Analyzing ecological networks of species interactions, bioRxiv, 2017-03-01
Networks provide one of the best representations for ecological communities, composed of many species with sometimes complex connections between them. Yet the methodological literature allowing one to analyze and extract meaning from ecological networks is dense, fragmented, and unwelcoming. We provide a general overview to the field of using networks in community ecology, outlining both the intent of the different measures, their assumptions, and the contexts in which they can be used. When methodologically justified, we suggest good practices to use in the analysis of ecological networks. We anchor this synopsis with examples from empirical studies, and conclude by highlighting what identified as needed future developments in the field.
biorxiv ecology 0-100-users 2017orco mutagenesis causes loss of antennal lobe glomeruli and impaired social behavior in ants, bioRxiv, 2017-03-01
Life inside ant colonies is orchestrated with a diverse set of pheromones, but it is not clear how ants perceive these social cues. It has been proposed that pheromone perception in ants evolved via expansions in the numbers of odorant receptors (ORs) and antennal lobe glomeruli. Here we generate the first mutant lines in ants by disrupting orco, a gene required for the function of all ORs. We find that orco mutants exhibit severe deficiencies in social behavior and fitness, suggesting that they are unable to perceive pheromones. Surprisingly, unlike in Drosophila melanogaster, orco mutant ants also lack most of the approximately 500 antennal lobe glomeruli found in wild-types. These results illustrate that ORs are essential for ant social organization, and raise the possibility that, similar to mammals, receptor function is required for the development andor maintenance of the highly complex olfactory processing areas in the ant brain.
biorxiv evolutionary-biology 100-200-users 2017A global perspective on bioinformatics training needs, bioRxiv, 2017-02-28
AbstractIn the last decade, life-science research has become increasingly data-intensive and computational. Nevertheless, basic bioinformatics and data stewardship are still only rarely taught in life-science degree programmes, creating a widening skills gap that spans educational levels and career roles. To better understand this situation, we ran surveys to determine how the skills dearth is affecting the need for bioinformatics training worldwide. Perhaps unsurprisingly, we found that respondents wanted more short courses to help boost their expertise and confidence in data analysis and interpretation. However, it was evident that most respondents appreciated their need for training only after designing their experiments and collecting their data. This is clearly rather late in the research workflow, and suboptimal from a training perspective, as skills acquired to address a specific need at a particular time are seldom retained, engendering a cycle of low confidence in trainees. To ensure that such skill gaps do not continue to create barriers to the progress of research, we argue that universities should strive to bring their life-science curricula into the digital-data era. Meanwhile, the demand for point-of-need training in bioinformatics and data stewardship will grow. While this situation persists, international groups like GOBLET are increasing their efforts to enlarge the community of trainers and quench the global thirst for bioinformatics training.
biorxiv scientific-communication-and-education 100-200-users 2017Enabling cross-study analysis of RNA-Sequencing data, bioRxiv, 2017-02-28
AbstractDriven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data. While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources poses a great challenge, due to differences in sample and data processing. Here, we present a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment and gene expression quantification as well as batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA) and have successfully corrected for study-specific biases, enabling comparative analysis across studies. The normalized data are available for download via GitHub (at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.commskccRNAseqDB>httpsgithub.commskccRNAseqDB<jatsext-link>).
biorxiv bioinformatics 0-100-users 2017MAGIC A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data, bioRxiv, 2017-02-26
ABSTRACTSingle-cell RNA-sequencing is fast becoming a major technology that is revolutionizing biological discovery in fields such as development, immunology and cancer. The ability to simultaneously measure thousands of genes at single cell resolution allows, among other prospects, for the possibility of learning gene regulatory networks at large scales. However, scRNA-seq technologies suffer from many sources of significant technical noise, the most prominent of which is ‘dropout’ due to inefficient mRNA capture. This results in data that has a high degree of sparsity, with typically only ~10% non-zero values. To address this, we developed MAGIC (Markov Affinity-based Graph Imputation of Cells), a method for imputing missing values, and restoring the structure of the data. After MAGIC, we find that two- and three-dimensional gene interactions are restored and that MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and our newly generated epithelial-to-mesenchymal transition dataset.
biorxiv bioinformatics 100-200-users 2017Multiplexed confocal and super-resolution fluorescence imaging of cytoskeletal and neuronal synapse proteins, bioRxiv, 2017-02-26
ABSTRACTNeuronal synapses contain dozens of protein species whose expression levels and localizations are key determinants of synaptic transmission and plasticity. The spectral properties of fluorophores used in conventional microscopy limit the number of measured proteins to four species within a given sample. The ability to perform high-throughput confocal or super-resolution imaging of many proteins simultaneously without limitation in target number imposed by this spectral limit would enable large-scale characterization of synaptic protein networks in situ. Here, we introduce PRISM Probe-based Imaging for Sequential Multiplexing, a method that sequentially utilizes either high affinity Locked Nucleic Acid (LNA) or low affinity DNA probes to enable diffraction-limited confocal and PAINT-based super-resolution imaging. High-affinity LNA probes offer high-throughput, confocal-based imaging compared with PAINT, which uses low affinity probes to realize localization-based super-resolution imaging. Simultaneous immunostaining of all targets is performed prior to imaging, followed by sequential LNADNA probe exchange that requires only minutes under mild wash conditions. We apply PRISM to quantify the co-expression levels and nanometer-scale organization of one dozen cytoskeletal and synaptic proteins within individual neuronal synapses. Our approach is scalable to dozens of target proteins and is compatible with high-content screening platforms commonly used to interrogate phenotypic changes associated with genetic and drug perturbations in a variety of cell types.
biorxiv bioengineering 0-100-users 2017