Genomics,proteomics and bioinformatics: all in the same boat

A report on the Genomics, Proteomics and Bioinformatics for Medicine (GPBM) 2002 meeting, St. Petersburg to Moscow, Russia, 22-30 June 2002.     

Integromics: challenges in data integration

A report on Barnett International's 4th annual Bioinformatics and Data Integration conference, Philadelphia, USA, 7-8 March 2002.     

Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences

MOTIVATION: In 2001 and 2002, we published two papers (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.     

Infrastructure for the life sciences: design and implementation of the UniProt website

Background  

The UniProt consortium was formed in 2002 by groups from the Swiss Institute of Bioinformatics (SIB), the European Bioinformatics Institute (EBI) and the Protein Information Resource (PIR) at Georgetown University, and soon afterwards the website was set up as a central entry point to UniProt resources. Requests to this address were redirected to one of the three organisations' websites. While these sites shared a set of static pages with general information about UniProt, their pages for searching and viewing data were different. To provide users with a consistent view and to cut the cost of maintaining three separate sites, the consortium decided to develop a common website for UniProt. Following several years of intense development and a year of public beta testing, the domain was switched to the newly developed site described in this paper in July 2008.     

OrthoGUI: graphical presentation of Orthostrapper results

SUMMARY: Orthostrapper is a program that calculates orthology support values for pairs of sequences in a multiple alignment (Storm and Sonnhammer, Bioinformatics, 18, 92-99, 2002). Here we present OrthoGUI, a web interface and display tool for Orthostrapper analysis. OrthoGUI visualizes the Orthostrapper output in both tabular and tree representations, and can also apply a clustering algorithm to identify groups of multiple orthologs, which are indicated by colour coding. AVAILABILITY: http://www.cgb.ki.se/OrthoGUI CONTACT: erik.sonnhammer@cgb.ki.se     

生物信息数据库知识产权保护初探

生物信息技术是当今生命科学乃至整个自然科学的重大前沿领域之一,生物信息数据库是生物信息技术中非常重要的部分,对生命科学的发展起着重要的推动作用。本文叙述了生物信息数据库的特点及国内外知识产权保护现状,初步讨论了生物信息数据库知识产权保护中存在的问题,提出了保护我国生物信息数据库的建议。     

数据挖掘在生物信息学中的应用

借助各种应用数学和计算机技术 ,将大量积累并急需处理的生物信息数据利用起来 ,探索生物信息中的规律 ,是当前国内国际生物信息学研究的热点和重点。其中数据挖掘技术在生物信息研究中发挥着巨大的作用。     

Multi-class cancer classification via partial least squares with gene expression profiles

MOTIVATION: Discrimination between two classes such as normal and cancer samples and between two types of cancers based on gene expression profiles is an important problem which has practical implications as well as the potential to further our understanding of gene expression of various cancer cells. Classification or discrimination of more than two groups or classes (multi-class) is also needed. The need for multi-class discrimination methodologies is apparent in many microarray experiments where various cancer types are considered simultaneously. RESULTS: Thus, in this paper we present the extension to the classification methodology proposed earlier Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50) to classify cancer samples from multiple classes. The methodologies proposed in this paper are applied to four gene expression data sets with multiple classes: (a) a hereditary breast cancer data set with (1) BRCA1-mutation, (2) BRCA2-mutation and (3) sporadic breast cancer samples, (b) an acute leukemia data set with (1) acute myeloid leukemia (AML), (2) T-cell acute lymphoblastic leukemia (T-ALL) and (3) B-cell acute lymphoblastic leukemia (B-ALL) samples, (c) a lymphoma data set with (1) diffuse large B-cell lymphoma (DLBCL), (2) B-cell chronic lymphocytic leukemia (BCLL) and (3) follicular lymphoma (FL) samples, and (d) the NCI60 data set with cell lines derived from cancers of various sites of origin. In addition, we evaluated the classification algorithms and examined the variability of the error rates using simulations based on randomization of the real data sets. We note that there are other methods for addressing multi-class prediction recently and our approach is along the line of Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50). CONTACT: dnguyen@stat.tamu.edu; dmrocke@ucdavis.edu     

A review of bioinformatics degrees in Australia

Bioinformatics has been a hot topic in Australia's biotechnology circles for the past five years. As with biotechnology in the 1990s, there has been a sudden increase in the number of Bioinformatics undergraduate degrees. For students in the 2005 intake there are six undergraduate Bioinformatics degrees to choose from and another five Bioinformatics streams within a Bachelor of Science degree. The courses vary from three to four years of full-time study. This report aims at dissecting each of these degrees to determine where the differences lie, to give the prospective students an idea as to which degree suits their career goals and to give an overview of the pedagogy of Australian bioinformatics education.     

