Dataset Information

Getting the Most Out of Your Crystals: Data Collection at the New High-Flux, Microfocus MX Beamlines at NSLS-II.

ABSTRACT: Advances in synchrotron technology are changing the landscape of macromolecular crystallography. The two recently opened beamlines at NSLS-II-AMX and FMX-deliver high-flux microfocus beams that open new possibilities for crystallographic data collection. They are equipped with state-of-the-art experimental stations and automation to allow data collection on previously intractable crystals. Optimized data collection strategies allow users to tailor crystal positioning to optimally distribute the X-ray dose over its volume. Vector data collection allows the user to define a linear trajectory along a well diffracting volume of the crystal and perform rotational data collection while moving along the vector. This is particularly well suited to long, thin crystals. We describe vector data collection of three proteins-Akt1, PI3K?, and CDP-Chase-to demonstrate its application and utility. For smaller crystals, we describe two methods for multicrystal data collection in a single loop, either manually selecting multiple centers (using H108A-PHM as an example), or "raster-collect", a more automated approach for a larger number of crystals (using CDP-Chase as an example).

SUBMITTER: Miller MS

PROVIDER: S-EPMC6384729 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Getting the Most Out of Your Crystals: Data Collection at the New High-Flux, Microfocus MX Beamlines at NSLS-II.

Miller Michelle S MS Maheshwari Sweta S Shi Wuxian W Gao Yuan Y Chu Nam N Soares Alexei S AS Cole Philip A PA Amzel L Mario LM Fuchs Martin R MR Jakoncic Jean J Gabelli Sandra B SB

Molecules (Basel, Switzerland) 20190130 3

Advances in synchrotron technology are changing the landscape of macromolecular crystallography. The two recently opened beamlines at NSLS-II-AMX and FMX-deliver high-flux microfocus beams that open new possibilities for crystallographic data collection. They are equipped with state-of-the-art experimental stations and automation to allow data collection on previously intractable crystals. Optimized data collection strategies allow users to tailor crystal positioning to optimally distribute the ...[more]

PMID: 30704096

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Project description:Background. A common research goal in transcriptome projects is to find genes that are differentially expressed in different phenotype classes. Biologists might wish to validate such gene candidates experimentally, or use them for downstream systems biology analysis. Producing a coherent differential gene expression analysis from RNA-seq count data requires an understanding of how numerous sources of variation such as the replicate size, the hypothesized biological effect size, and the specific method for making differential expression calls interact. We believe an explicit demonstration of such interactions in real RNA-seq data sets is of practical interest to biologists. Results. Using two large public RNA-seq data sets-one representing strong, and another mild, biological effect size-we simulated different replicate size scenarios, and tested the performance of several commonly-used methods for calling differentially expressed genes in each of them. We found that, when biological effect size was mild, RNA-seq experiments should focus on experimental validation of differentially expressed gene candidates. Importantly, at least triplicates must be used, and the differentially expressed genes should be called using methods with high positive predictive value (PPV), such as NOISeq or GFOLD. In contrast, when biological effect size was strong, differentially expressed genes mined from unreplicated experiments using NOISeq, ASC and GFOLD had between 30 to 50% mean PPV, an increase of more than 30-fold compared to the cases of mild biological effect size. Among methods with good PPV performance, having triplicates or more substantially improved mean PPV to over 90% for GFOLD, 60% for DESeq2, 50% for NOISeq, and 30% for edgeR. At a replicate size of six, we found DESeq2 and edgeR to be reasonable methods for calling differentially expressed genes at systems level analysis, as their PPV and sensitivity trade-off were superior to the other methods'. Conclusion. When biological effect size is weak, systems level investigation is not possible using RNAseq data, and no meaningful result can be obtained in unreplicated experiments. Nonetheless, NOISeq or GFOLD may yield limited numbers of gene candidates with good validation potential, when triplicates or more are available. When biological effect size is strong, NOISeq and GFOLD are effective tools for detecting differentially expressed genes in unreplicated RNA-seq experiments for qPCR validation. When triplicates or more are available, GFOLD is a sharp tool for identifying high confidence differentially expressed genes for targeted qPCR validation; for downstream systems level analysis, combined results from DESeq2 and edgeR are useful.

Dataset Information

Getting the Most Out of Your Crystals: Data Collection at the New High-Flux, Microfocus MX Beamlines at NSLS-II.

Publications

Getting the Most Out of Your Crystals: Data Collection at the New High-Flux, Microfocus MX Beamlines at NSLS-II.

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