To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

https://journals.sagepub.com/doi/pdf/10.1177/1177932219899051

Multi-omics Data Integration, Interpretation, and Its Application.

Background FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data. Objectives This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by “GO FAIR” which addresses the identified gaps that such process has when dealing with health datasets. Methods A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing. Results A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow. Discussion Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets. Conclusion Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture.

/pdf/from-raw-data-to-fair-data-the-fairification-workflow-for-1z5m8r7wfc.pdf

From Raw Data to FAIR Data: The FAIRification Workflow for Health Research

Mirk protein kinase is activated by MKK3 and functions as a transcriptional activator of HNF1α. Vol. 277 (2002) 25040-25046

Background: Liver cancer is a substantial disease burden in China. As one of the primary diagnostic tools for detecting liver cancer, dynamic contrast-enhanced computed tomography provides detailed evidences for diagnosis that are recorded in free-text radiology reports.
Objective: The aim of our study was to apply a deep learning model and rule-based natural language processing (NLP) method to identify evidences for liver cancer diagnosis automatically.
Methods: We proposed a pretrained, fine-tuned BERT (Bidirectional Encoder Representations from Transformers)-based BiLSTM-CRF (Bidirectional Long Short-Term Memory-Conditional Random Field) model to recognize the phrases of APHE (hyperintense enhancement in the arterial phase) and PDPH (hypointense in the portal and delayed phases). To identify more essential diagnostic evidences, we used the traditional rule-based NLP methods for the extraction of radiological features. APHE, PDPH, and other extracted radiological features were used to design a computer-aided liver cancer diagnosis framework by random forest.
Results: The BERT-BiLSTM-CRF predicted the phrases of APHE and PDPH with an F1 score of 98.40% and 90.67%, respectively. The prediction model using combined features had a higher performance (F1 score, 88.55%) than those using APHE and PDPH (84.88%) or other extracted radiological features (83.52%). APHE and PDPH were the top 2 essential features for liver cancer diagnosis.
Conclusions: This work was a comprehensive NLP study, wherein we identified evidences for the diagnosis of liver cancer from Chinese radiology reports, considering both clinical knowledge and radiology findings. The BERT-based deep learning method for the extraction of diagnostic evidence achieved state-of-the-art performance. The high performance proves the feasibility of the BERT-BiLSTM-CRF model in information extraction from Chinese radiology reports. The findings of our study suggest that the deep learning–based method for automatically identifying evidences for diagnosis can be extended to other types of Chinese clinical texts.

/pdf/use-of-bert-bidirectional-encoder-representations-from-4nfx0jp5zf.pdf

Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework

Usher syndrome is a rare disorder causing retinitis pigmentosa, together with sensorineural hearing loss. Due to the phenotypic and genetic heterogeneity of this disease, the best method to screen the causative mutations is by high-throughput sequencing. In this study, we tested a semiconductor chip based sequencing approach with 77 unrelated patients, as a molecular diagnosis routine. In addition, Multiplex Ligation-dependent Probe Amplification and microarray-based Comparative Genomic Hybridization techniques were applied to detect large rearrangements, and minigene assays were performed to confirm the mRNA processing aberrations caused by splice-site mutations. The designed panel included all the USH causative genes (MYO7A, USH1C, CDH23, PCDH15, USH1G, CIB2, USH2A, ADGRV1, WHRN and CLRN1) as well as four uncertainly associated genes (HARS, PDZD7, CEP250 and C2orf71). The outcome showed an overall mutation detection ratio of 82.8% and allowed the identification of 42 novel putatively pathogenic mutations. Furthermore, we detected two novel nonsense mutations in CEP250 in a patient with a disease mimicking Usher syndrome that associates visual impairment due to cone-rod dystrophy and progressive hearing loss. Therefore, this approach proved reliable results for the molecular diagnosis of the disease and also allowed the consolidation of the CEP250 gene as disease causative for an Usher-like phenotype.

/pdf/high-throughput-sequencing-for-the-molecular-diagnosis-of-55l4gmspkb.pdf

High-throughput sequencing for the molecular diagnosis of Usher syndrome reveals 42 novel mutations and consolidates CEP250 as Usher-like disease causative.

Using natural language processing to extract clinically useful information from Chinese electronic medical records.

Ion Torrent Personal Genome Machine (PGM) technology is a mid-length read, low-cost and high-speed next-generation sequencing platform with a relatively high insertion and deletion (indel) error rate. A full systematic assessment of the effectiveness of various error correction algorithms in PGM viral datasets (e.g., hepatitis B virus (HBV)) has not been performed. We examined 19 quality-trimmed PGM datasets for the HBV reverse transcriptase (RT) region and found a total error rate of 0.48% ± 0.12%. Deletion errors were clearly present at the ends of homopolymer runs. Tests using both real and simulated data showed that the algorithms differed in their abilities to detect and correct errors and that the error rate and sequencing depth significantly affected the performance. Of the algorithms tested, Pollux showed a better overall performance but tended to over-correct ‘genuine’ substitution variants, whereas Fiona proved to be better at distinguishing these variants from sequencing errors. We found that the combined use of Pollux and Fiona gave the best results when error-correcting Ion Torrent PGM viral data.

