MA Plots Research Articles

A Computational Workflow for Analysis of 3' Tag-Seq Data.

RNA-sequencing (RNA-seq) is a gold-standard method to profile genome-wide changes in gene expression. RNA-seq uses high-throughput sequencing technology to quantify the amount of RNA in a biological sample. With the increasing popularity of RNA-seq, many variations on the protocol have been proposed to extract unique and relevant information from biological samples. 3' Tag-Seq (also called TagSeq, 3' Tag-RNA-Seq, and Quant-Seq 3' mRNA-Seq) is one RNA-seq variation where the 3' end of the transcript is selected and amplified to yield one copy of cDNA from each transcript in the biological sample. We present a simple, easy-to-use, and publicly available computational workflow to analyze 3' Tag-Seq data. The workflow begins by trimming sequence adapters from raw FASTQ files. The trimmed sequence reads are checked for quality using FastQC and aligned to the reference genome, and then read counts are obtained using STAR. Differential gene expression analysis is performed using DESeq2, based on differential analysis of gene count data. The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots. This protocol is intended for users specifically interested in analyzing 3' Tag-Seq data, and thus normalizations based on transcript length are not performed within the workflow. Future updates to this workflow could include custom analyses based on the gene counts table as well as data visualization enhancements. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Running the 3' Tag-Seq workflow Support Protocol: Generating genome indices.

Gene prioritization based on random walks with restarts and absorbing states, to define gene sets regulating drug pharmacodynamics from single-cell analyses.

Prioritizing genes for their role in drug sensitivity, is an important step in understanding drugs mechanisms of action and discovering new molecular targets for co-treatment. To formalize this problem, we consider two sets of genes X and P respectively composing the gene signature of cell sensitivity at the drug IC50 and the genes involved in its mechanism of action, as well as a protein interaction network (PPIN) containing the products of X and P as nodes. We introduce Genetrank, a method to prioritize the genes in X for their likelihood to regulate the genes in P. Genetrank uses asymmetric random walks with restarts, absorbing states, and a suitable renormalization scheme. Using novel so-called saturation indices, we show that the conjunction of absorbing states and renormalization yields an exploration of the PPIN which is much more progressive than that afforded by random walks with restarts only. Using MINT as underlying network, we apply Genetrank to a predictive gene signature of cancer cells sensitivity to tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL), performed in single-cells. Our ranking provides biological insights on drug sensitivity and a gene set considerably enriched in genes regulating TRAIL pharmacodynamics when compared to the most significant differentially expressed genes obtained from a statistical analysis framework alone. We also introduce gene expression radars, a visualization tool embedded in MA plots to assess all pairwise interactions at a glance on graphical representations of transcriptomics data. Genetrank is made available in the Structural Bioinformatics Library (https://sbl.inria.fr/doc/Genetrank-user-manual.html). It should prove useful for mining gene sets in conjunction with a signaling pathway, whenever other approaches yield relatively large sets of genes.

GraphBio: A shiny web app to easily perform popular visualization analysis for omics data.

Background: Massive amounts of omics data are produced and usually require sophisticated visualization analysis. These analyses often require programming skills, which are difficult for experimental biologists. Thus, more user-friendly tools are urgently needed. Methods and Results: Herein, we present GraphBio, a shiny web app to easily perform visualization analysis for omics data. GraphBio provides 15 popular visualization analysis methods, including heatmap, volcano plots, MA plots, network plots, dot plots, chord plots, pie plots, four quadrant diagrams, Venn diagrams, cumulative distribution curves, principal component analysis (PCA), survival analysis, receiver operating characteristic (ROC) analysis, correlation analysis, and text cluster analysis. It enables experimental biologists without programming skills to easily perform popular visualization analysis and get publication-ready figures. Conclusion: GraphBio, as an online web application, is freely available at http://www.graphbio1.com/en/ (English version) and http://www.graphbio1.com/ (Chinese version). The source code of GraphBio is available at https://github.com/databio2022/GraphBio.

