Bioinfo Labs  |   CEPCEB  |   IIGB  |   UC Riverside

Thomas Girke

Professor of Bioinformatics
Institute for Integrative Genome Biology (IIGB)
Department of Botany and Plant Sciences
1207F Genomics Building
University of California, Riverside, CA 92521
Phone/Cell/Text: (951) 732-7072


Ph.D., University Hamburg, Germany
Postdoctorate, Michigan State University


Research Interests

My research focuses on the development of computational data analysis methods for genome biology and small molecule discovery. This includes discovery-oriented data mining projects, as well as algorithm and software development projects for data types from a variety of high-throughput technologies such as next generation sequencing (NGS), genome-wide profiling approaches and chemical genomics. As part of the multidisciplinary nature of my field, I frequently collaborate with experimental scientists on data analysis projects of complex biological networks. Another important activity is the development of integrated data analysis systems for the open source software projects R and Bioconductor. The following gives a short summary of a few selected projects in my group.

Tools for Analyzing Next Generation Sequence Data 

Reference-Assisted Transcriptome Assembly 
Owing to the complexity and often incomplete representation of transcripts in RNA-Seq libraries, the assembly of high-quality transcriptomes can be extremely challenging. To improve this, my group is developing algorithms for guiding these assemblies with genomic sequences of related organisms as well as reducing the complexity in NGS libraries. The software tools we have published for this purpose so far include SEED (Bao et al., 2011) and BRANCH (Bao et al., 2013). BRANCH is a reference assisted post-processing method for enhancing de novo transcriptome assemblies (Figure 1). It can be used in combination with most de novo transcriptome assembly software tools. The assembly improvements are achieved with help from partial or complete genomic sequence information. They can be obtained by sequencing and assembling a genomic DNA sample in addition to the RNA samples required for a transcriptome assembly project. This approach is practical because it requires only preliminary genome assembly results in form of contigs. Nowadays, the latter can be generated with very reasonable cost and time investments. In case the genome sequence of a closely related organism is available, one can skip the genome assembly step and use the related gene sequences instead. This type of reference assisted assembly approach provides many attractive opportunities for improving de novo NGS assemblies in the future by making use of the rapidly growing number of reference genome information available to us.

Figure 1: Outline of BRANCH algorithm published in Bao et al., 2013(a) Read alignments against preassembled transcripts 
and closely related genomic reference. (b) Junction graph based on this alignment result. (c) Assembly of extended transcripts.

Modeling Gene Expression Networks from RNA-Seq and ChIP-Seq Data
As part of several collaborative research projects, my group has developed a variety of data analysis pipelines for profiling data from next generation sequencing projects (e.g. RNA-Seq and ChIP-Seq), microarray experiments and high-throughput small molecule screens. Most of the data analysis resources developed by these projects are described in the associated online manuals for next generation data analysis. Recent research publications of these projects include Yang et al., 2013, Zou et al., 2013, Yadav et al., 2013, Yadav et al., 2011, Mustroph et al., 2009, etc.  

Software Resources for Small Molecule Discovery and Chemical Genomics

Software tools for modeling the similarities among drug-like small molecules and high-throughput screening data are important for many applications in drug discovery and chemical genomics. In this area we are working on the development of the ChemmineR environment (Cao et al., 2008, Backman et al., 2011). This modular software infrastructure consists currently of five R/Bioconductor packages along with a user-friendly web interface, named ChemMine Tools that is intended for non-expert users (Figures 2-3). The integration of cheminformatic tools with the R programming environment has many advantages for small molecule discovery, such as easy access to a wide spectrum of statistical methods, machine learning algorithms and graphic utilities. Currently, the ChemmineR toolkit provides utilities for processing large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries and screening results with a wide spectrum of algorithms. More recently, we have developed for this infrastructure the fmcsR algorithms which is the first mismatch tolerant maximum common substructure search tool in the field (Wang et al., 2013). In our comparisons with related structure similarity search tools, fmcsR showed the best virtual screening (VS) performance


Figure 2: ChemmineR small molecule modeling environment with its add-on packages and selected functionalities.

Figure 3: Selectivity Analysis with ChemmineR and bioassayR 

Functional Annotation of Gene and Protein Sequences

Computational methods for characterizing the functions of protein sequences play an important role in the discovery of novel molecular, biochemical and regulatory activities. To facilitate this process, we have developed the sub-HMM algorithm that extends the application spectrum of profile HMMs to motif discovery and active site prediction in protein sequences (Horan et al. 2010). Its most interesting utility is the identification of the functionally relevant residues in proteins of known and unknown function (Figure 4). Additionally, sub-HMMs can be used for highly localized sequence similarity searches that focus on shorter conserved features rather than entire domains or global similarities. As part of this study we have predicted a comprehensive set of putative active sites for all protein families available in the Pfam database which has become a valuable knowledge resource for characterizing protein functions in the future.

Figure 4: Illustration of the sub-HMM extraction process from conserved protein domains, here Pfam desaturase domain (PF00487).

Open Positions

Postdoctoral position in comparative genomics - filled
This position requires several years of experience in computational biology, statistics, database design and data mining with strong publications records in several of these areas. The candidate should be proficient in at least one of the common programming languages that are used in bioinformatics: C, Python, Java, Perl or R. Experience with web and database programming is also beneficial. To apply for this position, please email your CV and a statement of research interests to

Postdoctoral position in cheminformatics - filled
This position requires experience in computational chemistry, drug-informatics, QSAR/phramacophore modeling and data mining with strong publications records in several of these areas. The candidate should be proficient in at least one of the common programming languages that are used in cheminformatics: C, Python, Java, Perl or R. Experience with web and database programming is also beneficial. To apply for this position, please email your CV and a statement of research interests to

Undergraduate research positions filled
Several postions are available for undergraduate students who are interested in participating in challenging database and software development projects for various research projects in bioinformatics and cheminformatics. The required skills are experience with open-source databases (MySQL/PostgreSQL), application and web programming with Python, Perl and/or Java. The minimum time commitment is 15-20h per week for a period of at least 18 months. Full-time employment during the summer break is possible. To apply for these positions, please email your CV with a detailed outline of your computational skills to

Subpages (2): Lab Seminars Publications