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James Taylor

Publications and source records attributed to James Taylor.

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Enhancing pre-defined workflows with ad hoc analytics using Galaxy, Docker and Jupyter

What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., list of variable sites). The subsequent exploratory stage is much more ad hoc and requires development of custom scripts making it problematic for biomedical researchers. Here we describe a hybrid platform combining common analysis pathways with exploratory environments. It aims at fully encompassing and simplifying the \"raw data-to-publication\" pathway and making it reproducible.

Bioinformatics

Genome-wide comparative analysis reveals human- mouse regulatory landscape and evolution

BackgroundBecause species-specific gene expression is driven by species-specific regulation, understanding the relationship between sequence and function of the regulatory regions in different species will help elucidate how differences among species arise. Despite active experimental and computational research, the relationships among sequence, conservation, and function are still poorly understood.\n\nResultsWe compared transcription factor occupied segments (TFos) for 116 human and 35 mouse TFs in 546 human and 125 mouse cell types and tissues from the Human and the Mouse ENCODE projects. We based the map between human and mouse TFos on a one-to-one nucleotide cross-species mapper, bnMapper, that utilizes whole genome alignments (WGA).\n\nOur analysis shows that TFos are under evolutionary constraint, but a substantial portion (25.1% of mouse and 25.85% of human on average) of the TFos does not have a homologous sequence on the other species; this portion varies among cell types and TFs. Furthermore, 47.67% and 57.01% of the homologous TFos sequence shows binding activity on the other species for human and mouse respectively. However, 79.87% and 69.22% is repurposed such that it binds the same TF in different cells or different TFs in the same cells. Remarkably, within the set of TFos not showing conservation of occupancy, the corresponding genome regions in the other species are preferred locations of novel TFos. These events suggest that a substantial amount of functional regulatory sequences is exapted from other biochemically active genomic material.\n\nDespite substantial repurposing of TFos, we did not find substantial changes in their predicted target genes, suggesting that CRMs buffer evolutionary events allowing little or no change in the TF - target gene associations. Thus, the small portion of TFos with strictly conserved occupancy underestimates the degree of conservation of regulatory interactions.\n\nConclusionWe mapped regulatory sequences from an extensive number of TFs and cell types between human and mouse. A comparative analysis of this correspondence unveiled the extent of the shared regulatory sequence across TFs and cell types under study. Importantly, a large part of the shared regulatory sequence repurposed on the other species. This sequence, fueled by turnover events, provides a strong case for exaptation in regulatory elements.

Bioinformatics

HiFive: a tool suite for easy and efficient HiC and 5C data analysis

The chromatin interaction assays 5C and HiC have advanced our understanding of genomic spatial organization but analysis approaches for these data are limited by usability and flexibility. The HiFive tool suite provides efficient data handling and a variety of normalization approaches for easy, fast analysis and method comparison. Integration of MPI-based parallelization allows scalability and rapid processing time. In addition to single-command analysis of an entire experiment from mapped reads to interaction values, HiFive has been integrated into the open-source, web-based platform Galaxy to connect users with computational resources and a graphical interface. HiFive is open-source software available from http://taylorlab.org/software/hifive/.

Bioinformatics