MOTIVATION: On-target gene knockdown, using siRNA, ideally results from binding fully complementary regions in mRNA transcripts to induce direct cleavage. Off-target siRNA gene knockdown can occur through several modes, one being a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated off-target effects occur when the ∼8 nucleotides at the 5' end of the guide strand, called a seed region, bind the 3' untranslated regions of mRNA, causing reduced translation. Experiments using siRNA knockdown paired with RNA-seq can be used to detect siRNA sequences with off-target effects driven by the seed region. However, there are limited computational tools designed specifically for detecting siRNA off-target effects mediated by the seed region in differential gene expression experiments. RESULTS: SeedMatchR is an R package developed to provide users a single, unified resource for detecting and visualizing seed-mediated off-target effects of siRNA using RNA-seq experiments. SeedMatchR is designed to extend current differential expression analysis tools, such as DESeq2, by annotating results with predicted seed matches. Using publicly available data, we demonstrate the ability of SeedMatchR to detect cumulative changes in differential gene expression attributed to siRNA seed region activity. AVAILABILITY: SeedMatchR is available on CRAN. Documentation and example workflows are available through the SeedMatchR GitHub page at https://github.com/tacazares/SeedMatchR.

BACKGROUND: RNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here we introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment. RESULTS: Developed as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, we have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of this workflow. Source code is available here: workflow: https://code.bmi.osumc.edu/gadepalli.3/BISR-RNAseq-ICIBM2019 and shiny: https://code.bmi.osumc.edu/gadepalli.3/BISR_RNASeq_ICIBM19. Example dataset is demonstrated here: https://dataportal.bmi.osumc.edu/RNA_Seq/. CONCLUSION: The workflow allows for the analysis (alignment, QC, gene-wise counts generation) of raw RNAseq data and seamless integration of quality analysis and differential expression results into a configurable R shiny web application.

BACKGROUND: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. RESULTS: In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network. CONCLUSIONS: We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.

Enabling data analysis in large data depositories for high throughput experimental data such as gene microarrays and ChIP-seq is challenging. In this paper, we discuss three methods for integrating QUEST, a data depository for epigenetic experiments, with a web-based data analysis platform GenePattern. These methods are universal and can serve as an exemplary implementation resolving the dilemma facing many similar database systems in integrating data analysis tools.

In this paper, we present a study on the relationships between Chronic Lymphocytic Leukemia (CLL) related clinical attributes using network visualization and analysis techniques. This work is the first step of our long-term project to identify novel biomarkers for CLL, working in coordination with the NCI-funded CLL Research Consortium (cll.ucsd.edu). By computing Spearman correlation coefficients for 125 clinical attributes, we established an attribute network for CLL. Using network visualization techniques we identified a core network with peripheral nodes around it. An important observation is that many cytogenetic attributes are in the core network, indicating the connection between karyotypes (e.g., abnormality in Chromosome 17) and other clinical attributes. Since these karyotypes are linked to CLL etiology in many ways, the corresponding clinical attributes may be further selected and tested as markers for disease screening. The correlation analysis results are also consistent with previous studies using a knowledge engineering based approach for establishing relationships between such attributes.