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INTRODUCTIONGenome-wide association studies have led to numerous genetic loci associated with Alzheimer’s disease (AD). Whole-genome sequencing (WGS) now permit genome-wide analyses to identify rare variants contributing to AD risk.METHODSWe performed single-variant and spatial clustering-based testing on rare variants (minor allele frequency ≤1%) in a family-based WGS-based association study of 2,247 subjects from 605 multiplex AD families, followed by replication in 1,669 unrelated individuals.RESULTSWe identified 13 new AD candidate loci that yielded consistent rare-variant signals in discovery and replication cohorts (4 from single-variant, 9 from spatial-clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, CLSTN2.DISCUSSIONDownstream analyses of these novel loci highlight synaptic function, in contrast to common AD-associated variants, which implicate innate immunity. These loci have not been previously associated with AD, emphasizing the ability of WGS to identify AD-associated rare variants, particularly outside of coding regions.

With the advent of whole genome-sequencing (WGS) studies, family-based designs enable sex-specific analysis approaches that can be applied to only affected individuals; tests using family-based designs are attractive because they are completely robust against the effects of population substructure. These advantages make family-based association tests (FBATs) that use siblings as well as parents especially suited for the analysis of late-onset diseases such as Alzheimer’s Disease (AD). However, the application of FBATs to assess sex-specific effects can require additional filtering steps, as sensitivity to sequencing errors is amplified in this type of analysis. Here, we illustrate the implementation of robust analysis approaches and additional filtering steps that can minimize the chances of false positive-findings due to sex-specific sequencing errors. We apply this approach to two family-based AD datasets and identify four novel loci (GRID1, RIOK3, MCPH1, ZBTB7C) showing sex-specific association with AD risk. Following stringent quality control filtering, the strongest candidate is ZBTB7C (Pinter = 1.83 × 10−7), in which the minor allele of rs1944572 confers increased risk for AD in females and protection in males. ZBTB7C encodes the Zinc Finger and BTB Domain Containing 7C, a transcriptional repressor of membrane metalloproteases (MMP). Members of this MMP family were implicated in AD neuropathology.

Recent genome-wide association studies identified the angiotensin-converting enzyme gene (ACE) as an Alzheimer's disease (AD) risk locus. However, the pathogenic mechanism by which ACE causes AD is unknown. Using whole-genome sequencing, we identified rare ACE coding variants in AD families and investigated one, ACE1 R1279Q, in knockin (KI) mice. Similar to AD, ACE1 was increased in neurons, but not microglia or astrocytes, of KI brains, which became elevated further with age. Angiotensin II (angII) and angII receptor AT1R signaling were also increased in KI brains. Autosomal dominant neurodegeneration and neuroinflammation occurred with aging in KI hippocampus, which were absent in the cortex and cerebellum. Female KI mice exhibited greater hippocampal electroencephalograph disruption and memory impairment compared to males. ACE variant effects were more pronounced in female KI mice, suggesting a mechanism for higher AD risk in women. Hippocampal neurodegeneration was completely rescued by treatment with brain-penetrant drugs that inhibit ACE1 and AT1R. Although ACE variant-induced neurodegeneration did not depend on β-amyloid (Aβ) pathology, amyloidosis in 5XFAD mice crossed to KI mice accelerated neurodegeneration and neuroinflammation, whereas Aβ deposition was unchanged. KI mice had normal blood pressure and cerebrovascular functions. Our findings strongly suggest that increased ACE1/angII signaling causes aging-dependent, Aβ-accelerated selective hippocampal neuron vulnerability and female susceptibility, hallmarks of AD that have hitherto been enigmatic. We conclude that repurposed brain-penetrant ACE inhibitors and AT1R blockers may protect against AD.

Information integration and workflow technologies for data analysis have always been major fields of investigation in bioinformatics. A range of popular workflow suites are available to support analyses in computational biology. Commercial providers tend to offer prepared applications remote to their clients. However, for most academic environments with local expertise, novel data collection techniques or novel data analysis, it is essential to have all the flexibility of open source tools and open source workflow descriptions. Workflows in data-driven science such as computational biology have considerably gained in complexity. New tools or new releases with additional features arrive at an enormous pace, new reference data or concepts for quality control are emerging. A well-abstracted workflow and the exchange of the same across work groups has an enormous impact on the efficiency of research and the further development of the field. High-throughput sequencing adds to the avalanche of data available in the field; efficient computation and, in particular, parallel execution motivate the transition from traditional scripts and Makefiles to workflows. We here review the extant software development and distribution model with a focus on the role of integration testing and discuss the effect of Common Workflow Language (CWL) on distributions of open source scientific software to swiftly and reliably provide the tools demanded for the execution of such formally described workflows. It is contended that, alleviated from technical differences for the execution on local machines, clusters or the cloud, communities also gain the technical means to test workflow-driven interaction across several software packages.

