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IEEE International Conference on bioinformatics and Biomedicine (IEEE BIBM)

作者： Zhang, La Li, Xiao-peng Lai, Jia-qi Zhang, Lei Guangzhou Univ Chinese Med Affiliated Hosp 2 Guangzhou Guangdong Peoples R China Guangdong Prov Hosp Chinese Med Guangzhou Guangdong Peoples R China Guangzhou Univ Chinese Med Clin Coll 2 Class 2016 Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781509030507

Network pharmacology which is based on bioinformatics and system biology knowledge has been employed into the field of Traditional Chinese Medicine (TCM) researches in recent years. In order to summarize current available bioinformatics databases, especially those can be used for TCM formulae study;a systematic search of Chinese literature by May 2017 was conducted. Results showed that research methods of network pharmacology have been widely applied in a variety of diseases and TCM formulae since 2012. Four comprehensive TCM bioinformatics databases with multi-functions were briefly introduced.

关键词： bioinformatics databases network pharmacology Chinese medicine formulae systematic review

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Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding

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CURRENT COMPUTER-AIDED DRUG DESIGN 2024年第6期20卷 1013-1024页

作者： Qu, Xiaohan Du, Guoxia Hu, Jing Cai, Yongming Guangdong Pharmaceut Univ Sch Med Informat Engn Guangzhou Peoples R China Guangdong Prov Tradit Chinese Med Precis Med Big D Guangzhou Peoples R China

Background In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman *** Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary *** The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in

关键词： Deep learning drug development bioinformatics databases classifier design and evaluation feature representation topology

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MiniDBG: A Novel and Minimal De Bruijn Graph for Read Mapping

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2024年第1期21卷 129-142页

作者： Yu, Changyong Zhao, Yuhai Zhao, Chu Jin, Jianyu Mao, Keming Wang, Guoren Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

The De Bruijn graph (DBG) has been widely used in the algorithms for indexing or organizing read and reference sequences in bioinformatics. However, a DBG model that can locate each node, edge and path on sequence has not been proposed so far. Recently, DBG has been used for representing reference sequences in read mapping tasks. In this process, it is not a one-to-one correspondence between the paths of DBG and the substrings of reference sequence. This results in the false path on DBG, which means no substrings of reference producing the path. Moreover, if a candidate path of a read is true, we need to locate it and verify the candidate on sequence. To solve these problems, we proposed a DBG model, called MiniDBG, which stores the position lists of a minimal set of edges. With the position lists, MiniDBG can locate any node, edge and path efficiently. We also proposed algorithms for generating MiniDBG based on an original DBG and algorithms for locating edges or paths on sequence. We designed and ran experiments on real datasets for comparing them with BWT-based and position list-based methods. The experimental results show that MiniDBG can locate the edges and paths efficiently with lower memory costs.

关键词： bioinformatics Genomics Indexing Costs Task analysis Memory management Data structures Graph algorithms Indexing methods bioinformatics databases Queries

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StLiter: A Novel Algorithm to Iteratively Build the Compacted de Bruijn Graph From Many Complete Genomes

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND bioinformatics 2022年第4期19卷 2471-2483页

作者： Yu, Changyong Mao, Keming Zhao, Yuhai Chang, Cheng Wang, Guoren Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Coll Software Shenyang 110819 Peoples R China Beijing Inst Life Natl Ctr Prot Sci Beijing Beijing Proteome Res Ctr State Key Lab Prote Beijing 102206 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

