MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.

MOTIVATION: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher. RESULTS: We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms. AVAILABILITY AND IMPLEMENTATION: MOViDA is implemented in Python using PyTorch library and freely available for download at https://github.com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380.

Altered centriolar cohesion by CEP250 and appendages impact outcome of patients with pancreatic cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading cause of cancer death worldwide. PDACs are characterized by centrosome aberrations, but whether centrosome-related genes influence patient outcomes has not been tested. METHODS: Publicly available RNA-sequencing data of patients diagnosed with PDAC were interrogated with unsupervised approaches to identify centrosome protein-encoding genes with prognostic relevance. Candidate genes were validated by immunohistochemistry and multiplex immunofluorescence in a set of clinical PDAC and normal pancreatic tissues. RESULTS: Results showed that two genes CEP250 and CEP170, involved in centrosome linker and centriolar subdistal appendages, were expressed at high levels in PDAC tissues and were correlated with prognosis of PDAC patients in independent databases. Large clustered γ-tubulin-labelled centrosomes were linked together by aberrant circular and planar-shaped CEP250 arrangements in CEP250-high expressing PDACs. Furthermore, PDACs displayed prominent centrosome separation and reduced CEP164-centrosomal labelling associated with acetylated-tubulin staining compared to normal pancreatic tissues. Interestingly, in a small validation cohort, CEP250-high expressing patients had shorter disease free- and overall-survival and almost none of those who received gemcitabine plus nab-paclitaxel first-line therapy achieved a clinical response. In contrast, weak CEP250 expression was associated with long-term survivors or responses to medical treatments. CONCLUSIONS: Alteration of the centriolar cohesion and appendages has effect on the survival of patients with PDAC.

A metabolic function of FGFR3-TACC3 gene fusions in cancer.

Chromosomal translocations that generate in-frame oncogenic gene fusions are notable examples of the success of targeted cancer therapies. We have previously described gene fusions of FGFR3-TACC3 (F3-T3) in 3% of human glioblastoma cases. Subsequent studies have reported similar frequencies of F3-T3 in many other cancers, indicating that F3-T3 is a commonly occuring fusion across all tumour types. F3-T3 fusions are potent oncogenes that confer sensitivity to FGFR inhibitors, but the downstream oncogenic signalling pathways remain unknown. Here we show that human tumours with F3-T3 fusions cluster within transcriptional subgroups that are characterized by the activation of mitochondrial functions. F3-T3 activates oxidative phosphorylation and mitochondrial biogenesis and induces sensitivity to inhibitors of oxidative metabolism. Phosphorylation of the phosphopeptide PIN4 is an intermediate step in the signalling pathway of the activation of mitochondrial metabolism. The F3-T3-PIN4 axis triggers the biogenesis of peroxisomes and the synthesis of new proteins. The anabolic response converges on the PGC1α coactivator through the production of intracellular reactive oxygen species, which enables mitochondrial respiration and tumour growth. These data illustrate the oncogenic circuit engaged by F3-T3 and show that F3-T3-positive tumours rely on mitochondrial respiration, highlighting this pathway as a therapeutic opportunity for the treatment of tumours with F3-T3 fusions. We also provide insights into the genetic alterations that initiate the chain of metabolic responses that drive mitochondrial metabolism in cancer.

Prediction and pan-cancer analysis of mammalian transcripts involved in target directed miRNA degradation.

It is currently unknown how many RNA transcripts are able to induce degradation of microRNAs (miRNA) via the mechanism known as target-directed miRNA degradation (TDMD). We developed TDMDfinder, a computational pipeline that identifies 'high confidence' TDMD interactions in the Human and Mouse transcriptomes by combining sequence alignment and feature selection approaches. Our predictions suggested that TDMD is widespread, with potentially every miRNA controlled by endogenous targets. We experimentally tested 37 TDMDfinder predictions, of which 17 showed TDMD effects as measured by RT-qPCR and small RNA sequencing, linking the miR-17, miR-19, miR-30, miR-221, miR-26 and miR-23 families to novel endogenous TDMDs. In some cases, TDMD was found to affect different members of the same miRNA family selectively. Features like complementarity to the miRNA 3' region, bulge size and hybridization energy appeared to be the main factors determining sensitivity. Computational analyses performed using the multiomic TCGA platform substantiated the involvement of many TDMD transcripts in human cancer and highlighted 36 highly significant interactions, suggesting TDMD as a new potential oncogenic mechanism. In conclusion, TDMDfinder provides the first inventory of bona fide human and mouse TDMDs. Available as a free webtool, TDMDfinder allows users to search for any TDMD interaction of interest by customizing its selection criteria.

