Partial protein domains: evolutionary insights and bioinformatics challenges

Protein domains are generally thought to correspond to units of evolution. New research raises questions about how such domains are defined with bioinformatics tools and sheds light on how evolution has enabled partial domains to be viable.With the rapid expansion in the number of determined protein sequences - over 92 million in UniProt in March 2015 - an ever-increasing number of biologists are using bioinformatics tools for annotation of these sequences. One widely used strategy is to identify occurrences of Pfam families within the sequence of interest [1]. A Pfam family is a multiple sequence alignment of the occurrences of a particular domain both in different species and in different regions of the same protein. The concept underpinning Pfam is that proteins typically comprise one or more domains (regions), each of which is an evolutionary unit that generally has a well-defined biological function. A significant sequence similarity between a query protein and a Pfam family provides the basis for annotations. Two recent articles [2,3] in Genome Biology evaluate the implications of having the query sequence only matching part of a Pfam family, which is an intriguing finding, given that a Pfam family is considered to be an evolutionary unit.     

生物信息学对计算机科学发展的机遇与挑战

生物信息学是一个发展很快的新兴学科,是计算机应用的最重要的领域之一,同时生物信息学的发展又给计算机学科提出了许多新的课题,从而促进计算机学科自身的发展。从数据库技术、海量存储技术、数据挖掘、计算几何、DNA计算、网格计算、机器学习、人工心智、web service等方面,就生物信息学对计算机科学发展的促进作用进行了论述。     

Establishing a master's degree programme in bioinformatics: challenges and opportunities

The development of the Bioinformatics MS degree program at the University of Illinois, the challenges and opportunities associated with such a process, and the current structure of the program is described. This program has departed from earlier University practice in significant ways. Despite the existence of several interdisciplinary programs at the University, a few of which grant degrees, this is the first interdisciplinary program that grants degrees and formally recognises departmental specialisation areas. The program, which is not owned by any particular department but by the Graduate College itself, is operated in a franchise-like fashion via several departmental concentrations. With four different colleges and many more departments involved in establishing and operating the program, the logistics of the operation are of considerable complexity but result in significant interactions across the entire campus.     

Structural biology and bioinformatics in drug design: opportunities and challenges for target identification and lead discovery

Impressive progress in genome sequencing, protein expression and high-throughput crystallography and NMR has radically transformed the opportunities to use protein three-dimensional structures to accelerate drug discovery, but the quantity and complexity of the data have ensured a central place for informatics. Structural biology and bioinformatics have assisted in lead optimization and target identification where they have well established roles; they can now contribute to lead discovery, exploiting high-throughput methods of structure determination that provide powerful approaches to screening of fragment binding.     

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.     

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.     

Machine learning in bioinformatics

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.     

生物信息学中的分布式计算

生物信息学的发展给计算机技术带来了挑战,通过介绍当前在生物信息学研究领域已经得到广泛使用或正在研发的各种分布式计算平台、工具或研究项目,以此来概观生物信息学领域中分布式计算应用的现状。     

生物信息学在昆虫学研究中的应用

随着深度测序和基因芯片技术的不断发展,基因组、转录组、表达谱数据大量积累。目前,至少有10多个昆虫的基因组已被测序,30多个昆虫的转录组数据被报道。显然,传统的生物统计学方法无法处理如此海量的生物数据。量变引发质变,生物数据的大量积累催生了一门新兴学科,生物信息学。生物信息学融合了统计学、信息科学和生物学等各学科的理论和研究内容,在医学、基础生物学、农业科学以及昆虫学等方面获得了广泛的应用。生物信息学的目标是存储数据、管理数据和数据挖掘。因此,建立维护生物学数据库、设计开发基于模式识别、机器学习、数据挖掘等方法的生物软件,以及运用上述工具进行深度的数据挖掘,是生物信息学的重要研究内容。本文首先简要介绍了生物信息学的历史、研究现状及其在昆虫学科中的应用,然后综述了昆虫基因组学和转录组学的研究进展,最后对生物信息学在昆虫学研究中的应用前景进行了展望。     

Time: The Biggest Pattern in Natural History Research

We distinguish between four cosmological transitions in the history of Western intellectual thought, and focus on how these cosmologies differentially define matter, space and time. We demonstrate that how time is conceptualized significantly impacts a cosmology’s notion on causality, and hone in on how time is conceptualized differentially in modern physics and evolutionary biology. The former conflates time with space into a single space–time continuum and focuses instead on the movement of matter, while the evolutionary sciences have a tradition to understand time as a given when they cartography how organisms change across generations over or in time, thereby proving the phenomenon of evolution. The gap becomes more fundamental when we take into account that phenomena studied by chrono-biologists demonstrate that numerous organisms, including humans, have evolved a “sense” of time. And micro-evolutionary/genetic, meso-evolutionary/developmental and macro-evolutionary phenomena including speciation and extinction not only occur by different evolutionary modes and at different rates, they are also timely phenomena that follow different periodicities. This article focusses on delineating the problem by finding its historical roots. We conclude that though time might be an obsolete concept for the physical sciences, it is crucial for the evolutionary sciences where evolution is defined as the change that biological individuals undergo in/over or through time.     

Evolving research trends in bioinformatics

The cross-disciplinary nature of bioinformatics entails co-evolution with other biomedical disciplines, whereby some bioinformatics applications become popular in certain disciplines and, in turn, these disciplines influence the focus of future bioinformatics development efforts. We observe here that the growth of computational approaches within various biomedical disciplines is not merely a reflection of a general extended usage of computers and the Internet, but due to the production of useful bioinformatics databases and methods for the rest of the biomedical scientific community. We have used the abstracts stored both in the MEDLINE database of biomedical literature and in NIH-funded project grants, to quantify two effects. First, we examine the biomedical literature as a whole and find that the use of computational methods has become increasingly prevalent across biomedical disciplines over the past three decades, while use of databases and the Internet have been rapidly increasing over the past decade. Second, we study the recent trends in the use of bioinformatics topics. We observe that molecular sequence databases are a widely adopted contribution in biomedicine from the field of bioinformatics, and that microarray analysis is one of the major new topics engaged by the bioinformatics community. Via this analysis, we were able to identify areas of rapid growth in the use of informatics to aid in curriculum planning, development of computational infrastructure and strategies for workforce education and funding.