Recently, the compacted de Bruijn graph (cDBG) of complete genome sequences was successfully used in read mapping due to its ability to deal with the repetitions in genomes. However, current approaches are not flexible enough to fit frequently building the graphs with different k-mer lengths. Instead of building the graph directly, how can we build the compacted de Bruijin graph of longer k-mer based on the one of short k-mer? In this article, we present StLiter, a novel algorithm to build the compacted de Bruijn graph either directly from genome sequences or indirectly based on the graph of a short k-mer. For 100 simulated human genomes, StLiter can construct the graph of k-mer length 15-18 in 2.5-3.2 hours with maximal similar to 70GB memory in the case of without considering the reverese complements of the reference genomes. And it costs 4.5-5.9 hours when considering the reverse complements. In experiments, we compared StLiter with TwoPaCo, the state-of-art method for building the graph, on 4 datasets. For k-mer length 15-18, StLiter can build the graph 5-9 times faster than TwoPaCo using less maximal memory cost. For k-mer length larger than 18, given the graph of a short (k-x)-mer, such as x= 1-2, compared with TwoPaCo building the graph directly, StLiter can also build the graph more efficiently. The source codes of StLiter can be downloaded from web site https://***/BioLab-cz/StLiter.

关键词： Graph algorithm indexing methods bioinformatics databases queries

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An overview of long noncoding RNAs: Biology, functions, therapeutics, analysis methods, and bioinformatics tools

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CELL BIOCHEMISTRY AND FUNCTION 2022年第8期40卷 800-825页

作者： Jafari-Raddani, Farideh Davoodi-Moghaddam, Zeinab Yousefi, Amir-Mohammad Ghaffari, Seyed H. Bashash, Davood Shahid Beheshti Univ Med Sci Sch Allied Med Sci Dept Hematol & Blood Banking Tehran Iran Univ Tehran Med Sci Shariati Hosp Hematol Oncol & Stem Cell Transplantat Res Ctr Sch Med Tehran Iran

Long noncoding RNAs (lncRNAs) are a diverse class of RNAs whose functions are widespread in all branches of life and have been the focus of attention in the last decade. While a huge number of lncRNAs have been identified, there is still much work to be done and plenty to be learned. In the current review, we begin with the biogenesis and function of lncRNAs as they are involved in the different cellular processes from regulating the architecture of chromosomes to controlling translation and post-translation modifications. Questions on how overexpression, mutations, or deficiency of lncRNAs can affect the cellular status and result in the pathogenesis of various human diseases are responded to. Besides, we allocate an overview of several studies, concerning the application of lncRNAs either as diagnostic and prognostic biomarkers or novel therapeutics. We also introduce the currently available techniques to explore details of lncRNAs such as their function, cellular localization, and structure. In the last section, as exponentially growing data in this area need to be gathered and organized in comprehensive databases, we have a particular focus on presenting general and specialized databases. Taken together, with this review, we aim to provide the latest information on different aspects of lncRNAs to highlight their importance in physiopathologic states and take a step towards helping future studies.

关键词： bioinformatics databases epigenetic gene expression long noncoding RNA therapeutics

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Exploring the mechanism of artificial selection signature in Chinese indigenous pigs by leveraging multiple bioinformatics database tools

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BMC GENOMICS 2023年第1期24卷 743-743页

作者： Feng, Xueyan Diao, Shuqi Liu, Yuqiang Xu, Zhiting Li, Guangzhen Ma, Ye Su, Zhanqin Liu, Xiaohong Li, Jiaqi Zhang, Zhe South China Agr Univ Coll Anim Sci State Key Lab Swine & Poultry Breeding Ind Guangdong Prov Key Lab Agroanim Genom & Mol Breedi Guangzhou 510642 Peoples R China Sun Yat Sen Univ Sch Life Sci State Key Lab Biocontrol Guangzhou 510275 Peoples R China

