# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

## Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

## Blog Post number 3

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

## Blog Post number 2

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

## Blog Post number 1

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool. Read more

## Measuring hand hygiene compliance rates at hospital entrances

Published in American journal of infection control, 2015

Background Despite the importance of hand hygiene in the health care setting, there are no studies evaluating hand hygiene compliance at hospital entrances. Methods The study was prospectively performed over a 33-week period from March 30, 2014-November 15, 2014, to evaluate hand hygiene compliance in 2 hospital reception areas. We compared electronic handwash counters with the application of radiofrequency identification (GOJO SMARTLINK) (electronic observer) that counts each activation of alcohol gel dispensers to direct observation (human observer) via remote review of video surveillance. Read more

Recommended citation: Vaidotas, M., Yokota, P. K. O., Marra, A. R., Camargo, T. Z. S., da Silva Victor, E., Gysi, D. M., ... & Edmond, M. B. (2015). Measuring hand hygiene compliance rates at hospital entrances. American journal of infection control, 43(7), 694-696. https://www.sciencedirect.com/science/article/pii/S0196655315001534

## Combined Experimental and System-Level Analyses Reveal the Complex Regulatory Network of miR-124 during Human Neurogenesis

Published in Cell Systems, 2018

Non-coding RNAs regulate many biological processes including neurogenesis. The brain-enriched miR-124 has been assigned as a key player of neuronal differentiation via its complex but little understood regulation of thousands of annotated targets. To systematically chart its regulatory functions, we used CRISPR/ Cas9 gene editing to disrupt all six miR-124 alleles in human induced pluripotent stem cells. Upon neuronal induction, miR-124-deleted cells underwent neurogenesis and became functional neurons, albeit with altered morphology and neurotransmitter specification. Using RNA-induced-silencing-complex precipitation, we identified 98 high-confidence miR-124 targets, of which some directly led to decreased viability. By performing advanced transcription-factor-network analysis, we identified indirect miR-124 effects on apoptosis, neuronal subtype differentiation, and the regulation of previously uncharacterized zinc finger transcription factors. Our data emphasize the need for combined experimental- and system-level analyses to comprehensively disentangle and reveal miRNA functions, including their involvement in the neurogenesis of diverse neuronal cell types found in the human brain. Read more

Recommended citation: Kutsche, L. K., Gysi, D. M., Fallmann, J., Lenk, K., Petri, R., Swiersy, A., … Busskamp, V. (2018). Combined Experimental and System-Level Analyses Reveal the Complex Regulatory Network of miR-124 during Human Neurogenesis. Cell Systems, 1–15. https://doi.org/10.1016/j.cels.2018.08.011 https://doi.org/10.1016/j.cels.2018.08.011

## wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool

Published in BMC Bioinformatics, 2018

Background Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network. Read more

Recommended citation: Gysi, D. M., Voigt, A., Fragoso, T.M, Almaas, E., Nowick, K. (2018). wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool. BMC Bioinformatics, 19:392. https://doi.org/10.1186/s12859-018-2351-7 https://doi.org/10.1186/s12859-018-2351-7

## Honey bee virus causes context-dependent changes in host social behavior

Published in PNAS, 2020

Recommended citation: Geffre, A. C., Gernat, T., Harwood, G. P., Jones, B. M., Morselli Gysi, D., Hamilton, A. R., … Dolezal, A. G. (2020). Honey bee virus causes context-dependent changes in host social behavior. Proceedings of the National Academy of Sciences, 202002268. https://doi.org/10.1073/pnas.2002268117 https://doi.org/10.1073/pnas.2002268117

## Construction, comparison and evolution of networks in life sciences and other disciplines

Published in Royal Society Interface, 2020

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research. Read more

Recommended citation: Gysi, D. M., & Nowick, K. (2020). Construction, comparison and evolution of networks in life sciences and other disciplines. Journal of The Royal Society Interface, 17(166), 20190610. https://doi.org/10.1098/rsif.2019.0610 https://doi/10.1098/rsif.2019.0610

# wTO: Computing Weighted Topological Overlaps (wTO) & Consensus wTO Network

Computes the Weighted Topological Overlap with positive and negative signs (wTO) networks (Nowick et al. (2009) <doi:10.1073/pnas.0911376106>) given a data frame containing the mRNA count/ expression/ abundance per sample, and a vector containing the interested nodes of interaction (a subset of the elements of the full data frame). Read more

# Co-Expression Differential Network Analysis: CoDiNA

The usage of the Co-expression Differential Network analysis has been growing by the Biological and Medical science for the analysis of complex systems or diseases. We have developed a method that is able to compare as many networks as desired, by caracterizing both links and nodes that are common, different or specific to each network. More information can be found at [arXiv:1802.00828]. Read more

# RichR: Enrichment for Diseases in a Set of Genes

It provides a list of genes associated to diseases (g2d$clean and g2d$complete) based on the following 4 publications (GS2D, Fontaine (2016) [doi:10.18547/gcb.2016.vol2.iss1.e33], DisGeNET, Pinero (2016) [doi:10.1093/nar/gkw943] Berto2016, Berto (2016) [doi:10.3389/fgene.2016.00031] and PsyGeNET, Sacristan (2015) [doi:10.1093/bioinformatics/btv301]). Those lists were combined and manually curated to have matching disease names. When provided a list of gene names, it calculates the disease enrichment of the gene set. The enrichment is calculated using proportion test and Fisher’s exact test. Adjusted fdr p-values are returned alongside with p-values combined using the Fisher’s method. Read more

## Evolution of gene co-expression networks implicated in cognitive functions in primates

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XIII Herbstseminar der Bioinformatik! Read more

## wTO: an R package to calculate weighted topological overlap networks

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XIV Herbstseminar der Bioinformatik! Read more

## Co-expression differential network analysis

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Complex networks: theory, methods, and applications (4th edition) Read more

## Co-expression differential network analysis: CoDiNA

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XXIX International Biometric Conference Read more

## Basic statistics using R.

Short course ministered, Leipzig University, 2017