Social norms and reinforced sex-role stereotypes inhibit the type of formal and informal interaction between male and female peers which is conducive to building effective working relationships. This chapter aims to analyze the factors within an organization and an individual which give rise to and potentially reinforce covert discrimination within a traditional business organization. Sex-role standards can be defined as the sum of socially designated behaviors that differentiate between men and women. The influence of sex role stereotyping may be more prevalent with respect to female leadership behaviors. The organization structure represents the environment within which an individual works and, in the broadest sense, reflects the values and goals of its managers with respect to the utilization of human resources. Organization design, in terms of specific job duties and responsibilities, and the individuals filling the positions, reflects the basic sex-role stereotypes prevalent in our society.

Standardized benchmarking methods and tools are essential to robust accuracy assessment of NGS variant calling. Benchmarking variant calls requires careful attention to definitions of performance metrics, sophisticated comparison approaches, and stratification by variant type and genome context. To address these needs, the Global Alliance for Genomics and Health (GA4GH) Benchmarking Team convened representatives from sequencing technology developers, government agencies, academic bioinformatics researchers, clinical laboratories, and commercial technology and bioinformatics developers for whom benchmarking variant calls is essential to their work. This team addressed challenges in (1) matching variant calls with different representations, (2) defining standard performance metrics, (3) enabling stratification of performance by variant type and genome context, and (4) developing and describing limitations of high-confidence calls and regions that can be used as “truth”. Our methods are publicly available on GitHub (https://github.com/ga4gh/benchmarking-tools) and in a web-based app on precisionFDA, which allow users to compare their variant calls against truth sets and to obtain a standardized report on their variant calling performance. Our methods have been piloted in the precisionFDA variant calling challenges to identify the best-in-class variant calling methods within high-confidence regions. Finally, we recommend a set of best practices for using our tools and critically evaluating the results.

Hackathons or Hackathon-style events, describe increasingly popular time-bounded intensive events across different fields and sectors. Often cited examples of hackathons include the demanding overnight competitive coding events, but there are many design variations for different audiences and with divergent aims. They offer a new form of collaboration by affording explicit, predictable, time-bounded spaces for interdependent work and engaging with new audiences. This one-day workshop will bring together researchers, experienced event organizers, and practitioners to share and discuss their practical experiences. Empirical insights from studying these events may help position the CHI community to better study, plan and design hackathon-style events and socio-technical systems that support new modes of production and collaboration.

Time-bounded collaborative events in which teams work together under intense time pressure are becoming increasingly popular. While hackathons, that is, competitive overnight coding events, are one of the more prevalent examples of this phenomenon, there are many more distinct event design variations for different audiences and with divergent aims, such as sprints, codefests, hack-days, edit-a-thons and so on. Taken together, these events offer new opportunities and challenges for cooperative work by affording explicit, predictable, time-bounded spaces for interdependent work and access to new audiences of collaborators. This one-day workshop brings together researchers interested in the phenomenon, experienced event organizers, and participants interested in running their own events to consolidate research to-date, share practical experiences, and understand what benefits different event variations may offer, how they may be applied in other contexts, and how insights from studying these events may contribute to CSCW knowledge.

It is currently thought that life-long blood cell production is driven by the action of a small number of multipotent haematopoietic stem cells. Evidence supporting this view has been largely acquired through the use of functional assays involving transplantation. However, whether these mechanisms also govern native non-transplant haematopoiesis is entirely unclear. Here we have established a novel experimental model in mice where cells can be uniquely and genetically labelled in situ to address this question. Using this approach, we have performed longitudinal analyses of clonal dynamics in adult mice that reveal unprecedented features of native haematopoiesis. In contrast to what occurs following transplantation, steady-state blood production is maintained by the successive recruitment of thousands of clones, each with a minimal contribution to mature progeny. Our results demonstrate that a large number of long-lived progenitors, rather than classically defined haematopoietic stem cells, are the main drivers of steady-state haematopoiesis during most of adulthood. Our results also have implications for understanding the cellular origin of haematopoietic disease.

Clinical adoption of human genome sequencing requires methods with known accuracy of genotype calls at millions or billions of positions across a genome. Previous work showing discordance amongst sequencing methods and algorithms has made clear the need for a highly accurate set of genotypes across a whole genome that could be used as a benchmark. We present methods to make highly confident SNP, indel, and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias towards any method by integrating and arbitrating between 14 datasets from 5 sequencing technologies, 7 mappers, and 3 variant callers. Regions for which no confident genotype call could be made are identified as uncertain, and classified into different reasons for uncertainty. Our highly confident genotype calls are publicly available on the Genome Comparison and Analytic Testing (GCAT) website to enable real-time benchmarking of any method.

