One-pot oligosaccharide synthesis: reactivity tuning by post-synthetic modification of aglyconElectronic supplementary information (ESI) available: experimental data. See http://www.rsc.org/suppdata/cc/b4/b405886k/

2004 ◽  
pp. 1960 ◽  
Author(s):  
Lijun Huang ◽  
Zhen Wang ◽  
Xuefei Huang
Keyword(s):  
Experimental Data ◽  
Supplementary Information ◽  
Oligosaccharide Synthesis ◽  
One Pot

scholarly journals Haplotype assembly of autotetraploid potato using integer linear programing

2019 ◽  
Vol 35 (18) ◽  
pp. 3279-3286 ◽  
Author(s):  
Enrico Siragusa ◽  
Niina Haiminen ◽  
Richard Finkers ◽  
Richard Visser ◽  
Laxmi Parida
Keyword(s):  
Experimental Data ◽  
Experimental Studies ◽  
Simulated Data ◽  
Supplementary Information ◽  
Linear Programs ◽  
Plant Genomics ◽  
Optimal Method ◽  
Haplotype Assembly ◽  
Open Issue ◽  
Removal Model

Abstract Summary Haplotype assembly of polyploids is an open issue in plant genomics. Recent experimental studies on highly heterozygous autotetraploid potato have shown that available methods do not deliver satisfying results in practice. We propose an optimal method to assemble haplotypes of highly heterozygous polyploids from Illumina short-sequencing reads. Our method is based on a generalization of the existing minimum fragment removal model to the polyploid case and on new integer linear programs to reconstruct optimal haplotypes. We validate our methods experimentally by means of a combined evaluation on simulated and experimental data based on 83 previously sequenced autotetraploid potato cultivars. Results on simulated data show that our methods produce highly accurate haplotype assemblies, while results on experimental data confirm a sensible improvement over the state of the art. Availability and implementation Executables for Linux at http://github.com/Computational Genomics/HaplotypeAssembler. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Modeling cell proliferation in human acute myeloid leukemia xenografts

2019 ◽  
Vol 35 (18) ◽  
pp. 3378-3386 ◽  
Author(s):  
Marco S Nobile ◽  
Thalia Vlachou ◽  
Simone Spolaor ◽  
Daniela Bossi ◽  
Paolo Cazzaniga ◽  
...  
Keyword(s):  
Experimental Data ◽  
Cell Proliferation ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Time Series Data ◽  
Supplementary Information ◽  
Series Data ◽  
Proliferative Potential ◽  
Proliferating Cells ◽  
Acute Myeloid

Abstract Motivation Acute myeloid leukemia (AML) is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. Results In this work, we present ProCell, a novel modeling and simulation framework to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events. We apply ProCell to compare different models of cell proliferation in AML, notably leveraging experimental data derived from human xenografts in mice. ProCell is coupled with Fuzzy Self-Tuning Particle Swarm Optimization, a swarm-intelligence settings-free algorithm used to automatically infer the models parameterizations. Our results provide new insights on the intricate organization of AML cells with highly heterogeneous proliferative potential, highlighting the important role played by quiescent cells and proliferating cells characterized by different rates of division in the progression and evolution of the disease, thus hinting at the necessity to further characterize tumor cell subpopulations. Availability and implementation The source code of ProCell and the experimental data used in this work are available under the GPL 2.0 license on GITHUB at the following URL: https://github.com/aresio/ProCell. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals HypercubeME: two hundred million combinatorially complete datasets from a single experiment

2019 ◽  
Author(s):  
Laura Avino Esteban ◽  
Lyubov R Lonishin ◽  
Daniil Bobrovskiy ◽  
Gregory Leleytner ◽  
Natalya S Bogatyreva ◽  
...  
Keyword(s):  
Experimental Data ◽  
High Throughput ◽  
Recursive Algorithm ◽  
Random Mutagenesis ◽  
Higher Order ◽  
Supplementary Information ◽  
Single Experiment ◽  
Manual Curation ◽  
Complete Dataset ◽  
Genotype Space

Abstract Motivation Epistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a “combinatorially complete dataset”. So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets. Results We present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199,847,053 unique combinatorially complete genotype combinations of dimensionality ranging from two to twelve. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data. Availability https://github.com/ivankovlab/HypercubeME.git Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals One-pot oligosaccharide synthesis: latent-active method of glycosylations and radical halogenation activation of allyl glycosides

2019 ◽  
Vol 91 (9) ◽  
pp. 1451-1470 ◽  
Author(s):  
Rita Pal ◽  
Anupama Das ◽  
Narayanaswamy Jayaraman
Keyword(s):  
Acid Catalysis ◽  
Oligosaccharide Synthesis ◽  
Protecting Group ◽  
Functional Importance ◽  
Central Importance ◽  
One Pot ◽  
Active Method ◽  
Powerful Approach ◽  
Oxocarbenium Ion ◽  
Allyl Glycoside

Abstract Chemical glycosylations occupy a central importance to synthesize tailor-made oligo- and polysaccharides of functional importance. Generation of the oxocarbenium ion or the glycosyl cation is the method of choice in order to form the glycosidic bond interconnecting a glycosyl moiety with a glycosyl/aglycosyl moiety. A number of elegant methods have been devised that allow the glycosyl cation formation in a fairly stream-lined manner to a large extent. The latent-active method provides a powerful approach in the protecting group controlled glycosylations. In this context, allyl glycosides have been developed to meet the requirement of latent-active reactivities under appropriate glycosylation conditions. Radical halogenation provides a newer route of activation of allyl glycosides to an activated allylic glycoside. Such an allylic halide activation subjects the glycoside reactive under acid catalysis, leading to the conversion to a glycosyl cation and subsequent glycosylation with a number of acceptors. The complete anomeric selectivity favoring the 1,2-trans-anomeric glycosides points to the possibility of a preferred conformation of the glycosyl cation. This article discusses about advancements in the selectivity of glycosylations, followed by delineating the allylic halogenation of allyl glycoside as a glycosylation method and demonstrates synthesis of a repertoire of di- and trisaccharides, including xylosides, with varied protecting groups.

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