Briefings Bioinform.最新文献

{"title":"FRANC: a unified framework for multi-way local ancestry deconvolution with high density SNP data","authors":"Ephifania Geza, N. Mulder, E. Chimusa, G. Mazandu","doi":"10.1093/bib/bbz117","DOIUrl":"https://doi.org/10.1093/bib/bbz117","url":null,"abstract":"\u0000 Several thousand genomes have been completed with millions of variants identified in the human deoxyribonucleic acid sequences. These genomic variations, especially those introduced by admixture, significantly contribute to a remarkable phenotypic variability with medical and/or evolutionary implications. Elucidating local ancestry estimates is necessary for a better understanding of genomic variation patterns throughout modern human evolution and adaptive processes, and consequences in human heredity and health. However, existing local ancestry deconvolution tools are accessible as individual scripts, each requiring input and producing output in its own complex format. This limits the user’s ability to retrieve local ancestry estimates. We introduce a unified framework for multi-way local ancestry inference, FRANC, integrating eight existing state-of-the-art local ancestry deconvolution tools. FRANC is an adaptable, expandable and portable tool that manipulates tool-specific inputs, deconvolutes ancestry and standardizes tool-specific results. To facilitate both medical and population genetics studies, FRANC requires convenient and easy to manipulate input files and allows users to choose output formats to ease their use in further potential local ancestry deconvolution applications.","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128197007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flexible k-mers with variable-length indels for identifying binding sequences of protein dimers","authors":"Chenyang Hong, Kevin Y. Yip","doi":"10.1093/bib/bbz101","DOIUrl":"https://doi.org/10.1093/bib/bbz101","url":null,"abstract":"\u0000 Many DNA-binding proteins interact with partner proteins. Recently, based on the high-throughput consecutive affinity-purification systematic evolution of ligands by exponential enrichment (CAP-SELEX) method, many such protein pairs have been found to bind DNA with flexible spacing between their individual binding motifs. Most existing motif representations were not designed to capture such flexibly spaced regions. In order to computationally discover more co-binding events without prior knowledge about the identities of the co-binding proteins, a new representation is needed. We propose a new class of sequence patterns that flexibly model such variable regions and corresponding algorithms that identify co-bound sequences using these patterns. Based on both simulated and CAP-SELEX data, features derived from our sequence patterns lead to better classification performance than patterns that do not explicitly model the variable regions. We also show that even for standard ChIP-seq data, this new class of sequence patterns can help discover co-bound events in a subset of sequences in an unsupervised manner. The open-source software is available at https://github.com/kevingroup/glk-SVM.","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115903446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Next Generation Microarray Bioinformatics: Methods and Protocols. Edited by Junbai Wang, Aik Choon Tan and Tianhai Tian","authors":"D. Lin","doi":"10.1093/bib/bbs068","DOIUrl":"https://doi.org/10.1093/bib/bbs068","url":null,"abstract":"This book caught my eye by its title. While it is unusual to relate ‘next generation’ to microarrays, ‘next generation’ is widely used to refer to a variety of rapidly advancing DNA sequencing technologies. Since 2004, the Next Generation Sequencing (NGS) technologies have increased throughput more than a million fold over the first-generation sequencing (called Sanger sequencing) at the time of this review. The availability of massive digital sequence information at base pair resolution not only provides higher sensitivity and specificity on numeric measurements than what microarrays can offer but also allows inferences to be made regarding genetic variations within the same set of sequences, which are added benefits that are not available in microarrays. The important questions are as follows: Will NGS completely replace microarrays? Where will microarray technologies go? How can we apply what we learned from microarray Bioinformatics to NGS Bioinformatics? I started reading with curiosity about how this book can help address these questions. The volume does not cover all of the latest microarray and NGS applications. Instead, the authors have assembled a set of popular techniques and have focused on explaining how they work. Although the topics are highly technical and specific, most chapters are well written and provide sufficient technical and mathematic details as well as references for a well-trained bioinformatician to implement the algorithms, use the software tools and follow the protocols therein. The book is a good resource for someone who wants to get a general idea of, or seek guidance for, a particular microarray application. Each chapter is written in a format consisting of four sections after an abstract to make it different from a journal publication. First, ‘Introduction’ gives a general overview of the topic. Then ‘Materials and Methods’ sections describe the details of a particular application or a protocol and use one or more examples to illustrate how algorithms or software tools work. At the end, there is a unique ‘Notes’ section to provide tips, highlight pitfalls or clarify key concepts. Each chapter is written to be read independently, so there are nominal overlaps among some chapters. The book is a collection of 26 technological reports and reviews at intermediate to advanced levels. They are partitioned into five sections: (i) Introduction and Resources for Microarray Bioinformatics; (ii) Microarray Data Analysis (Top–Down Approach), which details the analysis of systematic characterizations of the cell to understand the functions and associations of components; (iii) Microarray Bioinformatics in Systems Biology (Bottom–Up Approach), which describes modeling individual components, their interactions based on microarray results and the inference of regulatory relationships at the system level; (iv) Next Generation Sequencing Data Analysis, a bonus section that is not implied in the book title and (v) Emerging Applicati","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130864939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Principles of Computational Modeling in NeuroscienceDavid Sterratt, Bruce Graham, Andrew Gillies and David Willshaw","authors":"Li Shen","doi":"10.1093/bib/bbs003","DOIUrl":"https://doi.org/10.1093/bib/bbs003","url":null,"abstract":"Understanding