How to test bioinformatics software?

Biophysical Reviews - Bioinformatics is the application of computational, mathematical and statistical techniques to solve problems in biology and medicine. Bioinformatics programs developed for...     

Bioinformatics and systems biology research update from the 15<Superscript>th</Superscript> International Conference on Bioinformatics (InCoB2016)

The International Conference on Bioinformatics (InCoB) has been publishing peer-reviewed conference papers in BMC Bioinformatics since 2006. Of the 44 articles accepted for publication in supplement issues of BMC Bioinformatics, BMC Genomics, BMC Medical Genomics and BMC Systems Biology, 24 articles with a bioinformatics or systems biology focus are reviewed in this editorial. InCoB2017 is scheduled to be held in Shenzen, China, September 20–22, 2017.     

Bioinformatics education dissemination with an evolutionary problem solving perspective

Bioinformatics is central to biology education in the 21st century. With the generation of terabytes of data per day, the application of computer-based tools to stored and distributed data is fundamentally changing research and its application to problems in medicine, agriculture, conservation and forensics. In light of this 'information revolution,' undergraduate biology curricula must be redesigned to prepare the next generation of informed citizens as well as those who will pursue careers in the life sciences. The BEDROCK initiative (Bioinformatics Education Dissemination: Reaching Out, Connecting and Knitting together) has fostered an international community of bioinformatics educators. The initiative's goals are to: (i) Identify and support faculty who can take leadership roles in bioinformatics education; (ii) Highlight and distribute innovative approaches to incorporating evolutionary bioinformatics data and techniques throughout undergraduate education; (iii) Establish mechanisms for the broad dissemination of bioinformatics resource materials and teaching models; (iv) Emphasize phylogenetic thinking and problem solving; and (v) Develop and publish new software tools to help students develop and test evolutionary hypotheses. Since 2002, BEDROCK has offered more than 50 faculty workshops around the world, published many resources and supported an environment for developing and sharing bioinformatics education approaches. The BEDROCK initiative builds on the established pedagogical philosophy and academic community of the BioQUEST Curriculum Consortium to assemble the diverse intellectual and human resources required to sustain an international reform effort in undergraduate bioinformatics education.     

gamma-Turn types prediction in proteins using the support vector machines

Recently, two different models have been developed for predicting gamma-turns in proteins by Kaur and Raghava [2002. An evaluation of beta-turn prediction methods. Bioinformatics 18, 1508-1514; 2003. A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. Protein Sci. 12, 923-929]. However, the major limitation of previous methods is inability in predicting gamma-turns types. Thus, there is a need to predict gamma-turn types using an approach which will be useful in overall tertiary structure prediction. In this work, support vector machines (SVMs), a powerful model is proposed for predicting gamma-turn types in proteins. The high rates of prediction accuracy showed that the formation of gamma-turn types is evidently correlated with the sequence of tripeptides, and hence can be approximately predicted based on the sequence information of the tripeptides alone.     

自然语言处理技术在生物信息学中的应用

从信息处理的角度来看,生物信息学与自然语言处理中的许多问题是非常相似的,因此,可以将一些自然语言处理中的经典方法应用到生物信息学文字中。本文介绍了自然语言处理和生物信息学中共有的问题,如比对、分类、预测等,以及这些问题的解决方法。通过对两个领域形似问题的分析可知,优秀的自然语言处理技术也可用来解决生物信息学方面的问题,并且一些还未在生物信息学领域得到应用的自然语言理解技术也有其潜在的应用价值。最后给出了一个分类问题的解决方案,演示了如何在生物数据上应用算法进行实验。     

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.     

MATLAB 7.X生物信息工具箱的应用——基因序列分析(一)

MATLAB 7.X生物信息工具箱为广大用户提供了一个用于基因组和蛋白质组分析的综合环境,它利用数据库资源,使科学研究事半功倍,在工具箱提供的开放环境里,用户甚至可以按照自己的目的来设计和利用分析工具.本文主要介绍了MATLAB7.X生物信息工具箱在基因序列分析中的应用,包括确定核苷酸组成,密码子组成,氨基酸转化和组成等,所有操作简便高效,结果可视化程度高.     

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。