Thiede, S. N. | bioRxiv Science

bioRxiv2021

Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our educational materials on GitHub.

scientific communication and education↗

bioRxiv2019

prewas: Data pre-processing for more informative bacterial GWAS

While variant identification pipelines are becoming increasingly standardized, less attention has been paid to the pre-processing of variants prior to their use in bacterial genome-wide association studies (bGWAS). Three nuances of variant pre-processing that impact downstream identification of genetic associations include the separation of variants at multiallelic sites, separation of variants in overlapping genes, and referencing of variants relative to ancestral alleles. Here we demonstrate the importance of these variant pre-processing steps on diverse bacterial genomic datasets and present prewas, an R package, that standardizes the pre-processing of multiallelic sites, overlapping genes, and reference alleles before bGWAS. This package facilitates improved reproducibility and interpretability of bGWAS results. Prewas enables users to extract maximal information from bGWAS by implementing multi-line representation for multiallelic sites and variants in overlapping genes. Prewas outputs a binary SNP matrix that can be used for SNP-based bGWAS and will prevent the masking of minor alleles during bGWAS analysis. The optional binary gene matrix output can be used for gene-based bGWAS which will enable users to maximize the power and evolutionary interpretability of their bGWAS studies. Prewas is available for download from GitHub. DATA SUMMARYO_LIprewas is available from GitHub under the MIT License (URL: https://github.com/Snitkin-Lab-Umich/prewas) and can be installed using the command devtools::install_github("Snitkin-Lab-Umich/prewas") C_LIO_LICode to perform analyses is available from GitHub under the MIT License (URL: https://github.com/Snitkin-Lab-Umich/prewas_manuscript_analysis) C_LIO_LIAll genomes are publicly available on NCBI (see Table S1 for more details) C_LI O_TBL View this table: org.highwire.dtl.DTLVardef@137f5b6org.highwire.dtl.DTLVardef@e4a12dorg.highwire.dtl.DTLVardef@11b2a3org.highwire.dtl.DTLVardef@1e71da1org.highwire.dtl.DTLVardef@4a7286_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable S1:C_FLOATNO O_TABLECAPTIONSources for bacterial datasets C_TABLECAPTION C_TBL IMPACT STATEMENTIn between variant calling and performing bacterial genome-wide association studies (bGWAS) there are many decisions regarding processing of variants that have the potential to impact bGWAS results. We discuss the benefits and drawbacks of various variant pre-processing decisions and present the R package prewas to standardize single nucleotide polymorphism (SNP) pre-processing, specifically to incorporate multiallelic sites and prepare the data for gene-based analyses. We demonstrate the importance of these considerations by highlighting the prevalence of multiallelic sites and SNPs in overlapping genes within diverse bacterial genomes and the impact of reference allele choice on gene-based analyses.

bioinformatics↗