Jianlin Cheng, from the University of Missouri, presented his research into model quality assessment methods – ways of choosing the model that is closest to the native structure from a set of decoys. Their method (MULTICOM) is a consensus method, which calcualtes an average rank from 14 different quality assessment methods. By combining this average rank with clustering and model combination to select five top models for a target, their method produces good results – in CASP11, the group were ranked third when considering the top-ranked models and second when considering the top five.

Accounting for the cell cycle in single cell RNA-seq

The current ubiquitous of RNA-seq throughout academia speaks volumes to the strength of the methodology.  It provides a transcript-wide measure of a cell’s expression at the point of cell lysis; from which one can investigate gene fusion, SNPs and changes in expression, to name only a few possibilities.  Traditionally, these measurement are made using a cell culture and as such the expression levels, and derived results, are based on averages taken over a number of cells. Recent advances have allowed the resolution to increase to the point where measurements can now instead be made on single isolated cells. With this increase in capability, it should now be possible to measure and identify subpopulations within a given culture. However, the inherent variability of expression, such as that caused by the cell cycle, often overshadows any change that could be attributed to these subpopulations. If one could characterise this variability, then this could be removed from the data and perhaps these subpopulations would then be elucidated.

Oliver Stegle gave a great presentation on doing exactly this for the cell cycle. They modeled the different phases as a set of latent variables such that they are inferred directly from the data (rather than merely observed). Via this model, they estimated that upwards of 30% of the inherent variability could be accounted for, and hence subtracted from the data. Applying such a technique to culture of T cells, they were able to identify the the different stages of differentiation of naive T cells into T helper 2 cells. Crucially, these would of been obscured had the cell cycle not been identified. Given this success with just accounting for the cell cycle, Stegle suggested that their technique can be expanded upon to elucidate other sources of gene expression heterogeneity while making it easier to identify these cellular subpopulations.

Dr. Julia Shifman from Hebrew University of Jerusalem studies protein-protein interactions. In her 3DSiG presentation she focused on the presence of “cold-spots” in protein sequence where in sillico mutations to several different amino acids improve the binding affinity. Such cold-spots are often observed at the periphery of the complex, where no interaction is observed.

Alex Cornish from Imperial College London presented his work on the structural difference between cell-type specific interaction networks. To generate these, he weighted protein-protein interaction network edges by cell-type specific gene expression data from the FANTOM5 project. Using these cell-type specific networks, he finds that it is possible to associate specific networks with specific diseases based on the distances between disease-associated genes in the networks. Furthermore, these disease – cell type associations can be used to assess the similarity between diseases.

Barry Grant presented an overview of the research activity in his group — namely nucleotide switch proteins (e.g. GTPases, such as Ras and Kinesin). In the case of Kinesin, the group used simple statistical methods such as principal components analysis to draw inferences between conformation and functional states. The group then used correlated motions to construct a community network that describes how certain groups of residues behave in certain conformational states.

Bumps and traffic lights along the translation of secretory proteins
Shelley Mahlab, Michal LinialIn her talk, Michal described how a theoretical measure of translation speed, tAI, shows the differences between the translation speeds of proteins targeted to different locations. Proteins with a signal peptide (either secreted or transmembrane) have significantly slower codons than proteins without, over approximately the first 30 codons, perhaps allowing more time for binding to the signal recognition particle. Transmembrane proteins had a lower predicted speed overall, which may be important for their insertion and correct folding.