Biology subjects

Erb, I.

Publications and source records attributed to Erb, I..

3 recordsLinked to original sources

bioRxiv2018

Impaired development of neocortical circuits contributes to the neurological alterations in DYRK1A haploinsufficiency syndrome

Autism spectrum disorders are early onset neurodevelopmental disorders characterized by deficits in social communication and restricted repetitive behaviors, yet they are quite heterogeneous in terms of their genetic basis and phenotypic manifestations. Recently, de novo pathogenic mutations in DYRK1A, a chromosome 21 gene associated to neuropathological traits of Down syndrome, have been identified in patients presenting a recognizable syndrome included in the autism spectrum. These mutations produce DYRK1A kinases with partial or complete absence of the catalytic domain, or they represent missense mutations located within this domain. Here, we undertook an extensive biochemical characterization of the DYRK1A missense mutations reported to date and show that most of them, but not all, result in enzymatically dead DYRK1A proteins. We also show that haploinsufficient Dyrk1a+/- mutant mice mirror the neurological traits associated with the human pathology, such as defective social interactions, stereotypic behaviors and epileptic activity. These mutant mice present altered proportions of excitatory and inhibitory neocortical neurons and synapses. Moreover, we provide evidence that alterations in the production of cortical excitatory neurons are contributing to these defects. Indeed, by the end of the neurogenic period, the expression of developmental regulated genes involved in neuron differentiation and/or activity is altered. Therefore, our data indicate that altered neocortical neurogenesis could critically affect the formation of cortical circuits, thereby contributing to the neuropathological changes in DYRK1A haploinsufficiency syndrome.

neuroscience↗

bioRxiv2017

Understanding sequencing data as compositions: anoutlook and review

MotivationAlthough seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g., gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e., library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that renders invalid many conventional analyses, including distance measures, correlation coefficients, and multivariate statistical models.\n\nResultsThe purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study.

bioinformatics↗

bioRxiv2017

Differential Proportionality - A Normalization-Free Approach To Differential Gene Expression

Gene expression data, such as those generated by next generation sequencing technologies (RNA-seq), are of an inherently relative nature: the total number of sequenced reads has no biological meaning. This issue is most often addressed with various normalization techniques which all face the same problem: once information about the total mRNA content of the origin cells is lost, it cannot be recovered by mere technical means. Additional knowledge, in the form of an unchanged reference, is necessary; however, this reference can usually only be estimated. Here we propose a novel method where sample normalization is unnecessary, but important insights can be obtained nevertheless. Instead of trying to recover absolute abundances, our method is entirely based on ratios, so normalization factors cancel by default. Although the differential expression of individual genes cannot be recovered this way, the ratios themselves can be differentially expressed (even when their constituents are not). Yet, most current analyses are blind to these cases, while our approach reveals them directly. Specifically, we show how the differential expression of gene ratios can be formalized by decomposing log-ratio variance (LRV) and deriving intuitive statistics from it. Although small LRVs have been used to detect proportional genes in gene expression data before, we focus here on the change in proportionality factors between groups of samples (e.g. tissue-specific proportionality). For this, we propose a statistic that is equivalent to the squared t-statistic of one-way ANOVA, but for gene ratios. In doing so, we show how precision weights can be incorporated to account for the peculiarities of count data, and, moreover, how a moderated statistic can be derived in the same way as the one following from a hierarchical model for individual genes. We also discuss approaches to deal with zero counts, deriving an expression of our statistic that is able to incorporate them. In providing a detailed analysis of the connections between the differential expression of genes and the differential proportionality of pairs, we facilitate a clear interpretation of new concepts. The proposed framework is applied to a data set from GTEx consisting of 98 samples from the cerebellum and cortex, with selected examples shown. A computationally efficient implementation of the approach in R has been released as an addendum to the propr package.1

bioinformatics↗