Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization

© 2015 Elsevier Ltd. All rights reserved. Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request

Integration of multiple data sources to prioritize candidate genes using discounted rating system

Background: Identifying disease gene from a list of candidate genes is an important task in bioinformatics. The main strategy is to prioritize candidate genes based on their similarity to known disease genes. Most of existing gene prioritization methods access only one genomic data source, which is noisy and incomplete. Thus, there is a need for the integration of multiple data sources containing different information. Results: In this paper, we proposed a combination strategy, called discounted rating system (DRS). We performed leave one out cross validation to compare it with N-dimensional order statistics (NDOS) used in Endeavour. Results showed that the AUC (Area Under the Curve) values achieved by DRS were comparable with NDOS on most of the disease families. But DRS worked much faster than NDOS, especially when the number of data sources increases. When there are 100 candidate genes and 20 data sources, DRS works more than 180 times faster than NDOS. In the framework of DRS, we give different weights for different data sources. The weighted DRS achieved significantly higher AUC values than NDOS. Conclusion: The proposed DRS algorithm is a powerful and effective framework for candidate gene prioritization. If weights of different data sources are proper given, the DRS algorithm will perform better

GeneFishing to reconstruct context specific portraits of biological processes.

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications

Gene prioritization and clustering by multi-view text mining

<p>Abstract</p> <p>Background</p> <p>Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model.</p> <p>Results</p> <p>We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods.</p> <p>Conclusions</p> <p>In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification.</p

MetaRanker 2.0: a web server for prioritization of genetic variation data

MetaRanker 2.0 is a web server for prioritization of common and rare frequency genetic variation data. Based on heterogeneous data sets including genetic association data, protein–protein interactions, large-scale text-mining data, copy number variation data and gene expression experiments, MetaRanker 2.0 prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 is made freely available at www.cbs.dtu.dk/services/MetaRanker-2.0

