct

generally produces inactive proteins or

misfolded proteins that can be toxic.

IV. BIOLOGICAL NETWORKS AND

SYSTEM BIOLOGY

The properties of network topology are

widely applied in many scientific fields,

including bioinformatics, leading to the

construction of large-scale biological

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networks represented as "omes" like biome,

interactome, microbiome.

The above emphasized molecular sequence

analysis, annotation, prediction, and

molecular modelling-associated to

bioinformatics methods are also the

principal for organizing, building, and

genetic and biochemical pathways of

complex cellular processes. These signal

transduction comprise reception, and gene

co-expression and gene regulation. Such

molecular networks incorporate various data

types, including DNA sequences, regulatory

RNA, proteins, gene expression data,

secondary metabolites, and other small

molecules, which may be connected

physically and functionally. The structuring

and organization of such physically and

functionally connected molecular networks

of cellular processes can be achieved only

by applying the combination of simulative,

iterative, and model-oriented bioinformatics

approaches.

V. SOFTWARE, ANALYSIS TOOLS,

SERVICES, AND WORKFLOW

The key driving force for the present and

future development of bioinformatics

software and tools, which help in the

progress of genome decoding technologies,

consequent required for their analyses,

accretion of large volume biological data, as

well as the development of computer

technologies, visualization, graphics, and

networking techniques and molecular

modelling.

Moreover, the availability of numerous

shared object models, open-source codes,

and community-maintained plug-ins helps

collect new ideas from the community and

perform innovative in silico experiments on

current "Big Data."

These all make way for research groups and

bioinformatics corporations to experiment,

work, and create a more innovative

generation of bioinformatics tools and

software that are user-friendly and must

perform integrated and extended analysis

with better visualization and graphical

outputs.

Some of the open-source software packages

include UGENE, EMBOSS, GenGIS,

GENtle, BioPerl, PathVisio, GenoCAD,

GenomeSpace, Biopython, Bioclipse,

GeWorkbench, .NET Bio, Apache Taverna,

BioJS, Bioconductor, BioJava, and

BioRuby.

VI. TEXT MINING

Bioinformatics research and application aim

to use computational algorithms and

bioinformatics tools to gather, construct, and

structure the increasing body of biomedical

literature permutes scientists to question,

mine, study, and synthesize the particular

literature and published articles of their

research interest.

Therefore, text mining and biomedical

literature play a significant role in the

scientific enhancement, inventions, and

application and integration of discoveries to

society through extracting information (EI)

and assessing the relationships of

publications.

Biomedical literature text mining uses

various "text mining & data mining" tools,

visualization and navigation, information

retrieval, applying techniques such as data

clustering and extraction, and text

categorization and summarization.

VII. CONCLUSION

Systematic literature reviews used to collect

data for bioinformatics analysis. It is a

technique used to discover, estimate and

summarize the state-of-the-art of a precise

theme in the literature. Systematic Literature

Review permits gathering literature

information restrictively that allows a

Copyright © 2021 pubrica. All rights reserved 2

rigorous methodological analysis with lower

bias than the traditional reviews. The

objective is to build a general vision of a

specific question and give it a reasonable

summary of the literature. Hence, to perform

systematic reviews is essential to follow a

pre-established and well-defined protocol.

REFERENCES

1. Zomaya, A. Y. (2005). Parallel computing for

bioinformatics and computational biology. Wiley.

2. Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad,

B., Dettling, M., Dudoit, S., ... & Zhang, J. (2004).

Bioconductor: open software development for

computational biology and bioinformatics. Genome

Biology, 5(10), 1-16.