Highlights d SERGIO simulates stochastic gene expression in steadystate or differentiating cells d Simulations of RNA splicing enable RNA velocity estimation from generated data d Simulations show technical noise to greatly impact accuracy of network inference d Simulations recapitulate key regulators of T cell differentiation program

DNA origami nanostructures can be used to functionalize solid-state nanopores for single molecule studies. In this study, we characterized a nanopore in a DNA origami-graphene heterostructure for DNA detection. The DNA origami nanopore is functionalized with a specific nucleotide type at the edge of the pore. Using extensive molecular dynamics (MD) simulations, we computed and analyzed the ionic conductivity of nanopores in heterostructures carpeted with one or two layers of DNA origami on graphene. We demonstrate that a nanopore in DNA origami-graphene gives rise to distinguishable dwell times for the four DNA base types, whereas for a nanopore in bare graphene, the dwell time is almost the same for all types of bases. The specific interactions (hydrogen bonds) between DNA origami and the translocating DNA strand yield different residence times and ionic currents. We also conclude that the speed of DNA translocation decreases due to the friction between the dangling bases at the pore mouth and the sequencing DNA strands.

Osteosarcoma (OS) is the most common form of primary bone cancer in humans. The early detection and subsequent control of metastasis has been challenging in OS. Lipids are important constituents of cells that maintain structural integrity that can be converted into lipid-signaling molecules and are reprogrammed in cancerous states. Here, we investigate the global lipidomic differences in metastatic (143B) and nonmetastatic (HOS) human OS cells as compared with normal fetal osteoblast cells (FOB) using lipidomics. We detect 15 distinct lipid classes in all three cell lines that included over 1,000 lipid species across various classes including phospholipids, sphingolipids and ceramides, glycolipids, and cholesterol. We identify a key class of lipids, diacylglycerols, which are overexpressed in metastatic OS cells as compared with their nonmetastatic or nontumorigenic counterparts. As a proof of concept, we show that blocking diacylglycerol synthesis reduces cellular viability and reduces cell migration in metastatic OS cells. Thus, the differentially regulated lipids identified in this study might aid in biomarker discovery, and the synthesis and metabolism of specific lipids could serve as future targets for therapeutic development.-Roy, J., P. Dibaeinia, T. M. Fan, S. Sinha, and A. Das. Global analysis of osteosarcoma lipidomes reveal altered lipid profiles in metastatic versus nonmetastatic cells. J. Lipid Res. 2019. 60: 375-387. Supplementary key words lipidomics • mass spectrometry • diacylglycerol • cholesterolOsteosarcoma (OS) is the most common form of primary bone cancer in humans. The treatment options for OS consists of multiagent induction chemotherapy, tumor excision, and adjuvant chemotherapy. The survival rates remain poor, despite aggressive treatment. In patients with localized disease, 5-year survival rates are approximately 65%; however, in the case of metastatic disease, the 5-year survival rates have plateaued to about 20% (1, 2). Although progress has been made toward improving treatment Manuscript

Motivation ASTRAL is the current leading method for species tree estimation from phylogenomic datasets (i.e., hundreds to thousands of genes) that addresses gene tree discord resulting from incomplete lineage sorting (ILS). ASTRAL is statistically consistent under the multi-locus coalescent model (MSC), runs in polynomial time, and is able to run on large datasets. Key to ASTRAL’s algorithm is the use of dynamic programming to find an optimal solution to the MQSST (maximum quartet support supertree) within a constraint space that it computes from the input. Yet, ASTRAL can fail to complete within reasonable timeframes on large datasets with many genes and species, because in these cases the constraint space it computes is too large. Results Here we introduce FASTRAL, a phylogenomic estimation method. FASTRAL is based on ASTRAL, but uses a different technique for constructing the constraint space. The technique we use to define the constraint space maintains statistical consistency and is polynomial time; thus we prove that FASTRAL is a polynomial time algorithm that is statistically consistent under the MSC. Our performance study on both biological and simulated data sets demonstrates that FASTRAL matches or improves on ASTRAL with respect to species tree topology accuracy (and under high ILS conditions it is statistically significantly more accurate), while being dramatically faster—especially on datasets with large numbers of genes and high ILS—due to using a significantly smaller constraint space. Availability FASTRAL is available in open-source form at https://github.com/PayamDiba/FASTRAL. Supplementary information Supplementary data are available at Bioinformatics online.

A common approach to benchmarking of single-cell transcriptomics tools is to generate synthetic data sets that resemble experimental data in their statistical properties.However, existing single-cell simulators do not incorporate known principles of transcription factor-gene regulatory interactions that underlie expression dynamics.Here we present SERGIO, a simulator of single-cell gene expression data that models the stochastic nature of transcription as well as linear and non-linear influences of multiple transcription factors on genes according to a user-provided gene regulatory network. SERGIO is capable of simulating any number of cell types in steady-state or cells differentiating to multiple fates according to a provided trajectory, reporting both unspliced and spliced transcript counts in single-cells. We show that data sets generated by SERGIO are comparable with experimental data in terms of multiple statistical measures. We also illustrate the use of SERGIO to benchmark several popular single-cell analysis tools, including GRN inference methods.