Mapping the complexity of transcription control in higher eukaryotes

Recent large analyses suggest the importance of combinatorial regulation by broadly expressed transcription factors rather than expression domains characterized by highly specific factors

linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type

Summary: An essential element when analysing the structure, function, and dynamics of biological networks is the identification of communities of related nodes. An algorithm proposed recently enhances this process by clustering the links between nodes, rather than the nodes themselves, thereby allowing each node to belong to multiple overlapping or nested communities. The R package ‘linkcomm’ implements this algorithm and extends it in several aspects: (i) the clustering algorithm handles networks that are weighted, directed, or both weighted and directed; (ii) several visualization methods are implemented that facilitate the representation of the link communities and their relationships; (iii) a suite of functions are included for the downstream analysis of the link communities including novel community-based measures of node centrality; (iv) the main algorithm is written in C++ and designed to handle networks of any size; and (v) several clustering methods are available for networks that can be handled in memory, and the number of communities can be adjusted by the user

An automated workflow for parallel processing of large multiview SPIM recordings

Multiview light sheet fluorescence microscopy (LSFM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance cluster (HPC). Here we introduce an automated workflow for processing large multiview, multi-channel, multi-illumination time-lapse LSFM data on a single workstation or in parallel on a HPC. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing LSFM data in a fraction of the time required to collect it.Comment: 13 pages with supplement, LATEX; 1 table, 1 figure, 2 supplementary figures, 2 supplementary lists, 2 supplementary tables; corrected error in results table, results unchange

CATMAID: collaborative annotation toolkit for massive amounts of image data

Summary: High-resolution, three-dimensional (3D) imaging of large biological specimens generates massive image datasets that are difficult to navigate, annotate and share effectively. Inspired by online mapping applications like GoogleMaps™, we developed a decentralized web interface that allows seamless navigation of arbitrarily large image stacks. Our interface provides means for online, collaborative annotation of the biological image data and seamless sharing of regions of interest by bookmarking. The CATMAID interface enables synchronized navigation through multiple registered datasets even at vastly different scales such as in comparisons between optical and electron microscopy

Globally optimal stitching of tiled 3D microscopic image acquisitions

Motivation: Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks

An Adaptive Threshold in Mammalian Neocortical Evolution

Expansion of the neocortex is a hallmark of human evolution. However, it remains an open question what adaptive mechanisms facilitated its expansion. Here we show, using gyrencephaly index (GI) and other physiological and life-history data for 102 mammalian species, that gyrencephaly is an ancestral mammalian trait. We provide evidence that the evolution of a highly folded neocortex, as observed in humans, requires the traversal of a threshold of 10^9 neurons, and that species above and below the threshold exhibit a bimodal distribution of physiological and life-history traits, establishing two phenotypic groups. We identify, using discrete mathematical models, proliferative divisions of progenitors in the basal compartment of the developing neocortex as evolutionarily necessary and sufficient for generating a fourteen-fold increase in daily prenatal neuron production and thus traversal of the neuronal threshold. We demonstrate that length of neurogenic period, rather than any novel progenitor-type, is sufficient to distinguish cortical neuron number between species within the same phenotypic group.Comment: Currently under review; 38 pages, 5 Figures, 13 Supplementary Figures, 2 Table

Fully Unsupervised Probabilistic Noise2Void

Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing. A new category of DL methods such as Noise2Void or Noise2Self can be used fully unsupervised, requiring nothing but the noisy data. However, this comes at the price of reduced reconstruction quality. The recently proposed Probabilistic Noise2Void (PN2V) improves results, but requires an additional noise model for which calibration data needs to be acquired. Here, we present improvements to PN2V that (i) replace histogram based noise models by parametric noise models, and (ii) show how suitable noise models can be created even in the absence of calibration data. This is a major step since it actually renders PN2V fully unsupervised. We demonstrate that all proposed improvements are not only academic but indeed relevant.Comment: Accepted at ISBI 202

Computational identification of Drosophila microRNA genes

BACKGROUND: MicroRNAs (miRNAs) are a large family of 21-22 nucleotide non-coding RNAs with presumed post-transcriptional regulatory activity. Most miRNAs were identified by direct cloning of small RNAs, an approach that favors detection of abundant miRNAs. Three observations suggested that miRNA genes might be identified using a computational approach. First, miRNAs generally derive from precursor transcripts of 70-100 nucleotides with extended stem-loop structure. Second, miRNAs are usually highly conserved between the genomes of related species. Third, miRNAs display a characteristic pattern of evolutionary divergence. RESULTS: We developed an informatic procedure called 'miRseeker', which analyzed the completed euchromatic sequences of Drosophila melanogaster and D. pseudoobscura for conserved sequences that adopt an extended stem-loop structure and display a pattern of nucleotide divergence characteristic of known miRNAs. The sensitivity of this computational procedure was demonstrated by the presence of 75% (18/24) of previously identified Drosophila miRNAs within the top 124 candidates. In total, we identified 48 novel miRNA candidates that were strongly conserved in more distant insect, nematode, or vertebrate genomes. We verified expression for a total of 24 novel miRNA genes, including 20 of 27 candidates conserved in a third species and 4 of 11 high-scoring, Drosophila-specific candidates. Our analyses lead us to estimate that drosophilid genomes contain around 110 miRNA genes. CONCLUSIONS: Our computational strategy succeeded in identifying bona fide miRNA genes and suggests that miRNAs constitute nearly 1% of predicted protein-coding genes in Drosophila, a percentage similar to the percentage of miRNAs recently attributed to other metazoan genomes