LiverAtlas: a unique integrated knowledge database for systems‐level research of liver and hepatic disease

Zhang, Yanqiong; Yang, Chunyuan; Wang, Shaochuang; Chen, Tao; Li, Mansheng; Xue, Wei; Li, Dongsheng; Wang, Kang; Ma, Jie; Wu, Songfeng; Zhang, Xueli; Zhu, Yunping; Wu, Jinsheng; He, Fuchu

doi:10.1111/liv.12173

Cited by 19 publications

(20 citation statements)

References 35 publications

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“…We found that the differentially expressed transcripts and proteins screened in the HCC cell lines, which emphasized the metastatic versus nonmetastatic potentials, were different from those genes screened in liver carcinogenesis by proteome, which focused on the tumorous versus nontumorous tissues . Our results added useful information to the previously published LiverAtlas , including a total 627 metastasis‐relative genes listed in Supporting Information Table 2 and many newly identified genes such as IPO5 , SON , PITRM1 , PAICS , SF3B2 , FGFR1OP , SEC63 , MTO1 , PIAS4 , DDX49 , YTHDC2 , and MRPL57 shown in Supporting Information Table 2C of which 28 were overlapped genes between the 351 significant transcripts and 304 proteins.…”

Section: Resultsmentioning

confidence: 78%

Transcriptome and proteome of human hepatocellular carcinoma reveal shared metastatic pathways with significant genes

et al. 2015

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Previously isolated pathways screened from individual genes were investigated at either the transcriptional or translational level; however, the consistency between the pathways screened at the gene expression levels was obscure in metastatic human hepatocellular carcinoma (HCC). To elucidate this question, we performed a transcriptomic (16,353 genes) and proteomic (7861 proteins) analysis simultaneously on six metastatic HCC cell lines against two nonmetastatic HCC cell lines, with all HBV traceable and close genetic-backgrounds for a comparative study. The quantitative and integrated results showed that significant genes were screened differentially with 351 transcripts from the transcriptome and 304 proteins from the proteome, with limited overlapping genes (7%). However, we discovered that these discrete 351 transcripts and 304 proteins screened share extrusive significant-pathways/networks with a 77% overlap, including active TGF-β, RAS, NFκB, and Wnt, and inactive HNF4A, which are responsible for HCC metastasis. We conclude that the discrete, but significant genes predicted by either ome play intrinsically important roles in the linkage of responsible pathways shared by both omes in HCC metastasis.

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Section: Resultsmentioning

confidence: 78%

Transcriptome and proteome of human hepatocellular carcinoma reveal shared metastatic pathways with significant genes

et al. 2015

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“…28,29 With the accumulation of proteome data sets generated from different tissues and cell lines, to date, 50−60% of protein-coding genes have been identified at the proteome level. 3,35 However, because of limitations associated with the detection sensitivity and dynamic range of available mass spectrometers, data saturation could be easily reached.…”

Section: Approaching the Sample Number-independent Data Saturation Stmentioning

confidence: 99%

Systematic Analysis of Missing Proteins Provides Clues to Help Define All of the Protein-Coding Genes on Human Chromosome 1

