Andrian Yang

How Difficult Is Inference of Mammalian Causal Gene Regulatory Networks

Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on pieces of experimental genetic perturbation evidence from manually reading primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.

Falco: a quick and flexible single-cell RNA-seq processing framework on the cloud

Summary Single-cell RNA-seq (scRNA-seq) is increasingly used in a range of biomedical studies. Nonetheless, current RNA-seq analysis tools are not specifically designed to efficiently process scRNA-seq data due to their limited scalability. Here we introduce Falco, a cloud-based framework to enable paralellization of existing RNA-seq processing pipelines using big data technologies of Apache Hadoop and Apache Spark for performing massively parallel analysis of large scale transcriptomic data. Using two public scRNA-seq datasets and two popular RNA-seq alignment/feature quantification pipelines, we show that the same processing pipeline runs 2.6-145.4 times faster using Falco than running on a highly optimized standalone computer. Falco also allows users to utilize low-cost spot instances of Amazon Web Services, providing a ∼65% reduction in cost of analysis. Availability and Implementation Falco is available via a GNU General Public License at https://github.com/VCCRI/Falco/. Contact j.ho@victorchang.edu.au. Supplementary information Supplementary data are available at Bioinformatics online.

iSyTE 2.0: a database for expression-based gene discovery in the eye

Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (integrated Systems Tool for Eye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches.

A cloud-based framework for applying metamorphic testing to a bioinformatics pipeline

Testing of bioinformatics software often suffers from the oracle problem, especially when testing software that analyses human genome sequencing data. Metamorphic testing has been proposed to alleviate the oracle problem. Nonetheless, smaller research or clinical centres may be challenged by the complexity and resources required to implement a suitable metamorphic testing framework in practice. This paper presents a case study on how a cloud-based metamorphic testing framework can be applied to a widely used genomic sequencing pipeline, and discusses the future of implementing large-scale on-demand automated metamorphic testing using cloud-based resources.

Scalability and Validation of Big Data Bioinformatics Software.

This review examines two important aspects that are central to modern big data bioinformatics analysis – software scalability and validity. We argue that not only are the issues of scalability and validation common to all big data bioinformatics analyses, they can be tackled by conceptually related methodological approaches, namely divide-and-conquer (scalability) and multiple executions (validation). Scalability is defined as the ability for a program to scale based on workload. It has always been an important consideration when developing bioinformatics algorithms and programs. Nonetheless the surge of volume and variety of biological and biomedical data has posed new challenges. We discuss how modern cloud computing and big data programming frameworks such as MapReduce and Spark are being used to effectively implement divide-and-conquer in a distributed computing environment. Validation of software is another important issue in big data bioinformatics that is often ignored. Software validation is the process of determining whether the program under test fulfils the task for which it was designed. Determining the correctness of the computational output of big data bioinformatics software is especially difficult due to the large input space and complex algorithms involved. We discuss how state-of-the-art software testing techniques that are based on the idea of multiple executions, such as metamorphic testing, can be used to implement an effective bioinformatics quality assurance strategy. We hope this review will raise awareness of these critical issues in bioinformatics.

Light-focusing human micro-lenses generated from pluripotent stem cells model lens development and drug-induced cataract in vitro

ABSTRACT Cataracts cause vision loss and blindness by impairing the ability of the ocular lens to focus light onto the retina. Various cataract risk factors have been identified, including drug treatments, age, smoking and diabetes. However, the molecular events responsible for these different forms of cataract are ill-defined, and the advent of modern cataract surgery in the 1960s virtually eliminated access to human lenses for research. Here, we demonstrate large-scale production of light-focusing human micro-lenses from spheroidal masses of human lens epithelial cells purified from differentiating pluripotent stem cells. The purified lens cells and micro-lenses display similar morphology, cellular arrangement, mRNA expression and protein expression to human lens cells and lenses. Exposing the micro-lenses to the emergent cystic fibrosis drug Vx-770 reduces micro-lens transparency and focusing ability. These human micro-lenses provide a powerful and large-scale platform for defining molecular disease mechanisms caused by cataract risk factors, for anti-cataract drug screening and for clinically relevant toxicity assays. Highlighted Article: Using human pluripotent stem cells, robust and reliable methods are described for large-scale production of purified human lens epithelial cells, and for subsequent large-scale generation of clinically relevant, light-focusing micro-lenses.

