SPRING a kinetic interface for visualizing high dimensional single-cell expression data, bioRxiv, 2016-11-30
MotivationSingle-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, existing data visualization approaches are imperfect for studying continuous gene expression topologies.ResultsForce-directed layouts of k-nearest-neighbor graphs can visualize continuous gene expression topologies in a manner that preserves high-dimensional relationships and allows manually exploration of different stable two-dimensional representations of the same data. We implemented an interactive web-tool to visualize single-cell data using force-directed graph layouts, called SPRING. SPRING reveals more detailed biological relationships than existing approaches when applied to branching gene expression trajectories from hematopoietic progenitor cells. Visualizations from SPRING are also more reproducible than those of stochastic visualization methods such as tSNE, a state-of-the-art tool.Availability<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpskleintools.hms.harvard.edutoolsspring.html>httpskleintools.hms.harvard.edutoolsspring.html<jatsext-link>,<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comAllonKleinLabSPRING>httpsgithub.comAllonKleinLabSPRING<jatsext-link>Contactcalebsw@gmail.com, allon_klein@hms.harvard.edu
biorxiv bioinformatics 0-100-users 2016Cryo-EM structure of haemoglobin at 3.2 Å determined with the Volta phase plate, bioRxiv, 2016-11-18
With the advent of direct electron detectors, the perspectives of cryo-electron microscopy (cryo-EM) have changed in a profound way1. These cameras are superior to previous detectors in coping with the intrinsically low contrast of radiation-sensitive organic materials embedded in amorphous ice, and so they have enabled the structure determination of several macromolecular assemblies to atomic or near-atomic resolution. According to one theoretical estimation, a few thousand images should suffice for calculating the structure of proteins as small as 17 kDa at 3 Å resolution2. In practice, however, we are still far away from this theoretical ideal. Thus far, protein complexes that have been successfully reconstructed to high-resolution by single particle analysis (SPA) have molecular weights of ~100 kDa or larger3. Here, we report the use of Volta phase plate in determining the structure of human haemoglobin (64 kDa) at 3.2 Å. Our results demonstrate that this method can be applied to complexes that are significantly smaller than those previously studied by conventional defocus-based approaches. Cryo-EM is now close to becoming a fast and cost-effective alternative to crystallography for high-resolution protein structure determination.
biorxiv biophysics 100-200-users 2016Tractography-based connectomes are dominated by false-positive connections, bioRxiv, 2016-11-08
AbstractFiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.
biorxiv neuroscience 200-500-users 2016Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Neural Networks, bioRxiv, 2016-11-03
AbstractIn the human genome, distal enhancers are involved in regulating target genes through proxi-mal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions. Here we report a new computational method (named “SPEID”) using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes. This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.
biorxiv bioinformatics 0-100-users 2016WhatsHap fast and accurate read-based phasing, bioRxiv, 2016-11-03
AbstractRead-based phasing allows to reconstruct the haplotypes of a sample purely from sequencing reads. While phasing is an important step for answering questions about population genetics, compound heterozygosity, and to aid in clinical decision making, there has been a lack of accurate, usable and standards-based software.WhatsHap is a production-ready tool for highly accurate read-based phasing. It was designed from the beginning to leverage third-generation sequencing technologies, whose long reads can span many variants and are therefore ideal for phasing. WhatsHap works also well with second-generation data, is easy to use and will phase not only SNVs, but also indels and other variants. It is unique in its ability to combine read-based with pedigree-based phasing, allowing to further improve accuracy if multiple related samples are provided.
biorxiv bioinformatics 0-100-users 2016The megabase-sized fungal genome of Rhizoctonia solani assembled from nanopore reads only, bioRxiv, 2016-11-02
AbstractThe ability to quickly obtain accurate genome sequences of eukaryotic pathogens at low costs provides a tremendous opportunity to identify novel targets for therapeutics, develop pesticides with increased target specificity and breed for resistance in food crops. Here, we present the first report of the ~54 MB eukaryotic genome sequence of Rhizoctonia solani, an important pathogenic fungal species of maize, using nanopore technology. Moreover, we show that optimizing the strategy for wet-lab procedures aimed to isolate high quality and ultra-pure high molecular weight (HMW) DNA results in increased read length distribution and thereby allowing generation of the most contiguous genome assembly for R. solani to date. We further determined sequencing accuracy and compared the assembly to short-read technologies. With the current sequencing technology and bioinformatics tool set, we are able to deliver an eukaryotic fungal genome at low cost within a week. With further improvements of the sequencing technology and increased throughput of the PromethION sequencer we aim to generate near-finished assemblies of large and repetitive plant genomes and cost-efficiently perform de novo sequencing of large collections of microbial pathogens and the microbial communities that surround our crops.
biorxiv genomics 100-200-users 2016