Разработка и создание анимационной карты животного мира

The article is devoted to the concept development of the wildlife animated school map with the representation of animated animal symbols and sounds. Currently, the modern school is undergoing a major modernization; this is due to the release of new educational standards, the creation of new student training programs, the creation and release of new textbooks, and also adjustment of approaches and teaching methods for modern children to new technologies and technics. The system of cartographic support for the educational process also requires revision, since a lot of maps are already outdated, some maps that had been included in textbooks were simply excluded when textbooks were republished, and maps for individual regions are not included in textbooks at all. Therefore, the issue of developing and creating modern cartographic works is more relevant than ever and requires its solution. This article is devoted to the development and creation of an animated map of the wild life using the HTML language, which will be accompanied by animated symbols and sounds of each animal.

Mobility Community Reports data: Geovisual analytics and cartographic synthesis of behaviour changes due to COVID-19 pandemic in Europe

Mobility Community Reports data: Geovisual analytics and cartographic synthesis of behaviour changes due to COVID-19 pandemic in Europe

OmicplotR: visualizing omic datasets as compositions

BackgroundDifferential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method.ResultsomicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset.ConclusionsomicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience.

Changes in DNA methylation pattern of apple long-term in vitro shoot culture and acclimatized plants

DNA methylation is a process of epigenetic modification that can alter the functionality of a genome. Using whole-genome bisulfite sequencing, this study quantify the level of DNA methylation in the epigenomes of two diploid apple (Malus x domestica) scion cultivars ('McIntosh' and 'Húsvéti rozmaring') derived from three environmental conditions: in vivo mother plants in an orchard, in vitro culture, and acclimatized in vitro plants. The global DNA methylation levels were not dependent on the source of plant material, and the average level of DNA methylation was 49.77%, 34.65% and 8.77% in CpG, CHG and CHH contexts, respectively. Significant differences in DNA methylation were identified in 586 (specifically 334, 201 and 131 in CpG, CHG and CHH contexts, respectively) out of 45,116 genes, including promoter and coding sequences. These were classified as differentially methylated genes (DMGs). This is a 1.3% difference in the level of DNA methylation of genes in response to a change in the environment. Differential methylation was visualised by MA plots and functional genomic maps were established for biological processes, molecular functions and cellular components. When the DMGs were considered, in vitro tissue culture resulted in the highest level of methylation, but it was lower in acclimatized in vitro plants which was similar to that in the mother tree. Methylation patterns of the two scions differed, indicating cultivar-specific epigenetic regulation of gene expression during adaptation to various environments. After selecting genes that displayed differences larger than ±10% in CpG and CHG contexts, or larger than ±1.35% in the CHH context from among the DMGs, they were annotated in Blast2 GO v5.1.12 for Gene Ontology. DMGs identified as MD07G1113000 (protein transport), MD08G1041600 (extracellular space), MD09G1054800 (phosphatidic acid binding), and MD10G1265800 (not annotated) were methylated in all three contexts in in vitro shoots. These DNA methylation results suggest that epigenetic changes may contribute to the adaptation of apple to environmental changes by modifying the epigenome and thereby gene expression.

Landscape Evaluation Based on Gaofen Satellite in the Southern Part of the Nile Delta, Egypt

Landscape segmentation and classification is fundamental to landscape research because it provides an important frame of reference for researchers to communicate and compare their work. Anthropogenic human activities mainly lead to landscape changes. The present study aims to assess the impact of anthropogenic activities on landscape classification of the Nile Delta using remote sensing and GIS techniques. Field survey, digital databases and GIS capabilities are applied for landscapes classification. Vector data using a lot of maps and raster data using satellite image have the ability to give obvious classification about landscape. Results showed that the anthropogenic impacts affect negatively on the landscape classification. Using GF2, landscapes are classified into major eight classes: cultivated land, garden land, woodland, grassland, bare land, urban land, water bodies and mining land. It was showed that the urban occupies the highest percentage of the study area. Urban construction and development areas centered on the capital Cairo city and the city of Giza are dumbbell-shaped to the east. Bare lands occupy the second percentage of the study area, and it may be distributed on around the Nile Delta, southeast of Cairo City and southwest of Giza City. According to vegetation cover, three classes were applied as the sequence: Cultivated land > Garden land > Grass land. These classes depend mainly on the River Nile. Vegetation cover may be based mainly on the water from the Nile River. In addition, mining land occupies the least percentage of the study area. The main distribution of mines and mineral exploration is also very small, but it is distributed on the edge of the city. Landscape metric as Fractal Dimension (Frac) and the Square Pixel (SqP) was applied to validate the segmentation and classification. These metrics indicated that the landscape classification is related to natural and human changes. These changes were related to unplanned management of new projects and some anthropogenic activities.