Integrative analysis identifies co-dependent gene expression regulation of BRG1 and CHD7 at distal regulatory sites in embryonic stem cells

Motivation: DNA binding proteins such as chromatin remodellers, transcription factors (TFs), histone modifiers and co‐factors often bind cooperatively to activate or repress their target genes in a cell type‐specific manner. Nonetheless, the precise role of cooperative binding in defining cell‐type identity is still largely uncharacterized. Results: Here, we collected and analyzed 214 public datasets representing chromatin immunoprecipitation followed by sequencing (ChIP‐Seq) of 104 DNA binding proteins in embryonic stem cell (ESC) lines. We classified their binding sites into those proximal to gene promoters and those in distal regions, and developed a web resource called Proximal And Distal (PAD) clustering to identify their co‐localization at these respective regions. Using this extensive dataset, we discovered an extensive co‐localization of BRG1 and CHD7 at distal but not proximal regions. The comparison of co‐localization sites to those bound by either BRG1 or CHD7 alone showed an enrichment of ESC master TFs binding and active chromatin architecture at co‐localization sites. Most notably, our analysis reveals the co‐dependency of BRG1 and CHD7 at distal regions on regulating expression of their common target genes in ESC. This work sheds light on cooperative binding of TF binding proteins in regulating gene expression in ESC, and demonstrates the utility of integrative analysis of a manually curated compendium of genome‐wide protein binding profiles in our online resource PAD. Availability and Implementation: PAD is freely available at http://pad.victorchang.edu.au/ and its source code is available via an open source GPL 3.0 license at https://github.com/VCCRI/PAD/ Contact: pengyi.yang@sydney.edu.au or j.ho@victorchang.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

starmap: Immersive visualisation of single cell data using smartphone-enabled virtual reality

We report a new smartphone-enabled virtual reality (VR) program, starmap (https://vccri.github.io/starmap/), that enables immersive visualisation of single-cell data for hundreds of thousands of cells using a mobile-enabled web browser and low-cost VR head mount device.

Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

Motivation Read alignment is an important step in RNA-seq analysis as the result of alignment forms the basis for further downstream analyses. However, recent studies have shown that published alignment tools have variable mapping sensitivity and do not necessarily align reads which should have been aligned, a problem we termed as the false-negative non-alignment problem. Results We have developed Scavenger, a pipeline for recovering unaligned reads using a novel mechanism which utilises information from aligned reads. Scavenger performs recovery of unaligned reads by re-aligning unaligned reads against a putative location derived from aligned reads with sequence similarity against unaligned reads. We show that Scavenger can successfully recover unaligned reads in both simulated and real RNA-seq datasets, including single-cell RNA-seq data. The reads recovered contain more genetic variants compared to previously aligned reads, indicating that divergence between personal and reference genomes plays a role in the false-negative non-alignment problem. We also explored the impact of read recovery on downstream analyses, in particular gene expression analysis, and showed that Scavenger is able to both recover genes which were previously non-expressed and also increase gene expression, with lowly expressed genes having the most impact from the addition of recovered reads. We also found that the majority of genes with >1 fold change in expression after recovery are categorised as pseudogenes, indicating that pseudogene expression can be affected by the false-negative non-alignment problem. Scavenger helps to solve the false-negative non-alignment problem through recovery of unaligned reads using information from previously aligned reads. Availability Scavenger is available via an open source license in https://github.com/VCCRI/Scavenger/ Contact j.ho@victorchang.edu.au

PBrowse: a web-based platform for real-time collaborative exploration of genomic data.

Abstract Genome browsers are widely used for individually exploring various types of genomic data. A handful of genome browsers offer limited tools for collaboration among multiple users. Here, we describe PBrowse, an integrated real-time collaborative genome browser that enables multiple users to simultaneously view and access genomic data, thereby harnessing the wisdom of the crowd. PBrowse is based on the Dalliance genome browser and has a re-designed user and data management system with novel collaborative functionalities, including real-time collaborative view, track comment and an integrated group chat feature. Through the Distributed Annotation Server protocol, PBrowse can easily access a wide range of publicly available genomic data, such as the ENCODE data sets. We argue that PBrowse represents a paradigm shift from using a genome browser as a static data visualization tool to a platform that enables real-time human–human interaction and knowledge exchange in a collaborative setting. PBrowse is available at http://pbrowse.victorchang.edu.au, and its source code is available via an open source BSD 3 license at http://github.com/VCCRI/PBrowse.