Investigating the Expansion of the D. biarmipes Muller F Element

From the Washington University Senior Honors Thesis Abstracts (WUSHTA), Spring 2016. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Kristin G. Sobotka, Undergraduate Research Coordinator; Jennifer Kohl. Mentor: Sarah Elgi

Additional file 1: of ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference

Table S1. Parameters passed to each pipeline. Default, AUC-optimized and reproducibility-optimized pipelines for ATAC-seq and DNase1-seq are shown using each footprinting algorithm. Parameters for each pipeline are listed. These parameters correspond to Fig. 7, and these pipelines can be found at github.com/ChioriniLab . (PDF 182 kb

Additional file 4: of ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference

Figure S3. Visual inspection reveals consistent overlap between HOMER peaks and OCRs at peak size of 200. (a) Read coverage for DNase1-seq (top) and ATAC-seq (bottom) shown underneath open chromatin regions called by HOMER at peak sizes ranging from 10 base pairs to 2000 base pairs. Peak reproducibility between replicates was shown to be higher with lower peak sizes. Visualized using the Broad Institute’s Integrative Genomics Viewer (software. broadinstitute.org/software/igv /). (b,c,d) Metrics of reproducibility and biological information plotted against the HOMER argument minDist for all pipelines. Pipelines could have a minDist of 0, 50 or 500, and these values had no effect on correlation between replicates or recapitulation of known ChIP-seq. (PDF 1272 kb

Correction to: ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference

Following the publication of this article [1], the authors informed us of the following typographical errors in the Results section (the changes are marked in bold)

ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference

Abstract Background Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data. Results We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads. Conclusions We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling

Additional file 2: of ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference

Figure S1. FastQC Reveals Tn5 Tags and PCR Chimerism. Non-random distributions of the first ~ 10 bases after tagmentation correspond to the Tn5 tagmentation after library primers are removed. This non-random region at the beginning of the read is characteristics of ATAC-seq data. In the ATAC-seq sample from Buenrostro, a second non-random region can be seen, suggesting PCR chimerism. (PDF 57 kb

Additional file 3: of ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference

Figure S2. Trimming reads improves alignment of the GM12878 ATAC-seq reads. Tn5 transposase attaches mosaic end (ME) tags that need to be trimmed from the 5′ end of the read. Additionally, however, trimming low-quality base pairs from the 3′ end of the ATAC-seq reads so that all reads had the same length improved alignment to the genome (shown in green). With a 3 billion base pair genome, the chance that a sequence of a certain length will align randomly is high for sequences shorter than 17 base pairs. To minimize random alignment while removing low-quality base pairs for this ATAC-seq data, we trimmed the reads to a final length of 20 base pairs. (PDF 13 kb

Identification of RNA aptamer which specifically interacts with PtdIns(3)P

The phosphinositide Ptdlns(3)P plays an important role in autophagy; however, the detailed mechanism of its activity remains unclear. Here, we used a Systematic Evolution of Ligands by EXponential enrichment (SELEX) screening approach to identify an RNA aptamer of 40 nucleotides that specifically recognizes and binds to intracellular lysosomal Ptdlns(3)P. Binding occurs in a magnesium concentration- and pH-dependent manner, and consequently inhibits autophagy as determined by LC3II/I conversion, p62 degradation, formation of LC3 puncta, and lysosomal accumulation of Phafin2. These effects in turn inhibited lysosomal acidification, and the subsequent hydrolytic activity of cathepsin D following induction of autophagy. Given the essential role of Ptdlns(3)P as a key targeting molecule for autophagy induction, identification of this novel Ptdlns(3)P RNA aptamer provides new opportunities for investigating the biological functions and mechanisms of phosphoinositides. (C) 2019 The Authors. Published by Elsevier Inc