Strand-Specific RNA-Seq Analyses of Fruiting Body Development in Coprinopsis cinerea.

The basidiomycete fungus Coprinopsis cinerea is an important model system for multicellular development. Fruiting bodies of C. cinerea are typical mushrooms, which can be produced synchronously on defined media in the laboratory. To investigate the transcriptome in detail during fruiting body development, high-throughput sequencing (RNA-seq) was performed using cDNA libraries strand-specifically constructed from 13 points (stages/tissues) with two biological replicates. The reads were aligned to 14,245 predicted transcripts, and counted for forward and reverse transcripts. Differentially expressed genes (DEGs) between two adjacent points and between vegetative mycelium and each point were detected by Tag Count Comparison (TCC). To validate RNA-seq data, expression levels of selected genes were compared using RPKM values in RNA-seq data and qRT-PCR data, and DEGs detected in microarray data were examined in MA plots of RNA-seq data by TCC. We discuss events deduced from GO analysis of DEGs. In addition, we uncovered both transcription factor candidates and antisense transcripts that are likely to be involved in developmental regulation for fruiting.

The simulation and mapping of building performance indicators based on European weather stations

Due to the climate change debate, a lot of research and maps of external climate parameters are available. However, maps of indoor climate performance parameters are still lacking. This paper presents a methodology for obtaining maps of performances of similar buildings that are virtually spread over whole Europe. The produced maps are useful for analyzing regional climate influence on building performance indicators such as energy use and indoor climate. This is shown using the Bestest building as a reference benchmark. An important application of the mapping tool is the visualization of potential building measures over the EU. Also the performances of single building components can be simulated and mapped. It is concluded that the presented method is efficient as it takes less than 15min to simulate and produce the maps on a 2.6GHz/4GB computer. Moreover, the approach is applicable for any type of building.

Push Pins, Dots, Customisation, Brands and Services: The Three Waves of Making Digital Maps

This is not the article that I set out to write. The working title for that article was going to be something along the lines of cartography is subjective; my favourite map probably isn’t your favourite map. But every blog post, article or conference talk I write has to start somewhere and armed with this working title I set about trying to find my favourite map. I was spoilt for choice as I had a vast array of sources to choose from. On Flickr there’s the photostream of the Norman B Leventhal Map Center at the Boston Public Library with over 3000 maps to choose from Figure 1. Also on Flickr are all the maps I’ve come across and have favourited. Then there are my tagged social bookmarks on Delicious plus a plethora of mapping and cartography related articles that sit within my browser’s bookmarks. That’s a lot of maps and that’s not even counting those that are offline in my reasonably large collection of map books. I soon found out that reviewing all of these maps was by no means as simple a task as I’d first thought. I’d expected a favourite to leap out of the browser’s window or book’s page at me. Maps were certainly getting my attention but for the wrong reasons. I was able to discount maps I didn’t like or maps I was ambivalent about, but even getting the beginnings of a short list of favourites was proving a thankless task. There were just too many good maps. Time was passing and I wasn’t anywhere near finding a favourite map, let alone writing an article about it. But as I continued browsing maps, I noticed there was something else on my laptop’s screen that was vying for my attention. Actually there were two other things. One was a terminal window that was open and logged into a remote server somewhere on the Internet where I keep a large stash of geographic and mapping data. The other was the icon for the TextMate text editor, sitting in the dock of my Mac, which I use for coding things, usually maps related code. Realisation slowly dawned on me that my favourite map was yet to come into being; it was the next one that I would make, and the one after that and so on. What I would use to make my next map is what this article should be about. Despite working with geographical data for almost 30 years, it was not until 2007 that I made the first map for myself. Now I’m not a cartographer or a professional map-maker; if I had to describe myself as anything I’d term myself as a hybrid of a geotechnologist, in the literal sense of someone who works with geography and technology, and a neogeographer. The combination of geotechnology, of neogeography and more traditional cartographical disciplines has given us repeated phases of acceleration and disruption. In my opinion, today’s uses of digital maps are in the tail end of the second wave of innovation and we’re starting to see the beginnings of a third wave. Each wave overlaps, there’s no clear border or delineation between them, but each wave has distinct characteristics. When talking about making and using digital maps, my focus is on the way in which consumers, developers and the web use resources to create map based experiences, rather than the process of gathering the spatial data that underpins a digital map. The first wave of digital maps was the combination of a variety of factors slowly coming together.

