Background: Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients. Methods: An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein-protein interaction (PPI) networks. Then, functional clusters in PPI networks were determined. Enrichr, a gene list enrichment analysis tool, was utilized for the functional enrichment of clusters. Results: A total of 12, 2, and 4 functional clusters from 619, 52, and 119 DEGs were determined in the lung, peripheral blood mononuclear cell (PBMC), and skin tissues, respectively. Analysis revealed that the tumor necrosis factor (TNF) signaling pathway was enriched significantly in the three investigated tissues as a common pathway. In addition, clusters associated with inflammation and immunity were common in the three investigated tissues. However, clusters related to the fibrosis process were common in lung and skin tissues. Conclusions: Analysis indicated that there were common pathological clusters that contributed to the pathogenesis of SSc in different tissues. Moreover, it seems that the common pathways in distinct tissues stem from a diverse set of genes.
Aligning single-molecule sequencing (SMS) reads to a reference genome has been computationally challenging due to the high sequencing error rates in this technology. Short distances between consecutive errors in SMS reads confront finding seeds, subsequences of the reads with exact matches to the reference, that specifically target a unique genomic position. To overcome this issue, one can look for similarities, rather than exact matches. MinHash, a locality-sensitive hashing (LSH) scheme, measures the similarity of two sequences by listing all k-mers of each one and approximating the fraction of common k-mers between them using a family of hash functions, which usually includes hundreds to thousands of different hash functions in order to increase the measurement accuracy. MinHash is used to address various bioinformatics problems, including the assembly of SMS reads. Here, we enhance both the efficiency and accuracy of the MinHash scheme by algorithmic techniques. We use a single hash function, rather than hundreds or thousands of different hash functions as used in the other MinHash-based algorithms, without losing the accuracy. We also double the size of the seed sequences by allowing one sequencing error of any form inside a pair of k-mers, which has a significant impact on the accuracy. We show algorithm, called Aryana-LoR, outperforms the accuracy of the other existing SMS aligners in both E-coli and Human genomes.AvailabilityAryana-LoR is freely available at https://gitlab.com/hnikaein/aryana-LoR
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