No abstract

Understanding the mechanisms underlying gene regulation is paramount to comprehend the translation from genotype to phenotype. The two are connected by gene expression, and it is generally thought that variation in transcription factor (TF) function is an important determinant of phenotypic evolution. We analyzed publicly available genome-wide chromatin immunoprecipitation experiments for 27 TFs in Arabidopsis thaliana and constructed an experimental network containing 46,619 regulatory interactions and 15,188 target genes. We identified hub targets and highly occupied target (HOT) regions, which are enriched for genes involved in development, stimulus responses, signaling, and gene regulatory processes in the currently profiled network. We provide several lines of evidence that TF binding at plant HOT regions is functional, in contrast to that in animals, and not merely the result of accessible chromatin. HOT regions harbor specific DNA motifs, are enriched for differentially expressed genes, and are often conserved across crucifers and dicots, even though they are not under higher levels of purifying selection than non-HOT regions. Distal bound regions are under purifying selection as well and are enriched for a chromatin state showing regulation by the Polycomb repressive complex. Gene expression complexity is positively correlated with the total number of bound TFs, revealing insights in the regulatory code for genes with different expression breadths. The integration of noncanonical and canonical DNA motif information yields new hypotheses on cobinding and tethering between specific TFs involved in flowering and light regulation.

A gene regulatory network (GRN) is a collection of regulatory interactions between transcription factors (TFs) and their target genes. GRNs control different biological processes and have been instrumental to understand the organization and complexity of gene regulation. Although various experimental methods have been used to map GRNs in Arabidopsis thaliana, their limited throughput combined with the large number of TFs makes that for many genes our knowledge about regulating TFs is incomplete. We introduce TF2Network, a tool that exploits the vast amount of TF binding site information and enables the delineation of GRNs by detecting potential regulators for a set of co-expressed or functionally related genes. Validation using two experimental benchmarks reveals that TF2Network predicts the correct regulator in 75–92% of the test sets. Furthermore, our tool is robust to noise in the input gene sets, has a low false discovery rate, and shows a better performance to recover correct regulators compared to other plant tools. TF2Network is accessible through a web interface where GRNs are interactively visualized and annotated with various types of experimental functional information. TF2Network was used to perform systematic functional and regulatory gene annotations, identifying new TFs involved in circadian rhythm and stress response.

BackgroundThe present study aimed to define the optimal number of atlases for automatic multi-atlas-based brachial plexus (BP) segmentation and to compare Simultaneous Truth and Performance Level Estimation (STAPLE) label fusion with Patch label fusion using the ADMIRE® software. The accuracy of the autosegmentations was measured by comparing all of the generated autosegmentations with the anatomically validated gold standard segmentations that were developed using cadavers.Materials and methodsTwelve cadaver computed tomography (CT) atlases were used for automatic multi-atlas-based segmentation. To determine the optimal number of atlases, one atlas was selected as a patient and the 11 remaining atlases were registered onto this patient using a deformable image registration algorithm. Next, label fusion was performed by using every possible combination of 2 to 11 atlases, once using STAPLE and once using Patch. This procedure was repeated for every atlas as a patient.The similarity of the generated automatic BP segmentations and the gold standard segmentation was measured by calculating the average Dice similarity (DSC), Jaccard (JI) and True positive rate (TPR) for each number of atlases. These similarity indices were compared for the different number of atlases using an equivalence trial and for the two label fusion groups using an independent sample-t test.ResultsDSC’s and JI’s were highest when using nine atlases with both STAPLE (average DSC = 0,532; JI = 0,369) and Patch (average DSC = 0,530; JI = 0,370). When comparing both label fusion algorithms using 9 atlases for both, DSC and JI values were not significantly different. However, significantly higher TPR values were achieved in favour of STAPLE (p < 0,001). When fewer than four atlases were used, STAPLE produced significantly lower DSC, JI and TPR values than did Patch (p = 0,0048).ConclusionsUsing 9 atlases with STAPLE label fusion resulted in the most accurate BP autosegmentations (average DSC = 0,532; JI = 0,369 and TPR = 0,760). Only when using fewer than four atlases did the Patch label fusion results in a significantly more accurate autosegmentation than STAPLE.