Research

Computational Biology // Massey University

Overview

Generally speaking, we work on biological and biomedical problems in a data-driven manner using computational and statistical techniques to discover new knowledge. Some areas of interest to us are exemplified below. However, this is not an exhaustive list and we are interested in and work on other aspects within the field of biology. Feel free to browse through our publications and tools to learn more about current and past research interests.


Proximal Gene Regulation

We make use of transcriptomics data stemming from technologies like RNA-seq or CAGE to understand the how genes are expressed and regulated on the transcriptional level. For example, through CAGE (see Figure) we study 5' transcription starts sites (promoters) of RNAs and explore their transcriptional output (expression levels) in different conditions [1]. Also, we investigate if particular genes get transcribed from different promoters in particular biological conditions (alternative promoter usage), which might be related to distinct regulatory features at promoters and/or might impact splice variants of genes. For example, we are investigating the transcriptional responses of mouse macrophages to stimulation and infection [2, 3, 4]. We are also studying the use of transcriptomics profiles for cancer (sub-)type classification, as well as investigating the relationships between host transcriptomes and bacterial communities.


Distal Gene Regulation

Distal cis-regulatory control can be facilitated through enhancers (see Figure). Chromatin forms loops to bring enhancer regions physically close to a gene's promoter region. Enhancers are known to be tissue-specific and different enhancers may drive regulation in different biological conditions. Enhancer loci show expression of short enhancer RNAs (eRNA) who's expression correlates with the gene expression coming from the promoter. For example, we are studying the influence of enhancers on the regulation of genes during mouse macrophage activation as well as tuberculosis infection. Enhancer chromosomal regions are characterised by particular combinations of histone modifications, which we study through the analyses of ChIP-seq datasets. As enhancer-promoter chromosomal regions interact with each other, we also investigate data stemming from chromatin conformation capture approaches, e.g. Hi-C, 4C, etc.


Regulatory Network Dynamics

We are interested in understanding the regulatory network dynamics in different biological conditions. Are certain ncRNAs more important in a certain condition in driving the observed biological phenotypes? Similarly, are certain transcription factors (TFs) more important in regulating the expression of such ncRNAs? We establish regulatory connections through mining public available regulatory datasets (e.g. TF ChIP-seq data, ncRNA-protein or protein-protein interaction data) or through co-expression networks (e.g. by using small RNA-seq, mRNA-seq, or CAGE data). We use network and graph theoretical as well as machine learning approaches to deduce the importance of entities within the networks of biological conditions to identify sub-populations that e.g. exert the most influence on the network or are dysregulated [5, 6, 7, 8, 9, 10].


Variations / SNPs / eQTLs

We study how DNA variations (e.g. SNPs) influence the regulation of gene expression. For example, recently many SNPs have been identified that are located within non-coding areas of the genome but have been shown to influence the expression of genes (e.g. eQTL SNPs). We are interested in understanding how those SNPs facilitate the observed change in expression of the associated gene(s), e.g. are those SNPs located in regions that are being bound by TFs, e.g. promoters [11] or enhancers?


Data Mining and Visualisation

Another area of research involves the identification, extraction, storage, integration, and representation of biological/biomedical data. We are interested in data mining and machine learning techniques to harvest, mine and integrate biological data from various heterogenous sources. We study and use database and web technologies to create knowledge-bases and web applications that help researchers find useful information and/or help analyse their data. Examples of projects in this domain include a customised knowledge-base to study the influence of non-coding regulatory RNA on immunological processes [12], a database that helps in identifying regulatory variations in promoter regions of microRNAs [11], as well as a database that facilitates the identification of transcription co-factors and TF interactions [9, 10].

References

  1. A promoter-level mammalian expression atlas. Nature, 2014, 507:462–470
  2. Redefining the transcriptional regulatory dynamics of classically and alternatively activated macrophages by deepCAGE transcriptomics. Nucleic Acids Research, 2015, doi: 10.1093/nar/gkv646
  3. Batf2/Irf1 Induces Inflammatory Responses in Classically Activated Macrophages, Lipopolysaccharides, and Mycobacterial Infection. The Journal of Immunology, 2015, doi:10.4049/jimmunol.1402521
  4. IL-4Rα-activated alternative macrophages are not decisive for Mycobacterium tuberculosis pathology and bacterial burden in mice. PLoS ONE, 2015, 19;10(3):e0121070.
  5. An atlas of combinatorial transcriptional regulation in mouse and man. Cell, 2010,140(5):744-52
  6. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nature Genetics, 2009, 41(5):553-62
  7. Network analysis of microRNAs and their regulation in human ovarian cancer. BMC Syst Biol. 2011,5:183
  8. Deciphering the transcriptional circuitry of microRNA genes expressed during human monocytic differentiation. BMC Genomics, 2009 10:595
  9. TcoF-DB: Dragon Database for Human Transcription Co-Factors and Transcription Factor Interacting Proteins. Nucleic Acids Research, 2011, doi:10.1093/nar/gkq945
  10. TcoF-DB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions. Nucleic Acids Research, 2016, doi: 10.1093/nar/gkw1007
  11. dPORE-miRNA: Polymorphic Regulation of MicroRNA Genes. PLoS ONE, 2011, 6(2):e16657
  12. IRNdb: The database of immunologically relevant non-coding RNAs. Database, 2016

Contact

Dr. Sebastian Schmeier
Research Group Leader
Senior Lecturer in Bioinformatics/Genomics

Massey University, INMS
Auckland, New Zealand
+64 9 414 0800 (ext: 43538)

Publications // latest

Genome-wide identification and analysis of transcribed enhancers during macrophage polarization. bioRxiv preprint

Distinct gut microbiome patterns associate with consensus molecular subtypes of colorectal cancer. bioRxiv preprint

Consensus molecular classification of colorectal cancer and association with the colonic microbiota. Diseases of the Colon & Rectum, 2017, 60(6):E90-E91 (In proceedings: Annual Meeting of the American Society of Colon and Rectal Surgeons)

Workshops // upcoming

[ Nov 2017 ] Software Carpentry Workshop, Massey University Auckland

Tweets // latest