Yuji Zhang

Calculating Sample Size Estimates for RNA Sequencing Data

BACKGROUND Given the high technical reproducibility and orders of magnitude greater resolution than microarrays, next-generation sequencing of mRNA (RNA-Seq) is quickly becoming the de facto standard for measuring levels of gene expression in biological experiments. Two important questions must be taken into consideration when designing a particular experiment, namely, 1) how deep does one need to sequence? and, 2) how many biological replicates are necessary to observe a significant change in expression? RESULTS Based on the gene expression distributions from 127 RNA-Seq experiments, we find evidence that 91% ± 4% of all annotated genes are sequenced at a frequency of 0.1 times per million bases mapped, regardless of sample source. Based on this observation, and combining this information with other parameters such as biological variation and technical variation that we empirically estimate from our large datasets, we developed a model to estimate the statistical power needed to identify differentially expressed genes from RNA-Seq experiments. CONCLUSIONS Our results provide a needed reference for ensuring RNA-Seq gene expression studies are conducted with the optimally sample size, power, and sequencing depth. We also make available both R code and an Excel worksheet for investigators to calculate for their own experiments.

Effects of Velocity Modulation during Surgical Needle Insertion

Precise interstitial intervention is essential for many medical diagnostic and therapeutic procedures. But accurate insertion and placement of surgical needle in soft tissue is quite challenging. The understanding of the interaction between surgical needle and soft tissue is very important to develop new devices and systems to achieve better accuracy and to deliver quality treatment. In this paper we present the effects of velocity (linear, rotational, and oscillatory) modulation on needle force and target deflection. We have experimentally verified our hypothesis that needle insertion with continuous rotation reduces target movement and needle force significantly. We have observed little changes in force and target deflection in rotational oscillation (at least at lower frequency) of the needle

TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data

Summary: TREAT (Targeted RE-sequencing Annotation Tool) is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing. It provides a rich integration of publicly available as well as in-house developed annotations and visualizations for variants, variant-hosting genes and host-gene pathways. Availability and implementation: TREAT is freely available to non-commercial users as either a stand-alone annotation and visualization tool, or as a comprehensive workflow integrating sequencing alignment and variant calling. The executables, instructions and the Amazon Cloud Images of TREAT can be downloaded at the website: http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm Contact: Hossain.Asif@mayo.edu; Kocher.JeanPierre@mayo.edu Supplementary information: Supplementary data are provided at Bioinformatics online.

Reverse engineering module networks by PSO-RNN hybrid modeling

BackgroundInferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell; (2) complex dynamic and nonlinear relationships exist among genes; (3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules.ResultsThis study presents a novel GRN inference method by integrating gene expression data and gene functional category information. The inference is based on module network model that consists of two parts: the module selection part and the network inference part. The former determines the optimal modules through fuzzy c-mean (FCM) clustering and by incorporating gene functional category information, while the latter uses a hybrid of particle swarm optimization and recurrent neural network (PSO-RNN) methods to infer the underlying network between modules. Our method is tested on real data from two studies: the development of rat central nervous system (CNS) and the yeast cell cycle process. The results are evaluated by comparing them to previously published results and gene ontology annotation information.ConclusionThe reverse engineering of GRNs in time course gene expression data is a major obstacle in system biology due to the limited number of time points. Our experiments demonstrate that the proposed method can address this challenge by: (1) preprocessing gene expression data (e.g. normalization and missing value imputation) to reduce the data noise; (2) clustering genes based on gene expression data and gene functional category information to identify biologically meaningful modules, thereby reducing the dimensionality of the data; (3) modeling GRNs with the PSO-RNN method between the modules to capture their nonlinear and dynamic relationships. The method is shown to lead to biologically meaningful modules and networks among the modules.

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

BackgroundIntegrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information.ResultsThe proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations.ConclusionThe major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (e.g., LOOCV) and biologically (e.g., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.

Robotic system for prostate brachytherapy.

In contemporary brachytherapy procedures, needle placement at the desired target is challenging for a variety of reasons. A robot-assisted brachytherapy system can potentially improve needle placement and seed delivery, resulting in enhanced therapeutic outcome. In this paper we present a robotic system with 16 degrees of freedom (DOF) (9 DOF for the positioning module and 7 DOF for the surgery module) that has been developed and fabricated for prostate brachytherapy. Strategies to reduce needle deflection and target movement were incorporated after extensive experimental validation. Provision for needle motion and force feedback was included in the system to improve robot control and seed delivery. Preliminary experimental results reveal that the prototype system is sufficiently accurate in placing brachytherapy needles.

SAAP-RRBS

Summary: Reduced representation bisulfite sequencing (RRBS) is a cost-effective approach for genome-wide methylation pattern profiling. Analyzing RRBS sequencing data is challenging and specialized alignment/mapping programs are needed. Although such programs have been developed, a comprehensive solution that provides researchers with good quality and analyzable data is still lacking. To address this need, we have developed a Streamlined Analysis and Annotation Pipeline for RRBS data (SAAP-RRBS) that integrates read quality assessment/clean-up, alignment, methylation data extraction, annotation, reporting and visualization. This package facilitates a rapid transition from sequencing reads to a fully annotated CpG methylation report to biological interpretation. Availability and implementation: SAAP-RRBS is freely available to non-commercial users at the web site http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm. Contact: baheti.saurabh@mayo.edu or sun.zhifu@mayo.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

