Biology subjects

Johnson, W. E.

Publications and source records attributed to Johnson, W. E..

44 records · Page 3Linked to original sources

bioRxiv2020

ComBat-Seq: batch effect adjustment for RNA-Seq count data

The benefit of integrating batches of genomic data to increase statistical power in differential expression is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data. Many existing methods for batch effect adjustment assume continuous, bell-shaped Gaussian distributions for data. However in RNA-Seq studies where data are skewed, over-dispersed counts, this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used to better capture the properties of counts. We developed a batch correction method, ComBat-Seq, using negative binomial regression. ComBat-Seq retains the integer nature of count data in RNA-Seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-Seq adjusted data result in better statistical power and control of false positives in differential expression, compared to data adjusted by the other available methods. We further demonstrated in a real data example where ComBat-Seq successfully removes batch effects and recovers the biological signal in the data.

bioinformatics↗

bioRxiv2020

Functional variants within the 5UTR support reduced exon usage as a molecular mechanism underlying the quantitative trait gene Hnrnph1 for reduced methamphetamine sensitivity

We previously identified a 210 kb region on chromosome 11 (50.37-50.58 Mb, mm10) containing two protein-coding genes (Hnrnph1, Rufy1) that was necessary for reduced methamphetamine-induced locomotor activity in C57BL/6J congenic mice harboring DBA/2J polymorphisms. Gene editing of a small deletion in the first coding exon supported Hnrnph1 as a quantitative trait gene. We have since shown that Hnrnph1 mutants also exhibit reduced methamphetamine-induced reward, reinforcement, and dopamine release. However, the quantitative trait variants (QTVs) that modulate Hnrnph1 function at the molecular level are not known. Nine single nucleotide polymorphisms and seven indels distinguish C57BL/6J from DBA/2J within Hnrnph1, including four variants within the 5 untranslated region (UTR). Here, we show that a 114 kb introgressed region containing Hnrnph1 and Rufy1 was sufficient to cause a decrease in MA-induced locomotor activity. Gene-level transcriptome analysis of striatal tissue from 114 kb congenics versus Hnrnph1 mutants identified a nearly perfect correlation of fold-change in expression for those differentially expressed genes that were common to both mouse lines, indicating functionally similar effects on the transcriptome and behavior. Exon-level analysis (including noncoding exons) revealed decreased 5 UTR usage of Hnrnph1 and immunoblot analysis identified a corresponding decrease in hnRNP H protein in 114 kb congenic mice. Molecular cloning of the Hnrnph1 5 UTR containing all four variants (but none of them individually) upstream of a reporter induced a decrease in reporter signal in both HEK293 and N2a cells, thus identifying a set of QTVs underlying molecular regulation of Hnrnph1.

neuroscience↗

bioRxiv2019

Robustifying genomic classifiers to batch effects via ensemble learning

Genomic data are often produced in batches due to practical restrictions, which may lead to unwanted variation in data caused by discrepancies across processing batches. Such \"batch effects\" often have negative impact on downstream biological analysis and need careful consideration. In practice, batch effects are usually addressed by specifically designed software, which merge the data from different batches, then estimate batch effects and remove them from the data. Here we focus on classification and prediction problems, and propose a different strategy based on ensemble learning. We first develop prediction models within each batch, then integrate them through ensemble weighting methods. In contrast to the typical approach of removing batch effects from the merged data, our method integrates predictions rather than data. We provide a systematic comparison between these two strategies, using studies targeting diverse populations infected with tuberculosis. In one study, we simulated increasing levels of heterogeneity across random subsets of the study, which we treat as simulated batches. We then use the two methods to develop a genomic classifier for the binary indicator of disease status. We evaluate the accuracy of prediction in another independent study targeting a different population cohort. We observed a turning point in the level of heterogeneity, after which our strategy of integrating predictions yields better discrimination in independent validation than the traditional method of integrating the data. These observations provide practical guidelines for handling batch effects in the development and evaluation of genomic classifiers.

