BlastFrost Fast querying of 100,000s of bacterial genomes in Bifrost graphs, bioRxiv, 2020-01-23
AbstractBlastFrost is a highly efficient method for querying 100,000s of genome assemblies. It builds on Bifrost, a recently developed dynamic data structure for compacted and colored de Bruijn graphs from bacterial genomes. BlastFrost queries a Bifrost data structure for sequences of interest, and extracts local subgraphs, thereby enabling the efficient identification of the presence or absence of individual genes or single nucleotide sequence variants. Here we describe the algorithms and implementation of BlastFrost. We also present two exemplar practical applications. In the first, we determined the presence of the individual genes within the SPI-2 Salmonella pathogenicity island within a collection of 926 representative genomes in minutes. In the second application, we determined the existence of known single nucleotide polymorphisms associated with fluoroquinolone resistance in the genes gyrA, gyrB and parE among 190, 209 Salmonella genomes. BlastFrost is available for download at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comnluhmannBlastFrost>httpsgithub.comnluhmannBlastFrost<jatsext-link>.
biorxiv bioinformatics 0-100-users 2020Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin, bioRxiv, 2020-01-23
Since the SARS outbreak 18 years ago, a large number of severe acute respiratory syndrome related coronaviruses (SARSr-CoV) have been discovered in their natural reservoir host, bats1-4. Previous studies indicated that some of those bat SARSr-CoVs have the potential to infect humans5-7. Here we report the identification and characterization of a novel coronavirus (nCoV-2019) which caused an epidemic of acute respiratory syndrome in humans, in Wuhan, China. The epidemic, started from December 12th, 2019, has caused 198 laboratory confirmed infections with three fatal cases by January 20th, 2020. Full-length genome sequences were obtained from five patients at the early stage of the outbreak. They are almost identical to each other and share 79.5% sequence identify to SARS-CoV. Furthermore, it was found that nCoV-2019 is 96% identical at the whole genome level to a bat coronavirus. The pairwise protein sequence analysis of seven conserved non-structural proteins show that this virus belongs to the species of SARSr-CoV. The nCoV-2019 virus was then isolated from the bronchoalveolar lavage fluid of a critically ill patient, which can be neutralized by sera from several patients. Importantly, we have confirmed that this novel CoV uses the same cell entry receptor, ACE2, as SARS-CoV.
biorxiv microbiology 500+-users 2020Human immune system variation during one year, bioRxiv, 2020-01-23
SUMMARYThe human immune system varies extensively between individuals, but variation within individuals over time has not been well characterized. Systems-level analyses allow for simultaneous quantification of many interacting immune system components, and the inference of global regulatory principles. Here we present a longitudinal, systems-level analysis in 99 healthy adults, 50 to 65 years of age and sampled every 3rd month during one year. We describe the structure of inter-individual variation and characterize extreme phenotypes along a principal curve. From coordinated measurement fluctuations, we infer relationships between 115 immune cell populations and 750 plasma proteins constituting the blood immune system. While most individuals have stable immune systems, the degree of longitudinal variability is an individual feature. The most variable individuals, in the absence of overt infections, exhibited markers of poor metabolic health suggestive of a functional link between metabolic and immunologic homeostatic regulation.HIGHLIGHTSLongitudinal variation in immune cell composition during one yearInter-individual variation can be described along a principal curveImmune cell and protein relationships are inferredVariability over time is an individual feature correlating with markers of poor metabolic health
biorxiv immunology 0-100-users 2020Post-prediction Inference, bioRxiv, 2020-01-23
AbstractMany modern problems in medicine and public health leverage machine learning methods to predict outcomes based on observable covariates [1, 2, 3, 4]. In an increasingly wide array of settings, these predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes [1, 5, 6, 7, 8, 9]. We call inference with predicted outcomes post-prediction inference. In this paper, we develop methods for correcting statistical inference using outcomes predicted with an arbitrary machine learning method. Rather than trying to derive the correction from the first principles for each machine learning tool, we make the observation that there is typically a low-dimensional and easily modeled representation of the relationship between the observed and predicted outcomes. We build an approach for the post-prediction inference that naturally fits into the standard machine learning framework. We estimate the relationship between the observed and predicted outcomes on the testing set and use that model to correct inference on the validation set and subsequent statistical models. We show our postpi approach can correct bias and improve variance estimation (and thus subsequent statistical inference) with predicted outcome data. To show the broad range of applicability of our approach, we show postpi can improve inference in two totally distinct fields modeling predicted phenotypes in repurposed gene expression data [10] and modeling predicted causes of death in verbal autopsy data [11]. We have made our method available through an open-source R package [<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comSiruoWangpostpi>httpsgithub.comSiruoWangpostpi<jatsext-link>]
biorxiv bioinformatics 0-100-users 2020High-resolution cryo-EM using beam-image shift at 200 keV, bioRxiv, 2020-01-22
ABSTRACTRecent advances in single-particle cryo-electron microscopy (cryo-EM) data collection utilizes beam-image shift to improve throughput. Despite implementation on well-aligned 300 keV cryo-EM instruments, it remains unknown how well beam-image shift data collection affects data quality on 200 keV instruments and whether any aberrations can be computationally corrected. To test this, we collected and analyzed a cryo-EM dataset of aldolase at 200 keV using beam-image shift. This analysis shows that beam tilt on the instrument initially limited the resolution of aldolase to 5.6Å. After iterative rounds of aberration correction and particle polishing in RELION, we were able to obtain a 2.8Å structure. This analysis indicates that software correction of microscope misalignment can provide a dramatic improvement in resolution.
biorxiv biophysics 0-100-users 2020A super sensitive auxin-inducible degron system with an engineered auxin-TIR1 pair, bioRxiv, 2020-01-21
AbstractAuxin-Inducible Degron (AID) technology enables conditional depletion of targeted proteins. However, the applicability of the AID in vertebrate cells has been limited due to cytotoxicity caused by high auxin concentrations. Here, we establish an improved AID system using an engineered orthogonal auxin-TIR1 pair, which exhibits over 1,000 times stronger binding. With ~1,000-fold less auxin concentration, we achieved to generate the AID-based knockout cells in various human and mouse cell lines in a single transfection.
biorxiv bioengineering 0-100-users 2020