Biology subjects

Ackermann, J.

Publications and source records attributed to Ackermann, J..

4 recordsLinked to original sources

bioRxiv2018

Mapping the genetics of neuropsychological traits to the molecular network of the human brain using a data integrative approach

MotivationComplex neuropsychiatric conditions including autism spectrum disorders are among the most heritable neurodevelopmental disorders with distinct profiles of neuropsychological traits. A variety of genetic factors modulate these traits (phenotypes) underlying clinical diagnoses. To explore the associations between genetic factors and phenotypes, genome-wide association studies are broadly applied. Stringent quality checks and thorough downstream analyses for in-depth interpretation of the associations are an indispensable prerequisite. However, in the area of neuropsychology there is no framework existing, which besides performing association studies also affiliates genetic variants at the brain and gene network level within a single framework.\n\nResultsWe present a novel bioinformatics approach in the field of neuropsychology that integrates current state-of-the-art tools, algorithms and brain transcriptome data to elaborate the association of phenotype and genotype data. The integration of transcriptome data gives an advantage over the existing pipelines by directly translating genetic associations to brain regions and developmental patterns. Based on our data integrative approach, we identify genetic variants associated with Intelligence Quotient (IQ) in an autism cohort and found their respective genes to be expressed in specific brain areas.\n\nConclusionOur data integrative approach revealed that IQ is related to early down-regulated and late up-regulated gene modules implicated in frontal cortex and striatum, respectively. Besides identifying new gene associations with IQ we also provide a proof of concept, as several of the identified genes in our analysis are candidate genes related to intelligence in autism, intellectual disability, and Alzheimers disease. The framework provides a complete extensive analysis starting from a phenotypic trait data to its association at specific brain areas at vulnerable time points within a timespan of four days.\n\nAvailability and ImplementationOur framework is implemented in R and Python. It is available as an in-house script, which can be provided on demand.\n\nContactafsheen.yousaf@kgu.de

bioinformatics↗

bioRxiv2018

isiKnock: in silico knockouts in biochemical pathways

SummaryisiKnock is a new software that automatically conducts in silico knockouts for mathematical models of biochemical pathways. The software allows for the prediction of the behavior of biological systems after single or multiple knockout. The implemented algorithm applies transition invariants and the novel concept of Manatee invariants. A knockout matrix visualizes the results. The tool enables the analysis of dependencies, for example, in signal flows from the receptor activation to the cell response at steady state.\n\nAvailability and ImplementationisiKnock is an open-source tool, freely available at http://www.bioinformatik.uni-frankfurt.de/tools/isiKnock/index.php. It requires at least Java 8 and runs under Microsoft Windows, Linux, and Mac OS.\n\nContactina.koch@bioinformatik.uni-frankfurt.de

bioinformatics↗

bioRxiv2017

Bioinformatics analysis quantifies neighborhood preferences of cancer cells in Hodgkin lymphoma.

MotivationHodgkin lymphoma is a tumor of the lymphatic system and represents one of the most frequent lymphoma in the Western world. It is characterized by Hodgkin cells and Reed-Sternberg cells, which exhibit a broad morphological spectrum. The cells are visualized by immunohistochemical staining of tissue sections. In pathology, tissue images are mainly manually evaluated, relying on the expertise and experience of pathologists. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of cancer cells is of great interest.\n\nResultsHere, we systematically quantified and investigated cancer cell properties and their spatial neighborhood relations by applying statistical analyses to whole slide images of Hodgkin lymphoma and lymphadenitis, which describes a non-cancerous inflammation of the lymph node. We differentiated cells by their morphology and studied the spatial neighborhood relation of more than 400,000 immunohistochemically stained cells. We found that, according to their morphological features, the cells exhibited significant preferences for and aversions to cells of specific profiles as nearest neighbor. We quantified differences between Hodgkin lymphoma and lymphadenitis concerning the neighborhood relations of cells and the sizes of cells. The approach can easily be applied to other cancer types.\n\nContactina.koch@bioinformatik.uni-frankfurt.de

bioinformatics↗

bioRxiv2017

Reversed depth in anti-correlated random dot stereograms and central-peripheral difference in visual inference

1Two images of random black and white dots, one for each eye, can represent object surfaces in a threedimensional scene when the dots correspond interocularly in a random dot stereogram (RDS). The spatial disparities between the corresponding dots represent depths of object surfaces. If the dots become anti-correlated such that a black dot in one monocular image corresponds to a white dot in the other, disparity-tuned neurons in the primary visual cortex (V1) respond as if their preferred disparities become non-preferred and vice versa, thereby reversing the disparity signs reported to higher visual areas. Humans have great difficulty perceiving the reversed depth, or any depth at all, in anti-correlated RDSs. We report that the reversed depth is more easily perceived when the RDSs are viewed in peripheral visual field, supporting a recently proposed central-peripheral dichotomy in mechanisms of feedback from higher to lower visual cortical areas for visual inference.

neuroscience↗