Genetic and biochemical analyses of RNA interference (RNAi) and microRNA (miRNA) pathways have revealed proteins such as Argonaute and Dicer as essential cofactors that process and present small RNAs to their targets. Well-validated small RNA pathway cofactors such as these show distinctive patterns of conservation or divergence in particular animal, plant, fungal and protist species. We compared 86 divergent eukaryotic genome sequences to discern sets of proteins that show similar phylogenetic profiles with known small RNA cofactors. A large set of additional candidate small RNA cofactors have emerged from functional genomic screens for defects in miRNA- or short interfering RNA (siRNA)-mediated repression in Caenorhabditis elegans and Drosophila melanogaster, and from proteomic analyses of proteins co-purifying with validated small RNA pathway proteins. The phylogenetic profiles of many of these candidate small RNA pathway proteins are similar to those of known small RNA cofactor proteins. We used a Bayesian approach to integrate the phylogenetic profile analysis with predictions from diverse transcriptional coregulation and proteome interaction data sets to assign a probability for each protein for a role in a small RNA pathway. Testing high-confidence candidates from this analysis for defects in RNAi silencing, we found that about one-half of the predicted small RNA cofactors are required for RNAi silencing. Many of the newly identified small RNA pathway proteins are orthologues of proteins implicated in RNA splicing. In support of a deep connection between the mechanism of RNA splicing and small-RNA-mediated gene silencing, the presence of the Argonaute proteins and other small RNA components in the many species analysed strongly correlates with the number of introns in those species.

Message from the ISCB: The Bioinformatics Open Source Conference (BOSC) is a yearly meeting organized by the Open Bioinformatics Foundation (OBF), a non-profit group dedicated to promoting the practice and philosophy of Open Source software development and Open Science within the biological research community. BOSC has been run since 2000 as a two-day Special Interest Group (SIG) before the annual ISMB conference. The 17th annual BOSC (http://www.open-bio.org/wiki/BOSC_2016) took place in Orlando, Florida in July 2016. As in previous years, the conference was preceded by a two-day collaborative coding event open to the bioinformatics community. The conference brought together nearly 100 bioinformatics researchers, developers and users of open source software to interact and share ideas about standards, bioinformatics software development, and open and reproducible science.

International audience; To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.

Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research.

Long noncoding RNAs (lncRNAs) have important regulatory roles and can function at the level of chromatin. To determine where lncRNAs bind to chromatin, we developed capture hybridization analysis of RNA targets (CHART), a hybridization-based technique that specifically enriches endogenous RNAs along with their targets from reversibly cross-linked chromatin extracts. CHART was used to enrich the DNA and protein targets of endogenous lncRNAs from flies and humans. This analysis was extended to genome-wide mapping of roX2 , a well-studied ncRNA involved in dosage compensation in Drosophila . CHART revealed that roX2 binds at specific genomic sites that coincide with the binding sites of proteins from the male-specific lethal complex that affects dosage compensation. These results reveal the genomic targets of roX2 and demonstrate how CHART can be used to study RNAs in a manner analogous to chromatin immunoprecipitation for proteins.

Mounting evidence suggests that malignant tumors are initiated and maintained by a subpopulation of cancerous cells with biological properties similar to those of normal stem cells. However, descriptions of stem-like gene and pathway signatures in cancers are inconsistent across experimental systems. Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), we have developed the Stem Cell Discovery Engine (SCDE)—an online database of curated CSC experiments coupled to the Galaxy analytical framework. The SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. Our initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. A linked Galaxy framework provides a comprehensive, flexible environment populated with novel tools for gene list comparisons against molecular signatures in GeneSigDB and MSigDB, curated experiments in the SCDE and pathways in WikiPathways. The SCDE is available at http://discovery.hsci.harvard.edu.

Laboratory scale experiments were conducted to investigate the effect of a phytoremediation system using poplar trees on the smear zone-soil gases for petroleum hydrocarbons contaminated soil. Eight clear polyvinyl chloride (PVC) columns (1.5 mx0.1 m) were packed with the contaminated soil covered with clean top soil. Five ports were installed on the front side of each column for soil gas sampling. Hybrid poplar trees (Populus deltoides X nigra DN34) were grown in 4 columns. The remaining four columns were used as controls (no trees). Three of the tree columns and 3 of the controls had induced air flow. An experimental apparatus was established to allow for the induced air to flow through the air flow columns. Average air flow rates of 0.43 L/d, 0.71 L/d and 1.50 L/d were tested. The columns were operated for 78 days. During the experiments, the effect of oxygen addition, through the induced air, on the development of poplar roots in the hydrocarbons contaminated soil was investigated. In addition, the compositions and concentrations of the soil gases at different soil depths were evaluated during the course of the experiments.

