miND pipeline AWS EC2 installation and setup v2

miND is a NGS data analysis pipeline for smallRNA sequencing data. In this protocol, the pipeline is setup and run on an AWS EC2 instance with example data from a public repository. Please see the publication paper on F1000 for more details on the pipeline and how to use it.

miND pipeline AWS EC2 installation and setup v1

miND is a NGS data analysis pipeline for smallRNA sequencing data. In this protocol, the pipeline is setup and run on an AWS EC2 instance with example data from a public repository. Please see the publication paper on F1000 for more details on the pipeline and how to use it.

COVID-19 Variant Detection with a High-Fidelity CRISPR-Cas12 Enzyme

Laboratory tests for the accurate and rapid identification of SARS-CoV-2 variants have the potential to guide the treatment of COVID-19 patients and inform infection control and public health surveillance efforts. Here we present the development and validation of a COVID-19 variant DETECTR assay incorporating loop-mediated isothermal amplification (LAMP) followed by CRISPR-Cas12 based identification of single nucleotide polymorphism (SNP) mutations in the SARS-CoV-2 spike (S) gene. This assay targets the L452R, E484K, and N501Y mutations associated with nearly all circulating viral lineages. In a comparison of three different Cas12 enzymes, only the newly identified enzyme CasDx1 was able to accurately identify all three targeted SNP mutations. We developed a data analysis pipeline for CRISPR-based SNP identification using the assay from 91 clinical samples (Ct < 30), yielding an overall SNP concordance and agreement with SARS-CoV-2 lineage classification of 100% compared to viral whole-genome sequencing. These findings highlight the potential utility of CRISPR-based mutation detection for clinical and public health diagnostics.

Anonymiced Shareable Data: Using mice to Create and Analyze Multiply Imputed Synthetic Datasets

Synthetic datasets simultaneously allow for the dissemination of research data while protecting the privacy and confidentiality of respondents. Generating and analyzing synthetic datasets is straightforward, yet, a synthetic data analysis pipeline is seldom adopted by applied researchers. We outline a simple procedure for generating and analyzing synthetic datasets with the multiple imputation software mice (Version 3.13.15) in R. We demonstrate through simulations that the analysis results obtained on synthetic data yield unbiased and valid inferences and lead to synthetic records that cannot be distinguished from the true data records. The ease of use when synthesizing data with mice along with the validity of inferences obtained through this procedure opens up a wealth of possibilities for data dissemination and further research on initially private data.

A Reproducible MEEG Data Analysis Workflow with conda, Snakemake, and R Markdown

This tutorial is devoted to computational reproducibility, which is an ability to recreate the reported results using the original data and code. Previous studies show that this is impossible for a large percentage of studies with published data and code. We find this situation to be a serious problem for science in general and for the cognitive neuroscience in particular. In this tutorial, we focused on three sources of irreproducibility: differences in software environment, utilization of out-of-date derivative files, and human errors during manual copying of figures, tables, and numbers to the manuscript. We describe three tools that solve these issues: conda, Snakemake, and R Markdown, respectively. Together, they form an effective toolkit that can help researchers achieve reproducibility of their analyses. We demonstrate an application of this toolkit by reimplementing a published data analysis pipeline applied to an open MEEG dataset. Main strengths and weaknesses of our and other approaches are discussed.

Analysis Strategies for MHz XPCS at the European XFEL

The nanometer length-scale holds precious information on several dynamical processes that develop from picoseconds to seconds. In the past decades, X-ray scattering techniques have been developed to probe the dynamics at such length-scales on either ultrafast (sub-nanosecond) or slow ((milli-)second) time scales. With the start of operation of the European XFEL, thanks to the MHz repetition rate of its X-ray pulses, even the intermediate μs range have become accessible. Measuring dynamics on such fast timescales requires the development of new technologies such as the Adaptive Gain Integrating Pixel Detector (AGIPD). μs-XPCS is a promising technique to answer many scientific questions regarding microscopic structural dynamics, especially for soft condensed matter systems. However, obtaining reliable results with complex detectors at free-electron laser facilities is challenging and requires more sophisticated analysis methods compared to experiments at storage rings. Here, we discuss challenges and possible solutions to perform XPCS experiments with the AGIPD at European XFEL; in particular, at the Materials Imaging and Dynamics (MID) instrument. We present our data analysis pipeline and benchmark the results obtained at the MID instrument with a well-known sample composed by silica nanoparticles dispersed in water.

Examining the emission and transmission spectra of WASP-79b with retrieval

&lt;p&gt;Retrieval tools provide a way of determining an exoplanet atmosphere's temperature structure and composition with an observed planetary spectrum, working backwards to determine the chemistry and temperature by iteratively comparing synthetic spectra that have been constructed via a forward model to the observed spectra and determining a best-fit result (Barstow and Heng, 2020). This talk will be presenting the emission and reanalysed transmission spectrum and retrieval analysis of WASP-79b, an inflated hot Jupiter first detected by Smalley et al. (2012). Previous transmission spectra of WASP-79b has been analysed in Sozten et al. (2020), Skaf et al. (2020), and Rathcke et al. (2021); all studies agreeing on detections of H2O with various confidence levels, with the latter finding moderate evidence of an H- bound-free opacity compared to iron hydride abundance found by the other studies. Using the publicly available \verb+Iraclis+ data analysis pipeline and the Bayesian atmospheric retrieval framework TauREx 3, we will be adding to the global picture of this planet by examining the Hubble Space Telescope emission spectra as captured by the Wide Field Camera 3 G141 grism (PI: David Sing, proposal ID: 14767).&amp;#160;&lt;/p&gt;