BackgroundChinese indigenous pigs in Yunnan exhibit considerable phenotypic diversity, but their population structure and the biological interpretation of signatures of artificial selection require further investigation. To uncover population genetic diversity, migration events, and artificial selection signatures in Chinese domestic pigs, we sampled 111 Yunnan pigs from four breeds in Yunnan which is considered to be one of the centres of livestock domestication in China, and genotyped them using Illumina Porcine SNP60K BeadChip. We then leveraged multiple bioinformatics database tools to further investigate the signatures and associated complex *** structure and migration analyses showed that Diannanxiaoer pigs had different genetic backgrounds from other Yunnan pigs, and Gaoligongshan may undergone the migration events from Baoshan and Saba pigs. Intriguingly, we identified a possible common target of sharing artificial selection on a 265.09 kb region on chromosome 5 in Yunnan indigenous pigs, and the genes on this region were associated with cardiovascular and immune systems. We also detected several candidate genes correlated with dietary adaptation, body size (e.g., PASCIN1, GRM4, ITPR2), and reproductive performance. In addition, the breed-sharing gene MMP16 was identified to be a human-mediated gene. Multiple lines of evidence at the mammalian genome, transcriptome, and phenome levels further supported the evidence for the causality between MMP16 variants and the metabolic diseases, brain development, and cartilage tissues in Chinese pigs. Our results suggested that the suppression of MMP16 would directly lead to inactivity and insensitivity of neuronal activity and skeletal development in Chinese indigenous *** this study, the population genetic analyses and identification of artificial selection signatures of Yunnan indigenous pigs help to build an understanding of the effect of human-mediated selection mechanisms on phen

关键词： Chinese indigenous pigs Artificial selection Genetic diversity bioinformatics databases

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In silico identification of novel lncRNAs with a potential role in diagnosis of gastric cancer

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2020年第7期38卷 1954-1962页

作者： Delshad, Encieh Shamsabadi, Fatemeh T. Bahramian, Shabbou Mehravar, Fatemeh Maghsoudi, Hossein Shafiee, Mohammad Golestan Univ Med Sci Golestan Res Ctr Gastroenterol & Hepatol Gorgan Golestan Iran Golestan Univ Med Sci Fac Adv Technol Med Dept Med Biotechnol Gorgan Golestan Iran Golestan Univ Med Sci Hlth Management & Social Dev Res Ctr Gorgan Golestan Iran Payame Noor Univ Dept Biotechnol Tehran Iran

Gastric cancer (GC) is the second leading cause of cancer-related deaths in the world. Due to the shortage of adequate symptoms in the early stages, it is diagnosed when the tumor has spread to distant organs. Early recognition of GC enhances the chance of successful treatment. Molecular mechanisms of GC are still poorly understood. LncRNAs are emerging as new players in cancer in both oncogene and tumor suppressor roles. High-throughput technologies such as RNA-Seq, have revealed thousands of lncRNAs which are dysregulated in GC. In this study, we retrieved lncRNAs obtained by High-throughput technologies from OncoLnc database. Consequently, retrieved lncRNAs were compared in literature-based databases including PubMed. As a result, two lists, including experimentally validated lncRNAs and predicted lncRNAs were provided. We found 43 predicted lncRNAs that had not been experimentally validated in GC, so far. Further bioinformatics analyses were performed to obtain the expression profile of predicted lncRNAs in tumor and normal tissues. Also, the roles and targets of predicted lncRNAs in GC were identified by related databases. Finally, using the GEPIA database was reviewed the significant relationship of predicted lncRNAs with the survival of GC patients. By recognizing the lncRNAs involved in initiation and progression of GC, they may be considered as potential biomarkers in the GC early diagnosis or targeted treatment and lead to novel therapeutic strategies. Communicated by Ramaswamy H. Sarma

关键词： Gastric cancer predicted lncRNAs in silico analysis bioinformatics databases high-throughput technology

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Natural logic knowledge bases and their graph form

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DATA & KNOWLEDGE ENGINEERING 2020年 129卷 101848-101848页

作者： Andreasen, Troels Bulskov, Henrik Jensen, Per Anker Nilsson, Jurgen Fischer Roskilde Univ Comp Sci Roskilde Denmark Copenhagen Business Sch Management Soc & Commun Copenhagen Denmark Tech Univ Denmark Math & Comp Sci Lyngby Denmark

