Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions. In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.

Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions. In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.

This Festschrift volume, published in honour of Peter Buneman, contains contributions written by some of his colleagues, former students, and friends. In celebration of his distinguished career a colloquium was held in Edinburgh, Scotland, 27-29 October, 2013. The articles presented herein belong to some of the many areas of Peter's research interests.

This volume contains the papers presented at the 12th Annual International ConferenceonREsearchin COmputationalMolecularBiology(RECOMB2008) held in Singapore, March 30-April 2, 2008. The RECOMB conference series was founded in 1997 by Sorin Istrail, Pavel Pevzner and Michael Waterman. Its history is summarized in the table herein. The general development of the RECOMB conference series is guided by a Steering Committee. RECOMB 2008 was hosted by the National University of Singapore and held at the University Cultural Centre. The Conference Chair was Limsoon Wong, who was supported by a 16-member-strong Organizing Committee. Out of 193 papers submitted to RECOMB 2008, the Program Committee (PC) selected 34 papers for presentation at the conference. The PC consisted of 46 members, who could further draw on the help of subreviewers. Three PC members with possible help of the external reviewers ?rst reviewed each paper, subsequently a Web-based discussion led to the ?nal selection of papers. This year, RECOMB and the journal Genome Research teamed up to - fer authors Genome Research as an alternative forum for publication. Appro- mately 55 of the RECOMB submissions, selected by a subset of the PC headed by Sera?m Batzoglou, were forwarded to Genome Research for consideration.

This book constitutes the refereed proceedings of the International Workshop on Pattern Recognition in Bioinformatics, PRIB 2006, held in Hong Kong, within the scope of the 18th International Conference on Pattern Recognition, ICPR 2006. The book presents 19 revised full papers, covering all topics of the creation and maintenance of biological databases, and the discovery of knowledge from life sciences data. Includes an introduction to Pattern Recognition in Bioinformatics.

This volume contains papers presented at the 18th International Conference on Genome Informatics (GIW 2007) held at the Biopolis, Singapore from December 3 to 5, 2007. The GIW Series provides an international forum for the presentation and discussion of original research papers on all aspects of bioinformatics, computational biology and systems biology. Its scope includes biological sequence analysis, protein folding prediction, gene regulatory network, clustering algorithms, comparative genomics, and text mining. Boasting a history of 18 years, GIW is likely the longest-running international bioinformatics conference.A total of 16 papers were selected for presentation at GIW 2007 and inclusion in this book. The notable authors include Ming Li (University of Waterloo, Canada), Minoru Kanehisa (Kyoto University, Japan), Vladimir Kuznetsov (Genome Institute of Singapore), Tao Jiang (UC Riverside, USA), Christos Ouzounis (European Bioinformatics Institute, UK), and Satoru Miyano (University of Tokyo, Japan). In addition, this book contains abstracts from the five invited speakers: Frank Eisenhaber (Bioinformatics Institute, Singapore), Sir David Lane (Institute of Molecular and Cell Biology, Singapore), Hanah Margalit (The Hebrew University of Jerusalem, Israel), Lawrence Stanton (Genome Institute of Singapore), and Michael Zhang (Cold Spring Harbor Laboratory, USA).

Computer scientists have increasingly been enlisted as "bioinformaticians" to assist molecular biologists in their research. This book is a practical introduction to bioinformatics for these computer scientists. The chapters are in-depth discussions by expert bioinformaticians on both general techniques and specific approaches to a range of selected bioinformatics problems. The book is organized into clusters of chapters on the following topics:
- Overview of modern molecular biology and a broad spectrum of techniques from computer science -- data mining, machine learning, mathematical modeling, sequence alignment, data integration, workflow development, etc.
- In-depth discussion of computational recognition of functional and regulatory sites in DNA sequences.
- Incisive discussion of computational prediction of secondary structure of RNA sequences.
- Overview of computational prediction of protein cellular localization, and selected discussions of inference of protein function.
- Overview of methods for discovering protein-protein interactions.
- Detailed discussion of approaches to gene expression analysis for the diagnosis of diseases, the treatment of diseases, and the understanding of gene functions.
- Case studies on analysis of phylogenies, functional annotation of proteins, construction of purposebuilt integrated biological databases, and development of workflows underlying the large-scale-effort gene discovery.
- Written in a practical, in-depth tutorial style
- Covers a broad range of bioinformatics topics and of techniques used in bioinformatics
- Comprehensive overviews of the development of various approaches in a number of selectedtopics
- In-depth exposition of a number of important topics
- Contributions by prominent researchers: Vladimir Bajic, Ming Li, Kenta Nakai, Limsoon Wong, Cathy Wu, etc.
- Extensive, integrated references to background literature

Computer scientists have increasingly been enlisted as "bioinformaticians" to assist molecular biologists in their research. This book is a practical introduction to bioinformatics for these computer scientists. The chapters are in-depth discussions by expert bioinformaticians on both general techniques and specific approaches to a range of selected bioinformatics problems. The book is organized into clusters of chapters on the following topics:* Overview of modern molecular biology and a broad spectrum of techniques from computer science - data mining, machine learning, mathematical modeling, sequence alignment, data integration, workflow development, etc.* In-depth discussion of computational recognition of functional and regulatory sites in DNA sequences.* Incisive discussion of computational prediction of secondary structure of RNA sequences.* Overview of computational prediction of protein cellular localization, and selected discussions of inference of protein function.* Overview of methods for discovering protein-protein interactions.* Detailed discussion of approaches to gene expression analysis for the diagnosis of diseases, the treatment of diseases, and the understanding of gene functions.* Case studies on analysis of phylogenies, functional annotation of proteins, construction of purpose-built integrated biological databases, and development of workflows underlying the large-scale-effort gene discovery.