Bingqing Xie

Lynx: a database and knowledge extraction engine for integrative medicine

We have developed Lynx (http://lynx.ci.uchicago.edu)—a web-based database and a knowledge extraction engine, supporting annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Its underlying knowledge base (LynxKB) integrates various classes of information from >35 public databases and private collections, as well as manually curated data from our group and collaborators. Lynx provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization to assist the user in extracting meaningful knowledge from LynxKB and experimental data, whereas its service-oriented architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

Disease Gene Prioritization Using Network and Feature

Identifying high-confidence candidate genes that are causative for disease phenotypes, from the large lists of variations produced by high-throughput genomics, can be both time-consuming and costly. The development of novel computational approaches, utilizing existing biological knowledge for the prioritization of such candidate genes, can improve the efficiency and accuracy of the biomedical data analysis. It can also reduce the cost of such studies by avoiding experimental validations of irrelevant candidates. In this study, we address this challenge by proposing a novel gene prioritization approach that ranks promising candidate genes that are likely to be involved in a disease or phenotype under study. This algorithm is based on the modified conditional random field (CRF) model that simultaneously makes use of both gene annotations and gene interactions, while preserving their original representation. We validated our approach on two independent disease benchmark studies by ranking candidate genes using network and feature information. Our results showed both high area under the curve (AUC) value (0.86), and more importantly high partial AUC (pAUC) value (0.1296), and revealed higher accuracy and precision at the top predictions as compared with other well-performed gene prioritization tools, such as Endeavour (AUC-0.82, pAUC-0.083) and PINTA (AUC-0.76, pAUC-0.066). We were able to detect more target genes (9/18/19/27) on top positions (1/5/10/20) compared to Endeavour (3/11/14/23) and PINTA (6/10/13/18). To demonstrate its usability, we applied our method to a case study for the prediction of molecular mechanisms contributing to intellectual disability and autism. Our approach was able to correctly recover genes related to both disorders and provide suggestions for possible additional candidates based on their rankings and functional annotations.

Prediction of candidate genes for neuropsychiatric disorders using feature-based enrichment

The progress in understanding of molecular mechanisms underlying common heritable disorders (e.g. autism, schizophrenia, diabetes) depends on the availability of new bioinformatics approaches for identification of their characteristic genetic variations and associated multidimensional patterns of inheritance. High-throughput genome-wide studies (e.g. sequencing, gene expression profiling) result in hundreds of potential candidate genes. Prioritizing these genes and finding the best candidates contributing to a disease phenotype is one of the most important problems of genomics. We present an approach for prioritization of disease candidate genes using Support Vector Machine (SVM) and ontology associations. Features are extracted from both hierarchical and non-hierarchical ontology space (e.g user defined customized ontologies, Gene Ontology(GO) ). We select a subset of features according to enrichment scores in a training set of genes and use these to train a classifier using SVM. Ranking of the genes in the query set (e.g. the results of gene expression analysis) is based on a distance from the decision boundary to data points. Results obtained using the proposed approach to the analysis of several neurological disorders (autism, mental retardation, and agenesis of corpus callosum) are presented.

An integrative computational approach for prioritization of genomic variants

An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.

DEEPre: sequence-based enzyme EC number prediction by deep learning

Motivation Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency‐caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number. Results We propose an end‐to‐end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manually crafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross‐fold validation experiments conducted on two large‐scale datasets show that DEEPre improves the prediction performance over the previous state‐of‐the‐art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low‐homology dataset. Two case studies demonstrate DEEPres ability to capture the functional difference of enzyme isoforms. Availability and implementation The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre. Contact xin.gao@kaust.edu.sa Supplementary information Supplementary data are available at Bioinformatics online.

High-Throughput Translational Medicine: Challenges and Solutions

Recent technological advances in genomics now allow producing biological data at unprecedented tera- and petabyte scales. Yet, the extraction of useful knowledge from this voluminous data presents a significant challenge to a scientific community. Efficient mining of vast and complex data sets for the needs of biomedical research critically depends on seamless integration of clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships accumulated in a plethora of publicly available databases. Furthermore, such experimental data should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining. Translational projects require sophisticated approaches that coordinate and perform various analytical steps involved in the extraction of useful knowledge from accumulated clinical and experimental data in an orderly semiautomated manner. It presents a number of challenges such as (1) high-throughput data management involving data transfer, data storage, and access control; (2) scalable computational infrastructure; and (3) analysis of large-scale multidimensional data for the extraction of actionable knowledge.We present a scalable computational platform based on crosscutting requirements from multiple scientific groups for data integration, management, and analysis. The goal of this integrated platform is to address the challenges and to support the end-to-end analytical needs of various translational projects.

The functional domains for Bax∆2 aggregate-mediated caspase 8-dependent cell death

ABSTRACT Bax&Dgr;2 is a functional pro‐apoptotic Bax isoform having alterations in its N‐terminus, but sharing the rest of its sequence with Bax&agr;. Bax&Dgr;2 is unable to target mitochondria due to the loss of helix &agr;1. Instead, it forms cytosolic aggregates and activates caspase 8. However, the functional domain(s) responsible for Bax&Dgr;2 behavior have remained elusive. Here we show that disruption of helix &agr;1 makes Bax&agr; mimic the behavior of Bax&Dgr;2. However, the other alterations in the Bax&Dgr;2 N‐terminus have no significant impact on aggregation or cell death. We found that the hallmark BH3 domain is necessary but not sufficient for aggregation‐mediated cell death. We also noted that the core region shared by Bax&agr; and Bax&Dgr;2 is required for the formation of large aggregates, which is essential for Bax&Dgr;2 cytotoxicity. However, aggregation by itself is unable to trigger cell death without the C‐terminus. Interestingly, the C‐terminal helical conformation, not its primary sequence, appears to be critical for caspase 8 recruitment and activation. As Bax&Dgr;2 shares core and C‐terminal sequences with most Bax isoforms, our results not only reveal a structural basis for Bax&Dgr;2‐induced cell death, but also imply an intrinsic potential for aggregate‐mediated caspase 8‐dependent cell death in other Bax family members. HIGHLIGHTSDisruption of helix &agr;1 makes Bax&agr; mimic Bax&Dgr;2 behavior.Bax&Dgr;2 aggregation is essential but not sufficient for cytotoxicity.The C‐terminus is critical for caspase 8‐dependent cell death.The C‐terminal helix &agr;9 structure is the key for caspase 8 recruitment.The Bax family may have an intrinsic capability for caspase 8 activation.

Lynx web services for annotations and systems analysis of multi-gene disorders

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform.

Lynx: a knowledge base and an analytical workbench for integrative medicine

Lynx (http://lynx.ci.uchicago.edu) is a web-based database and a knowledge extraction engine. It supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

A machine learning based lecture video segmentation and indexing algorithm

Video segmentation and indexing are important steps in multi-media document understanding and information retrieval. This paper presents a novel machine learning based approach for automatic structuring and indexing of lecture videos. By indexing video content, we can support both topic indexing and semantic querying of multimedia documents. In this paper, our proposed approach extracts features from video images and then uses these features to construct a model to label video frames. Using this model, we are able to segment and indexing videos with accuracy of 95% on our test collection.