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The underrepresentation of non-Europeans in human genetic studies so far has limited the diversity of individuals in genomic datasets and led to reduced medical relevance for a large proportion of the world's population. Population-specific reference genome datasets as well as genome-wide association studies in diverse populations are needed to address this issue. Here we describe the pilot phase of the GenomeAsia 100K Project. This includes a whole-genome sequencing reference dataset from 1,739 individuals of 219 population groups and 64 countries across Asia. We catalogue genetic variation, population structure, disease associations and founder effects. We also explore the use of this dataset in imputation, to facilitate genetic studies in populations across Asia and worldwide. Using whole-genome sequencing data from 1,739 individuals, the GenomeAsia 100K Project catalogues genetic variation, population structure and disease associations to facilitate genetic studies in Asian populations and increase representation in genetics studies worldwide. [ABSTRACT FROM AUTHOR]

Planning a symposium organized by PhD students is a challenging prospect. Insight from the organizers of three such symposia sheds light on the highs and lows of the experience. [ABSTRACT FROM AUTHOR]

Summary: Breast cancer is one of the most frequent cancers among women. Extensive studies into themolecular heterogeneity of breast cancer have produced a plethora ofmolecular subtype classification and prognosis prediction algorithms, as well as numerous gene expression signatures. However, reimplementation of these algorithms is a tedious but important task to enable comparison of existing signatures and classification models between each other and with new models. Here, we present the genefu R/Bioconductor package, a multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer. Availability and implementation: The genefu package is available from Bioconductor. http://www. bioconductor.org/packages/devel/bioc/html/genefu.html. Source code is also available on Github https://github.com/bhklab/genefu. [ABSTRACT FROM AUTHOR]

A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that ‘‘random’’ gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.

Summary: Voltage-dependent Ca2+ channels (CaVs) represent the principal source of Ca2+ ions that trigger evoked neurotransmitter release from presynaptic boutons. Ca2+ influx is mediated mainly via CaV2.1 (P/Q-type) and CaV2.2 (N-type) channels, which differ in their properties. Their relative contribution to synaptic transmission changes during development and tunes neurotransmission during synaptic plasticity. The mechanism of differential recruitment of CaV2.1 and CaV2.2 to release sites is largely unknown. Here, we show that the presynaptic scaffolding protein Bassoon localizes specifically CaV2.1 to active zones via molecular interaction with the RIM-binding proteins (RBPs). A genetic deletion of Bassoon or an acute interference with Bassoon-RBP interaction reduces synaptic abundance of CaV2.1, weakens P/Q-type Ca2+ current-driven synaptic transmission, and results in higher relative contribution of neurotransmission dependent on CaV2.2. These data establish Bassoon as a major regulator of the molecular composition of the presynaptic neurotransmitter release sites. [ABSTRACT FROM AUTHOR]

Complete and accurate genome assembly and annotation is a crucial foundation for comparative and functional genomics. Despite this, few complete eukaryotic genomes are available, and genome annotation remains a major challenge. Here, we present a complete genome assembly of the skin commensal yeast Malassezia sympodialis and demonstrate how proteogenomics can substantially improve gene annotation. Through long-read DNA sequencing, we obtained a gap-free genome assembly for M. sympodialis (ATCC 42132), comprising eight nuclear and one mitochondrial chromosome. We also sequenced and assembled four M. sympodialis clinical isolates, and showed their value for understanding Malassezia reproduction by confirming four alternative allele combinations at the two mating-type loci. Importantly, we demonstrated how proteomics data could be readily integrated with transcriptomics data in standard annotation tools. This increased the number of annotated protein-coding genes by 14% (from 3612 to 4113), compared to using transcriptomics evidence alone. Manual curation further increased the number of protein-coding genes by 9% (to 4493). All of these genes have RNA-seq evidence and 87% were confirmed by proteomics. The M. sympodialis genome assembly and annotation presented here is at a quality yet achieved only for a few eukaryotic organisms, and constitutes an important reference for future host-microbe interaction studies., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The ability to extract, identify and annotate large amounts of biological data is a key feature of the "omics" era, and has led to an explosion in the amount of data available. One pivotal advance is the use of Next-Generation Sequencing (NGS) techniques such as RNA-sequencing (RNA-seq). RNA-seq uses data from millions of small mRNA transcripts or "reads" which are aligned to a reference genome. Comparative transcriptomics analyses using RNA-seq can provide the researcher with a comprehensive view of the cells' response to a given environment or stimulus.Here, we describe the NGS techniques (based on Illumina technology) that are routinely used for comparative transcriptome analysis of fungal species. We describe the entire process from isolation of RNA to computational identification of differentially expressed genes. We provide instructions to allow the beginner to implement packages in R such as Bioconductor. The methods described are not limited to yeast, and can also be applied to other eukaryotic organisms.