In 2018, the annual Bioinformatics Open Source Conference was held for the first time in conjunction with the Galaxy Community Conference, as an experiment to see if we could reach people in the bioinformatics community who aren’t part of the audience attracted by ISMB. Held in June 2018 at Reed College in Portland, Oregon, GCCBOSC (Galaxy Community Conference and Bioinformatics Open Source Conference) attracted over 300 participants from around the world. The meeting started with two days of training, followed by two days of talks and poster/demo sessions (with some joint and some parallel sessions). The joint sessions included well-received keynote talks by Tracy Teal, Fernando Pérez and Lucia Peixoto, as well as a panel discussion about documentation and training. After the main meeting, many attendees stayed for up to four additional collaboration days, an extended version of the Codefests that have been held in conjunction with previous BOSCs. GCCBOSC was a successful experiment. The organizers concluded that the best way to serve the broadest community of potential BOSC attendees will be to partner some years with the International Society for Computational Biology (ISMB) and others with GCC.

RNA-seq analysis involves multiple steps, from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates, objects and wrapper functions to post-process bcbio RNA sequencing output data. bcbioRNASeq helps automate the generation of high-level RNA-seq reports, facilitating the quality control analyses, identification of differentially expressed genes and functional enrichment analyses.

The engineering of biological components has been facilitated by de novo synthesis of gene-length DNA. Biological engineering at the level of pathways and genomes, however, requires a scalable and cost-effective assembly of DNA molecules that are longer than approximately 10 kb, and this remains a challenge. Here we present the development of pairwise selection assembly (PSA), a process that involves hierarchical construction of long-length DNA through the use of a standard set of components and operations. In PSA, activation tags at the termini of assembly sub-fragments are reused throughout the assembly process to activate vector-encoded selectable markers. Marker activation enables stringent selection for a correctly assembled product in vivo, often obviating the need for clonal isolation. Importantly, construction via PSA is sequence-independent, and does not require primary sequence modification (e.g. the addition or removal of restriction sites). The utility of PSA is demonstrated in the construction of a completely synthetic 91-kb chromosome arm from Saccharomyces cerevisiae.

As the pace of implementing personalized medicine concepts increases, high-throughput variant calling on hundreds of individual genomes per day is a reality that will likely be faced by sequencing facilities across the country in the near future. While the scientific best practices for human variant calling workflows have been well defined, they also pose serious computational challenges at this high scale. Therefore, efforts in both academia and the private sector have focused on developing alternative workflows that may substantially reduce the computational cost per individual genome. iSAAC is an "ultra-fast" variant calling workflow, designed by Illumina, Inc, and is claimed to be six times faster than BWA-GATK, with comparable sensitivity and specificity. This report is an independent review of iSAAC, mainly focused on the accuracy of variant calls. We note that iSAAC is indeed quite fast, and provide some benchmarks on a few hardware architectures. The overall conclusion from our analysis is that the iSAAC workflow has undergone substantial improvement from version 01.14.11.27 to iSAAC_2.0. The call accuracy is especially high on NA12878, however exomes and genomic data outside the Platinum sets tend to have a high fraction of false positive calls. We did not manage to reproduce the 99% sensitivity and specificity reported in the Illumina whitepaper, however that might be improved with further tweaking of the options. This report includes the information about some of the command-line parameters and documentation.

Integration of structural genomic data from a largely assembled rice genome sequence, with phylogenetic analysis of sequence samples for many other taxa, suggests that a polyploidization event occurred approximately 70 million years ago, before the divergence of the major cereals from one another but after the divergence of the Poales from the Liliales and Zingiberales. Ancient polyploidization and subsequent "diploidization" (loss) of many duplicated gene copies has thus shaped the genomes of all Poaceae cereal, forage, and biomass crops. The Poaceae appear to have evolved as separate lineages for approximately 50 million years, or two-thirds of the time since the duplication event. Chromosomes that are predicted to be homoeologs resulting from this ancient duplication event account for a disproportionate share of incongruent loci found by comparison of the rice sequence to a detailed sorghum sequence-tagged site-based genetic map. Differential gene loss during diploidization may have contributed many of these incongruities. Such predicted homoeologs also account for a disproportionate share of duplicated sorghum loci, further supporting the hypothesis that the polyploidization event was common to sorghum and rice. Comparative gene orders along paleo-homoeologous chromosomal segments provide a means to make phylogenetic inferences about chromosome structural rearrangements that differentiate among the grasses. Superimposition of the timing of major duplication events on taxonomic relationships leads to improved understanding of comparative gene orders, enhancing the value of data from botanical models for crop improvement and for further exploration of genomic biodiversity. Additional ancient duplication events probably remain to be discovered in other angiosperm lineages.

The importance of angiosperms to sustaining humanity by providing a wide range of ‘ecosystem services’ warrants increased exploration of their genomic diversity. The nearly completed sequences for two species representing the major angiosperm subclasses, specifically the dicot Arabidopsis thaliana and the monocot Oryza sativa, provide a foundation for comparative analysis across the angiosperms. The angiosperms also exemplify some challenges to be faced as genomics makes new inroads into describing biotic diversity, in particular polyploidy (genome-wide chromatin duplication), and much larger genome sizes than have been studied to date.

