http://www.biomedcentral.com/bmcbioinformatics/ The editor's pick of recent articles published by BMC Bioinformatics 2013-06-07T00:00:00Z Background: The development of next-generation sequencing instruments has led to the generation of millions of short sequences in a single run. The process of aligning these reads to a reference genome is time consuming and demands the development of fast and accurate alignment tools. However, the current proposed tools make different compromises between the accuracy and the speed of mapping. Moreover, many important aspects are overlooked while comparing the performance of a newly developed tool to the state of the art. Therefore, there is a need for an objective evaluation method that covers all the aspects. In this work, we introduce a benchmarking suite to extensively analyze sequencing tools with respect to various aspects and provide an objective comparison. Results: We applied our benchmarking tests on 9 well known mapping tools, namely, Bowtie, Bowtie2, BWA, SOAP2, MAQ, RMAP, GSNAP, Novoalign, and mrsFAST (mrFAST) using synthetic data and real RNA-Seq data. MAQ and RMAP are based on building hash tables for the reads, whereas the remaining tools are based on indexing the reference genome. The benchmarking tests reveal the strengths and weaknesses of each tool. The results show that no single tool outperforms all others in all metrics. However, Bowtie maintained the best throughput for most of the tests while BWA performed better for longer read lengths. The benchmarking tests are not restricted to the mentioned tools and can be further applied to others. Conclusion: The mapping process is still a hard problem that is affected by many factors. In this work, we provided a benchmarking suite that reveals and evaluates the different factors affecting the mapping process. Still, there is no tool that outperforms all of the others in all the tests. Therefore, the end user should clearly specify his needs in order to choose the tool that provides the best results. http://www.biomedcentral.com/1471-2105/14/184 Ayat Hatem Doruk Bozda¿ Amanda E Toland Ümit V Çatalyürek BMC Bioinformatics 2013, 14:184 2013-06-07T00:00:00Z 10.1186/1471-2105-14-184 Test of nine short read mapping tools <p>A benchmarking suite to analyze well-known next generation sequencing tools, mapping short reads to a reference genome, with respect to various technological and algorithmic perspectives and addressing issues using multiple approaches such as multiple simulators.</p> /content/figures/1471-2105-14-184-toc.gif BMC Bioinformatics 1471-2105 14 184 2013-06-07T00:00:00Z PDF Background: In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies. Results: To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments. Conclusions: In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs. http://www.biomedcentral.com/1471-2105/14/157 Jen-hwa Chu Angela Rogers Iuliana Ionita-Laza Katayoon Darvishi Ryan E Mills Charles Lee Benjamin A Raby BMC Bioinformatics 2013, 14:157 2013-05-09T00:00:00Z 10.1186/1471-2105-14-157 Family data used in copy number detection <p>Employing Gaussian mixture models for intensity-based copy number variation array data performed on all family members simultaneously provides advance towards better quality CNV calls and should lead to more powerful family-based CNV association tests.</p> /content/figures/1471-2105-14-157-toc.gif BMC Bioinformatics 1471-2105 14 157 2013-05-09T00:00:00Z XML User-centred design (UCD) is a type of user interface design in which the needs and desires of users are taken into account at each stage of the design process for a service or product; often for software applications and websites. Its goal is to facilitate the design of software that is both useful and easy to use. To achieve this, you must characterise users’ requirements, design suitable interactions to meet their needs, and test your designs using prototypes and real life scenarios.For bioinformatics, there is little practical information available regarding how to carry out UCD in practice. To address this we describe a complete, multi-stage UCD process used for creating a new bioinformatics resource for integrating enzyme information, called the Enzyme Portal (http://www.ebi.ac.uk/enzymeportal). This freely-available service mines and displays data about proteins with enzymatic activity from public repositories via a single search, and includes biochemical reactions, biological pathways, small molecule chemistry, disease information, 3D protein structures and relevant scientific literature.We employed several UCD techniques, including: persona development, interviews, ‘canvas sort’ card sorting, user workflows, usability testing and others. Our hope is that this case study will motivate the reader to apply similar UCD approaches to their own software design for bioinformatics. Indeed, we found the benefits included more effective decision-making for design ideas and technologies; enhanced team-working and communication; cost effectiveness; and ultimately a service that more closely meets the needs of our target audience. http://www.biomedcentral.com/1471-2105/14/103 Paula de Matos Jennifer A Cham Hong Cao Rafael Alcántara Francis Rowland Rodrigo Lopez Christoph Steinbeck BMC Bioinformatics 2013, 14:103 2013-03-20T00:00:00Z 10.1186/1471-2105-14-103 User-centred design for bioinformatics <p>The first &lsquo;how to&rsquo; guide for applying User-Centred Design (UCD) to websites for bioinformatics involves biomedical researchers rather than developers to design better web experiences, for example The Enzyme Portal</p> /content/figures/1471-2105-14-103-toc.gif BMC Bioinformatics 1471-2105 14 103 2013-03-20T00:00:00Z XML Background: Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient “partial“ clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. Results: We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either Cα RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. Conclusions: The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance. http://www.biomedcentral.com/1471-2105/14/11 Jianjun Zhou David S Wishart BMC Bioinformatics 2013, 14:11 2013-01-16T00:00:00Z 10.1186/1471-2105-14-11 Rapid decoy selection in protein structures <p>HS-Forest performs rapid, but incomplete,&nbsp;clustering for decoy selection in protein structure prediction, generating multiple independent trees for a consensus result by means of local sensitive hashing.&nbsp;</p> /content/figures/1471-2105-14-11-toc.gif BMC Bioinformatics 1471-2105 14 11 2013-01-16T00:00:00Z XML