This paper describes how knowledge bases can be represented in and reasoned with in natural logic. Natural logic is a regimented fragment of natural language possessing a well-defined logical semantics. As such, natural logic may be considered an attractive alternative among the various knowledge representation logics such as description logics. Our version of natural logic expands formal ontologies with affirmative propositions expressing a variety of relationships between concepts. It comprises (nested) restrictive relative clauses and prepositional phrases and, as a new construct, adverbial prepositional phrases. The natural logic knowledge base is to be used for deductive query answering applying inference rules. This is facilitated by introduction of DATALOG as an embedding meta-logic. The inference rules are stated in DATALOG and act directly on the natural logic formulations. The knowledge base propositions are decomposed into a graph form enabling path finding between concepts. The examples in the paper are derived from text source life-science descriptions.

关键词： Natural Logic Knowledge management applications Ontologies Query Metalogic bioinformatics databases

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Automated detection of records in biological sequence databases that are inconsistent with the literature

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JOURNAL OF BIOMEDICAL INFORMATICS 2017年 71卷 229-240页

作者： Bouadjenek, Mohamed Reda Verspoor, Karin Zobel, Justin Univ Melbourne Dept Comp & Informat Syst Parkville Vic 3053 Australia

We investigate and analyse the data quality of nucleotide sequence databases with the objective of automatic detection of data anomalies and suspicious records. Specifically, we demonstrate that the published literature associated with each data record can be used to automatically evaluate its quality, by cross-checking the consistency of the key content of the database record with the referenced publications. Focusing on GenBank, we describe a set of quality indicators based on the relevance paradigm of information retrieval (IR). Then, we use these quality indicators to train an anomaly detection algorithm to classify records as "confident" or "suspicious". Our experiments on the PubMed Central collection show assessing the coherence between the literature and database records, through our algorithms, is an effective mechanism for assisting curators to perform data cleansing. Although fewer than 0.25% of the records in our data set are known to be faulty, we would expect that there are many more in GenBank that have not yet been identified. By automated comparison with literature they can be identified with a precision of up to 10% and a recall of up to 30%, while strongly outperforming several baselines. While these results leave substantial room for improvement, they reflect both the very imbalanced nature of the data, and the limited explicitly labelled data that is available. Overall, the obtained results show promise for the development of a new kind of approach to detecting low-quality and suspicious sequence records based on literature analysis and consistency. From a practical point of view, this will greatly help curators in identifying inconsistent records in large-scale sequence databases by highlighting records that are likely to be inconsistent with the literature. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Data analysis Data quality bioinformatics databases Anomaly detection

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BioGraph: a web application and a graph database for querying and analyzing bioinformatics resourcesN

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BMC SYSTEMS BIOLOGY 2018年第Sup5期12卷 98-98页

作者： Messina, Antonio Fiannaca, Antonino La Paglia, Laura La Rosa, Massimo Urso, Alfonso Natl Res Council Italy CNR ICAR Via Ugo La Malfa 153 Palermo Italy

Background: Several online databases provide a large amount of biomedical data of different biological entities. These resources are typically stored in systems implementing their own data model, user interface and query language. On the other hand, in many bioinformatics scenarios there is often the need to use more than one resource. The availability of a single bioinformatics platform that integrates many biological resources and services is, for those reasons a fundamental issue. Description: Here, we present BioGraph, a web application that allows to query, visualize and analyze biological data belonging to several online available sources. BioGraph is built upon our previously developed graph database called BioGraphDB, that integrates and stores heterogeneous biological resources and make them available by means of a common structure and a unique query language. BioGraph implements state-of-the-art technologies and provides pre-compiled bioinformatics scenarios, as well as the possibility to perform custom queries and obtaining an interactive and dynamic visualization of results. Conclusion: We present a case study about functional analysis of microRNA in breast cancer in order to demonstrate the functionalities of the system. BioGraph is freely available at http://***. Source files are available on GitHub at https://***/IcarPA-TBlab/BioGraph

关键词： Integrated databases bioinformatics databases Graph databases BioGraphDB miRNA

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