The Semple Lab

Stuart’s developed a clever new approach to time series data (Aitken et al, 2015), fitting expression profiles over time to a series of archetypical models or ‘kinetic signatures’. Unlike the dominant forms of analysis (clustering, differential expression between time points) this allows us to detect profiles of interest even if the profile of interest is entirely unique or involves lowly expressed transcripts. That turns out to be particularly handy when studying ncRNA dynamics.

James’ interesting survey of where and when (and even how) duplicated regions diverge in terms of their chromatin structure appears in Genome Biology and Evolution (Prendergast et al, 2014). I realise nobody wants to hear scientists moaning about the peer review process (yet again). But jeez – this paper went through several journals over the course of ~18 months – and is more or less unchanged as a result. Someone needs to at least try to find an alternative for the stodgy, inefficient process we’ve ended up with. Maybe this is it?

The FANTOM5 Consortium publishes an atlas of transcriptional activity covering 975 human and 399 mouse samples, including primary cells, tissues and cancer cell lines (FANTOM Consortium, 2014). Better still, the data were generated using single-molecule sequencing, avoiding the biases introduced by other approaches. As ever it was great fun to be involved in this unique international collaboration. All data are freely available from the FANTOM5 site.

Emily Chambers’ study of higher order chromatin structure is published in PLOS Comp Biol (Chambers et al, 2013). For the first time this manuscript describes the regions of the mammalian genome that appear to have diverged at the level of their chromatin structure, rather than at the level of the DNA sequence. Intriguingly some of the regions involved contain genes involved in the development of the mammalian body plan in embryogenesis.

Our lab has been elected to this FP7 European Community-funded Network of Excellence. The goals of Epigenesys is to encourage collaboration and understanding between the fields of epigenetics and computational systems biology, and also to communicate the science to the wider public.

James’ mammoth study (Prendergast et al, 2012) of human chromatin variation is published; it involved remapping 1.3 billion sequencing reads. He shows that it is possible to find variable sites in the human genome (SNPs) where the different variants (alleles) present carry different chromatin structures. These sites seem to be surprisingly rare in embryonic stem cells, but there is evidence that they affect gene expression and are associated with human disease.

James’ impressive analysis of human evolutionary divergence patterns finally appears online as an epub at Genome Research (Prendergast and Semple, 2011). For the first time these data suggest that selection has acted relatively recently (since divergence from chimpanzee) across many regions of the human genome to alter the physical landscape, the chromatin structure, present. This also implies that the chromatin structures seen at many locations, often far away from known genes, affect critical cellular functions and have played important roles in the origin of our species.

We contributed preliminary analyses to the publication (Diez-Roux et al, 2011) of the freely accessible Eurexpress digital transcriptome atlas (http://www.eurexpress.org), of the E14.5 mouse embryo. Over 15,000 genes were annotated for hundreds of anatomical structures and regions, down to (almost) cellular level, allowing the identification of tissue-specific and tissue-overlapping gene networks. We illustrated the value of the Eurexpress atlas by finding novel coexpressed clusters of genes active in particular structures such as the developing eye.

Suzuki et al (2009) examine the genome-wide dynamics of promoter usage in a human leukemia cell line, using high throughput sequencing of RNA over a time course of growth arrest and differentiation. Many key transcription regulators and their target genes were identified and validated by systematic siRNA knockdown. The results emphasise the enormous and daunting complexity of networks maintaining cellular states.