#computational-biology

[bradly-alicea]

Check out our presentation tothe OHBMx Twitter conference (#44, Developmental Braitenberg Vehicles)! Open-source software, simulations of brain and behavior, and theory development! To learn more, read our recent preprint on the arXiv (or join our project community on ResearchGate).

[bradly-alicea]

Here is some reading for you holiday: “Towards a Digital Diatom: image processing and deep learning analysis of Bacillaria paradoxa dynamic morphology”. Now live on biorXiv. Deep Learning, Biomechanics, and more in a fascinating organism! 

Also be sure to check out our Github repository, recent presentation from DevoWormML (slides, lecture), and data repository at the Open Science Framework.

[bradly-alicea]

Check out the latest Synthetic Daisies post about "Things That Just Happened in London...", in this case an OpenWorm Foundation Open House event sponsored by the Royal Society. Check out this week's happenings through social media reports and pictures from the event.

[bradly-alicea]

Demography, classification, and Synthetic Daisies! For this month's Carnival of Evolution, I wrote a post called "Playing the Long Game of Human Biological Variation" which critically examines the concept of racial classification and human intraspecific variation from both an epistomological and technical viewpoint. 

[bradly-alicea]

Is it possible that the three domains of life are not monophyletic? A new study in Nature Reviews Microbiology [1] using cutting-edge phylogenetic methods suggests that no they are not [2]. As an alternative, they suggest the ring of life hypothesis summarized above, with reticulations between each domain's clade. Perhaps we are all not just Eukaryotic. Or something like that.

[1] McInerney, J.O., O’Connell, M.J., and Pisani, D.   The hybrid nature of the Eukaryota and a consilient view of life on Earth. Nature Reviews Microbiology, doi:10.1038/nrmicro3271 (2014).

[2] McInerney, J.   The three domains of life hypothesis has been falsified. Bioinformatics and Molecular Evolution Unit blog (2014).

[bradly-alicea]

Here are four new papers on human genomic variation. The first [1] is a review of genome mosaicism, or variation across cells in the same human body. Mosaicism results from errors in either chromosome segregation during mitosis or DNA replication. In neurons from the frontal cortex [2], mosaicism is responsible for variation in chromosomal complements and copy number variants (CNVs). This variation comes in the form of aneuploidies, retrotransposons, and large-scale CNV differences (in 13-41% of neurons sampled). 

In [3], variation in chromatin states across the genome is explored. One finding suggests that variable regions are enriched in SNPs relative to nonvariable regions, which may be due to negative selection. The expression of heterozygous SNPs with allele-specific signals are highest for active marks. These is also variation in methylation switches (active/repressed or active/weakly active states) which results in enhancer and core promoter-specific states.

Finally, functional genomic elements can be more explicitly linked to chromatin signatures. This was done in [4] by finding the cis-regulatory variants that most affect chromatin states. In this study, five post-transcriptional modifiers and three transcription factors were used to show these trends across 14 individuals. It was found that allele-specific patterns of association (between genomic function and chromatin regulation) exist.

[1] Lupski, J.R.   One Human, Multiple Genomes: Genome Mosaicism. Science, 341, 358-359 (2013).

[2] McConnell, M.J.   Mosaic Copy Number Variation in Human Neurons. Science, 342, 631-637 (2013).

[3] Kasowski, M. et.al  Extensive Variation in Chromatin States Across Humans. 750-752. Science, 342, 750 (2013).

[4] Kilpinen, H. et.al   Chromatin Structure, and Transcription Coordinated Effects of Sequence Variation on DNA Binding, Science, 342, 744-747 (2013).

[quantitative-biology]

Authors: Margaret Wu, Grace S. Chiu, Lin Lu

The ability to quantitatively assess the health of an ecosystem is often of great interest to those tasked with monitoring and conserving ecosystems. For decades, research in this area has relied upon multimetric indices of various forms. Although indices may be numbers, many are constructed based on procedures that are highly qualitative in nature, thus limiting the quantitative rigour of the practical interpretations made from these indices. The statistical modelling approach to construct the latent health factor index (LHFI) was recently developed to express ecological data, collected to construct conventional multimetric health indices, in a rigorous quantitative model that integrates qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modelling approach allows (a) statistical inference of health for observed sites and (b) prediction of health for unobserved sites, all accompanied by formal uncertainty statements. Thus far, the LHFI approach has been demonstrated and validated on freshwater ecosystems. The goal of this paper is to adapt this approach to modelling estuarine ecosystem health, particularly that of the previously unassessed system in Richibucto in New Brunswick, Canada. Field data correspond to biotic health metrics that constitute the AZTI marine biotic index (AMBI) and abiotic predictors preconceived to influence biota. We also briefly discuss related LHFI research involving additional metrics that form the infaunal trophic index (ITI). Our paper is the first to construct a scientifically sensible model to rigorously identify the collective explanatory capacity of salinity, distance downstream, channel depth, and silt-clay content --- all regarded a priori as qualitatively important abiotic drivers --- towards site health in the Richibucto ecosystem.

[quantitative-biology]

Authors: Satoru Morita, Jin Yoshimura

We study a stochastic linear discrete metapolulation model to understand the effect of risk spreading by dispersion. We calculate analytically the stable distribution of populations that live in different habitats. The result shows that the simultaneous distribution of the populations has a complicated self-similar structure, but a population at each habitat follows a log-normal distribution.