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The Kinase Analysis for Calibrating the Activity from the NIK-IKK1 Sophisticated Induced via the Noncanonical NF-κB Walkway.
BACKGROUND The identification of all matches of a large set of position weight matrices (PWMs) in long DNA sequences requires significant computational resources for which a number of efficient yet complex algorithms have been proposed. RESULTS We propose BLAMM, a simple and efficient tool inspired by high performance computing techniques. The workload is expressed in terms of matrix-matrix products that are evaluated with high efficiency using optimized BLAS library implementations. The algorithm is easy to parallelize and implement on CPUs and GPUs and has a runtime that is independent of the selected p-value. In terms of single-core performance, it is competitive with state-of-the-art software for PWM matching while being much more efficient when using multithreading. Additionally, BLAMM requires negligible memory. For example, both strands of the entire human genome can be scanned for 1404 PWMs in the JASPAR database in 13 min with a p-value of 10-4 using a 36-core machine. On a dual GPU system, the same task can be performed in under 5 min. CONCLUSIONS BLAMM is an efficient tool for identifying PWM matches in large DNA sequences. Its C++ source code is available under the GNU General Public License Version 3 at https//github.com/biointec/blamm.BACKGROUND Noonan syndrome (NS), an autosomal dominant developmental genetic disorder, is caused by germline mutations in genes associated with the RAS / mitogen-activated protein kinase (MAPK) pathway. In several studies PTPN11 is one of the genes with a significant number of pathogenic variants in NS-affected patients. Therefore, clinically diagnosed NS individuals are initially tested for pathogenic variants in PTPN11 gene to confirm the relationship before studying genotype-phenotype correlation. METHODS Individuals (363) with clinically diagnosed NS from four hospitals in South India were recruited and the exons of PTPN11 gene were sequenced. RESULTS Thirty-two previously described pathogenic variants in eight different exons in PTPN11 gene were detected in 107 patients, of whom 10 were familial cases. Exons 3, 8 and 13 had the highest number of pathogenic variants. The most commonly identified pathogenic variants in this series were in exon 8 (c.922A > G, c.923A > G), observed in 22 of the affected. Congenital cardiac anomalies were present in 84% of the mutation-positive cohort, the majority being defects in the right side of the heart. The most common facial features were downward-slanting palpebral fissures, hypertelorism and low-set posteriorly rotated ears. Other clinical features included short stature (40%), pectus excavatum (54%) and, in males, unilateral or bilateral cryptorchidism (44%). CONCLUSION The clinical features and mutational spectrum observed in our cohort are similar to those reported in other large studies done worldwide. This is the largest case series of NS-affected individuals with PTPN11 mutations described till date from India.BACKGROUND The actual task of electrocardiographic examinations is to increase the reliability of diagnosing the condition of the heart. Within the framework of this task, an important direction is the solution of the inverse problem of electrocardiography, based on the processing of electrocardiographic signals of multichannel cardio leads at known electrode coordinates in these leads (Titomir et al. Noninvasiv electrocardiotopography, 2003), (Macfarlane et al. Comprehensive Electrocardiology, 2nd ed. (Chapter 9), 2011). RESULTS In order to obtain more detailed information about the electrical activity of the heart, we carry out a reconstruction of the distribution of equivalent electrical sources on the heart surface. In this area, we hold reconstruction of the equivalent sources during the cardiac cycle at relatively low hardware cost. ECG maps of electrical potentials on the surface of the torso (TSPM) and electrical sources on the surface of the heart (HSSM) were studied for different times of the cardiac cycle. We carried out a visual and quantitative comparison of these maps in the presence of pathological regions of different localization. For this purpose we used the model of the heart electrical activity, based on cellular automata. CONCLUSIONS The model of cellular automata allows us to consider the processes of heart excitation in the presence of pathological regions of various sizes and localization. Vismodegib It is shown, that changes in the distribution of electrical sources on the surface of the epicardium in the presence of pathological areas with disturbances in the conduction of heart excitation are much more noticeable than changes in ECG maps on the torso surface.BACKGROUND Hi-C is a molecular biology technique to understand the genome spatial structure. However, data obtained from Hi-C experiments is biased. Therefore, several methods have been developed to model Hi-C data and identify significant interactions. Each method receives its own Hi-C data structure and only work on specific operating systems. RESULTS We introduce MHiC (Multi-function Hi-C data analysis tool), a tool to identify and visualize statistically signifiant interactions from Hi-C data. The MHiC tool (i) works on different operating systems, (ii) accepts various Hi-C data structures from different Hi-C analysis tools such as HiCUP or HiC-Pro, (iii) identify significant Hi-C interactions with GOTHiC, HiCNorm and Fit-Hi-C methods and (iv) visualizes interactions in Arc or Heatmap diagram. MHiC is an open-source tool which is freely available for download on https//github.com/MHi-C. CONCLUSIONS MHiC is an integrated tool for the analysis of high-throughput chromosome conformation capture (Hi-C) data.BACKGROUND In the field of protein engineering and biotechnology, the discovery and characterization of structural patterns is highly relevant as these patterns can give fundamental insights into protein-ligand interaction and protein function. This paper presents GSP4PDB, a bioinformatics web tool that enables the user to visualize, search and explore protein-ligand structural patterns within the entire Protein Data Bank. RESULTS We introduce the notion of graph-based structural pattern (GSP) as an abstract model for representing protein-ligand interactions. A GSP is a graph where the nodes represent entities of the protein-ligand complex (amino acids and ligands) and the edges represent structural relationships (e.g. distances ligand - amino acid). The novel feature of GSP4PDB is a simple and intuitive graphical interface where the user can "draw" a GSP and execute its search in a relational database containing the structural data of each PDB entry. The results of the search are displayed using the same graph-based representation of the pattern.
Here's my website: https://www.selleckchem.com/products/GDC-0449.html