A review of COVID-19 biomarkers and drug targets: resources and tools.

The stratification of patients at risk of progression of COVID-19 and their molecular characterization is of extreme importance to optimize treatment and to identify therapeutic options. The bioinformatics community has responded to the outbreak emergency with a set of tools and resource to identify biomarkers and drug targets that we review here. Starting from a consolidated corpus of 27 570 papers, we adopt latent Dirichlet analysis to extract relevant topics and select those associated with computational methods for biomarker identification and drug repurposing. The selected topics span from machine learning and artificial intelligence for disease characterization to vaccine development and to therapeutic target identification. Although the way to go for the ultimate defeat of the pandemic is still long, the amount of knowledge, data and tools generated so far constitutes an unprecedented example of global cooperation to this threat.

Adaptive one-class Gaussian processes allow accurate prioritization of oncology drug targets.

MOTIVATION: The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples. RESULTS: Since the absence of negative samples does not allow standard methods for automatic selection of hyperparameters, we propose a novel approach for hyperparameter selection of the kernel in One Class Gaussian Processes. We compare our methods with state-of-the-art approaches on benchmark datasets and then show its application to druggability prediction of oncology drugs. Our score reaches an AUC 0.90 on a set of clinical trial targets starting from a small training set of 102 validated oncology targets. Our score recovers the majority of known drug targets and can be used to identify novel set of proteins as drug target candidates. AVAILABILITY AND IMPLEMENTATION: The matrix of features for each protein is available at: https://bit.ly/3iLgZTa. Source code implemented in Python is freely available for download at https://github.com/AntonioDeFalco/Adaptive-OCGP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Lymphocyte antigen 6G6D-mediated modulation through p38α MAPK and DNA methylation in colorectal cancer.

In addition to being novel biomarkers for poor cancer prognosis, members of Lymphocyte antigen-6 (Ly6) gene family also play a crucial role in avoiding immune responses to tumors. However, it has not been possible to identify the underlying mechanism of how Ly6 gene regulation operates in human cancers. Transcriptome, epigenome and proteomic data from independent cancer databases were analyzed in silico and validated independently in 334 colorectal cancer tissues (CRC). RNA mediated gene silencing of regulatory genes, and treatment with MEK and p38 MAPK inhibitors were also tested in vitro. We report here that the Lymphocyte antigen 6G6D is universally downregulated in mucinous CRC, while its activation progresses through the classical adenoma-carcinoma sequence. The DNA methylation changes in LY6G6D promoter are intimately related to its transcript regulation, epigenomic and histological subtypes. Depletion of DNA methyltransferase 1 (DNMT1), which maintains DNA methylation, results in the derepression of LY6G6D expression. RNA-mediated gene silencing of p38α MAPK or its selective chemical inhibition, however, reduces LY6G6D expression, reducing trametinib's anti-inflammatory effects. Patients treated with FOLFOX-based first-line therapy experienced decreased survival due to hypermethylation of the LY6G6D promoter and decreased p38α MAPK signaling. We found that cancer-specific immunodominant epitopes are controlled by p38α MAPKs signaling and suppressed by DNA methylation in histological variants with Mucinous differentiation. This work provides a promising prospective for clinical application in diagnosis and personalized therapeutic strategies of colorectal cancer.

massiveGST: A Mann-Whitney-Wilcoxon Gene-Set Test Tool That Gives Meaning to Gene-Set Enrichment Analysis.

