Algebraic stability indicators for ranked lists in molecular profiling

MOTIVATION: We propose a method for studying the stability of biomarker lists obtained from functional genomics studies. It is common to adopt resampling methods to tune and evaluate marker-based diagnostic and prognostic systems in order to prevent selection bias. Such caution promotes honest estimation of class prediction, but leads to alternative sets of solutions. In microarray studies, the difference in lists may be bewildering, also due to the presence of modules of functionally related genes. Methods for assessing stability understand the dependency of the markers on the data or on the predictor's type and help selecting solutions. RESULTS: A computational framework for comparing sets of ranked biomarker lists is presented. Notions and algorithms are based on concepts from permutation group theory. We introduce several algebraic indicators and metric methods for symmetric groups, including the Canberra distance, a weighted version of Spearman's footrule. We also consider distances between partial lists and an aggregation of sets of lists into an optimal list based on voting theory (Borda count). The stability indicators are applied in practical situations to several synthetic, cancer microarray and proteomics datasets. The addressed issues are predictive classification, presence of modules, comparison of alternative biomarker lists, outlier removal, control of selection bias by randomization techniques and enrichment analysis. AVAILABILITY: Supplementary Material and software are available at the address http://biodcv.fbk.eu/listspy.html     

A comparison of common programming languages used in bioinformatics

Background  

The performance of different programming languages has previously been benchmarked using abstract mathematical algorithms, but not using standard bioinformatics algorithms. We compared the memory usage and speed of execution for three standard bioinformatics methods, implemented in programs using one of six different programming languages. Programs for the Sellers algorithm, the Neighbor-Joining tree construction algorithm and an algorithm for parsing BLAST file outputs were implemented in C, C++, C#, Java, Perl and Python.     

Algebraic comparison of metabolic networks, phylogenetic inference, and metabolic innovation

Background  

Comparison of metabolic networks is typically performed based on the organisms' enzyme contents. This approach disregards functional replacements as well as orthologies that are misannotated. Direct comparison of the structure of metabolic networks can circumvent these problems.     

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。     

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.     

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。     

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.     

Generations of interdisciplinarity in bioinformatics

Bioinformatics, a specialism propelled into relevance by the Human Genome Project and the subsequent -omic turn in the life science, is an interdisciplinary field of research. Qualitative work on the disciplinary identities of bioinformaticians has revealed the tensions involved in work in this “borderland.” As part of our ongoing work on the emergence of bioinformatics, between 2010 and 2011, we conducted a survey of United Kingdom-based academic bioinformaticians. Building on insights drawn from our fieldwork over the past decade, we present results from this survey relevant to a discussion of disciplinary generation and stabilization. Not only is there evidence of an attitudinal divide between the different disciplinary cultures that make up bioinformatics, but there are distinctions between the forerunners, founders and the followers; as inter/disciplines mature, they face challenges that are both inter-disciplinary and inter-generational in nature.     

Email lists in laboratory animal science

Email lists can be invaluable for acquiring information that may not be easily accessible in the published literature. The author discusses the general format and functioning of email lists and describes six lists that can be valuable tools for education, training, and information exchange in the field of laboratory animal science.     

Similarities of ordered gene lists

MOTIVATION: Many applications of microarray technology in clinical cancer studies aim at detecting molecular features for refined diagnosis. In this paper, we follow an opposite rationale: we try to identify common molecular features shared by phenotypically distinct types of cancer using a meta-analysis of several microarray studies. We present a novel algorithm to uncover that two lists of differentially expressed genes are similar, even if these similarities are not apparent to the eye. The method is based on the ordering in the lists. RESULTS: In a meta-analysis of five clinical microarray studies we were able to detect significant similarities in five of the ten possible comparisons of ordered gene lists. We included studies, where not a single gene can be significantly associated to outcome. The detection of significant similarities of gene lists from different microarray studies is a novel and promising approach. It has the potential to improve upon specialized cancer studies by exploring the power of several studies in one single analysis. Our method is complementary to previous methods in that it does not rely on strong effects of differential gene expression in a single study but on consistent ones across multiple studies.     

BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams

Background

Recently in a number of studies, it has been demonstrated that the innate immune system doesn't merely acts as the first line of defense but provides critical signals for the development of specific adaptive immune response. Innate immune system employs a set of receptors called pattern recognition receptors (PRRs) that recognize evolutionarily conserved patterns from pathogens called pathogen associated molecular patterns (PAMPs). In order to assist scientific community, a database PRRDB has been developed that provides extensive information about pattern recognition receptors and their ligands.

Results

The current version of database contains around 500 patterns recognizing receptors from 77 distinct organisms ranging from insects to human. This includes 177 Toll-like receptors, 124 are Scavenger receptors and 67 are Nucleotide Binding Site-Leucine repeats rich receptors. The database also provides information about 266 ligands that includes carbohydrates, proteins, nucleic acids, glycolipids, glycoproteins, lipopeptides. A number of web tools have been integrated in PRRDB in order to provide following services: i) searching on any field; ii) database browsing; and iii) BLAST search against the pattern-recognition receptors. PRRDB also provides external links to standard databases like Swiss-Prot and Pubmed.

Conclusion

PRRDB is a unique database of its kind, which provides comprehensive information about innate immunity. This database will be very useful in designing effective adjuvant for subunit vaccine and in understanding role of innate immunity. The database is available from the URL's in the Availabiltiy and requirements section.