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Characterizing the RNA targets and position-dependent splicing regulation by TDP-43

TDP-43 is a predominantly nuclear RNA-binding protein that forms inclusion bodies in frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS). The mRNA targets of TDP-43 in the human brain and its role in RNA processing are largely unknown. Using individual nucleotide-resolution ultraviolet cross-linking and immunoprecipitation (iCLIP), we found that TDP-43 preferentially bound long clusters of UG-rich sequences in vivo. Analysis of RNA binding by TDP-43 in brains from subjects with FTLD revealed that the greatest increases in binding were to the MALAT1 and NEAT1 noncoding RNAs. We also found that binding of TDP-43 to pre-mRNAs influenced alternative splicing in a similar position-dependent manner to Nova proteins. In addition, we identified unusually long clusters of TDP-43 binding at deep intronic positions downstream of silenced exons. A substantial proportion of alternative mRNA isoforms regulated by TDP-43 encode proteins that regulate neuronal development or have been implicated in neurological diseases, highlighting the importance of TDP-43 for the regulation of splicing in the brain.TDP-43 is a predominantly nuclear RNA-binding protein that forms inclusion bodies in frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS). The mRNA targets of TDP-43 in the human brain and its role in RNA processing are largely unknown. Using individual nucleotide-resolution ultraviolet cross-linking and immunoprecipitation (iCLIP), we found that TDP-43 preferentially bound long clusters of UG-rich sequences in vivo. Analysis of RNA binding by TDP-43 in brains from subjects with FTLD revealed that the greatest increases in binding were to the MALAT1 and NEAT1 noncoding RNAs. We also found that binding of TDP-43 to pre-mRNAs influenced alternative splicing in a similar position-dependent manner to Nova proteins. In addition, we identified unusually long clusters of TDP-43 binding at deep intronic positions downstream of silenced exons. A substantial proportion of alternative mRNA isoforms regulated by TDP-43 encode proteins that regulate neuronal development or have been implicated in neurological diseases, highlighting the importance of TDP-43 for the regulation of splicing in the brain.

Orange: from experimental machine learning to interactive data mining

Orange (www.ailab.si/orange) is a suite for machine learning and data mining. For researchers in machine learning, Orange offers scripting to easily prototype new algorithms and experimental procedures. For explorative data analysis, it provides a visual programming framework with emphasis on interactions and creative combinations of visual components.

Widespread binding of FUS along nascent RNA regulates alternative splicing in the brain.

Fused in sarcoma (FUS) and TAR DNA-binding protein 43 (TDP-43) are RNA-binding proteins pathogenetically linked to amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), but it is not known if they regulate the same transcripts. We addressed this question using crosslinking and immunoprecipitation (iCLIP) in mouse brain, which showed that FUS binds along the whole length of the nascent RNA with limited sequence specificity to GGU and related motifs. A saw-tooth binding pattern in long genes demonstrated that FUS remains bound to pre-mRNAs until splicing is completed. Analysis of FUS−/− brain demonstrated a role for FUS in alternative splicing, with increased crosslinking of FUS in introns around the repressed exons. We did not observe a significant overlap in the RNA binding sites or the exons regulated by FUS and TDP-43. Nevertheless, we found that both proteins regulate genes that function in neuronal development.

Analysis of CLIP and iCLIP methods for nucleotide-resolution studies of protein-RNA interactions

UV cross-linking and immunoprecipitation (CLIP) and individual-nucleotide resolution CLIP (iCLIP) are methods to study protein-RNA interactions in untreated cells and tissues. Here, we analyzed six published and two novel data sets to confirm that both methods identify protein-RNA cross-link sites, and to identify a slight uridine preference of UV-C-induced cross-linking. Comparing Nova CLIP and iCLIP data revealed that cDNA deletions have a preference for TTT motifs, whereas iCLIP cDNA truncations are more likely to identify clusters of YCAY motifs as the primary Nova binding sites. In conclusion, we demonstrate how each method impacts the analysis of protein-RNA binding specificity.