Gene-set enrichment analysis is the key methodology for obtaining biological information from transcriptomic space's statistical result. Since its introduction, Gene-set Enrichment analysis methods have obtained more reliable results and a wider range of application. Great attention has been devoted to global tests, in contrast to competitive methods that have been largely ignored, although they appear more flexible because they are independent from the source of gene-profiles. We analyzed the properties of the Mann-Whitney-Wilcoxon test, a competitive method, and adapted its interpretation in the context of enrichment analysis by introducing a Normalized Enrichment Score that summarize two interpretations: a probability estimate and a location index. Two implementations are presented and compared with relevant literature methods: an R package and an online web tool. Both allow for obtaining tabular and graphical results with attention to reproducible research.

Deep learning predicts short non-coding RNA functions from only raw sequence data.

Small non-coding RNAs (ncRNAs) are short non-coding sequences involved in gene regulation in many biological processes and diseases. The lack of a complete comprehension of their biological functionality, especially in a genome-wide scenario, has demanded new computational approaches to annotate their roles. It is widely known that secondary structure is determinant to know RNA function and machine learning based approaches have been successfully proven to predict RNA function from secondary structure information. Here we show that RNA function can be predicted with good accuracy from a lightweight representation of sequence information without the necessity of computing secondary structure features which is computationally expensive. This finding appears to go against the dogma of secondary structure being a key determinant of function in RNA. Compared to recent secondary structure based methods, the proposed solution is more robust to sequence boundary noise and reduces drastically the computational cost allowing for large data volume annotations. Scripts and datasets to reproduce the results of experiments proposed in this study are available at: https://github.com/bioinformatics-sannio/ncrna-deep.

RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes.

We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes.

Detection of long non-coding RNA homology, a comparative study on alignment and alignment-free metrics.

BACKGROUND: Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment-based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes. However, unlike protein coding genes the poor sequence conservation of long non-coding genes makes the identification of their homologs a challenging task. RESULTS: In this study we compare alignment-based and alignment-free string similarity metrics and look at promoter regions as a possible source of conserved information. We show that promoter regions encode relevant information for the conservation of long non-coding genes across species and that such information is better captured by alignment-free metrics. We perform a genome wide test of this hypothesis in human, mouse, and zebrafish. CONCLUSIONS: The obtained results persuaded us to postulate the new hypothesis that, unlike protein coding genes, long non-coding genes tend to preserve their regulatory machinery rather than their transcribed sequence. All datasets, scripts, and the prediction tools adopted in this study are available at https://github.com/bioinformatics-sannio/lncrna-homologs .

Identification of long non-coding transcripts with feature selection: a comparative study.

BACKGROUND: The unveiling of long non-coding RNAs as important gene regulators in many biological contexts has increased the demand for efficient and robust computational methods to identify novel long non-coding RNAs from transcripts assembled with high throughput RNA-seq data. Several classes of sequence-based features have been proposed to distinguish between coding and non-coding transcripts. Among them, open reading frame, conservation scores, nucleotide arrangements, and RNA secondary structure have been used with success in literature to recognize intergenic long non-coding RNAs, a particular subclass of non-coding RNAs. RESULTS: In this paper we perform a systematic assessment of a wide collection of features extracted from sequence data. We use most of the features proposed in the literature, and we include, as a novel set of features, the occurrence of repeats contained in transposable elements. The aim is to detect signatures (groups of features) able to distinguish long non-coding transcripts from other classes, both protein-coding and non-coding. We evaluate different feature selection algorithms, test for signature stability, and evaluate the prediction ability of a signature with a machine learning algorithm. The study reveals different signatures in human, mouse, and zebrafish, highlighting that some features are shared among species, while others tend to be species-specific. Compared to coding potential tools and similar supervised approaches, including novel signatures, such as those identified here, in a machine learning algorithm improves the prediction performance, in terms of area under precision and recall curve, by 1 to 24%, depending on the species and on the signature. CONCLUSIONS: Understanding which features are best suited for the prediction of long non-coding RNAs allows for the development of more effective automatic annotation pipelines especially relevant for poorly annotated genomes, such as zebrafish. We provide a web tool that recognizes novel long non-coding RNAs with the obtained signatures from fasta and gtf formats. The tool is available at the following url: http://www.bioinformatics-sannio.org/software/ .

