gganatogram: An R package for modular visualisation of anatograms and tissues based on ggplot2 [version 2; referees: 2 approved]

Displaying data onto anatomical structures is a convenient technique to quickly observe tissue related information. However, drawing tissues is a complex task that requires both expertise in anatomy and the arts. While web based applications exist for displaying gene expression on anatograms, other non-genetic disciplines lack similar tools. Moreover, web based tools often lack the modularity associated with packages in programming languages, such as R. Here I present gganatogram, an R package used to plot modular species anatograms based on a combination of the graphical grammar of ggplot2 and the publicly available anatograms from the Expression Atlas. This combination allows for quick and easy, modular, and reproducible generation of anatograms. Using only one command and a data frame with tissue name, group, colour, and  value, this tool enables the user to visualise specific human and mouse tissues with desired colours, grouped by a variable, or displaying a desired value, such as gene-expression, pharmacokinetics, or bacterial load across selected tissues. gganatogram consists of 5 highly annotated organisms, male/female human/mouse, and a cell anatogram. It further consists of 24 other less annotated organisms from the animal and plant kingdom. I hope that this tool will be useful by the wider community in biological sciences. Community members are welcome to submit additional anatograms, which can be incorporated into the package. A stable version gganatogram has been deposited to neuroconductor, and a development version can be found on github/jespermaag/gganatogram. An interactive shiny app of gganatogram can be found on https://jespermaag.shinyapps.io/gganatogram/, which allows for non-R users to create anatograms

gganatogram: An R package for modular visualisation of anatograms and tissues based on ggplot2 [version 1; referees: 2 approved]

Displaying data onto anatomical structures is a convenient technique to quickly observe tissue related information. However, drawing tissues is a complex task that requires both expertise in anatomy and the arts. While web based applications exist for displaying gene expression on anatograms, other non-genetic disciplines lack similar tools. Moreover, web based tools often lack the modularity associated with packages in programming languages, such as R. Here I present gganatogram, an R package used to plot modular species anatograms based on a combination of the graphical grammar of ggplot2 and the publicly available anatograms from the Expression Atlas. This combination allows for quick and easy, modular, and reproducible generation of anatograms. Using only one command and a data frame with tissue name, group, colour, and value, this tool enables the user to visualise specific human and mouse tissues with desired colours, grouped by a variable, or displaying a desired value, such as gene-expression, pharmacokinetics, or bacterial load across selected tissues. I hope that this tool will be useful by the wider community in biological sciences. Community members are welcome to submit additional anatograms, which can be incorporated into the package. A stable version gganatogram has been deposited to neuroconductor, and a development version can be found on github/jespermaag/gganatogram

Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data

RNA-sequencing has become the gold standard for whole-transcriptome gene expression quanti cation. Multiple algorithms have been developed to derive gene counts from sequencing reads. While a number of benchmarking studies have been conducted, the question remains how individual methods perform at accurately quantifying gene expression levels from RNA-sequencing reads. We performed an independent benchmarking study using RNA-sequencing data from the well established MAQCA and MAQCB reference samples. RNA-sequencing reads were processed using five workflows (Tophat-HTSeq, Tophat-Cuflinks, STAR-HTSeq, Kallisto and Salmon) and resulting gene expression measurements were compared to expression data generated by wet-lab validated qPCR assays for all protein coding genes. All methods showed high gene expression correlations with qPCR data. When comparing gene expression fold changes between MAQCA and MAQCB samples, about 85% of the genes showed consistent results between RNA-sequencing and qPCR data. Of note, each method revealed a small but speci c gene set with inconsistent expression measurements. A significant proportion of these method-specific inconsistent genes were reproducibly identified in independent datasets. These genes were typically smaller, had fewer exons, and were lower expressed compared to genes with consistent expression measurements. We propose that careful validation is warranted when evaluating RNA-seq based expression profiles for this specific gene set

The long noncoding RNA lncNB1 promotes tumorigenesis by interacting with ribosomal protein RPL35

The majority of patients with neuroblastoma due to MYCN oncogene amplification and consequent N-Myc oncoprotein over-expression die of the disease. Here our analyses of RNA sequencing data identify the long noncoding RNA lncNB1 as one of the transcripts most overexpressed in MYCN-amplified, compared with MYCN-non-amplified, human neuroblastoma cells and also the most over-expressed in neuroblastoma compared with all other cancers. lncNB1 binds to the ribosomal protein RPL35 to enhance E2F1 protein synthesis, leading to DEPDC1B gene transcription. The GTPase-activating protein DEPDC1B induces ERK protein phosphorylation and N-Myc protein stabilization. Importantly, lncNB1 knockdown abolishes neuroblastoma cell clonogenic capacity in vitro and leads to neuroblastoma tumor regression in mice, while high levels of lncNB1 and RPL35 in human neuroblastoma tissues predict poor patient prognosis. This study therefore identifies lncNB1 and its binding protein RPL35 as key factors for promoting E2F1 protein synthesis, N-Myc protein stability and N-Myc-driven oncogenesis, and as therapeutic targetsThe authors were supported by National Health & Medical Research Council Australia, National Institutes of Health USA (CA226959-01), Italian Association for Research on Cancer (AIRC), and Cancer Council New South Wales. P.Y.L. is a research fellow of Cancer Institute New South Wales

