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Rapid identification of clinically significant variants is key to the successful application of next generation sequencing technologies in clinical practice. The Melbourne Genomics Health Alliance (MGHA) variant prioritization framework employs a gene prioritization index based on clinician-generated a priori gene lists, and a variant prioritization index (VPI) based on rarity, conservation and protein effect. We used data from 80 patients who underwent singleton whole exome sequencing (WES) to test the ability of the framework to rank causative variants highly, and compared it against the performance of other gene and variant prioritization tools. Causative variants were identified in 59 of the patients. Using the MGHA prioritization framework the average rank of the causative variant was 2.24, with 76% ranked as the top priority variant, and 90% ranked within the top five. Using clinician-generated gene lists resulted in ranking causative variants an average of 8.2 positions higher than prioritization based on variant properties alone. This clinically driven prioritization approach significantly outperformed purely computational tools, placing a greater proportion of causative variants top or in the top 5 (permutation P-value=0.001). Clinicians included 40 of the 49 WES diagnoses in their a priori list of differential diagnoses (81%). The lists generated by PhenoTips and Phenomizer contained 14 (29%) and 18 (37%) of these diagnoses respectively. These results highlight the benefits of clinically led variant prioritization in increasing the efficiency of singleton WES data analysis and have important implications for developing models for the funding and delivery of genomic services.

The benefits of implementing high throughput sequencing in the clinic are quickly becoming apparent. However, few freely available bioinformatics pipelines have been built from the ground up with clinical genomics in mind. Here we present Cpipe, a pipeline designed specifically for clinical genetic disease diagnostics. Cpipe was developed by the Melbourne Genomics Health Alliance, an Australian initiative to promote common approaches to genomics across healthcare institutions. As such, Cpipe has been designed to provide fast, effective and reproducible analysis, while also being highly flexible and customisable to meet the individual needs of diverse clinical settings. Cpipe is being shared with the clinical sequencing community as an open source project and is available at http://cpipeline.org. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0191-x) contains supplementary material, which is available to authorized users.

Background Microsatellites are short (2-6bp) DNA sequences repeated in tandem, which make up approximately 3% of the human genome [1]. These loci are prone to frequent mutations and high polymorphism with the estimated mutation rates of 10 10 events per locus per generation, orders of magnitude higher than other parts of the genome [2]. Dozens of neurological and developmental disorders have been attributed to microsatellite expansions [3]. Microsatellites have also been implicated in a range of functions such as DNA replication and repair, chromatin organisation and regulation of gene expression [4]. Traditionally, microsatellite variation has been measured using capillary gel electrophoresis [5]. In addition to being time-consuming, and expensive, this method fails to reveal the full complexity at these loci because it does not directly sequence the fragment but only measure the number of bases in the repeat. Next-generation sequencing has the potential to address these problems. However, determining microsatellite lengths using next-generation sequencing data is difficult. In particular, polymerase slippage during PCR amplification introduces stutter noise. A small number of software tools have been written to genotype simple microsatellites in next-generation sequencing data [6-8], however they fail to address the issues of SNPs and compound repeats, and in some cases provide only approximate genotypes. We have begun to develop a microsatellite genotyping algorithm that addresses these issues, providing high accuracy as well as more detailed analysis of microsatellite loci. We have validated it using high depth amplicon sequencing data of microsatellites near the AVPR1A gene.

'Big data' analytics can be defined by the requirement for flexible, high throughput computational analysis methods applied to large, heterogeneous datasets. We propose an architectural approach to 'big data' challenges in which the movement of data is minimized, and analysis methods are implemented on the data as portable services. We term this approach 'data interlocking'. We demonstrate the feasibility of this approach through a domain specific implementation of a data interlocking architecture, in which an on-demand computational workbench provides portable high-throughput analysis methods to large genomic datasets on cloud infrastructure.

As science becomes increasingly interdisciplinary, we are expected to acquire both breadth of knowledge and depth of expertise. In bioinformatics, this is especially true. Keeping up to date with major techniques across multiple specialisations can be daunting, but you need not face it alone. A journal club is an excellent way to take in the scientific literature, keep up with developments in your field, and hone your communication and analytical skills. In general, a journal club is a group of people who meet regularly to discuss one or more scientific papers. The structure of such a club can vary. In the more traditional format, an individual studies a paper and then presents it to the group, usually in the form of PowerPoint slides, with time for questions. In some institutions, the journal club is for students only, designed to fulfill the requirements of a course or postgraduate program; attendance is obligatory, the scope of the literature is narrow, and the format is prescribed. The preparation of slides and a lecture may be required. Other kinds of journal clubs are just lab meetings in disguise, with the usual lab head and group members in attendance and one member nominated to present the paper. A formal style often fits well within an established academic structure—but what if your discipline is emerging? Consider the field of bioinformatics. Expertise may be spread across departments and institutions, and there may not be an obvious place or critical mass in any one lab for a traditional journal club. Research students, “pet bioinformaticians,” [1] and those interested in bioinformatics from adjoining fields all need a place to gather. We are pleased to offer an alternative structure to address this situation—an informal journal club, designed to bring together a diversity of backgrounds and career stages to discuss bioinformatics while building a network of like-minded peers. Additional benefits of such a journal club may include friendship and breakfast (see Rule 2)! We thoroughly recommend it to anyone who asks (as well as those who don’t). While this advice is drawn from our experiences in the Parkville Bioinformatics Journal Club, it is applicable to developing informal journal clubs of all disciplines. The advice contained in these rules will also help those who want to spice up their existing formal format. So don’t be a “lonely bioinformatician”[1], create a journal club! Follow these Ten Simple Rules to find out how.

Would Newton retweet your post on Twitter? How would Einstein view open source software? How would Darwin have handled a Wikipedia edit war? The way we do science is changing almost as fast as the volume of our data. Advice is needed; however, advice on leading a successful scientific life is usually confined to outdated memoirs, unrecorded weekly lab meetings, neglected blogs, or casual conversations at a conference. When we are faced with the challenges of how to be the best scientist we can, our instinctive reaction is to follow our usual pattern of inquiry—search the literature. This search left us wanting, until we discovered the PLOS Ten Simple Rules collection. We have found them to be a series of concise articles that capture the professional zeitgeist of being a scientist in an approachable manner. Many topics cover the professional (or “soft”) skills that are necessary for a modern scientific career, but are not part of a formal scientific education. [PB: Sad but true—teaching such skills should be a no-brainer.] These articles represent an invaluable chance to pass on advice and knowledge in a way that can be widely distributed, formally recognised, and—as an added benefit—cited. If (like us) you have read some articles in the Ten Simple Rules collection and appreciated their value, you may feel the urge to write one of your own. The collection provides a succinct and engaging format for advice on these skills. However, coming up with an article on soft skills need not be hard. Perhaps you have some insight, experience, or wisdom to impart. How would you do that? Is there practical advice for contributing to the Ten Simple Rules collection already available? What can we learn from the existing articles in the collection? If only there was an article with ten simple rules for writing a PLOS Ten Simple Rules article. If only that article could be peppered with insightful comments from the founder of the collection: Philip E. Bourne. This is that article.