Systems biology analysis reveals NFAT5 as a novel biomarker and master regulator of inflammatory breast cancer.

BACKGROUND: Inflammatory breast cancer (IBC) is the most rare and aggressive variant of breast cancer (BC); however, only a limited number of specific gene signatures with low generalization abilities are available and few reliable biomarkers are helpful to improve IBC classification into a molecularly distinct phenotype. We applied a network-based strategy to gain insight into master regulators (MRs) linked to IBC pathogenesis. METHODS: In-silico modeling and Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) on IBC/non-IBC (nIBC) gene expression data (n = 197) was employed to identify novel master regulators connected to the IBC phenotype. Pathway enrichment analysis was used to characterize predicted targets of candidate genes. The expression pattern of the most significant MRs was then evaluated by immunohistochemistry (IHC) in two independent cohorts of IBCs (n = 39) and nIBCs (n = 82) and normal breast tissues (n = 15) spotted on tissue microarrays. The staining pattern of non-neoplastic mammary epithelial cells was used as a normal control. RESULTS: Using in-silico modeling of network-based strategy, we identified three top enriched MRs (NFAT5, CTNNB1 or ß-catenin, and MGA) strongly linked to the IBC phenotype. By IHC assays, we found that IBC patients displayed a higher number of NFAT5-positive cases than nIBC (69.2% vs. 19.5%; p-value = 2.79 10(-7)). Accordingly, the majority of NFAT5-positive IBC samples revealed an aberrant nuclear expression in comparison with nIBC samples (70% vs. 12.5%; p-value = 0.000797). NFAT5 nuclear accumulation occurs regardless of WNT/ß-catenin activated signaling in a substantial portion of IBCs, suggesting that NFAT5 pathway activation may have a relevant role in IBC pathogenesis. Accordingly, cytoplasmic NFAT5 and membranous ß-catenin expression were preferentially linked to nIBC, accounting for the better prognosis of this phenotype. CONCLUSIONS: We provide evidence that NFAT-signaling pathway activation could help to identify aggressive forms of BC and potentially be a guide to assignment of phenotype-specific therapeutic agents. The NFAT5 transcription factor might be developed into routine clinical practice as a putative biomarker of IBC phenotype.

De novo reconstruction of gene regulatory networks from time series data, an approach based on formal methods.

Reverse engineering of gene regulatory relationships from genomics data is a crucial task to dissect the complex underlying regulatory mechanism occurring in a cell. From a computational point of view the reconstruction of gene regulatory networks is an undetermined problem as the large number of possible solutions is typically high in contrast to the number of available independent data points. Many possible solutions can fit the available data, explaining the data equally well, but only one of them can be the biologically true solution. Several strategies have been proposed in literature to reduce the search space and/or extend the amount of independent information. In this paper we propose a novel algorithm based on formal methods, mathematically rigorous techniques widely adopted in engineering to specify and verify complex software and hardware systems. Starting with a formal specification of gene regulatory hypotheses we are able to mathematically prove whether a time course experiment belongs or not to the formal specification, determining in fact whether a gene regulation exists or not. The method is able to detect both direction and sign (inhibition/activation) of regulations whereas most of literature methods are limited to undirected and/or unsigned relationships. We empirically evaluated the approach on experimental and synthetic datasets in terms of precision and recall. In most cases we observed high levels of accuracy outperforming the current state of art, despite the computational cost increases exponentially with the size of the network. We made available the tool implementing the algorithm at the following url: http://www.bioinformatics.unisannio.it.

A negative selection heuristic to predict new transcriptional targets.