Motivation: Whole genome duplications have played a major role in determining the structure of eukaryotic genomes. Current evidence revealing large blocks of duplicated chromatin yields new insights into the evolutionary history of species, but also presents a major challenge for researchers attempting to utilize comparative genomics techniques. Understanding the timing of duplication events relative to divergence among taxa is critical to accurate and comprehensive cross-species comparisons. Results: We describe a large-scale approach to estimate the timing of duplication events in a phylogenetic context. The methodology has been previously utilized for analysis of Arabidopsis and Saccharomyces duplication events. This new implementation provides a more flexible and reusable framework for these analyses. Scripts written in the Python programming language drive a number of freely available bioinformatics programs, creating a no-cost tool for researchers. The usefulness of the approach is demonstrated through genome-scale analysis of Arabidopsis and Oryza (rice) duplications. Availability: Software and documentation are freely available from http://plantgenome.agtec.uga.edu/bioinformatics/dating/

The cereal species, of central importance to our diet, began to diverge 50-70 million years ago. For the past few thousand years, these species have undergone largely parallel selection regimes associated with domestication and improvement. The rice genome sequence provides a platform for organizing information about diverse cereals, and together with genetic maps and sequence samples from other cereals is yielding new insights into both the shared and the independent dimensions of cereal evolution. New data and population-based approaches are identifying genes that have been involved in cereal improvement. Reduced-representation sequencing promises to accelerate gene discovery in many large-genome cereals, and to better link the under-explored genomes of 'orphan' cereals with state-of-the-art knowledge.

With a concept of producing polyhydroxybutyrate (PHB), a biodegradable thermoplastic polymer in maize chloroplasts, over 200 independent transgenic maize plants were produced. Five different constructs, pPHBA, pPHBB, pPHBC and pBY520 or pJS 101, were used in transformation experiments. PHB constructs contained phb genes from Alcaligenes eutrophus fused with the pea Rubisco small subunit (rbcS) transit peptide (TP) plus the first 24 amino acids of the mature rbcS protein. Plasmids BY520 and JS 101 contained the selectable marker bar gene linked with abiotic stress-related gene either hva 1 or mtl D. Southern blots on putative transgenic plants confirmed that the transgenic plants harbor transgenes from all five constructs. Transcription of transgenes was confirmed by Northern blot analyses. Western blot analyses using total cellular protein and protein from isolated chloroplasts confirmed that all the three genes produced their corresponding enzymes, and all PHB enzymes were targeted into maize chloroplasts. Immunofluorescent localization on both transgenic and non-transgenic maize chloroplasts treated with polyhydroxybutyrate synthase (PHBC)-antiserum also confirmed the targeting of PHBC enzyme to the chloroplasts of transgenic maize.

Conservation of gene order in vertebrates is evident after hundreds of millions of years of divergence, but comparisons of the Arabidopsis thaliana sequence to partial gene orders of other angiosperms (flowering plants) sharing common ancestry approximately 170-235 million years ago yield conflicting results. This difference may be largely due to the propensity of angiosperms to undergo chromosomal duplication ('polyploidization') and subsequent gene loss ('diploidization'); these evolutionary mechanisms have profound consequences for comparative biology. Here we integrate a phylogenetic approach (relating chromosomal duplications to the tree of life) with a genomic approach (mitigating information lost to diploidization) to show that a genome-wide duplication post-dates the divergence of Arabidopsis from most dicots. We also show that an inferred ancestral gene order for Arabidopsis reveals more synteny with other dicots (exemplified by cotton), and that additional, more ancient duplication events affect more distant taxonomic comparisons. By using partial sequence data for many diverse taxa to better relate the evolutionary history of completely sequenced genomes to the tree of life, we foster comparative approaches to the study of genome organization, consequences of polyploidy, and the molecular basis of quantitative traits.

The Bioinformatics Open Source Conference (BOSC) is a meeting organized by the Open Bioinformatics Foundation (OBF), a non-profit group dedicated to promoting the practice and philosophy of Open Source software development and Open Science within the biological research community. The 18th annual BOSC (http://www.open-bio.org/wiki/BOSC_2017) took place in Prague, Czech Republic in July 2017. The conference brought together nearly 250 bioinformatics researchers, developers and users of open source software to interact and share ideas about standards, bioinformatics software development, open and reproducible science, and this year’s theme, open data. As in previous years, the conference was preceded by a two-day collaborative coding event open to the bioinformatics community, called the OBF Codefest.

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants.