RNA-Seq 데이터에서 유전자의 랭킹을 책정하기 위한 네트워크 접근법을 사용한 정보 과학 시스템

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 생물정보학전공,2019. 8. 김선.RNA-seq 기술은 게놈 규모의 전사체를 고해상도로 분석 가능하게 만들었으나, 일반적으로 전사체 데이터에서 나타나는 유전자의 수는 많기 때문에 추가 분석 없이 연구 목표와 관련된 유전자를 식별하기가 어렵다. 따라서 전사체 데이터 분석은 종종 생물 네트워크, 유전자 정보 데이터베이스, 문헌 정보 같이 서로 다른 자원을 활용하여 분석하게 된다. 그러나 자원들 간의 관계는 이질적인 부분이 존재하여 서로 직접적으로 연결하여 해석하기 어려우며 어떠한 유전자가 실험 목표와 관련이 있는지를 구체적으로 이해하기 힘들다. 따라서 특정 연구 목표와 관련 있는 핵심 유전자를 효과적으로 결정하고 설명하기 위해서는 이러한 이질적인 자원을 효과적으로 통합할 강력한 전산 기법이 필요하다. 본 논문에서는 네트워크 기반 접근법을 사용하여 전사체 데이터를 분석하고 실험 목표와 관련 있는 유전자를 찾기 위한 세 가지 생물 정보 시스템을 개발했다. 첫 번째 연구는 RNA-Seq 데이터의 특성을 활용하여 샘플 수가 적은 유전자 녹아웃 (KO) 마우스 실험에서 중요한 유전자를 찾기 위한 정보학 시스템을 개발하였다. 이 시스템은 유전자 조절 네트워크 (GRN)와 패스웨이 정보를 사용하여 유의함이 적은 Differentially Expressed Gene (DEG)를 제거하고 단일 염기 변이 (SNV) 정보를 사용하여 샘플 간 유전적 차이로 인해 다를 수 있는 유전자를 제거한다. 이 연구는 네트워크와 SNV 정보의 통합을 통해서 후보 유전자의 수를 유의미하게 줄일 수 있음을 보여주었다. 두 번째 연구는 사용자의 실험 목표를 반영할 수 있는 유전자 랭킹 시스템인 CLIP-GENE을 개발하였다. CLIP-GENE은 쥐의 전사인자 KO 실험에서 유전자를 랭킹하기 위한 통합 분석 웹 서비스이다. CLIP-GENE은 후보 유전자에 랭킹을 부여하기 위해 GRN, SNV 정보를 이용하여 샘플 개체 간의 차이가 있고 덜 유의미한 후보 유전자를 제거하고 텍스트 마이닝 기술과 단백질-단백질 상호작용 네트워크 정보를 이용하여 사용자의 실험 목표와 관련된 유전자를 랭킹한다. 마지막 연구는 벤 다이어그램을 사용하여 다수의 RNA-Seq 실험을 비교분석 할수 있는 정보 시스템을 개발하였다. RNA-Seq 실험은 일반적으로 비교 및 대조군의 샘플을 비교하여 DEG를 생성하고 벤 다이어그램을 통하여 샘플 간의 차이를 분석한다. 그러나 벤 다이어그램 상에서의 각 영역은 다양한 비율의 DEG를 포함하고 있으며, 특정 영역의 DEG는 서로 다른 비교군(혹은 대조군)에 의한 DEG이기에 단순히 유전자 목록 간의 차이를 비교하는 것은 적절하지 못하다. 이러한 문제를 해결하기 위해 벤 다이어그램과 네트워크 전파(Network Propagation)를 사용한 통합 분석 프레임워크인 Venn-diaNet이 개발했다. Venn-diaNet은 다수의 DEG 목록이 있는 실험의 유전자를 랭킹할 수 있는 정보 시스템이다. 우리는 Venn-diaNet이 서로 다른 조건에서 생물학적 실험을 비교함으로써 원본 논문에 보고된 연구 결과를 재현 할 수 있음을 보여주었다. 정리하면 이 논문은 전사체 데이터로부터 유전자를 랭킹할 수있는 정보 시스템을 개발하기 위해 네트워크 기반 분석법을 다양한 자원들과 결합하였으며, 다른 연구자의 편리한 사용 경험을 위해 친화적인 UI를 가진 웹도구 또는 소프트웨어 패키지로 제작 및 배포하였다.Transcriptomic analysis, the measurement of transcripts on the genome scale, is now routinely performed in high resolution. Since the number of genes obtained in the transcriptome data is usually large, it is difficult for researchers to identify genes that are relevant to their research goals, without additional analysis. Analysis of transcriptome data is often performed utilizing heterogeneous resources such as biological networks, annotated gene information, and published literature. However, the relationship among heterogeneous resources is often too complicated to decipher which genes are relevant to the experimental design. Therefore, powerful computational methods should be coupled with these heterogeneous resources in order to effectively determine and illustrate key genes that are relevant to specific research goals. In my doctoral study, I have developed three bioinformatics systems that use network approaches to analyze transcriptome data and rank genes that are relevant to the experimental design. The first study was conducted to develop a bioinformatics system that could be used to analyze RNA-Seq data of gene knockout (KO) mice, where the sample number is small. In this case, the main objectives were to investigate how the KO gene affects the expression of other genes and identify the key genes that contribute significantly to the phenotypic difference. To address these questions, I developed a gene prioritization system that utilizes the characteristics of RNA-Seq data. The system prioritizes genes by removing the less informative differentially expressed genes (DEGs) using gene regulatory network (GRN) and biological pathways. Next, it filters out genes that might be different due to genetic differences between samples using single nucleotide variant (SNV) information. Consequently, this study demonstrated that the integration of networks and SNV information was able to increase the performance of gene prioritization. The second study was conducted to develop a gene prioritization system that allows the user to specify the context of the experiment. This study was inspired by the fact that the currently available analysis methods for transcriptome data do not fully consider the experimental design of gene KO studies. Therefore, I envisaged that users would prefer an analysis method that took into consideration the characteristics of the KO experiments and could be guided by the context of the researcher who has designed and performed the biological experiment. Therefore, I developed CLIP-GENE, a web service of the condition-specific context-laid integrative analysis for prioritizing genes in mouse TF KO experiments. CLIP-GENE prioritizes genes of KO experiments by removing the less informative DEGs using GRN, discards genes that might have sample variance, using SNV information, and ranks genes that are related to the user's context using the text-mining technique, as well as considering the shortest path of protein-protein interaction (PPI) from the KO gene to the target genes. The last study was conducted to develop an informative system that could be used to compare multiple RNA-Seq experiments using Venn diagrams. In general, RNA-Seq experiments are performed to compare samples between control and treated groups, producing a set of DEGs. Each region in a Venn diagram (a subset of DEGs) generally contains a large number of genes that could complicate the determination of the important and relevant genes. Moreover, simply comparing the list of DEGs from different experiments could be misleading because some of the DEG lists may have been measured using different controls. To address these issues, Venn-diaNet was developed, an analysis framework that integrates Venn diagram and network propagation to prioritize genes for experiments that have multiple DEG lists. We demonstrated that Venn-diaNet was able to reproduce research findings reported in the original papers by comparing two, three, and eight biological experiments measured in different conditions. I believe that Venn-diaNet can be very useful for researchers to determine genes for their follow-up studies. In summary, my doctoral study aimed to develop computational tools that can prioritize genes from transcriptome data. To achieve this goal, I combined network approaches with multiple heterogeneous resources in a single computational environment. All three informatics systems are deployed as software packages or web tools to support convenient access to researchers, eliminating the need for installation or learning any additional software packages.Abstract Chapter 1 Introduction 1.1 Challenges of analyzing RNA-seq data 1.1.1 Excessive amount of databases and analysis methods 1.1.2 Knowledge bias that prioritizes less relevant genes 1.1.3 Complicated experiment designs 1.2 My approach to address the challenges for the analysis of RNA-seq data 1.3 Background 1.3.1 Differentially expressed genes 1.3.2 Gene prioritization 1.4 Outline of the thesis Chapter 2 A filtering strategy that combines GRN, SNV information to enhances the gene prioritization in mouse KO studies with small number of samples 2.1 Background 2.2 Methods 2.2.1 First filter: DEG 2.2.2 Second filter: GRN 2.2.3 Third filter: Biological Pathway 2.2.4 Final filter: SNV 2.3 Results and Discussion 2.4 Discussion Chapter 3 An integration of data-fusion and text-mining strategy to prioritize context-laid genes in mouse TF KO experiments 3.1 Background 3.2 Methods 3.2.1 Selection of initial candidate genes 3.2.2 Prioritizing genes with the user context and PPI 3.3 Results and Discussion 3.3.1 Performance with the best context 3.3.2 Performance with the worst context 3.4 Discussion Chapter 4 Integrating Venn diagram to the network-based strategy for comparing multiple biological experiments 4.1 Background 4.2 Methods 4.2.1 Taking input data 4.2.2 Generating Venn diagram of DEG sets 4.2.3 Network propagation and gene ranking 4.3 Results and Discussion 4.3.1 Venn-diaNet for two experiments 4.3.2 Venn-diaNet for three experiments 4.3.3 Venn-diaNet for eight experiments 4.3.4Venn-diaNet performance with different PPI network 4.4 Discussion Chapter 5 Conclusion Bibliography 초록Docto