Kernel density weighted loess normalization improves the performance of detection within asymmetrical data

BackgroundNormalization of gene expression data has been studied for many years and various strategies have been formulated to deal with various types of data. Most normalization algorithms rely on the assumption that the number of up-regulated genes and the number of down-regulated genes are roughly the same. However, the well-known Golden Spike experiment presents a unique situation in which differentially regulated genes are biased toward one direction, thereby challenging the conclusions of previous bench mark studies.ResultsThis study proposes two novel approaches, KDL and KDQ, based on kernel density estimation to improve upon the basic idea of invariant set selection. The key concept is to provide various importance scores to data points on the MA plot according to their proximity to the cluster of the null genes under the assumption that null genes are more densely distributed than those that are differentially regulated. The comparison is demonstrated in the Golden Spike experiment as well as with simulation data using the ROC curves and compression rates. KDL and KDQ in combination with GCRMA provided the best performance among all approaches.ConclusionsThis study determined that methods based on invariant sets are better able to resolve the problem of asymmetry. Normalization, either before or after expression summary for probesets, improves performance to a similar degree.

Shawinigan arc magmatism in the Adirondack Lowlands as a consequence of closure of the Trans-Adirondack backarc basin

The Antwerp-Rossie metaigneous suite (ARS) represents arc magmatism related to closure of the Trans-Adirondack backarc basin during Shawinigan collisional orogenesis (ca. 1200–1160 Ma). The ARS is of calc-alkaline character, bimodal, and lacks intermediate compositions. Primarily intruding marble and pelitic gneiss, the ARS is spatially restricted to the Adirondack Lowlands southeast of the Black Lake fault. On discrimination diagrams, the ARS samples plot primarily within the volcanic arc granite fi elds. Incompatible elements show an arc-like signature with negative Nb, Ta, P, and Zr and positive Cs, Pb, La, and Nd anomalies relative to primitive mantle. Neodymium model ages (TDM, depleted mantle model) range from 1288 to 1634 Ma; the oldest ages (1613–1634) and smallest epsilon Nd (eNd) values are found in proximity to the Black Lake fault, delineating the extent of Laurentia prior to the Shawinigan orogeny. The epsilon Nd values at crystallization (1200 Ma) plot well below the depleted mantle curve. Geochemical and isotopic similarities to the Hermon granitic gneiss (HGG) (ca. 1182 Ma) and differences from the Hyde School Gneiss–Rockport Granite suites (1155–1180 Ma) suggest that arc plutonism rapidly transitioned into A-type AMCG (anorthosite-mangeritecharnockite-granite) plutonism. Given the short duration of Shawinigan subduction, apparently restricted extent of the ARS (Adirondack Lowlands), location outboard of the pre-Shawinigan Laurentian margin, intrusion into the Lowlands supracrustal sequence, bimodal composition, and recent discovery of enriched mantle rocks in the Lowlands, it is proposed the ARS formed as a consequence of subduction related to closure of a backarc basin that once extended between the Frontenac terrane and the Southern Adirondacks.