Computational biology comprises a wide range of different technologies and approaches. In order to combine different technologies to create more powerful workflows, multiple individuals and research groups generally need to collaborate. The format of data exchanged, the biology behind that data, and the individuals contributing the data or providing tools for its interpretation need to find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approach to using and combining the latest technologies. Traditionally, scientific conferences feature talks that present novel technologies or insights, followed up by informal discussions during coffee breaks - not necessarily involving the presenting party. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one collaborating research group typically visits the other. However, this does not scale well when the number of technologies or research groups is large. Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals sharing interests, commonly with prior acquaintance on mailing lists, to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies. Also, industrial participants in these discussions are rare, since their focus is on selling a product, not in forming collaborations that may not be immediately profitable. We here summarise our experience with Codefests, Hackathons and Sprints, which are interactive developer meetings in our field that aim to reduce the limitations of traditional scientific meetings described above, by strengthening the interaction among peers and letting the participants determine the schedule and topics covered at the meeting. These meetings are commonly run as loosely scheduled “unconferences� (self-organized identification of participants and topics for meetings) over at least two days, with early introductory talks to welcome and schedule contributors, followed by intensive collaborative coding sessions. We describe some differences in the way these meetings are organised, the audience is addressed, and in their outreach to their respective communities. The most prominent achievements of those meetings are summarised. Hackathons, Codefests and Springs share a stimulating atmosphere that encourages participants to jointly brainstorm and tackle problems of shared interest in a selfdriven proactive environment.

The Biopython project was formed in August 1999 as a collaboration to collect and produce open source bioinformatics tools written in Python, an object-oriented scripting language. It is modeled on the highly successful Bioperl project, but has the goal of making libraries available for people doing computations in Python. The philosophy of all the Bio* projects is that part of bioinformaticists' work involves software development. In order to prevent repeated efforts we believe that the field can be advanced more quickly if libraries that perform common programming functions are available. Thus, we hope to create a central source for high-quality bioinformatics tools that researchers can use.As an open source project, Biopython can be downloaded for free from the web site at http://www.biopython.org . Biopython libraries are currently under heavy development. This paper describes the current state of available Biopython tools, shows examples of their use in common bioinformatics problems, and describes plans for future development.

In July 2013, more than 100 bioinformatics researchers, developers and users of Open Source Software gathered in Berlin, Germany, to attend the 14th Annual Bioinformatics Open Source Conference (BOSC, http://www.open-bio.org/wiki/BOSC_2013, Harris et al., 2013). Since its inception in 2000, BOSC has provided bioinformatics developers with a forum for communicating the results of their latest efforts to the wider research community, and a focused environment for developers and users to interact and share ideas about standards, software development practices and practical techniques for solving bioinformatics problems. BOSC includes a 2 day ‘CodeFest’ preceding the formal conference, which provides a venue for developers to meet in person to work on or plan joint projects (Moller et al., 2013). The session topics at BOSC 2013 included Cloud and Parallel Computing, Visualization, Software Interoperability, Genome-scale Data Management, a session for updates on ongoing Open Source projects and two new sessions: Open Science and Reproducible Research and Translational Bioinformatics, recognizing the growing use of computational biology in medical applications. Slides from all of the presentations are available on the BOSC Web site, along with some of the posters and video recordings for selected talks, including the keynotes and panel discussion. Open Science is a movement dedicated to making all aspects of scientific knowledge production freely available for reuse and extension, including scientific data, methods and analyses. In response to the increasing traction that this movement has gained, BOSC 2013 featured a new session devoted explicitly to Open Science. One of the objectives of Open Science is the wider issue of making published research reproducible. Aside from openness in software licensing, this also includes openness of data and unhindered access to scientific papers themselves (Open Access). When researchers can freely access publications and the source code and data that support them, it becomes possible for them to recreate the steps that the authors went through to reach their conclusions and to then go beyond them. In this way, Open Science not only stands to provide the value of validating published results by recreating them but also to accelerate the pace of scientific discovery, by enabling researchers to more effectively build on the results of previous work, rather than having to reinvent tools and reassemble datasets. Each day of BOSC traditionally starts off with a keynote talk by a person of influence in Open Source bioinformatics. BOSC 2013’s first keynote speaker was Cameron Neylon, the Advocacy Director for the Public Library of Science, who is a prominent advocate for open science. Neylon discussed the cultural issues that are hindering open science, and spoke about the potential of openness in scientific collaborations for generating impact. Our second keynote speaker was Sean Eddy, a group leader at the Howard Hughes Medical Institute's Janelia Farm, who is perhaps best known as the author of the HMMER software suite (Eddy, 2011). He discussed how his own experience and practices show that dedicating effort to thorough engineering in tool development—which is often shunned as uninteresting and unpublishable—can be a key to creating a lasting impact. To stimulate discussion on controversial or multifaceted topics, BOSC includes a panel, in which panelists representing a range of viewpoints, answer questions from the audience. This year’s panel was on Strategies for Funding and Maintaining Open Source Software. To secure continued funding for a software project, researchers must be able to demonstrate its impact. The panelists explored ways to quantify usage of one’s software as a measure of impact. They agreed that traditional publications, and tracking their citations, still play an important role in publicizing and demonstrating the use of one’s software, but they are not the only metric. With the increasing reliance of more and more fields of biology on computational tools to manage and analyze their data, BOSC seems assured to stay relevant to life science, and thus to life scientists. Additional Information Plan to join us for BOSC: 15th Annual Bioinformatics Open Source Conference in Boston, USA, July 11–12, 2014. For more information visit http://www.open-bio.org/wiki/BOSC_2014.