The long noncoding RNA lncNB1 promotes tumorigenesis by interacting with ribosomal protein RPL35

The majority of patients with neuroblastoma due to MYCN oncogene amplification and consequent N-Myc oncoprotein over-expression die of the disease. Here our analyses of RNA sequencing data identify the long noncoding RNA lncNB1 as one of the transcripts most over-expressed in MYCN-amplified, compared with MYCN-non-amplified, human neuroblastoma cells and also the most over-expressed in neuroblastoma compared with all other cancers. lncNB1 binds to the ribosomal protein RPL35 to enhance E2F1 protein synthesis, leading to DEPDC1B gene transcription. The GTPase-activating protein DEPDC1B induces ERK protein phosphorylation and N-Myc protein stabilization. Importantly, lncNB1 knockdown abolishes neuroblastoma cell clonogenic capacity in vitro and leads to neuroblastoma tumor regression in mice, while high levels of lncNB1 and RPL35 in human neuroblastoma tissues predict poor patient prognosis. This study therefore identifies lncNB1 and its binding protein RPL35 as key factors for promoting E2F1 protein synthesis, N-Myc protein stability and N-Myc-driven oncogenesis, and as therapeutic targets

Additional file 1: Table S1 of Long noncoding RNAs in cancer: mechanisms of action and technological advancements

lncRNAs and references for Fig. 2. (PDF 106 kb

Computational analysis of ncRNA involved in cancer development and memory formation

The development of large-scale transcriptomic technologies has challenged many assumptions about the genome. One of the most striking observations is that the majority of the genome is expressed as transcripts that lack protein-coding potential. Many such transcripts are expressed in low quantities and are often only transcribed in certain tissues or cell subpopulations. Transcripts lacking coding-potential over 200 nt have been designated as long noncoding RNAs (lncRNA), many of which are in proximity to protein-coding genes, parading a previously undetected loci-complexity. Subsequent studies investigating individual lncRNAs have revealed that hundreds exhibit regulatory functions that are involved in almost all cellular processes.These transcripts’ characteristic low and specific expression has meant that studying and understanding the role of lncRNAs in health and disease is difficult. Consequently, much remains unknown regarding their expression and functions in different systems.This thesis uses bioinformatics analyses of RNA-sequencing to investigate the transcriptome in esophageal adenocarcinoma (EAC) development and long-term memory formation. Observing multiple developmental stages of cancer, and multiple time-points in memory formation, the investigations described in this thesis aim to elucidate and assign potential function and context for lncRNAs through expression correlation, network analysis, and guilt-by-association analysis.In EAC, this thesis identifies novel dysregulated lncRNAs highly upregulated in cancer. Many upregulated lncRNAs correlate with their neighbouring protein- coding gene. Further analysis suggests lncRNA involvement in the cell cycle and in immunological processes. Furthermore, machine learning reveals a novel 4-gene signature capable of segregating EAC from its precursor tissues on the mRNA level.Using in vivo long-term potentiation (LTP) as a surrogate for memory formation, this thesis identifies 71 novel lncRNAs correlating with known memory-associated genes. Additionally, this analysis observes activation of repeat elements in LTP, suggesting a role for retrotransposition in memory formation.Furthermore, investigation of the promoter epigenome of 954 memory-associated mRNAs, reveals widespread promoter methylation changes correlating with gene expression alterations.In summary, this thesis integrates the analysis of the protein-coding transcriptome with its noncoding counterpart to further characterise and explore the cellular alterations observed in the transition from the homeostasis of health to the genetic disorder of disease

Widespread promoter methylation of synaptic plasticity genes in long-term potentiation in the adult brain in vivo.

Background: DNA methylation is a key modulator of gene expression in mammalian development and cellular differentiation, including neurons. To date, the role of DNA modifications in long-term potentiation (LTP) has not been explored. Results: To investigate the occurrence of DNA methylation changes in LTP, we undertook the first detailed study to describe the methylation status of all known LTP-associated genes during LTP induction in the dentate gyrus of live rats. Using a methylated DNA immunoprecipitation (MeDIP)-array, together with previously published matched RNA-seq and public histone modification data, we discover widespread changes in methylation status of LTP-genes. We further show that the expression of many LTP-genes is correlated with their methylation status. We show that these correlated genes are enriched for RNA-processing, active histone marks, and specific transcription factors. These data reveal that the synaptic activity-evoked methylation changes correlates with pre-existing activation of the chromatin landscape. Finally, we show that methylation of Brain-derived neurotrophic factor (Bdnf) CpG-islands correlates with isoform switching from transcripts containing exon IV to exon I. Conclusions: Together, these data provide the first evidence of widespread regulation of methylation status in LTP-associated genes