complex neurobiological systems is one of the most difficult challenges in modern science. This book is focused on computational neuroscience, which provides a mathematical foundation and a rich set of computational approaches for understanding the principles and dynamics of the nervous system. Taking a bottom up strategy, the book offers comprehensive, step-by-step coverage on how to model the neuron and neural circuitry to understand the nervous system at multiple levels, from ion channels to networks. It has arisen from graduate courses to students in physical, mathematical and computer sciences, and can serve as an excellent text for courses in computational neuroscience.","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125413811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to Computational Genomics: A Case Studies Approach Nello Cristianini and Matthew W. Hahn","authors":"N. Ahituv","doi":"10.1093/bib/bbp009","DOIUrl":"https://doi.org/10.1093/bib/bbp009","url":null,"abstract":"","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122186302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Problems and Solutions in Biological Sequence Analysis, Mark Borodovsky, Svetlana Ekisheva","authors":"M. Perțea","doi":"10.1093/bib/bbn037","DOIUrl":"https://doi.org/10.1093/bib/bbn037","url":null,"abstract":"Since its first publication in 1998, Biological Sequence Analysis (BSA) by Durbin etal. has become a standard textbook for teaching bioinformatics. Part of the resources offered by BSA to understanding the underlying principles of bioinformatics is a set of workable exercises left to the interested reader to solve. Given the multidisciplinary background of bioinformatics, solving these problems requires integrating knowledge from various fields including genetics and molecular biology as well as mathematics and computer science. This is not an easy challenge for the numerous bioinformatics students who come with different abilities from a wide variety of educational backgrounds. Therefore I was glad to see this book offering not only step-by-step solutions to the problems presented in BSA but also to a large set of additional bioinformatics problems. From the first introductory chapter dealing with basic notions of probabilities to the more technical final chapter on more complex probabilistic concepts used in bioinformatics, this book follows exactly the structure of BSA. All problems presented in BSA are meticulously solved, and so are additional problems on topics concerning pairwise and multiple sequence alignments, hidden Markov models (HMMs) and building phylogenetic trees. The solutions often contain generalizations of particular cases and interesting remarks that draw the reader’s attention to specific results important for the analysis of biological sequences. Most problems have analytical solutions, but some of them are algorithmic examples. In such cases, the less inexperienced computer programmer can benefit from the Web Supplemental Materials including Perl and Cþþ implementations of those algorithms. As in BSA each chapter is concluded with a ‘Further reading’ section with more up-todate bibliographical references illustrating theoretical advances and research directions for the topics presented. I think that some of the most valuable parts of this book are the frequent theoretical introductions that broaden the fundamentals presented in BSA. The derivation of substitution matrices is briefly discussed in BSA. This is a critical step in scoring pairwise alignments, and I commend the authors for treating it thoroughly here. More attention is given to the PAM family in which case an ample discussion on the derivation of the amino acid substitution scores is followed by illustrative examples first in the context of a stationary Markov model, and later on in the context of an evolutionary model too. The statistical significance of pairwise alignments is addressed with exercises and theory topics describing the statistics of high-scoring pairs and the distribution of the length of the longest common word between random sequences. Theory and problems dealing with the selection of a particular probabilistic model and the estimation of its parameters expand the HMM chapter from BSA. More details are given for parameter estimation in the cas","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133972282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods in Microarray Normalization, Edited by Phillip Stafford","authors":"J. Daudin","doi":"10.1093/bib/bbn033","DOIUrl":"https://doi.org/10.1093/bib/bbn033","url":null,"abstract":"","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132456748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematics of Evolution and Phylogeny, Edited by Olivier Gascuel","authors":"J. Whitfield","doi":"10.1093/bib/bbn036","DOIUrl":"https://doi.org/10.1093/bib/bbn036","url":null,"abstract":"","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127158988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiac Gene Expression: Methods and Protocols.: Edited by Jun Zhang and Gregg Rokosh","authors":"N. Perumal","doi":"10.1093/bib/bbm062","DOIUrl":"https://doi.org/10.1093/bib/bbm062","url":null,"abstract":"PCT No. PCT/GB79/00117 Sec. 371 Date Mar. 17, 1980 Sec. 102(e) Date Mar. 17, 1980 PCT Filed Jul. 17, 1979 PCT Pub. No. WO80/00231 PCT Pub. Date Feb. 21, 1980.A die-cutting machine comprises two form-carrying rolls (10,12) operable in timed relation to produce alternate blanks, with minimum wastage between the blanks, from a web fed at constant speed by a conventional feed mechanism (14). The rolls (10,12) are driven by computer-controlled variable speed high impulse duty D.C. motors (28, 30) supplied with electrical power from thyristor controlled power supplies (36,38). Sensors (18, 24, 26) provide feedback signals representing web speed and instantaneous roll positions and speeds to the programmable computer units (32, 34). The use of the computer-controlled D.C. motors avoids backlash problems in previously proposed mechanical drives and allows greater flexibility, especially in maintaining registration between the rolls and preprinted areas on the web. A flywheel arrangement is employed to reduce the loading on the motors during large variations in roll speed.","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134520286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Analysis and Graphics Using R: An Example-Based Approach, Second Edition.: John Maindonald and John Braun","authors":"Zhaohui S. Qin","doi":"10.1093/bib/bbn012","DOIUrl":"https://doi.org/10.1093/bib/bbn012","url":null,"abstract":"","PeriodicalId":337078,"journal":{"name":"Briefings Bioinform.","volume":"324 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114804934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}