Background The impact of raltegravir-resistant HIV-1 minority variants (MVs) on raltegravir treatment failure is unknown. Illumina sequencing offers greater throughput than 454, but sequence analysis tools for viral sequencing are needed. We evaluated Illumina and 454 for the detection of HIV-1 raltegravir-resistant MVs. Methods A5262 was a single-arm study of raltegravir and darunavir/ritonavir in treatment-naive patients. Pre-treatment plasma was obtained from 5 participants with raltegravir resistance at the time of virologic failure. A control library was created by pooling integrase clones at predefined proportions. Multiplexed sequencing was performed with Illumina and 454 platforms at comparable costs. Illumina sequence analysis was performed with the novel snp-assess tool and 454 sequencing was analyzed with V-Phaser. Results Illumina sequencing resulted in significantly higher sequence coverage and a 0.095% limit of detection. Illumina accurately detected all MVs in the control library at ≥0.5% and 7/10 MVs expected at 0.1%. 454 sequencing failed to detect any MVs at 0.1% with 5 false positive calls. For MVs detected in the patient samples by both 454 and Illumina, the correlation in the detected variant frequencies was high (R2 = 0.92, P

Objective: To evaluate the utility of targeted exome sequencing for the molecular diagnosis of mitochondrial disorders, which exhibit marked phenotypic and genetic heterogeneity. Methods: We considered a diverse set of 102 patients with suspected mitochondrial disorders based on clinical, biochemical, and/or molecular findings, and whose disease ranged from mild to severe, with varying age at onset. We sequenced the mitochondrial genome (mtDNA) and the exons of 1,598 nuclear-encoded genes implicated in mitochondrial biology, mitochondrial disease, or monogenic disorders with phenotypic overlap. We prioritized variants likely to underlie disease and established molecular diagnoses in accordance with current clinical genetic guidelines. Results: Targeted exome sequencing yielded molecular diagnoses in established disease loci in 22% of cases, including 17 of 18 (94%) with prior molecular diagnoses and 5 of 84 (6%) without. The 5 new diagnoses implicated 2 genes associated with canonical mitochondrial disorders ( NDUFV1 , POLG2 ), and 3 genes known to underlie other neurologic disorders ( DPYD , KARS , WFS1 ), underscoring the phenotypic and biochemical overlap with other inborn errors. We prioritized variants in an additional 26 patients, including recessive, X-linked, and mtDNA variants that were enriched 2-fold over background and await further support of pathogenicity. In one case, we modeled patient mutations in yeast to provide evidence that recessive mutations in ATP5A1 can underlie combined respiratory chain deficiency. Conclusion: The results demonstrate that targeted exome sequencing is an effective alternative to the sequential testing of mtDNA and individual nuclear genes as part of the investigation of mitochondrial disease. Our study underscores the ongoing challenge of variant interpretation in the clinical setting.

Detection of somatic variants in tumor tissues is far from a solved problem. Heterogenous tumor materials complicate variant assessment in low frequency subclonal populations, which can play an important role in the development of drug resistance. Loss of heterozygosity, copy number variation and other ploidy changes can further change the expected distribution of variant metrics. We demonstrate improvements in the specificity and sensitivity of variant calling algorithms by optimising linear combinations of widely used filtering criteria such as allelic frequency, depth and p-value distributions. We specifically improve SNP and indels from the freely available VarDict caller (https://github.com/AstraZeneca-NGS/VarDictJava) and show that these optimisations can be generalised across a wide range of allelic frequencies. We validate our findings against synthetic and in vitro standards such as the ICGC-TCGA DREAM challenge truth set. Our proposed filtering strategy is widely applicable to other variant callers, and the updated filters for VarDict dramatically improve sensitivity and precision on low frequency variants which are crucial for our ability to reconstruct likely tumor populations from short read sequencing data and to study tumor evolution. Citation Format: Zhongwu Lai, Brad Chapman, Miika Ahdesmäki, Oliver Hofmann, Justin Johnson, Jonathan Dry. Optimizing the detection of subclonal somatic variants with VarDict. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 166.