A Multi-feature Reproducibility Assessment of Mass Spectral Data in Clinical Proteomic Studies

Abstract Background The use of mass spectrometry to investigate disease-associated proteins among thousands of candidates simultaneously creates challenges with the evaluation of operational and biological variation. Traditional statistical methods, which evaluate reproducibility of a single feature, are likely to provide an inadequate assessment of reproducibility. This paper proposes a systematic approach for the evaluation of the global reproducibility of multidimensional mass spectral data at the post-identification stage. Methods The proposed systematic approach combines dimensional reduction and permutation to test and summarize the reproducibility. First, principal component analysis is applied to the mean quantities from identified features of paired replicated samples. An eigenvalue test is used to identify the number of significant principal components which reflect the underlying correlation pattern of the multiple features. Second, a simulation-based permutation test is applied to the derived paired principal components. Third, a modified form of Bland Altman or MA plot is produced to visualize agreement between the replicates. Last, a discordance index is used to summarize the agreement. Results Application of this method to data from both a cardiac liquid chromatography tandem mass spectrometry experiment with iTRAQ labeling and simulation experiments derived from an ovarian cancer SELDI-MS experiment demonstrate that the proposed global reproducibility test is sensitive to the simulated systematic bias when the sample size is above 15. The two proposed test statistics (max t statistics and a sign score statistic) for the permutation tests are shown to be reliable. Conclusion The methodology presented in this paper provides a systematic approach for the global measurement of reproducibility in clinical proteomic studies.

Normalization and Statistical Analysis of Quantitative Proteomics Data Generated by Metabolic Labeling

Comparative proteomics is a powerful analytical method for learning about the responses of biological systems to changes in growth parameters. To make confident inferences about biological responses, proteomics approaches must incorporate appropriate statistical measures of quantitative data. In the present work we applied microarray-based normalization and statistical analysis (significance testing) methods to analyze quantitative proteomics data generated from the metabolic labeling of a marine bacterium (Sphingopyxis alaskensis). Quantitative data were generated for 1,172 proteins, representing 1,736 high confidence protein identifications (54% genome coverage). To test approaches for normalization, cells were grown at a single temperature, metabolically labeled with (14)N or (15)N, and combined in different ratios to give an artificially skewed data set. Inspection of ratio versus average (MA) plots determined that a fixed value median normalization was most suitable for the data. To determine an appropriate statistical method for assessing differential abundance, a -fold change approach, Student's t test, unmoderated t test, and empirical Bayes moderated t test were applied to proteomics data from cells grown at two temperatures. Inverse metabolic labeling was used with multiple technical and biological replicates, and proteomics was performed on cells that were combined based on equal optical density of cultures (providing skewed data) or on cell extracts that were combined to give equal amounts of protein (no skew). To account for arbitrarily complex experiment-specific parameters, a linear modeling approach was used to analyze the data using the limma package in R/Bioconductor. A high quality list of statistically significant differentially abundant proteins was obtained by using lowess normalization (after inspection of MA plots) and applying the empirical Bayes moderated t test. The approach also effectively controlled for the number of false discoveries and corrected for the multiple testing problem using the Storey-Tibshirani false discovery rate (Storey, J. D., and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440-9445). The approach we have developed is generally applicable to quantitative proteomics analyses of diverse biological systems.

Role for Med12 in Regulation of Nanog and Nanog Target Genes

Nanog, Oct4, and Sox2 form the core of a transcription factor network that maintains embryonic stem cells in the pluripotent state in both humans and mice. These critical factors have been implicated as both positive and negative regulators of transcription, varying by promoter and differentiation state of the cell. The Mediator complex, a ubiquitous conserved complex of approximately 30 subunits, facilitates transcription by coordinating RNA polymerase II binding to target promoters via gene-specific activators and can be divided into several functional subcomplexes. Med12 is part of a subcomplex of four proteins associated with the core Mediator complex and has been found to function both in repressing and activating transcription when recruited to target promoters. We identified an interaction between Med12 and Nanog and present evidence of involvement of Med12 in regulation of Nanog function. Gene expression analysis of embryonic stem cells knocked down for Med12 showed a similarity to Nanog knockdown, with increased expression of Nanog-repressed targets and decreased expression of Nanog-activated targets. Using chromatin immunoprecipitation, we found that Med12 and Nanog co-occupied Nanog target promoters in embryonic stem cells and that Med12 dissociated from target promoters upon differentiation with kinetics similar to Nanog. Our results indicate that Nanog and Med12 function in concert to regulate Nanog target genes and identify a novel role for Med12 in embryonic stem cell regulation.