/pdf/comparison-of-error-correction-algorithms-for-ion-torrent-4qyq1pnii6.pdf

Comparison of error correction algorithms for Ion Torrent PGM data: application to hepatitis B virus.

// Qi Pan 1 , Xianli Long 1 , Liting Song 1 , Dachun Zhao 2 , Xiaoyuan Li 3 , Dewei Li 4 , Min Li 5 , Jinxue Zhou 6 , Xia Tang 1 , Hong Ren 1 , Keyue Ding 1 1 Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Institute for Viral Hepatitis, Department of Infectious Diseases, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, P.R. China 2 Department of Pathology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, P.R. China 3 Department of Medical Oncology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, P.R. China 4 Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China 5 Department of Hepatobiliary Surgery, Suining Central Hospital, Suining, Sichuan Province, P. R. China 6 Department of Hepatobiliary Surgery, Henan Tumor Hospital, Zhenzhou, Henan Province, P.R. China Correspondence to: Keyue Ding, email: ding.keyue@cqmu.edu.cn Keywords: hepatocellular carcinoma, transcriptome sequencing, weighted gene co-expression network, hub gene Received: February 26, 2016      Accepted: May 05, 2016      Published: May 23, 2016 ABSTRACT Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and it remains a challenge to understand the genetic mechanisms underlying hepatocarcinogenesis. A global gene network of differential expression profiles in HCC has yet to be fully characterized. In the present study, we performed transcriptome sequencing (mRNA and lncRNA) in liver cancer and cirrhotic tissues of nine HCC patients. We identified differentially expressed genes (DEGs) and constructed a weighted gene co-expression network for the DEGs. In total, 755 DEGs (747 mRNA and eight lncRNA) were identified, and several co-expression modules were significantly associated with HCC clinical traits, including tumor location, tumor grade, and the α-fetoprotein (AFP) level. Of note, we identified 15 hub genes in the module associated with AFP level, and three ( SPX, AFP and ADGRE1 ) of four hub genes were validated in an independent HCC cohort ( n =78). Identification of hub genes for HCC clinical traits has implications for further understanding of the molecular genetic basis of HCC.

/pdf/transcriptome-sequencing-identified-hub-genes-for-3ue1p8hwwl.pdf

Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis

// Li Liang 1, * , Liting Song 1, * , Yi Yang 1 , Ling Tian 1 , Xiaoyuan Li 2 , Songfeng Wu 3 , Wenxun Huang 1 , Hong Ren 1 , Ni Tang 1 , Keyue Ding 1 1 Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Institute for Viral Hepatitis, Department of Infectious Diseases, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, 400010 PR China 2 Department of Medical Oncology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730 P.R. China 3 State Key Laboratory of Proteomics, National Protein Science Beijing Center, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, 102206 P.R. China * These authors contributed equally to this work Correspondence to: Ni Tang, email: nitang@cqmu.edu.cn Keyue Ding, email: ding.keyue@cqmu.edu.cn Keywords: personalized mutation-driver genes, multi-omics, validation, structure-function relationship, in vitro experiment Received: February 11, 2016      Accepted: May 08, 2016      Published: May 21, 2016 ABSTRACT Significant heterogeneity between different tumors prevents the discovery of cancer driver genes, especially in a patient-specific manner. We previously prioritized five personalized candidate mutation-driver genes in a hyper-mutated hepatocellular carcinoma patient using a multi-omics strategy. However, the roles of the prioritized driver genes and patient-specific mutations in hepatocarcinogenesis are unclear. We investigated the impact of the tumor-mutated allele on structure-function relationship of the encoded protein and assessed both loss- and gain-of-function of these genes and mutations on hepatoma cell behaviors in vitro . The prioritized mutation-driver genes act as tumor suppressor genes and inhibit cell proliferation and migration. In addition, the loss-of-function effect of the patient-specific mutations promoted cell proliferation and migration. Of note, the HNF1A S247T mutation significantly reduced the HNF1A transcriptional activity for hepatocyte nuclear factor 4 alpha ( HNF4A ) but did not disrupt nuclear localization of HNF1A. The results provide evidence for supporting the validity of our proposed multi-omics strategy, which supplies a new avenue for prioritizing mutation-drivers towards personalized cancer therapy.

/pdf/validation-of-a-multi-omics-strategy-for-prioritizing-8v9klqyyvx.pdf

Validation of a multi-omics strategy for prioritizing personalized candidate driver genes

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Using natural language processing to extract clinically useful information from Chinese electronic medical records.

Comparison of error correction algorithms for Ion Torrent PGM data: application to hepatitis B virus.

Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis

Validation of a multi-omics strategy for prioritizing personalized candidate driver genes