Current Issue : April - June Volume : 2016 Issue Number : 2 Articles : 6 Articles
Background\n\nInflammatory bowel disease (IBD) consists of two main disease-subtypes, Crohn�s disease (CD) and ulcerative colitis (UC); these subtypes share overlapping genetic and clinical features. Genome-wide microarray data enable unbiased documentation of alterations in gene expression that may be disease-specific. As genetic diseases are believed to be caused by genetic alterations affecting the function of signalling pathways, module-centric optimisation algorithms, whose aim is to identify sub-networks that are dys-regulated in disease, are emerging as promising approaches.\n\nResults\n\nIn order to account for the topological structure of molecular interaction networks, we developed an optimisation algorithm that integrates databases of known molecular interactions with gene expression data; such integration enables identification of differentially regulated network modules. We verified the performance of our algorithm by testing it on simulated networks; we then applied the same method to study experimental data derived from microarray analysis of CD and UC biopsies and human interactome databases. This analysis allowed the extraction of dys-regulated subnetworks under different experimental conditions (inflamed and uninflamed tissues in CD and UC). Optimisation was performed to highlight differentially expressed network modules that may be common or specific to the disease subtype.\n\nConclusions\n\nWe show that the selected subnetworks include genes and pathways of known relevance for IBD; in particular, the solutions found highlight cross-talk among enriched pathways, mainly the JAK/STAT signalling pathway and the EGF receptor signalling pathway. In addition, integration of gene expression with molecular interaction data highlights nodes that, although not being differentially expressed, interact with differentially expressed nodes and are part of pathways that are relevant to IBD. The method proposed here may help identifying dys-regulated sub-networks that are common in different diseases and sub-networks whose dys-regulation is specific to a particular disease....
Over the last years a great number of bacterial genomes were sequenced. Now one of the most important challenges of\ncomputational genomics is the functional annotation of nucleic acid sequences. In this study we presented the computational\nmethod and the annotation system for predicting biological functions using phylogenetic profiles. The phylogenetic profile of a\ngene was created by way of searching for similarities between the nucleotide sequence of the gene and 1204 reference genomes,\nwith further estimation of the statistical significance of found similarities. The profiles of the genes with known functions were\nused for prediction of possible functions and functional groups for the new genes. We conducted the functional annotation for\ngenes from 104 bacterial genomes and compared the functions predicted by our system with the already known functions. For the\ngenes that have already been annotated, the known function matched the function we predicted in 63% of the time, and in 86% of\nthe time the known function was found within the top five predicted functions. Besides, our system increased the share of annotated\ngenes by 19%. The developed system may be used as an alternative or complementary system to the current annotation systems...
Background\nNon-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects.\n\nResults\nThis work integrates two approaches - machine learning algorithms and topological parameter-based classification - to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early- and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements.\n\nConclusions\nWith the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs....
Sequencing and restriction analysis of genes like 16S rRNA and HSP60 are intensively used for molecular identification in the\nmicrobial communities. With aid of the rapid progress in bioinformatics, genome sequencing became the method of choice for\nbacterial identification. However, the genome sequencing technology is still out of reach in the developing countries. In this\npaper, we propose FN-Identify, a sequencing-free method for bacterial identification. FN-Identify exploits the gene sequences\ndata available in GenBank and other databases and the two algorithms that we developed, Create Scheme and Gene Identify,\nto create a restriction enzyme-based identification scheme. FN-Identify was tested using three different and diverse bacterial\npopulations (members of Lactobacillus, Pseudomonas, and Mycobacterium groups) in an in silico analysis using restriction enzymes\nand sequences of 16S rRNA gene.The analysis of the restriction maps of the members of three groups using the fragment numbers\ninformation only or along with fragments sizes successfully identified all of the members of the three groups using a minimum of\nfour and maximum of eight restriction enzymes. Our results demonstrate the utility and accuracy of FN-Identify method and its\ntwo algorithms as an alternative method that uses the standard microbiology laboratories techniques when the genome sequencing\nis not available....
The emerging genome-wide hairpin bisulfite sequencing (hairpin-BS-Seq) technique enables the determination of the methylation\npattern for DNA double strands simultaneously. Compared with traditional bisulfite sequencing (BS-Seq) techniques, hairpin-BSSeq\ncan determine methylation fidelity and increase mapping efficiency. However, no computational tool has been designed for the\nanalysis of hairpin-BS-Seq data yet. Here we present HBS-tools, a set of command line based tools for the preprocessing, mapping,\nmethylation calling, and summarizing of genome-wide hairpin-BS-Seq data. It accepts paired-end hairpin-BS-Seq reads to recover\nthe original (pre-bisulfite-converted) sequences using global alignment and then calls the methylation statuses for cytosines on both\nDNA strands after mapping the original sequences to the reference genome. After applying to hairpin-BS-Seq datasets, we found\nthat HBS-tools have a reduced mapping time and improved mapping efficiency compared with state-of-the-art mapping tools. The\nHBS-tools source scripts, along with user guide and testing data, are freely available for download....
The SIB Swiss Institute of Bioinformatics (www.\nisb-sib.ch) provides world-class bioinformatics\ndatabases, software tools, services and training to\nthe international life science community in academia\nand industry. These solutions allow life scientists to\nturn the exponentially growing amount of data into\nknowledge. Here, we provide an overview of SIB�s\nresources and competence areas, with a strong focus\non curated databases and SIB�s most popular\nand widely used resources. In particular, SIB�s\nBioinformatics resource portal ExPASy features over\n150 resources, including UniProtKB/Swiss-Prot, ENZYME,\nPROSITE, neXtProt, STRING, UniCarbKB,\nSugarBindDB, SwissRegulon, EPD, arrayMap, Bgee,\nSWISS-MODEL Repository, OMA, OrthoDB and other\ndatabases, which are briefly described in this article....
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