PreP+07: improvements of a user friendly tool to preprocess and analyse microarray data

BackgroundNowadays, microarray gene expression analysis is a widely used technology that scientists handle but whose final interpretation usually requires the participation of a specialist. The need for this participation is due to the requirement of some background in statistics that most users lack or have a very vague notion of. Moreover, programming skills could also be essential to analyse these data. An interactive, easy to use application seems therefore necessary to help researchers to extract full information from data and analyse them in a simple, powerful and confident way.ResultsPreP+07 is a standalone Windows XP application that presents a friendly interface for spot filtration, inter- and intra-slide normalization, duplicate resolution, dye-swapping, error removal and statistical analyses. Additionally, it contains two unique implementation of the procedures – double scan and Supervised Lowess-, a complete set of graphical representations – MA plot, RG plot, QQ plot, PP plot, PN plot – and can deal with many data formats, such as tabulated text, GenePix GPR and ArrayPRO. PreP+07 performance has been compared with the equivalent functions in Bioconductor using a tomato chip with 13056 spots. The number of differentially expressed genes considering p-values coming from the PreP+07 and Bioconductor Limma packages were statistically identical when the data set was only normalized; however, a slight variability was appreciated when the data was both normalized and scaled.ConclusionPreP+07 implementation provides a high degree of freedom in selecting and organizing a small set of widely used data processing protocols, and can handle many data formats. Its reliability has been proven so that a laboratory researcher can afford a statistical pre-processing of his/her microarray results and obtain a list of differentially expressed genes using PreP+07 without any programming skills. All of this gives support to scientists that have been using previous PreP releases since its first version in 2003.

Comparison of Normalization Methods for Defining Copy Number Variation Using Whole-genome SNP Genotyping Data

Abstract Precise and reliable identification of CNV is still im-portant to fully understand the effect of CNV on genetic diversity and background of complex diseases. SNP marker has been used frequently to detect CNVs, but the analysis of SNP chip data for identifying CNV has not been well established. We compared various nor-malization methods for CNV analysis and suggest opti-mal normalization procedure for reliable CNV call. Four normal Koreans and NA10851 HapMap male samples were genotyped using Affymetrix Genome-Wide Human SNP array 5.0. We evaluated the effect of median and quantile normalization to find the optimal normalization for CNV detection based on SNP array data. We also explored the effect of Robust Multichip Average (RMA) background correction for each normalization process. In total, the following 4 combinations of normalization were tried: 1) Median normalization without RMA back-ground correction, 2) Quantile normalization without RMA background correction, 3) Median normalization with RMA background correction, and 4) Quantile nor-malization with RMA background correction. CNV was called using SW-ARRAY algorithm. We applied 4 differ-ent combinations of normalization and compared the ef-fect using intensity ratio profile, box plot, and MA plot. When we applied median and quantile normalizations without RMA background correction, both methods showed similar normalization effect and the final CNV calls were also similar in terms of number and size. In both median and quantile normalizations, RMA back-ground correction resulted in widening the range of in-tensity ratio distribution, which may suggest that RMA background correction may help to detect more CNVs compared to no correction.Keywords: copy number variation, normalization, SNP, Robust Multiarray Average

Reversal of hypermethylation and reactivation of genes by dietary polyphenolic compounds

Reversal of hypermethylation and reactivation of genes by dietary polyphenolic compounds

A change of perspective

I seem to have been looking at a lot of maps over the past few weeks. Granted, some of them were for practical purposes (getting to the meetings I was attending, finding my way to Manchester town centre to kill time before my train home). But those were not the ones that made the biggest impression.