A brief history of bioinformatics

Abstract

It is easy for today's students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of 'Big Data' has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology.

Identification of Novel MicroRNAs and Their Diagnostic and Prognostic Significance in Oral Cancer

“…In order to best analyze the so-called “Big Data”, in recent years different researchers have created several bioinformatics software useful for a fast and efficient analysis of a large number of data thus interpretation through a process named “data mining” [43,44].…”

Identification of Immune-Related Prognostic Biomarkers Based on the Tumor Microenvironment in 20 Malignant Tumor Types With Poor Prognosis

“…With the development of high-throughput technologies, the omics sciences have advanced greatly, gaining unprecedented development; high-throughput methods have also promoted the progress of bioinformatics by generating thousands of massive datasets called “big data” ( 26 , 27 ). Thus, data mining has emerged to efficiently transform big data into useful information and knowledge, and several automated tools and techniques are used to intelligently assist data analysis ( 28 , 29 ). At present, TCGA is the primary database for multi-omic cancer data, and the ESTIMATE algorithm, a new data mining tool, can be used with TCGA data sets to estimate the numbers of stromal and immune cells in the tumor microenvironment and, thus, to assess tumor purity ( 30 ).…”

Four Key Genes are Biomarkers Associated with Immunity in Neuroglioma

“…With the advent of the big data era, gene sequencing has provided us with a wealth of gene chips. Through speci c research methods, people can use the genetic data in them for bioinformatics analysis, and can study the key genetic changes and epigenetic characteristics of glioma at the molecular level [5,6]. Moreover, the treatment of high-grade glioma is currently a di cult problem to overcome, mainly due to its strong heterogeneity and invasion and metastasis instinct.…”