MACE: model based analysis of ChIP-exo

Understanding the role of a given transcription factor (TF) in regulating gene expression requires precise mapping of its binding sites in the genome. Chromatin immunoprecipitation-exo, an emerging technique using λ exonuclease to digest TF unbound DNA after ChIP, is designed to reveal transcription factor binding site (TFBS) boundaries with near-single nucleotide resolution. Although ChIP-exo promises deeper insights into transcription regulation, no dedicated bioinformatics tool exists to leverage its advantages. Most ChIP-seq and ChIP-chip analytic methods are not tailored for ChIP-exo, and thus cannot take full advantage of high-resolution ChIP-exo data. Here we describe a novel analysis framework, termed MACE (model-based analysis of ChIP-exo) dedicated to ChIP-exo data analysis. The MACE workflow consists of four steps: (i) sequencing data normalization and bias correction; (ii) signal consolidation and noise reduction; (iii) single-nucleotide resolution border peak detection using the Chebyshev Inequality and (iv) border matching using the Gale-Shapley stable matching algorithm. When applied to published human CTCF, yeast Reb1 and our own mouse ONECUT1/HNF6 ChIP-exo data, MACE is able to define TFBSs with high sensitivity, specificity and spatial resolution, as evidenced by multiple criteria including motif enrichment, sequence conservation, direct sequence pileup, nucleosome positioning and open chromatin states. In addition, we show that the fundamental advance of MACE is the identification of two boundaries of a TFBS with high resolution, whereas other methods only report a single location of the same event. The two boundaries help elucidate the in vivo binding structure of a given TF, e.g. whether the TF may bind as dimers or in a complex with other co-factors.

Induction of an Inflammatory Loop by Interleukin-1β and Tumor Necrosis Factor-α Involves NF-kB and STAT-1 in Differentiated Human Neuroprogenitor Cells

Proinflammatory cytokines secreted from microglia are known to induce a secondary immune response in astrocytes leading to an inflammatory loop. Cytokines also interfere with neurogenesis during aging and in neurodegenerative diseases. The present study examined the mechanism of induction of inflammatory mediators at the transcriptional level in human differentiated neuroprogenitor cells (NPCs). Interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) induced the expression of cytokines and chemokines in differentiated human NPCs as shown by an immune pathway-specific array. Network motif (NM) analysis of these genes revealed 118 three-node NMs, suggesting complex interactions between inflammatory mediators and transcription factors. Immunofluorescent staining showed increases in the levels of IL-8 and CXCL10 proteins in neurons and glial cells. Findings from Taqman low density array suggested the synergistic actions of IL-1β and TNF-α in the induction of a majority of inflammatory genes by a mechanism involving NF-kB and STAT-1. Nuclear localization of these transcription factors in differentiated NPCs was observed following exposure to IL-1α and TNF-α. Further studies on CXCL10, a chemokine known to be elevated in the Alzheimers brain, showed that TNF-α is a stronger inducer of CXCL10 promoter when compared to IL-1β. The synergy between these cytokines was lost when ISRE or kB elements in CXCL10 promoter were mutated. Our findings suggest that the activation of inflammatory pathways in neurons and astrocytes through transcription factors including NF-kB and STAT-1 play important roles in neuroglial interactions and in sustaining the vicious cycle of inflammatory response.

Research Resource: Whole Transcriptome RNA Sequencing Detects Multiple 1α,25-Dihydroxyvitamin D3-Sensitive Metabolic Pathways in Developing Zebrafish

The biological role of vitamin D receptors (VDR), which are abundantly expressed in developing zebrafish (Danio rerio) as early as 48 h after fertilization, and before the development of a mineralized skeleton and mature intestine and kidney, is unknown. We probed the role of VDR in developing zebrafish biology by examining changes in expression of RNA by whole transcriptome shotgun sequencing (RNA-seq) in fish treated with picomolar concentrations of the VDR ligand and hormonal form of vitamin D(3), 1α,25-dihydroxyvitamin D(3) [1α,25(OH)(2)D(3))].We observed significant changes in RNAs of transcription factors, leptin, peptide hormones, and RNAs encoding proteins of fatty acid, amino acid, xenobiotic metabolism, receptor-activator of NFκB ligand (RANKL), and calcitonin-like ligand receptor pathways. Early highly restricted, and subsequent massive changes in more than 10% of expressed cellular RNA were observed. At days post fertilization (dpf) 2 [24 h 1α,25(OH)(2)D(3)-treatment], only four RNAs were differentially expressed (hormone vs. vehicle). On dpf 4 (72 h treatment), 77 RNAs; on dpf 6 (120 h treatment) 1039 RNAs; and on dpf 7 (144 h treatment), 2407 RNAs were differentially expressed in response to 1α,25(OH)(2)D(3). Fewer RNAs (n = 481) were altered in dpf 7 larvae treated for 24 h with 1α,25(OH)(2)D(3) vs. those treated with hormone for 144 h. At dpf 7, in 1α,25(OH)(2)D(3)-treated larvae, pharyngeal cartilage was larger and mineralization was greater. Changes in expression of RNAs for transcription factors, peptide hormones, and RNAs encoding proteins integral to fatty acid, amino acid, leptin, calcitonin-like ligand receptor, RANKL, and xenobiotic metabolism pathways, demonstrate heretofore unrecognized mechanisms by which 1α,25(OH)(2)D(3) functions in vivo in developing eukaryotes.