bioinformatics↗

bioRxiv2019

Decontamination of ambient RNA in single-cell RNA-seq with DecontX

Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq) and discover novel cellular heterogeneity in complex biological systems. However, ambient RNA present in the cell suspension can be incorporated into these droplets and aberrantly counted along with a cells native mRNA. This results in cross-contamination of transcripts between different cell populations and can potentially decrease the precision of downstream analyses. We developed a novel hierarchical Bayesian method called DecontX to estimate and remove contamination in individual cells from scRNA-seq data. DecontX accurately predicted the proportion of contaminated counts in a mixture of mouse and human cells. Decontamination of PBMC datasets removed aberrant expression of cell type specific marker genes from other cell types and improved overall separation of cell clusters. In general, DecontX can be incorporated into scRNA-seq workflows to assess quality of dissociation protocols and improve downstream analyses.

bioinformatics↗

bioRxiv2019

scruff: An R/Bioconductor package for preprocessing single-cell RNA-sequencing data

BackgroundSingle-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking.\n\nResultsHere we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and open-source R/Bioconductor package.\n\nConclusionsscruff streamlines the preprocessing of scRNA-seq data in a few simple R commands. It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data.

bioinformatics↗

bioRxiv2018

Mountain lion genomes provide insights into genetic rescue of inbred populations

Introduction paragraph/AbstractAcross the geographic range of mountain lions, which includes much of North and South America, populations have become increasingly isolated due to human persecution and habitat loss. To explore the genomic consequences of these processes, we assembled a high-quality mountain lion genome and analyzed a panel of resequenced individuals from across their geographic range. We found strong geographical structure and signatures of severe inbreeding in all North American populations. Tracts of homozygosity were rarely shared among populations, suggesting that assisted gene flow would restore local genetic diversity. However, the genome of an admixed Florida panther that descended from a translocated individual from Central America had surprisingly long tracts of homozygosity, indicating that genomic gains from translocation were quickly lost by local inbreeding. Thus, to sustain diversity, genetic rescue will need to occur at regular intervals, through repeated translocations or restoring landscape connectivity. Mountain lions provide a rare opportunity to examine the potential to restore diversity through genetic rescue, and to observe the long-term effects of translocation. Our methods and results provide a framework for genome-wide analyses that can be applied to the management of small and isolated populations.

evolutionary biology↗

bioRxiv2018

C57BL/6 substrain differences in inflammatory and neuropathic nociception and genetic mapping of a major quantitative trait locus underlying acute thermal nociception

Sensitivity to different pain modalities has a genetic basis that remains largely unknown. Employing closely related inbred mouse substrains can facilitate gene mapping of nociceptive behaviors in preclinical pain models. We previously reported enhanced sensitivity to acute thermal nociception in C57BL/6J (B6J) versus C57BL/6N (B6N) substrains. Here, we expanded on nociceptive phenotypes and observed an increase in formalin-induced inflammatory nociceptive behaviors and paw diameter in B6J versus B6N mice (Charles River Laboratories). No strain differences were observed in mechanical or thermal hypersensitivity or in edema following the Complete Freunds Adjuvant (CFA) model of inflammatory pain, indicating specificity in the inflammatory nociceptive stimulus. In the chronic nerve constriction injury (CCI), a model of neuropathic pain, no strain differences were observed in baseline mechanical threshold or in mechanical hypersensitivity up to one month post-CCI. We replicated the enhanced thermal nociception in the 52.5{degrees}C hot plate test in B6J versus B6N mice from The Jackson Laboratory. Using a B6J x B6N-F2 cross (N=164), we mapped a major quantitative trait locus (QTL) underlying hot plate sensitivity to chromosome 7 that peaked at 26 Mb (LOD=3.81, p<0.01; 8.74 Mb-36.50 Mb) that was more pronounced in males. Genes containing expression QTLs (eQTLs) associated with the peak nociceptive marker that are implicated in pain and inflammation include Ryr1, Cyp2a5, Pou2f2, Clip3, Sirt2, Actn4, and Ltbp4 (FDR < 0.05). Future studies involving positional cloning and gene editing will determine the quantitative trait gene(s) and potential pleiotropy of this locus across pain modalities.

genetics↗

bioRxiv2018

Interactive single cell RNA-Seq analysis with the Single Cell Toolkit (SCTK)

Single cell RNA-sequencing (scRNA-Seq) allows researchers to profile transcriptional activity in individual cells. However, the complex nature of these data and variability in study design and data generation requires sophisticated computational tools and informed analytical decisions. Here, we present the Single Cell Toolkit (SCTK), an interactive scRNA-Seq analysis package that enables users to perform scRNA-Seq analysis interactively using a command-line workflow or a graphical user interface (GUI) written in R/Shiny.

bioinformatics↗