BACKGROUND: Supervised machine learning approaches have been recently adopted in the inference of transcriptional targets from high throughput trascriptomic and proteomic data showing major improvements from with respect to the state of the art of reverse gene regulatory network methods. Beside traditional unsupervised techniques, a supervised classifier learns, from known examples, a function that is able to recognize new relationships for new data. In the context of gene regulatory inference a supervised classifier is coerced to learn from positive and unlabeled examples, as the counter negative examples are unavailable or hard to collect. Such a condition could limit the performance of the classifier especially when the amount of training examples is low. RESULTS: In this paper we improve the supervised identification of transcriptional targets by selecting reliable counter negative examples from the unlabeled set. We introduce an heuristic based on the known topology of transcriptional networks that in fact restores the conventional positive/negative training condition and shows a significant improvement of the classification performance. We empirically evaluate the proposed heuristic with the experimental datasets of Escherichia coli and show an example of application in the prediction of BCL6 direct core targets in normal germinal center human B cells obtaining a precision of 60%. CONCLUSIONS: The availability of only positive examples in learning transcriptional relationships negatively affects the performance of supervised classifiers. We show that the selection of reliable negative examples, a practice adopted in text mining approaches, improves the performance of such classifiers opening new perspectives in the identification of new transcriptional targets.

Exercise Equals the Mobilization of Visceral versus Subcutaneous Adipose Fatty Acid Molecules in Fasted Rats Associated with the Modulation of the AMPK/ATGL/HSL Axis.

Combining exercise with fasting is known to boost fat mass-loss, but detailed analysis on the consequential mobilization of visceral and subcutaneous WAT-derived fatty acids has not been performed. In this study, a subset of fasted male rats (66 h) was submitted to daily bouts of mild exercise. Subsequently, by using gas chromatography-flame ionization detection, the content of 22 fatty acids (FA) in visceral (v) versus subcutaneous (sc) white adipose tissue (WAT) depots was compared to those found in response to the separate events. Findings were related to those obtained in serum and liver samples, the latter taking up FA to increase gluconeogenesis and ketogenesis. Each separate intervention reduced scWAT FA content, associated with increased levels of adipose triglyceride lipase (ATGL) protein despite unaltered AMP-activated protein kinase (AMPK) Thr172 phosphorylation, known to induce ATGL expression. The mobility of FAs from vWAT during fasting was absent with the exception of the MUFA 16:1 n-7 and only induced by combining fasting with exercise which was accompanied with reduced hormone sensitive lipase (HSL) Ser563 and increased Ser565 phosphorylation, whereas ATGL protein levels were elevated during fasting in association with the persistently increased phosphorylation of AMPK at Thr172 both during fasting and in response to the combined intervention. As expected, liver FA content increased during fasting, and was not further affected by exercise, despite additional FA release from vWAT in this condition, underlining increased hepatic FA metabolism. Both fasting and its combination with exercise showed preferential hepatic metabolism of the prominent saturated FAs C:16 and C:18 compared to the unsaturated FAs 18:1 n-9 and 18:2 n-6:1. In conclusion, depot-specific differences in WAT fatty acid molecule release during fasting, irrelevant to their degree of saturation or chain length, are mitigated when combined with exercise, to provide fuel to surrounding organs such as the liver which is correlated with increased ATGL/ HSL ratios, involving AMPK only in vWAT.

Loss of Primary Cilia Potentiates BRAF/MAPK Pathway Activation in Rhabdoid Colorectal Carcinoma: A Series of 21 Cases Showing Ciliary Rootlet CoiledCoil (CROCC) Alterations.

A rhabdoid colorectal tumor (RCT) is a rare cancer with aggressive clinical behavior. Recently, it has been recognized as a distinct disease entity, characterized by genetic alterations in the SMARCB1 and Ciliary Rootlet Coiled-Coil (CROCC). We here investigate the genetic and immunophenotypic profiling of 21 RCTs using immunohistochemistry and next-generation sequencing. Mismatch repair-deficient phenotypes were identified in 60% of RCTs. Similarly, a large proportion of cancers exhibited the combined marker phenotype (CK7-/CK20-/CDX2-) not common to classical adenocarcinoma variants. More than 70% of cases displayed aberrant activation of the mitogen-activated protein kinase (MAPK) pathway with mutations prevalently in BRAF V600E. SMARCB1/INI1 expression was normal in a large majority of lesions. In contrast, ciliogenic markers including CROCC and γ-tubulin were globally altered in tumors. Notably, CROCC and γ-tubulin were observed to colocalize in large cilia found on cancer tissues but not in normal controls. Taken together, our findings indicate that primary ciliogenesis and MAPK pathway activation contribute to the aggressiveness of RCTs and, therefore, may constitute a novel therapeutic target.