Analysis of alternative splicing associated with aging and neurodegeneration in the human brain

Age is the most important risk factor for neurodegeneration; however, the effects of aging and neurodegeneration on gene expression in the human brain have most often been studied separately. Here, we analyzed changes in transcript levels and alternative splicing in the temporal cortex of individuals of different ages who were cognitively normal, affected by frontotemporal lobar degeneration (FTLD), or affected by Alzheimers disease (AD). We identified age-related splicing changes in cognitively normal individuals and found that these were present also in 95% of individuals with FTLD or AD, independent of their age. These changes were consistent with increased polypyrimidine tract binding protein (PTB)-dependent splicing activity. We also identified disease-specific splicing changes that were present in individuals with FTLD or AD, but not in cognitively normal individuals. These changes were consistent with the decreased neuro-oncological ventral antigen (NOVA)-dependent splicing regulation, and the decreased nuclear abundance of NOVA proteins. As expected, a dramatic down-regulation of neuronal genes was associated with disease, whereas a modest down-regulation of glial and neuronal genes was associated with aging. Whereas our data indicated that the age-related splicing changes are regulated independently of transcript-level changes, these two regulatory mechanisms affected expression of genes with similar functions, including metabolism and DNA repair. In conclusion, the alternative splicing changes identified in this study provide a new link between aging and neurodegeneration.

A function-decomposition method for development of hierarchical multi-attribute decision models

Function decomposition is a recent machine learning method that develops a hierarchical structure from class-labeled data by discovering new aggregate attributes and their descriptions. Each new aggregate attribute is described by an example set whose complexity is lower than the complexity of the initial set. We show that function decomposition can be used to develop a hierarchical multi-attribute decision model from a given unstructured set of decision examples. The method implemented in a system called HINT is experimentally evaluated on a real-world housing loans allocation problem and on the rediscovery of three hierarchical decision models. The experimentation demonstrates that the decomposition can discover meaningful and transparent decision models of high classification accuracy. We specifically study the effects of human interaction through either assistance or provision of background knowledge for function decomposition, and show that this has a positive effect on both the comprehensibility and classification accuracy.

VizRank: Data Visualization Guided by Machine Learning

Data visualization plays a crucial role in identifying interesting patterns in exploratory data analysis. Its use is, however, made difficult by the large number of possible data projections showing different attribute subsets that must be evaluated by the data analyst. In this paper, we introduce a method called VizRank, which is applied on classified data to automatically select the most useful data projections. VizRank can be used with any visualization method that maps attribute values to points in a two-dimensional visualization space. It assesses possible data projections and ranks them by their ability to visually discriminate between classes. The quality of class separation is estimated by computing the predictive accuracy of k-nearest neighbor classifier on the data set consisting of x and y positions of the projected data points and their class information. The paper introduces the method and presents experimental results which show that VizRanks ranking of projections highly agrees with subjective rankings by data analysts. The practical use of VizRank is also demonstrated by an application in the field of functional genomics.

Learning by discovering concept hierarchies

Abstract We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of switching circuits. To cope with high time complexity of finding an optimal decomposition, we propose a suboptimal heuristic algorithm. The method, implemented in program HINT (Hierarchy INduction Tool), is experimentally evaluated using a set of artificial and real-world learning problems. In particular, the evaluation addresses the generalization property of decomposition and its capability to discover meaningful hierarchies. The experiments show that HINT performs well in both respects.

Nomograms for visualizing support vector machines

We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphically encode the complete model on a single page. The dimensionality of the visualization does not depend on the number of attributes, but merely on the properties of the kernel. To represent the effect of each predictive feature on the log odds ratio scale as required for the nomograms, we employ logistic regression to convert the distance from the separating hyperplane into a probability. Case studies on selected data sets show that for a technique thought to be a black-box, nomograms can clearly expose its internal structure. By providing an easy-to-interpret visualization the analysts can gain insight and study the effects of predictive factors.

Methodological Review: Towards knowledge-based gene expression data mining

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks.