Evolving from bioinformatics in-the-small to bioinformatics in-the-large

We argue the significance of a fundamental shift in bioinformatics, from in-the-small to in-the-large. Adopting a large-scale perspective is a way to manage the problems endemic to the world of the small-constellations of incompatible tools for which the effort required to assemble an integrated system exceeds the perceived benefit of the integration. Where bioinformatics in-the-small is about data and tools, bioinformatics in-the-large is about metadata and dependencies. Dependencies represent the complexities of large-scale integration, including the requirements and assumptions governing the composition of tools. The popular make utility is a very effective system for defining and maintaining simple dependencies, and it offers a number of insights about the essence of bioinformatics in-the-large. Keeping an in-the-large perspective has been very useful to us in large bioinformatics projects. We give two fairly different examples, and extract lessons from them showing how it has helped. These examples both suggest the benefit of explicitly defining and managing knowledge flows and knowledge maps (which represent metadata regarding types, flows, and dependencies), and also suggest approaches for developing bioinformatics database systems. Generally, we argue that large-scale engineering principles can be successfully adapted from disciplines such as software engineering and data management, and that having an in-the-large perspective will be a key advantage in the next phase of bioinformatics development.     

Teaching bioinformatics

Bioinformatics is the name that has become associated with the theoretical and applied field of study that links computer science with modern biology. Within molecular biology specifically, bioinformatics is a generic term used to describe many of the analytical manipulations that can be carried out on sequences. Familiarity with the resources available and fundamental methods used for such analyses should be an essential part of a modern biology course, especially given the availability of WWW resources. In this article, some of these resources are summarised and their possible integration into a short practical undergraduate teaching unit is described.     

The apology of bioinformatics

The discussion about adequate understanding of the term "bioinformatics" is continued. The relationships between bioinformatics and experimental molecular biology are considered. The list of the main branches and achievements of modern bioinformatics is presented.     

Phylogenetic--evolutionary approaches to bioinformatics

Phylogenies of organisms are essential to investigating a range of evolutionary questions of interest to researchers in the field of bioinformatics. Phylogenies not only help to define how to study many evolutionary questions, they must also be taken into account when conducting statistical analyses. Here it is shown how phylogenies can be used to investigate variability along the sites of a gene, reconstruct ancestral states of ancient genes and proteins, identify and characterise events of parallel and convergent evolution, find events of gene duplication, analyse predictions from molecular clocks, seek evidence for correlated changes among different parts of the same gene or genome, and test theories of molecular evolution. A table of statistical and phylogenetic methods is presented.     

The origins of bioinformatics

Bioinformatics is often described as being in its infancy, but computers emerged as important tools in molecular biology during the early 1960s. A decade before DNA sequencing became feasible, computational biologists focused on the rapidly accumulating data from protein biochemistry. Without the benefits of super computers or computer networks, these scientists laid important conceptual and technical foundations for bioinformatics today.     

Earliest pages of bioinformatics

This review is a brief outline of the chronology and essence of early events in bioinformatics, covering the period from 1869 (discovery of DNA by Miescher) to 1980-1981 (beginning of massive sequencing). For the purpose of this review, bioinformatics is understood as a chapter of molecular biology dealing with the amino acid and nucleotide sequences and with the information they carry.     

Biggest challenges in bioinformatics

The third Heidelberg Unseminars in Bioinformatics (HUB) was held on 18th October 2012, at Heidelberg University, Germany. HUB brought together around 40 bioinformaticians from academia and industry to discuss the ‘Biggest Challenges in Bioinformatics’ in a ‘World Café’ style event.     

Distributed computing in bioinformatics

This paper provides an overview of methods and current applications of distributed computing in bioinformatics. Distributed computing is a strategy of dividing a large workload among multiple computers to reduce processing time, or to make use of resources such as programs and databases that are not available on all computers. Participating computers may be connected either through a local high-speed network or through the Internet.     

Open-source software accelerates bioinformatics

A report on the Wellcome Trust/Cold Spring Harbor Genome Informatics meeting, Cold Spring Harbor, USA, 7-11 May 2003.     

The growth of bioinformatics

Briefings in Bioinformatics, or BiB for short, will celebrateits 7th anniversary this year. The journal's mission has remainedunchanged throughout this period: we are committed to disseminatingknowledge on databases and computational tools for life sciencesthrough review articles. During its history, some 250 articleshave been published and they have been cited more than 2700times, making BiB the premier bioinformatics journal using theper-article citation impact measure. The common theme for thisissue is the cross-disciplinary nature of bioinformatics     

生物信息学技术进展

生物信息学是一门对生物信息进行采集、储存、传递、检索、分析和解读的学科,它已经渗透于现代生物学、数学、信息学、计算科学、统计学、物理、化学各个方面。本概述和分析了生物信息学研究中的一些方法。     

Forensic DNA and bioinformatics

The field of forensic science is increasingly based on biomolecular data and many European countries are establishing forensic databases to store DNA profiles of crime scenes of known offenders and apply DNA testing. The field is boosted by statistical and technological advances such as DNA microarray sequencing, TFT biosensors, machine learning algorithms, in particular Bayesian networks, which provide an effective way of evidence organization and inference. The aim of this article is to discuss the state of art potentialities of bioinformatics in forensic DNA science. We also discuss how bioinformatics will address issues related to privacy rights such as those raised from large scale integration of crime, public health and population genetic susceptibility-to-diseases databases.