Single-cell proteo-genomic reveals a comprehensive map of centrosome-associated spliceosome components.

Ribonucleoprotein (RNP) condensates are crucial for controlling RNA metabolism and splicing events in animal cells. We used spatial proteomics and transcriptomic to elucidate RNP interaction networks at the centrosome, the main microtubule-organizing center in animal cells. We found a number of cell-type specific centrosome-associated spliceosome interactions localized in subcellular structures involved in nuclear division and ciliogenesis. A component of the nuclear spliceosome BUD31 was validated as an interactor of the centriolar satellite protein OFD1. Analysis of normal and disease cohorts identified the cholangiocarcinoma as target of centrosome-associated spliceosome alterations. Multiplexed single-cell fluorescent microscopy for the centriole linker CEP250 and spliceosome components including BCAS2, BUD31, SRSF2 and DHX35 recapitulated bioinformatic predictions on the centrosome-associated spliceosome components tissue-type specific composition. Collectively, centrosomes and cilia act as anchor for cell-type specific spliceosome components, and provide a helpful reference for explore cytoplasmic condensates functions in defining cell identity and in the origin of rare diseases.

Loss of circadian rhythmicity in bdnf knockout zebrafish larvae.

Brain-derived neurotrophic factor (BDNF) plays a pivotal role in neuronal growth and differentiation, neuronal plasticity, learning, and memory. Using CRISPR/Cas9 technology, we generated a vital Bdnf null mutant line in zebrafish and carried out its molecular and behavioral characterization. Although no defects are evident on a morphological inspection, 66% of coding genes and 37% of microRNAs turned out to be differentially expressed in bdnf -/- compared with wild type sibling embryos. We deeply investigated the circadian clock pathway and confirmed changes in the rhythmic expression of clock (arntl1a, clock1a and clock2) and clock-controlled (aanat2) genes. The modulatory role of Bdnf on the zebrafish circadian clock was then validated by behavioral tests highlighting the absence of circadian activity rhythms in bdnf -/- larvae. The circadian behavior was partially rescued by pharmacological treatment. The bdnf -/- zebrafish line presented here is the first valuable and stable vertebrate model for the study of BDNF-related neurodevelopmental diseases.

VEGA: variational segmentation for copy number detection.

MOTIVATION: Genomic copy number (CN) information is useful to study genetic traits of many diseases. Using array comparative genomic hybridization (aCGH), researchers are able to measure the copy number of thousands of DNA loci at the same time. Therefore, a current challenge in bioinformatics is the development of efficient algorithms to detect the map of aberrant chromosomal regions. METHODS: We describe an approach for the segmentation of copy number aCGH data. Variational estimator for genomic aberrations (VEGA) adopt a variational model used in image segmentation. The optimal segmentation is modeled as the minimum of an energy functional encompassing both the quality of interpolation of the data and the complexity of the solution measured by the length of the boundaries between segmented regions. This solution is obtained by a region growing process where the stop condition is completely data driven. RESULTS: VEGA is compared with three algorithms that represent the state of the art in CN segmentation. Performance assessment is made both on synthetic and real data. Synthetic data simulate different noise conditions. Results on these data show the robustness with respect to noise of variational models and the accuracy of VEGA in terms of recall and precision. Eight mantle cell lymphoma cell lines and two samples of glioblastoma multiforme are used to evaluate the behavior of VEGA on real biological data. Comparison between results and current biological knowledge shows the ability of the proposed method in detecting known chromosomal aberrations. AVAILABILITY: VEGA has been implemented in R and is available at the address http://www.dsba.unisannio.it/Members/ceccarelli/vega in the section Download.