Involvement of wnt signaling pathways in the metamorphosis of the bryozoan bugula neritina
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KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Integrative Systems Biology Lab
KAUST Global Collaborative Research Program
Permanent link to this recordhttp://hdl.handle.net/10754/325301
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AbstractIn this study, we analyzed the metamorphosis of the marine bryozoan Bugula neritina. We observed the morphogenesis of the ancestrula. We defined three distinct pre-ancestrula stages based on the anatomy of the developing polypide and the overall morphology of pre-ancestrula. We then used an annotation based enrichment analysis tool to analyze the B. neritina transcriptome and identified over-representation of genes related to Wnt signaling pathways, suggesting its involvement in metamorphosis. Finally, we studied the temporal-spatial gene expression studies of several Wnt pathway genes. We found that one of the Wnt ligand, BnWnt10, was expressed spatially opposite to the Wnt antagonist BnsFRP within the blastemas, which is the presumptive polypide. Down-stream components of the canonical Wnt signaling pathway were exclusively expressed in the blastemas. Bn?catenin and BnFz5/8 were exclusively expressed in the blastemas throughout the metamorphosis. Based on the genes expression patterns, we propose that BnWnt10 and BnsFRP may relate to the patterning of the polypide, in which the two genes served as positional signals and contributed to the polarization of the blastemas. Another Wnt ligand, BnWnt6, was expressed in the apical part of the pre-ancestrula epidermis. Overall, our findings suggest that the Wnt signaling pathway may be important to the pattern formation of polypide and the development of epidermis. © 2012 Wong et al.
CitationWong YH, Wang H, Ravasi T, Qian P-Y (2012) Involvement of Wnt Signaling Pathways in the Metamorphosis of the Bryozoan Bugula neritina. PLoS ONE 7: e33323. doi:10.1371/journal.pone.0033323.
PublisherPublic Library of Science (PLoS)
PubMed Central IDPMC3308966
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Correlation of Respiratory Signals and Electrocardiogram Signals via Empirical Mode DecompositionEl Fiky, Ahmed Osama (2011-05-24) [Thesis]
Advisor: Kosel, Jürgen
Committee members: Alouini, Mohamed-Slim; Sundaramoorthi, GaneshRecently Electrocardiogram (ECG) signals are being broadly used as an essential diagnosing tool in different clinical applications as they carry a reliable representation not only for cardiac activities, but also for other associated biological processes, like respiration. However, the process of recording and collecting them has usually suffered from the presence of some undesired noises, which in turn affects the reliability of such representations.Therefore, de-noising ECG signals became a hot research field for signal processing experts to ensure better and clear representation of the different cardiac activities. Given the nonlinear and non-stationary properties of ECGs, it is not a simple task to cancel the undesired noise terms without affecting the biological physics of them. In this study, we are interested in correlating the ECG signals with respiratory parameters, specifically the lung volume and lung pressure. We have focused on the concept of de-noising ECG signals by means of signal decomposition using an algorithm called the Empirical Mode Decomposition (EMD) where the original ECG signals are being decomposed into a set of intrinsic mode functions (IMF). Then, we have provided criteria based on which some of these IMFs have been adapted to reconstruct de-noised ECG version. Finally, we have utilized de-noised ECGs as well as IMFs for to study the correlation with lung volume and lung pressure. These correlation studies have showed some clear resemblance especially between the oscillations of ECGs and lung pressures.
Software of prediction models for 12 poly(A) signal variants in human and feature vectors of regions covering [-300,poly(A) hexamer,+300] for these 12 signal variants.Albalawi, Fahad; Chahid, Abderrazak; Guo, Xingang; Albaradei, Somayah; Magana-Mora, Arturo; Jankovic, Boris R.; Uludag, Mahmut; Van Neste, Christophe; Essack, Magbubah; Laleg-Kirati, Taous-Meriem; Bajic, Vladimir B. (Mendeley Data, 2018-11-15) [Dataset]We used human genome hg38 from GENCODE folder at EBI ftp server (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/GRCh38.primary_assembly.genome.fa.gz) Positive set (PAS sequences) Using GENCODE annotation for poly(A) (ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/gencode.v28.polyAs.gff3.gz) We selected poly(A) signal annotation. Using bedtools-slop option, we found regions extended 300 bp upstream and 300 bp downstream of the poly(A) hexamer. With the bedtools-getfasta option, we extracted 606 bp fasta sequences from these regions. After eliminating duplicates, we obtained 37’516 presumed true functional poly(A) signal (PAS) sequences. Sequences from this set will be denoted as positive. Negative set (pseudo-PAS sequences) For the negative set, we looked for regions extended outside the region covering 1’000 bp upstream and downstream of the positive poly(A) hexamer signal using bedtools-complement. Homer tool was used to find matches for the 12 most frequent human poly(A) variants. Since the number of matches was huge, sampling was used to select 37’516 pseudo-PAS sequences. Sampling was done from each chromosome proportionally to the chromosomes lengths and also to the expected frequency of the poly(A) variants. Out of these predictions, for each PAS hexamer, we selected the same number of pseudo-PAS sequences as in the positive set. Training and testing sets We selected randomly from each of the positive and negative datasets 20% of sequences for the independent test data. The testing set thus consisted of 15’020 sequences. The remaining data represented the training set that consisted of 60’012 sequences. Both datasets are balanced relative to the true PAS and pseudo-PAS sequences. Processed sequences These sequences are processed by the Matlab code we provide, and sequences are converted to feature vectors of length 205. To the end of each of these features vectors, the class label ('1' for true PAS and '0' for pseudo-PAS) is added. These processed sequences are provided here. Prediction models In addition, we provide software encoding final models for prediction of each of the 12 PAS variants. For each PAS variant, there is a Linear Regression model and a Deep Neural Network (DNN) model.
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