Zhang¹,

Zhai

et al. 2013

J. Proteome Res.

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Our first proteomic exploration of human chromosome 1 began in 2012 (CCPD 1.0), and the genome-wide characterization of the human proteome through public resources revealed that 32-39% of proteins on chromosome 1 remain unidentified. To characterize all of the missing proteins, we applied an OMICS-integrated analysis of three human liver cell lines (Hep3B, MHCC97H, and HCCLM3) using mRNA and ribosome nascent-chain complex-bound mRNA deep sequencing and proteome profiling, contributing mass spectrometric evidence of 60 additional chromosome 1 gene products. Integration of the annotation information from public databases revealed that 84.6% of genes on chromosome 1 had high-confidence protein evidence. Hierarchical analysis demonstrated that the remaining 320 missing genes were either experimentally or biologically explainable; 128 genes were found to be tissue-specific or rarely expressed in some tissues, whereas 91 proteins were uncharacterized mainly due to database annotation diversity, 89 were genes with low mRNA abundance or unsuitable protein properties, and 12 genes were identifiable theoretically because of a high abundance of mRNAs/RNC-mRNAs and the existence of proteotypic peptides. The relatively large contribution made by the identification of enriched transcription factors suggested specific enrichment of low-abundance protein classes, and SRM/MRM could capture high-priority missing proteins. Detailed analyses of the differentially expressed genes indicated that several gene families located on chromosome 1 may play critical roles in mediating hepatocellular carcinoma invasion and metastasis. All mass spectrometry proteomics data corresponding to our study were deposited in the ProteomeXchange under the identifiers PXD000529, PXD000533, and PXD000535.

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“…Liver diseases are imported from DO [1], and UMLS [39]. These liver diseases are organized by Human Liver Disease Ontology (HuLDO) developed by ourselves [40]. HuLDO is a standardized method to classify and annotate human liver diseases.…”

Section: Construction and Contentmentioning

confidence: 99%

“…HuLDO is a standardized method to classify and annotate human liver diseases. It is a comprehensive lexicon which contains detailed information on hepatic disease and demonstrates the logical and medical relationships between different diseases [40]. To assess the quality of each entry, we use a semi-quantitative method which considers the reliability and the number of data sources.…”

Section: Construction and Contentmentioning

confidence: 99%

LiverWiki: a wiki-based database for human liver

Chen¹,

et al. 2017

BMC Bioinformatics

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BackgroundRecent advances in omics technology have produced a large amount of liver-related data. A comprehensive and up-to-date source of liver-related data is needed to allow biologists to access the latest data. However, current liver-related data sources each cover only a specific part of the liver. It is difficult for them to keep pace with the rapid increase of liver-related data available at those data resources. Integrating diverse liver-related data is a critical yet formidable challenge, as it requires sustained human effort.ResultsWe present LiverWiki, a first wiki-based database that integrates liver-related genes, homolog genes, gene expressions in microarray datasets and RNA-Seq datasets, proteins, protein interactions, post-translational modifications, associated pathways, diseases, metabolites identified in the metabolomics datasets, and literatures into an easily accessible and searchable resource for community-driven sharing. LiverWiki houses information in a total of 141,897 content pages, including 19,787 liver-related gene pages, 17,077 homolog gene pages, 50,251 liver-related protein pages, 36,122 gene expression pages, 2067 metabolites identified in the metabolomics datasets, 16,366 disease-related molecules, and 227 liver disease pages. Other than assisting users in searching, browsing, reviewing, refining the contents on LiverWiki, the most important contribution of LiverWiki is to allow the community to create and update biological data of liver in visible and editable tables. This integrates newly produced data with existing knowledge. Implemented in mediawiki, LiverWiki provides powerful extensions to support community contributions.ConclusionsThe main goal of LiverWiki is to provide the research community with comprehensive liver-related data, as well as to allow the research community to share their liver-related data flexibly and efficiently. It also enables rapid sharing new discoveries by allowing the discoveries to be integrated and shared immediately, rather than relying on expert curators. The database is available online at http://liverwiki.hupo.org.cn/.

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LiverAtlas: a unique integrated knowledge database for systems‐level research of liver and hepatic disease

Abstract: LiverAtlas is the most comprehensive liver and hepatic disease resource, which helps biologists and clinicians to analyse their data at the systems level and will contribute much to the biomarker discovery and diagnostic performance enhancement for liver diseases.

Cited by 19 publications

References 35 publications

Transcriptome and proteome of human hepatocellular carcinoma reveal shared metastatic pathways with significant genes

Transcriptome and proteome of human hepatocellular carcinoma reveal shared metastatic pathways with significant genes

Systematic Analysis of Missing Proteins Provides Clues to Help Define All of the Protein-Coding Genes on Human Chromosome 1

LiverWiki: a wiki-based database for human liver

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