Mapping the human brain's cortical-subcortical functional network organization, bioRxiv, 2017-10-20
Understanding complex systems such as the human brain requires characterization of the system's architecture across multiple levels of organization - from neurons, to local circuits, to brain regions, and ultimately large-scale brain networks. Here we focus on characterizing the human brain's large-scale network organization, as it provides an overall framework for the organization of all other levels. We developed a highly principled approach to identify cortical network communities at the level of functional systems, calibrating our community detection algorithm using extremely well-established sensory and motor systems as guides. Building on previous network partitions, we replicated and expanded upon well-known and recently-identified networks, including several higher-order cognitive networks such as a left-lateralized language network. We expanded these cortical networks to subcortex, revealing 358 highly-organized subcortical parcels that take part in forming whole-brain functional networks. Notably, the identified subcortical parcels are similar in number to a recent estimate of the number of cortical parcels (360). This whole-brain network atlas - released as an open resource for the neuroscience community - places all brain structures across both cortex and subcortex into a single large-scale functional framework, with the potential to facilitate a variety of studies investigating large-scale functional networks in health and disease.
biorxiv neuroscience 0-100-users 2017Amplification-free, CRISPR-Cas9 Targeted Enrichment and SMRT Sequencing of Repeat-Expansion Disease Causative Genomic Regions, bioRxiv, 2017-10-17
AbstractTargeted sequencing has proven to be an economical means of obtaining sequence information for one or more defined regions of a larger genome. However, most target enrichment methods require amplification. Some genomic regions, such as those with extreme GC content and repetitive sequences, are recalcitrant to faithful amplification. Yet, many human genetic disorders are caused by repeat expansions, including difficult to sequence tandem repeats.We have developed a novel, amplification-free enrichment technique that employs the CRISPR-Cas9 system for specific targeting multiple genomic loci. This method, in conjunction with long reads generated through Single Molecule, Real-Time (SMRT) sequencing and unbiased coverage, enables enrichment and sequencing of complex genomic regions that cannot be investigated with other technologies. Using human genomic DNA samples, we demonstrate successful targeting of causative loci for Huntington’s disease (HTT; CAG repeat), Fragile X syndrome (FMR1; CGG repeat), amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (C9orf72; GGGGCC repeat), and spinocerebellar ataxia type 10 (SCA10) (ATXN10; variable ATTCT repeat). The method, amenable to multiplexing across multiple genomic loci, uses an amplification-free approach that facilitates the isolation of hundreds of individual on-target molecules in a single SMRT Cell and accurate sequencing through long repeat stretches, regardless of extreme GC percent or sequence complexity content. Our novel targeted sequencing method opens new doors to genomic analyses independent of PCR amplification that will facilitate the study of repeat expansion disorders.
biorxiv genomics 0-100-users 2017Discrete attractor dynamics underlying selective persistent activity in frontal cortex, bioRxiv, 2017-10-16
AbstractShort-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To elucidate the mechanisms underlying this persistent activity we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modeling. During the delay epoch, both membrane potential and population activity of ALM neurons funneled towards discrete endpoints related to specific movement directions. These endpoints were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other endpoint, followed by incorrect actions. Our results are consistent with discrete attractor dynamics underlying short-term memory related to motor planning.
biorxiv neuroscience 0-100-users 2017A critical comparison of technologies for a plant genome sequencing project, bioRxiv, 2017-10-12
A high quality genome sequence of your model organism is an essential starting point for many studies. Old clone based methods are slow and expensive, whereas faster, cheaper short read only assemblies can be incomplete and highly fragmented, which minimises their usefulness. The last few years have seen the introduction of many new technologies for genome assembly. These new technologies and new algorithms are typically benchmarked on microbial genomes or, if they scale appropriately, human. However, plant genomes can be much more repetitive and larger than human, and plant biology makes obtaining high quality DNA free from contaminants difficult. Reflecting their challenging nature we observe that plant genome assembly statistics are typically poorer than for vertebrates. Here we compare Illumina short read, PacBio long read, 10x Genomics linked reads, Dovetail Hi-C and BioNano Genomics optical maps, singly and combined, in producing high quality long range genome assemblies of the potato species S. verrucosum. We benchmark the assemblies for completeness and accuracy, as well as DNA, compute requirements and sequencing costs. We expect our results will be helpful to other genome projects, and that these datasets will be used in benchmarking by assembly algorithm developers.
biorxiv genomics 0-100-users 2017A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction, bioRxiv, 2017-10-10
AbstractThe effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patient’s tumor. AstraZeneca carried out 11,576 experiments on 910 drug combinations across 85 cancer cell lines, recapitulating in vivo response profiles. These data, the largest openly available screen, were hosted by DREAM alongside deep molecular characterization from the Sanger Institute for a Challenge to computationally predict synergistic drug pairs and associated biomarkers. 160 teams participated to provide the most comprehensive methodological development and subsequent benchmarking to date. Winning methods incorporated prior knowledge of putative drug target interactions. For >60% of drug combinations synergy was reproducibly predicted with an accuracy matching biological replicate experiments, however 20% of drug combinations were poorly predicted by all methods. Genomic rationale for synergy predictions were identified, including antagonism unique to combined PIK3CBD inhibition with the ADAM17 inhibitor where synergy is seen with other PI3K pathway inhibitors. All data, methods and code are freely available as a resource to the community.
biorxiv bioinformatics 0-100-users 2017Assessment of batch-correction methods for scRNA-seq data with a new test metric, bioRxiv, 2017-10-10
AbstractSingle-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations. As with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch effect correction is often evaluated by visual inspection of dimension-reduced representations such as principal component analysis. This is inherently imprecise due to the high number of genes and non-normal distribution of gene expression. Here, we present a k-nearest neighbour batch effect test (kBET, <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comtheislabkBET>httpsgithub.comtheislabkBET<jatsext-link>) to quantitatively measure batch effects. kBET is easier to interpret, more sensitive and more robust than visual evaluation and other measures of batch effects. We use kBET to assess commonly used batch regression and normalisation approaches, and quantify the extent to which they remove batch effects while preserving biological variability. Our results illustrate that batch correction based on log-transformation or scran pooling followed by ComBat reduced the batch effect while preserving structure across data sets. Finally we show that kBET can pinpoint successful data integration methods across multiple data sets, in this case from different publications all charting mouse embryonic development. This has important implications for future data integration efforts, which will be central to projects such as the Human Cell Atlas where data for the same tissue may be generated in multiple locations around the world.[Before final publication, we will upload the R package to Bioconductor]
biorxiv bioinformatics 0-100-users 2017