FSM: Fast and scalable network motif discovery for exploring higher-order network organizations

Wang, Tao; Peng, Jiajie; Peng, Qidi; Wang, Yadong; Chen, Jin

doi:10.1016/j.ymeth.2019.07.008

Cited by 23 publications

(10 citation statements)

References 36 publications

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“…Many network-based computational methods have also been proposed in recent years ( Wang et al, 2019a , b ; Yang et al, 2019 ). For predicting disease genes, one of the initial methods is to simply count the number of disease-genes in the neighborhood of a candidate gene ( Oti et al, 2006 ).…”

Section: Introductionmentioning

confidence: 99%

Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning

Liu

Hou

et al. 2021

Front. Genet.

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Cerebral ischemic stroke (IS) is a complex disease caused by multiple factors including vascular risk factors, genetic factors, and environment factors, which accentuates the difficulty in discovering corresponding disease-related genes. Identifying the genes associated with IS is critical for understanding the biological mechanism of IS, which would be significantly beneficial to the diagnosis and clinical treatment of cerebral IS. However, existing methods to predict IS-related genes are mainly based on the hypothesis of guilt-by-association (GBA). These methods cannot capture the global structure information of the whole protein–protein interaction (PPI) network. Inspired by the success of network representation learning (NRL) in the field of network analysis, we apply NRL to the discovery of disease-related genes and launch the framework to identify the disease-related genes of cerebral IS. The utilized framework contains three main parts: capturing the topological information of the PPI network with NRL, denoising the gene feature with the participation of a stacked autoencoder (SAE), and optimizing a support vector machine (SVM) classifier to identify IS-related genes. Superior to the existing methods on IS-related gene prediction, our framework presents more accurate results. The case study also shows that the proposed method can identify IS-related genes.

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Section: Introductionmentioning

confidence: 99%

Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning

Liu

Hou

et al. 2021

Front. Genet.

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“…Advancements in next-generation sequencing technologies open opportunities to various biological analyses, such as de novo assemblies of bacterial and eukaryotic genomes, and species classification based on metagenomics studies [ 1 ]. Short read alignment is a common first step of various downstream analyses, such as variant calling [ 2 ], RNA abundance quantification [ 3 ], and expression quantitative trait locus (eQTL) analysis [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Fast and SNP-aware short read alignment with SALT

2021

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Background DNA sequence alignment is a common first step in most applications of high-throughput sequencing technologies. The accuracy of sequence alignments directly affects the accuracy of downstream analyses, such as variant calling and quantitative analysis of transcriptome; therefore, rapidly and accurately mapping reads to a reference genome is a significant topic in bioinformatics. Conventional DNA read aligners map reads to a linear reference genome (such as the GRCh38 primary assembly). However, such a linear reference genome represents the genome of only one or a few individuals and thus lacks information on variations in the population. This limitation can introduce bias and impact the sensitivity and accuracy of mapping. Recently, a number of aligners have begun to map reads to populations of genomes, which can be represented by a reference genome and a large number of genetic variants. However, compared to linear reference aligners, an aligner that can store and index all genetic variants has a high cost in memory (RAM) space and leads to extremely long run time. Aligning reads to a graph-model-based index that includes all types of variants is ultimately an NP-hard problem in theory. By contrast, considering only single nucleotide polymorphism (SNP) information will reduce the complexity of the index and improve the speed of sequence alignment. Results The SNP-aware alignment tool (SALT) is a fast, memory-efficient, and SNP-aware short read alignment tool. SALT uses 5.8 GB of RAM to index a human reference genome (GRCh38) and incorporates 12.8M UCSC common SNPs. Compared with a state-of-the-art aligner, SALT has a similar speed but higher accuracy. Conclusions Herein, we present an SNP-aware alignment tool (SALT) that aligns reads to a reference genome that incorporates an SNP database. We benchmarked SALT using simulated and real datasets. The results demonstrate that SALT can efficiently map reads to the reference genome with significantly improved accuracy. Incorporating SNP information can improve the accuracy of read alignment and can reveal novel variants. The source code is freely available at https://github.com/weiquan/SALT.

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“…However, the detailed understanding of the molecular mechanisms through which these egenes jointly affect disease phenotypes remains largely unclear, and their discovery is a challenging computational task (Cheng et al, 2019b;Peng et al, 2020a). Instead of analyzing binary relationships between single SNP and single gene, network-based analyses provide valuable insights into the higher-order structure of gene communities or pathways that those potential disease genes may work together in the etiology of complex diseases (Fagny et al, 2017;Cheng et al, 2019b;Peng et al, 2020b;Wang et al, 2020). And advances in deep learning and graph representation learning technologies improve the accuracy of identifying disease related biomarkers (Peng et al, 2019a,b).…”

Section: Introductionmentioning

confidence: 99%

Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome

Wang

Peng

Liu

et al. 2020

Front. Bioeng. Biotechnol.

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The study of disease-relevant gene modules is one of the main methods to discover disease pathway and potential drug targets. Recent studies have found that most disease proteins tend to form many separate connected components and scatter across the protein-protein interaction network. However, most of the research on discovering disease modules are biased toward well-studied seed genes, which tend to extend seed genes into a single connected subnetwork. In this paper, we propose N2V-HC, an algorithm framework aiming to unbiasedly discover the scattered disease modules based on deep representation learning of integrated multi-layer biological networks. Our method first predicts disease associated genes based on summary data of Genome-wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL) studies, and generates an integrated network on the basis of human interactome. The features of nodes in the network are then extracted by deep representation learning. Hierarchical clustering with dynamic tree cut methods are applied to discover the modules that are enriched with disease associated genes. The evaluation on real networks and simulated networks show that N2V-HC performs better than existing methods in network module discovery. Case studies on Parkinson's disease and Alzheimer's disease, show that N2V-HC can be used to discover biological meaningful modules related to the pathways underlying complex diseases.

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FSM: Fast and scalable network motif discovery for exploring higher-order network organizations

Cited by 23 publications

References 36 publications

Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning

Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning

Fast and SNP-aware short read alignment with SALT

Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome

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