Reliable detection of structural variation (SV) is playing an increasingly important role in cancer diagnostics and treatments. We introduce a best practice integrative workflow for detecting large structural variations (SVs) such as deletions, duplications, inversions, fusions and translocations from standard DNA re-sequencing techniques, annotating and prioritising for clinically actionable events. While established RNA-Seq fusion workflows exist to detect and filter fusion events in tumors detecting large structural changes from exome- and WGS data remains challenging. Current DNA-based SV callers predict a large percentage of false positive events, and the resulting event lists are not prioritised for validation. To streamline follow-up studies we automate running multiple structural variant callers (Manta, WHAM, Lumpy, MetaSV), identifying potentially disruptive large scale events. We evaluate the integrated workflow sensitivity and specificity against the ICGC-TCGA DREAM Mutation Calling Challenge data set, optimise filtering criteria based on quality and supporting information, and annotate the filtered calls using known cancer related alterations from CIViC and publicly available gene lists from AstraZeneca. This enables us to prioritise known and potentially interesting events over intergenic events or non-whole exon deletions. We show validation results from test data and clinically actionable inversions, duplications and fusion events from cancer cell lines. Our approach is based on a two tier approach of first focusing on known events that yield fusion transcripts such as the TACC3-FGFR3 tandem duplication and ALK-EML4 inversion, and secondarily on events in oncogenes and tumor suppressors of interest. While whole genome sequencing is the preferred approach to detecting these events, we also show how by design or by the nature of the breakpoint being close enough to exons, hybrid capture data can also be surprisingly useful for SV inference. Citation Format: Miika Ahdesmäki, Brad Chapman, Sally Luke, Hedley Carr, Daniel Stetson, Oliver Hofmann, Justin Johnson. Validated structural variant detection with prioritisation of known cancer related changes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3604.

The Genome Reference Consortium released human genome build 38 (hg38, GRCh38) two years ago and it offers major improvements over the six year old previous build, 37 (hg19, GRCh37). The new build reflects our increased understanding of the heterogeneity within human sub-populations, contains a large number of alternative genomic loci that better capture our knowledge of genome structure, and includes highly diverse human leukocyte antigen (HLA) regions enabling HLA typing. To help with the migration of clinical sequencing workflows we provide support for variant calling on build 38, validate methods using truth sets from the Genome in a Bottle consortium and Illumina's Platinum Genome and show that feature projection approaches are a viable intermediate approach to port existing annotation resources. We show how the refined genomic representation improves mapping and variant calling by reducing the number of false positive SNP calls and improving InDel detection, and how post-processing of sequence reads mapping to HLA regions enables accurate HLA genotyping from targeted panels, exome- and WGS data. Using Omixon HLA validation data, integrated OptiType HLA calling achieved 100% accuracy on 8 targeted panels and better than 90% accuracy on 1000 genome exome samples. This work provides a freely available, validated, ready to run pipeline making use of the improved heterogeneity representation in build 38 and demonstrating the value of moving towards a more accurate, graph-based representation of human genomes. Citation Format: Brad Chapman, Alison Meynert, Deanna Church, Justin Johnson, Oliver Hofmann. Improved clinical variant calling and HLA genotyping with GRCh38. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3636.

Background BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. Results The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. Conclusion We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

Cloud computing has revolutionized availability and access to computing and storage resources; making it possible to provision a large computational infrastructure with only a few clicks in a web browser. However, those resources are typically provided in the form of low-level infrastructure components that need to be procured and configured before use. In this protocol, we demonstrate how to utilize cloud computing resources to perform open-ended bioinformatics analyses, with fully automated management of the underlying cloud infrastructure. By combining three projects, CloudBioLinux, CloudMan, and Galaxy into a cohesive unit, we have enabled researchers to gain access to more than 100 preconfigured bioinformatics tools and gigabytes of reference genomes on top of the flexible cloud computing infrastructure. The protocol demonstrates how to setup the available infrastructure and how to use the tools via a graphical desktop interface, a parallel command line interface, and the web-based Galaxy interface.

Background A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Results Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Conclusions Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them.

[Background] Ongoing innovation in phylogenetics and evolutionary biology has been accompanied by a proliferation of software tools, data formats, analytical techniques and web servers. This brings with it the challenge of integrating phylogenetic and other related biological data found in a wide variety of formats, and underlines the need for reusable software that can read, manipulate and transform this information into the various forms required to build computational pipelines., [Results] We built a Python software library for working with phylogenetic data that is tightly integrated with Biopython, a broad-ranging toolkit for computational biology. Our library, Bio.Phylo, is highly interoperable with existing libraries, tools and standards, and is capable of parsing common file formats for phylogenetic trees, performing basic transformations and manipulations, attaching rich annotations, and visualizing trees. We unified the modules for working with the standard file formats Newick, NEXUS and phyloXML behind a consistent and simple API, providing a common set of functionality independent of the data source., [Conclusions] Bio.Phylo meets a growing need in bioinformatics for working with heterogeneous types of phylogenetic data. By supporting interoperability with multiple file formats and leveraging existing Biopython features, this library simplifies the construction of phylogenetic workflows. We also provide examples of the benefits of building a community around a shared open-source project. Bio.Phylo is included with Biopython, available through the Biopython website, http://biopython.org., We acknowledge the role of Google Summer of Code 2009 and the National Evolutionary Synthesis Center (NESCent) Phyloinformatics program in sponsoring and initiating this project. BI is supported by a FI-DGR from AGAUR (Catalonia, Spain). Harvard Bioinformatics Core provided for publication costs and moral support.