AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis.

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) is a technique widely used to investigate genome-wide chromatin accessibility. The recently published Omni-ATAC-seq protocol substantially improves the signal/noise ratio and reduces the input cell number. High-quality data are critical to ensure accurate analysis. Several tools have been developed for assessing sequencing quality and insertion size distribution for ATAC-seq data; however, key quality control (QC) metrics have not yet been established to accurately determine the quality of ATAC-seq data. Here, we optimized the analysis strategy for ATAC-seq and defined a series of QC metrics for ATAC-seq data, including reads under peak ratio (RUPr), background (BG), promoter enrichment (ProEn), subsampling enrichment (SubEn), and other measurements. We incorporated these QC tests into our recently developed ATAC-seq Integrative Analysis Package (AIAP) to provide a complete ATAC-seq analysis system, including quality assurance, improved peak calling, and downstream differential analysis. We demonstrated a significant improvement of sensitivity (20%-60%) in both peak calling and differential analysis by processing paired-end ATAC-seq datasets using AIAP. AIAP is compiled into Docker/Singularity, and it can be executed by one command line to generate a comprehensive QC report. We used ENCODE ATAC-seq data to benchmark and generate QC recommendations, and developed qATACViewer for the user-friendly interaction with the QC report. The software, source code, and documentation of AIAP are freely available at https://github.com/Zhang-lab/ATAC-seq_QC_analysis.

Transcriptomic analysis of bone and fibrous tissue morphogenesis during digit tip regeneration in the adult mouse.

Humans have limited regenerative potential of musculoskeletal tissues following limb or digit loss. The murine digit has been used to study mammalian regeneration, where stem/progenitor cells (the "blastema") completely regenerate the digit tip after distal, but not proximal, amputation. However, the molecular mechanisms responsible for this response remain to be determined. Here, we evaluated the spatiotemporal formation of bone and fibrous tissues after level-dependent amputation of the murine terminal phalanx and quantified the transcriptome of the repair tissue. Distal (regenerative) and proximal (non-regenerative) amputations showed significant differences in temporal gene expression and tissue regrowth over time. Genes that direct skeletal system development and limb morphogenesis are transiently upregulated during blastema formation and differentiation, including distal Hox genes. Overall, our results suggest that digit tip regeneration is controlled by a gene regulatory network that recapitulates aspects of limb development, and that failure to activate this developmental program results in fibrotic wound healing.

Transcriptional profiling of intramembranous and endochondral ossification after fracture in mice.

Bone fracture repair represents an important clinical challenge with nearly 1 million non-union fractures occurring annually in the U.S. Gene expression differs between non-union and healthy repair, suggesting there is a pattern of gene expression that is indicative of optimal repair. Despite this, the gene expression profile of fracture repair remains incompletely understood. In this work, we used RNA-seq of two well-established murine fracture models to describe gene expression of intramembranous and endochondral bone formation. We used top differentially expressed genes, enriched gene ontology terms and pathways, callus cellular phenotyping, and histology to describe and contrast these bone formation processes across time. Intramembranous repair, as modeled by ulnar stress fracture, and endochondral repair, as modeled by femur full fracture, exhibited vastly different transcriptional profiles throughout repair. Stress fracture healing had enriched differentially expressed genes associated with bone repair and osteoblasts, highlighting the strong osteogenic repair process of this model. Interestingly, the PI3K-Akt signaling pathway was one of only a few pathways uniquely enriched in stress fracture repair. Full fracture repair involved a higher level of inflammatory and immune cell related genes than did stress fracture repair. Full fracture repair also differed from stress fracture in a robust downregulation of ion channel genes following injury, the role of which in fracture repair is unclear. This study offers a broad description of gene expression in intramembranous and endochondral ossification across several time points throughout repair and suggests several potentially intriguing genes, pathways, and cells whose role in fracture repair requires further study.

SRmapper: a fast and sensitive genome-hashing alignment tool.

UNLABELLED: Modern sequencing instruments have the capability to produce millions of short reads every day. The large number of reads produced in conjunction with variations between reads and reference genomic sequences caused both by legitimate differences, such as single-nucleotide polymorphisms and insertions/deletions (indels), and by sequencer errors make alignment a difficult and computationally expensive task, and many reads cannot be aligned. Here, we introduce a new alignment tool, SRmapper, which in tests using real data can align 10s of billions of base pairs from short reads to the human genome per computer processor day. SRmapper tolerates a higher number of mismatches than current programs based on Burrows-Wheeler transform and finds about the same number of alignments in 2-8× less time depending on read length (with higher performance gain for longer read length). The current version of SRmapper aligns both single and pair-end reads in base space fastq format and outputs alignments in Sequence Alignment/Map format. SRmapper uses a probabilistic approach to set a default number of mismatches allowed and determines alignment quality. SRmapper's memory footprint (∼2.5 GB) is small enough that it can be run on a computer with 4 GB of random access memory for a genome the size of a human. Finally, SRmapper is designed so that its function can be extended to finding small indels as well as long deletions and chromosomal translocations in future versions. AVAILABILITY: http://www.umsl.edu/∼wongch/software.html.