My group continued to work on computational methods to study the dynamics of biological networks, impact of genetic variations and structural variation on gene expression, organismal phenotype in the context of complex diseases (with emphasis on studies related to cancer and its heterogeneity) , analysis of new high throughput experimental data, and developing methods for analysis of HT-SELEX data. We continue to develop a comprehensive software package, AptaTools, for analysis of HT-SELEX data. Revolutionizing the traditional Systematic Evolution of Ligands by EXponential Enrichment (SELEX), High-Throughput SELEX (HT-SELEX) has become the method of choice for the identification of aptamers - synthetic, single-stranded (ribo)nucleic molecules that bind to a given molecular target. In contrast to traditional SELEX, in which only data from the last selection cycle is obtained, HT-SELEX allows for sequencing of every round (including control cycles) which are then jointly analyzed and interpreted. However, given the vast amount of data generated by this technology, purely text-based tools to visualize and manipulate aptamers have proven impractical and time consuming for many researchers in this field.
Aimi ng at providing a comprehensive HT-SELEX data-analysis software. AptaTools is being designed with computational speed in mind and capable of simultaneously processing many selection cycles due to its parallel processing capabilities. Our most recent addition to the package is AptaMuta novel technique allowing for the identification of polymerase errors conferring an improved binding affinity relative to the parent sequence (1). We also designed A RESTful interface for secure database communication. There is an increasing base of users of AptaTool software. Some of these studies are in a collaboration with our group (2) some are carried independently. We also continued to work on methods to delineate genetic underpinnings cancer. Utilizing the strength of our group in designing new algorithmic methods, our primary interest was in developing on systems biology, pathways based approaches. Our most recent studies focused on utilizing the property of mutual exclusivity of cancer drivers in the context of network based approaches. We introduced a classification of mutual exclusivity into three basic classes: within tissue type exclusivity, across tissue type exclusivity and between tissue type exclusivity. We then combined across-cancer mutual exclusivity with interactions data to uncover pan-cancer dysregulated pathways. Our new method, Mutual Exclusivity Module Cover (MEMCover) not only identified previously known Pan-Cancer dysregulated subnetworks but also novel subnetworks whose across cancer role has not been appreciated well before. In addition, we demonstrate the existence of mutual exclusivity hubs, putatively corresponding to cancer drivers with strong growth advantages. Finally, we showed that while mutually exclusive pairs within or across cancer types are predominantly functionally interacting, the pairs in between cancer mutual exclusivity class are more often disconnected in functional networks (3). We also continued the collaboration with Brian Oliver's group on gene regulation in Drosophila (4). In particular, we undertook genome-wide analyses to identify DSX targets using in vivo occupancy, binding site prediction, and evolutionary conservation. We find that DSX(F) and DSX(M) bind thousands of the same targets in multiple tissues in both sexes, yet these targets have sex- and tissue-specific functions. Interestingly, DSX targets show considerable overlap with targets identified for mouse DMRT1. DSX targets include transcription factors and signaling pathway components providing for direct and indirect regulation of sex-biased expression (5). In addition, in collaboration with Artavanis-Tsakonas group we studied other aspect on transcription in this model organism. Specifically, TFs act through combinatorial interactions with other TFs, cofactors, and chromatin-remodeling proteins. We defined protein-protein interactions using a coaffinity purification/mass spectrometry method and study 459 Drosophila melanogaster transcription-related factors, representing approximately half of the established catalog of TFs. We probed this network in vivo, demonstrating functional interactions for many interacting proteins, and test the predictive value of our data set (6). Continuing a successful collaboration with David Levens (7,8) we continue our work on the impact of transcription induced supercoiling on gene regulation and formation of non-B-DNA structures . Focusing on more computational analysis, we leveraged 1000 human genome project analyzed the impact of nucleotide variations. We utilized genomic variants and expression quantitative trait loci (eQTL) data to analyze genome-wide variation propensities of potential non-B DNA regions and their relation to gene expression. Independent of genomic location, these regions were enriched in nucleotide variants. Our results are consistent with previously observed mutagenic properties of these regions and counter a previous study concluding that G-quadruplex regions have a reduced frequency of variants. While such mutagenicity might undermine functionality of these elements, we identified in potential non-B DNA regions a signature of negative selection. Yet, we found a depletion of eQTL-associated variants in potential non-B DNA regions, opposite to what might be expected from their proposed regulatory role. However, we also observed that genes downstream of potential non-B DNA regions showed higher expression variation between individuals. This coupling between mutagenicity and tolerance for expression variability of downstream genes may be a result of evolutionary adaptation, which allows reconciling mutagenicity of non-B DNA structures with their location in functionally important regions and their potential regulatory role (9). Finally, we continue to support other groups by providing computational expertise for their studies.

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Wang, Yijie; Hoinka, Jan; Liang, Yong et al. (2018) AptaBlocks: Designing RNA complexes and accelerating RNA-based drug delivery systems. Nucleic Acids Res 46:8133-8142
Shubham, Shambhavi; Hoinka, Jan; Banerjee, Soma et al. (2018) A 2'FY-RNA Motif Defines an Aptamer for Ebolavirus Secreted Protein. Sci Rep 8:12373
Guo, Jing; Chen, Hao; Yang, Peng et al. (2018) LDSplitDB: a database for studies of meiotic recombination hotspots in MHC using human genomic data. BMC Med Genomics 11:27
Fröhlich, Holger; Balling, Rudi; Beerenwinkel, Niko et al. (2018) From hype to reality: data science enabling personalized medicine. BMC Med 16:150
Hoinka, Jan; Backofen, Rolf; Przytycka, Teresa M (2018) AptaSUITE: A Full-Featured Bioinformatics Framework for the Comprehensive Analysis of Aptamers from HT-SELEX Experiments. Mol Ther Nucleic Acids 11:515-517
Huang, Xiaoqing; Wojtowicz, Damian; Przytycka, Teresa M (2017) Detecting presence of mutational signatures in cancer with confidence. Bioinformatics :
Kouzine, Fedor; Wojtowicz, Damian; Baranello, Laura et al. (2017) Permanganate/S1 Nuclease Footprinting Reveals Non-B DNA Structures with Regulatory Potential across a Mammalian Genome. Cell Syst 4:344-356.e7
Hacker, David E; Hoinka, Jan; Iqbal, Emil S et al. (2017) Highly Constrained Bicyclic Scaffolds for the Discovery of Protease-Stable Peptides via mRNA Display. ACS Chem Biol 12:795-804
Dao, Phuong; Kim, Yoo-Ah; Wojtowicz, Damian et al. (2017) BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions. PLoS Comput Biol 13:e1005695
Lee, Hangnoh; Cho, Dong-Yeon; Wojtowicz, Damian et al. (2017) Dosage-Dependent Expression Variation Suppressed on the Drosophila Male X Chromosome. G3 (Bethesda) :

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